SYSTEMS AND METHODS FOR DISHMACHINE HEALTH MONITORING

Abstract

Various examples are directed to systems and methods for dishwasher health monitoring. A method includes monitoring, using a control board, a dishwasher during operation of the dishwasher, and detecting, using the control board, an error condition of the dishwasher using signals from one or more sensors. The method also includes reporting, using a wireless communication transceiver, failure information of the error condition, and diagnosing, using the failure information and machine learning, a component failure of the dishwasher. The method further includes generating, based on the diagnosed component failure, service instructions for the component failure of the dishwasher, and displaying, for a user of the dishwasher, the service instructions including identifying information of replacement components for the dishwasher.

Description

CROSS-REFERENCE TO RELATED APPLICATION

This patent application claims the benefit of U.S. Provisional Patent Application No. 63/649,101, filed May 17, 2024, which is incorporated by reference herein in its entirety.

TECHNICAL FIELD

This application relates generally to washing machines and more particularly to systems and methods for health monitoring in a washing machine such as a dishwasher.

BACKGROUND

A dishwasher, also referred to as a dishmachine, warewasher or warewashing machine, is a machine for automatically cleaning articles, such as dishes, trays, laboratory equipment, dinnerware, and kitchenware. A common domestic dishwasher is an undercounter unit intended to be installed under a kitchen counter. Other types of dishwashers include industrial or commercial dishwashers for use in restaurants, hotels, and other commercial establishments with food services.

Common commercial dishwashers include a large number of moving parts and electrical controls that enable proper operation of the machines. In commercial settings, dishwasher service interruptions can have a large detrimental impact on the businesses and establishments that use the machines. Some dishwasher errors that may be relatively easy to fix may still result in long down times due to the need for calls to service professionals and time required for scheduling and performing a service call to diagnose and correct the errors.

What is needed is an improved health monitoring and reporting system for dishwashers that provides for shorter machine down times and more efficient machine operation.

SUMMARY

A system and method for health monitoring for dishwashers is provided. A method includes monitoring, using a control board, a dishwasher during operation of the dishwasher, and detecting, using the control board, an error condition of the dishwasher using signals from one or more sensors. The method also includes reporting, using a wireless communication transceiver, failure information of the error condition, and diagnosing, using the failure information and machine learning, a component failure of the dishwasher. The method further includes generating, based on the diagnosed component failure, service instructions for the component failure of the dishwasher, and displaying, for a user of the dishwasher, the service instructions including identifying information of replacement components for the dishwasher.

Various examples, include a system for health monitoring for dishwashers. The system includes an electronic control board including embedded sensors configured to monitor a dishwasher during operation of the dishwasher, a microprocessor configured to detect an error condition of the dishwasher using signals from one or more sensors, and a wireless communication transceiver configured to transmit failure information of the error condition. The system also includes one or more processors and a data storage system in communication with the one or more processors. The data storage system comprises instructions thereon that, when executed by the one or more processors, causes the one or more processors to diagnose, using the failure information and machine learning, a component failure of the dishwasher, generate, based on the diagnosed component failure, service instructions for the component failure of the dishwasher, and display, for a user of the dishwasher, the service instructions including identifying information of replacement components for the dishwasher.

This Summary is an overview of some of the teachings of the present application and not intended to be an exclusive or exhaustive treatment of the present subject matter. Further details about the present subject matter are found in the detailed description and appended claims. The scope of the present invention is defined by the appended claims and their legal equivalents.

BRIEF DESCRIPTION OF THE DRAWINGS

The drawings illustrate generally, by way of example, various embodiments discussed in the present document. The drawings are for illustrative purposes only and may not be to scale.

FIG. 1A illustrates a block diagram of a system for health monitoring of a dishwasher, according to various embodiments.

FIGS. 1B-1D illustrate perspective views of a dishwasher, according to various embodiments.

FIG. 1E illustrates benefits of the present system for health monitoring of a dishwasher, according to various embodiments.

FIG. 1F illustrates a circuit diagram of a control board for health monitoring of a dishwasher, according to various embodiments.

FIGS. 2A-2H illustrate graphical displays of the present system for health monitoring of a dishwasher, according to various embodiments.

FIG. 3 illustrates an example machine learning module for health monitoring of a dishwasher, according to various embodiments;

FIG. 4 illustrates a flowchart of a method for health monitoring of a dishwasher, according to various embodiments.

FIG. 5 is a block diagram of a machine in the example form of a computer system within which a set of instructions may be executed, for causing the machine to perform any one or more of the methodologies discussed herein.

DETAILED DESCRIPTION

The following detailed description of the present subject matter refers to subject matter in the accompanying drawings which show, by way of illustration, specific aspects and embodiments in which the present subject matter may be practiced. These embodiments are described in sufficient detail to enable those skilled in the art to practice the present subject matter. References to “an”, “one”, or “various” embodiments in this disclosure are not necessarily to the same embodiment, and such references contemplate more than one embodiment. The scope of the present invention is defined by the appended claims, along with the full scope of legal equivalents to which such claims are entitled.

Commercial dishwashers include a large number of moving parts and electrical controls that enable proper operation of the machines. In commercial settings, dishwasher service interruptions can have a large detrimental impact on the businesses and establishments that use the machines. Some dishwasher errors that may be relatively easy to fix may still result in long down times due to the need for calls to service professionals and time required for scheduling and performing a service call to diagnose and correct the errors.

The present subject matter provides a system for monitoring the operating health of a dishwasher. The system leverages machine learning to monitor, diagnose, and provide remedies for dishwasher maintenance and performance. The system uses a unique control board with embedded sensors that monitors a dishwasher for component failures and then reports failures to a user, either remotely using a cellular connected gateway or locally using a connection such as Bluetooth to a mobile phone application, in some examples. In various examples, the mobile application then translates the component failure diagnostics into step-by-step troubleshooting or replacement procedures and optionally logs the replaced component in a memory.

FIG. 1A illustrates a block diagram of a system for health monitoring of a dishwasher, according to various embodiments. The system 100 includes an electronic control board 110 including embedded sensors 116 configured to monitor a dishwasher during operation of the dishwasher, a microprocessor 114 (or microcontroller or the like) configured to detect an error condition of the dishwasher using signals from one or more sensors, and a wireless communication transceiver 112 configured to transmit failure information of the error condition. The system 100 also includes one or more processors 114, 124, 130, 140 and a data storage system in communication with the one or more processors. The data storage system comprises instructions thereon that, when executed by the one or more processors, causes the one or more processors to diagnose, using the failure information and machine learning, a component failure of a component 102 of the dishwasher, generate, based on the diagnosed component failure, service instructions for the component failure of the dishwasher, and display, for a user of the dishwasher, the service instructions including identifying information of replacement components for the dishwasher. A user interface or human machine interface (Ux/HMI) 120 may be used to interface between the control board 110 and other devices, the Ux/HMI 120 including a controller 124, a transmitter/receiver integrated circuit 126, a wireless communication circuit 122, and an antenna 128 for wireless communication. The Ux/HMI 120 may communicate with a cloud system 140 and/or a mobile device 130, such as a phone, tablet, or other computing device, in various embodiments.

In various embodiments, the instructions further cause the one or more processors to log the replacement components for the dishwasher in a memory. The instructions further cause the one or more processors to predict the component failure using the failure information and machine learning, in various embodiments. According to various embodiments, the one or more processors may include the microprocessor of the electronic control board 110, a processor of a mobile device 130, and/or a processor of a cloud-based system 140. The wireless communication transceiver may include a Bluetooth communication transceiver or a cellular communication transceiver, in some examples. The one or more sensors are configured to monitor electrical current used by a component of the dishwasher, in one embodiment. Other signals, such as temperature, acoustics, vibration, and the like, may be monitored without departing from the scope of the present subject matter. According to various embodiments, the one or more sensors may include the embedded sensor or sensors of the control board 110, a sensor or sensors on or in the dishwasher, and/or a sensor or sensors remote from the dishwasher and remote from the control board. In some embodiments, displaying the service instructions includes using a mobile application configured to execute on a user mobile device 130. In some embodiments, the service instructions may be displayed on a display on the dishwasher. For example, service instructions may be provided on a display of the dishwasher so that a customer may resolve minor issues without requiring a visit from a service associate. In various embodiments, service information is provided to a person or entity with remote access. In various embodiments the remote access is to a user mobile device. In various embodiments, the remote access is to the dishwasher. In various embodiments, the remote access is to a web page that collects service information from one or more dish machines. In various embodiments, the remote access is to a server having the service information. Other data sources and modes of communicating the service information are possible without departing from the scope of the present subject matter. Different sources and modes of communicating service information may be used in combination in various embodiments.

In one embodiment, a current sensor 116 (amp sensor) on the control board 110 detects if a component 102 has failed based on whether the component should be turned on because a relay is giving it power and there is no amperage being drawn (or an unexpected voltage is drawn) indicating that the component has failed or is near failure and should be replaced. The information from the sensor 116 is sent to a microcontroller unit (MCU) 114 to determine whether the component is functioning and to send a message regarding component status to the display board (Ux/HMI 120) which houses the wireless communication component 122. The display board sends a message to the cloud 140 through either a cellular gateway or a handheld device with an application 130, in various embodiments.

In one embodiment, an embedded sensor on the control board 110 measures power drawn by a fill valve of the dishwasher which indicates that the valve is energized, but a water level sensor in the dishwasher (and/or a flow meter on the water line to the dishwasher) indicate that water is not entering the dishwasher. In this embodiment, the control board MCU 114 may diagnose that a main water valve is closed.

The control board 110 may use edge computing at a local controller to analyze the data. A processor 114 may be used on the device to analyze data from the sensor 116 and determine likelihood of dishwasher component failure (e.g., using edge computing and/or machine learning), or the raw data may be sent to an external device or to the cloud for remote analysis. If it is determined that a component failure is likely, data may be sent either locally (e.g., using Bluetooth, ZigBee, Wi-Fi, LoRa, etc.) to a service associate, or to a central cellular gateway for further analysis and communication of activity to service associate. A number of wireless protocols may be used by the present device to communicate and report dishwasher health monitoring results or other data to one or more external devices (such as a computer, a smartphone, a tablet, etc.), to other devices, to a router, to a gateway, or the like. The wireless standards that may be used by the present subject matter include, but are not limited to, one or more of the following: LoRa, near-field communication (NFC), Bluetooth, Bluetooth Low Energy (BLE), Ethernet, Wi-Fi, WiMax, ZigBee, or cellular standard communications such as 3G, 4G, LTE, 5G. Other wireless standards may be used without departing from the scope of the present subject matter.

The system 100 may further include a display element such as a light emitting diode (LED) on a surface of the housing, or other type of display for providing device status or the like. In various examples, the control board 110 may also include wireless communication electronics connected to the controller and configured to provide for wireless communications with one or more external devices. The control board of the present subject matter includes a design that incorporates electrical sensors onto the circuit board to monitor the electrical signature (generally amperage draw) as a means to identify component failure. The control board may be used with a mobile application that receives information from the control board and provides a diagnostic to the user or service associate describing what has failed and further provides troubleshooting steps to the user by way of short videos (e.g., GIFs) that provide step by step instructions for repair of the dishwasher. In some embodiment, the mobile application then logs the part replaced after running a diagnostic cycle to confirm the issue has been fixed.

FIGS. 1B-ID illustrate perspective views of a dishwasher, according to various embodiments. FIG. 1B shows a dishmachine or dishwasher 150 having a wash changer 152, a wash tank 154, a wash pump and motor 158, and a rinse heater 156. In various embodiments, a control head 160 is affixed to or placed on top of the dishwasher 150 and used to control operation of the dishwasher 160. FIG. 1C shows a view of the dishwasher 150 showing upper wash arms 172 and rinse arms 174 used for circulating water to clean dishes in the dishwasher. FIG. 1D shows a view of the dishwasher 150 showing lower wash arms 172 and rinse arms 174. FIG. 1E illustrates benefits of the present system for health monitoring of a dishwasher, according to various embodiments. In various embodiments, the present system provides for increased customer satisfaction, decreased down time due of the dishwasher, and better reporting of results of cleaning and monitoring. Additional benefits include additional insights into quality and refurbishment of the dishwasher, predictive maintenance, faster and more efficient service visits, and improved training for users and service technicians. In various examples, the present subject matter provides a tool for faster identification and troubleshooting of component failures and predictive failures of components in the future. While the present machine health monitoring system is demonstrated for dishwashers, it may also be used for detergent dispensers, machine cleaning caddies, or other kitchen appliances such as ovens, grills, refrigerators, mixers, or the like. FIG. 1F illustrates a diagram of a control circuit board 180 for health monitoring of a dishwasher, such as control board 110 in FIG. 1A, according to various embodiments.

FIGS. 2A-2H illustrate graphical displays of the present system for health monitoring of a dishwasher, according to various embodiments. FIG. 2A shows screenshots of a mobile application of the present subject matter, providing digital controls to a user of the present system. In various examples, the mobile application provides the user with a diagnostic of the dishwasher, provides a part list and step-by-step instructions (textual or visual or both), provides confirmation that the repair was successful, and places components in a shopping cart for ease of reordering. Various examples of the mobile application include a status dashboard 202, a diagnostic summary 204, a self-diagnosis list 206, visual instructions 208, and confirmation of results 210.

FIG. 2B shows screenshots of a customer application for a customer having the dishwasher, providing savings template 212 for various machine modes, a machine data summary 214 for use of the dishwasher, and a service schedule calendar 216 for planning service of the dishwasher based on the diagnostics. The customer application provides data reporting from the dishwasher and provides customers with operational modes that the customer may activate, such as a ‘turbo mode’ that increases chemical products and runs faster cycles to help a customer get through a rush period while not increasing their chemical use. Another mode available on the customer application is an ‘energy saving’ mode that turns off heaters whenever the machine is idle for a programmable time period (15 minutes in one example). Yet another mode available on the customer application is a ‘ware specific’ mode for washing certain items. Other modes may be available on the customer application without departing from the scope of the present subject matter. In addition, the modes may be automatically activated and programmable from the customer application. For example, the customer may schedule the turbo mode from between 10 am to 2 pm.

FIG. 2C shows controllable sliders that a user, or a processor of the present subject matter, may adjust to control operation of the dishwasher, including but not limited to water usage, energy usage, cycle time, temperature and chemistry level. The present subject matter provides a tool for improved dishwasher troubleshooting that may be local or remote from the dishwasher and provides predictive component failure to enable replacement of the components before failure. The present subject matter also provides customer value by improving machine up time and providing additional features that are enabled by digital and remote control, such as a customer facing mobile application that gives customers options to select different cycle types such as a fast cycle or energy saving mode, which may be built into the dishmachine for improved automation.

FIG. 2D shows an illustration of adjusted sliders and a display screen of a mobile application in the instance when the present subject matter detects a wash arm obstruction of the dishwasher. In this embodiment, the health monitoring system of the present subject matter automatically detects the inefficient operation and diagnoses the wash arm obstruction, adjusts the slider settings, and notifies the user using a display in the mobile application. As depicted, in response to detecting the wash arm obstruction, the present system automatically increases water and energy usage, increases chemistry level, decreases cycle time speed, and makes no change to the operating temperature, by adjusting the sliders of FIG. 2C.

FIG. 2E shows an illustration of a display screen of a mobile application in the instance with the present subject matter predicts a failure of a component, according to one embodiment. In this embodiment, the present system uses sensor data, machine health history, and machine learning to identify components that may soon fail, so that they may be conveniently replaced outside of the user's busiest machine use times. The present subject matter continuously monitors component health and history for the dishwasher. In one example, the present subject matter detects that a wash pump motor is beginning to show signs that it may soon fail. The system then notifies a user, such as a service representative, that the component failure is imminent and that the component should be replaced. This prevents the potential service interruption and decreases the need for emergency service of the dishwasher, in various embodiments.

FIG. 2F shows an illustration of adjusted sliders and a display screen of a mobile application in the instance when the present subject matter automatically adjusts to busy time periods of the user. In this embodiment, the health monitoring system of the present subject matter automatically detects the busy periods and adjusts the slider settings, and notifies the user using a display in the mobile application. As depicted, in response to diagnosing a busy period, the present system automatically increases water and energy usage, increases chemistry level, increases cycle time speed, and increases the operating temperature, by adjusting the sliders of FIG. 2C.

FIG. 2G shows an illustration of adjusted sliders and a display screen of a mobile application in the instance when the present subject matter automatically adjusts operation of the dishwasher to provide energy cost reduction for the user. In this embodiment, the health monitoring system of the present subject matter automatically recognizes how it can save the user money in operating costs without sacrificing dishwasher performance and adjusts the slider settings, and notifies the user using a display in the mobile application. As depicted, in response to diagnosing an opportunity for energy cost reduction, the present system automatically decreases water and energy usage, decreases chemistry level, decreases cycle time speed, and decreases the operating temperature, by adjusting the sliders of FIG. 2C.

FIG. 2H shows an illustration of adjusted sliders and a display screen of a mobile application in the instance when the present subject matter detects a water softener of a water supply at the inlet of the dishwasher may be low or out of salt. In this embodiment, the health monitoring system of the present subject matter automatically detects the inefficient operation and diagnoses the water softener issue, adjusts the slider settings, and notifies the user using a display in the mobile application. As depicted, in response to detecting the wash arm obstruction, the present system automatically increases water and energy usage, increases chemistry level, decreases cycle time speed, and decreases the operating temperature, by adjusting the sliders of FIG. 2C.

FIG. 3 shows an example machine learning module 300 according to some examples of the present disclosure. The machine learning module 300 may be implemented in whole or in part by one or more computing devices. In some examples, the training module 310 may be implemented by a different device than the prediction module 320. In these examples, the model 120 may be created on a first machine and then sent to a second machine.

Machine learning module 300 utilizes a training module 310 and a prediction module 320. Training module 310 inputs training feature data 330 into feature determination module 350. The training feature data 330 may include data determined to be predictive of monitoring and diagnosing machine health of a dishwasher. Categories of training feature data may include tracked data, input data, image data, user data, other third-party data, or the like. Specific training feature data and prediction feature data 390 may include, for example one or more of: current tracked data, past tracked data, and the like.

Feature determination module 350 selects training vector 360 from the training feature data 330. The selected data may fill training vector 360 and comprises a set of the training feature data that is determined to be predictive of monitoring and diagnosing machine health of a dishwasher. In some examples, the tasks performed by the feature determination module 350 may be performed by the machine learning algorithm 370 as part of the learning process. Feature determination module 350 may remove one or more features that are not predictive of monitoring and diagnosing machine health of a dishwasher to train the model 120. This may produce a more accurate model that may converge faster. Information chosen for inclusion in the training vector 360 may be all the training feature data 330 or in some examples, may be a subset of all the training feature data 330.

In other examples, the feature determination module 350 may perform one or more data standardization, cleanup, or other tasks such as encoding non numerical features. For example, for categorical feature data, the feature determination module 350 may convert these features to numbers. In some examples, encodings such as “One Hot Encoding” may be used to convert the categorical feature data to numbers. This enables a representation of the categorical variables as binary vectors and provided a “probability-like” number for each label value to give the model more expressive power. One hot encoding represents a category as a vector whereby each possible category value is represented by one element in the vector. When the data is equal to that category value, the value of the vector is a ‘1’ and all other elements are zero (or vice versa).

The training vector 360 may be utilized (along with any applicable labels) by the machine learning algorithm 370 to produce a model 120. In some examples, other data structures other than vectors may be used. The machine learning algorithm 370 may learn one or more layers of a model. Example layers may include convolutional layers, dropout layers, pooling/up sampling layers, SoftMax layers, and the like. Example models may be a neural network, where each layer is comprised of a plurality of neurons that take a plurality of inputs, weight the inputs, input the weighted inputs into an activation function to produce an output which may then be sent to another layer. Example activation functions may include a Rectified Linear Unit (ReLu), and the like. Layers of the model may be fully or partially connected. In other examples, machine learning algorithm may be a gradient boosted tree and the model may be one or more data structures that describe the resultant nodes, leaves, edges, and the like of the tree.

In the prediction module 320, prediction feature data 390 may be input to the feature determination module 395. The prediction feature data 390 may include the data described above for the training feature data, but for a specific items such as dishwasher component failure identification or classification. In some examples, the prediction module 320 may be run sequentially for one or more items. Feature determination module 395 may operate the same, or differently than feature determination module 350. In some examples, feature determination modules 350 and 395 are the same modules or different instances of the same module. Feature determination module 395 produces vector 397, which is input into the model 120 to produce predictions 399. For example, the weightings and/or network structure learned by the training module 310 may be executed on the vector 397 by applying vector 397 to a first layer of the model 120 to produce inputs to a second layer of the model 120, and so on until the prediction 399 is output. As previously noted, other data structures may be used other than a vector (e.g., a matrix).

The training module 310 may operate in an offline manner to train the model 120. The prediction module 320, however, may be designed to operate in an online manner. It should be noted that the model 120 may be periodically updated via additional training and/or user feedback. For example, additional training feature data 330 may be collected. The feedback, along with the prediction feature data 390 corresponding to that feedback, may be used to refine the model by the training module 310.

In some example embodiments, results obtained by the model 120 during operation (e.g., outputs produced by the model in response to inputs) are used to improve the training data, which is then used to generate a newer version of the model. Thus, a feedback loop is formed to use the results obtained by the model to improve the model.

The machine learning algorithm 370 may be selected from among many different potential supervised or unsupervised machine learning algorithms. Examples of learning algorithms include artificial neural networks, convolutional neural networks, Bayesian networks, instance-based learning, support vector machines, decision trees (e.g., Iterative Dichotomiser 3, C4.5, Classification and Regression Tree (CART), Chi-squared Automatic Interaction Detector (CHAID), and the like), random forests, gradient boosted tree, linear classifiers, quadratic classifiers, k-nearest neighbor, linear regression, logistic regression, a region based CNN, a full CNN (for semantic segmentation), a mask R-CNN algorithm for instance segmentation, and hidden Markov models. Examples of unsupervised learning algorithms include expectation-maximization algorithms, vector quantization, and information bottleneck method.

The machine learning may include a machine learning model including a neural network. The machine learning model may include one or more of a long short-term memory (LSTM) network, bidirectional encoder representations from transformers (BERT), natural language processing (NLP), or an artificial intelligence (AI)-based knowledge tree, in various examples. In various examples, the artificial intelligence includes a large language model (LLM). Other types of machine learning models may be used without departing from the scope of the present subject matter.

FIG. 4 illustrates a flowchart of a method for health monitoring of a dishwasher, according to various embodiments. The method 400 includes monitoring, using a control board, a dishwasher during operation of the dishwasher, at step 402. At step 404, the method includes detecting, using the control board, an error condition of the dishwasher using signals from one or more sensors. The method 400 also includes reporting, using a wireless communication transceiver, failure information of the error condition, at step 406, and diagnosing, using the failure information and machine learning, a component failure of the dishwasher, at step 408. The method 400 further includes generating, based on the diagnosed component failure, service instructions for the component failure of the dishwasher, at step 410, and displaying, for a user of the dishwasher, the service instructions including identifying information of replacement components for the dishwasher, at step 412.

According to various embodiments, the method 400 further includes logging the replacement components for the dishwasher in a memory. The method 400 also includes predicting the component failure using the failure information and the machine learning, in various embodiments. In some examples, reporting failure information of the error condition includes reporting failure information to a mobile device application. Reporting failure information of the error condition includes reporting failure information to a cloud-based system, in some examples. In various examples, displaying the service instructions includes using a mobile application configured to execute on a user device. In some examples, the service instructions may be displayed on a display (such as a graphical user interface (GUI) or display panel) on the dishwasher. Displaying the service instructions includes displaying a status dashboard on a user device and/or on a display of the dishwasher, in various examples. In some embodiments, displaying the service instructions includes displaying a diagnostic summary on a user device and/or on a display of the dishwasher. Displaying the service instructions includes displaying text with step-by-step repair instructions on a user device and/or on a display of the dishwasher, in some embodiments. In various embodiments, displaying the service instructions includes displaying a video including step-by-step repair instructions on a user device and/or on a display of the dishwasher.

FIG. 5 illustrates a block diagram of an example machine 500 upon which any one or more of the techniques (e.g., methodologies) discussed herein may perform. In alternative embodiments, the machine 500 may operate as a standalone device or may be connected (e.g., networked) to other machines. In a networked deployment, the machine 500 may operate in the capacity of a server machine, a client machine, or both in server-client network environments. In an example, the machine 500 may act as a peer machine in peer-to-peer (P2P) (or other distributed) network environment. The machine 500 may be configured to perform the method of FIG. 4. The machine 500 may be in the form of a personal computer (PC), a tablet PC, a set-top box (STB), a personal digital assistant (PDA), a mobile telephone, a smart phone, a web appliance, a network router, switch or bridge, or any machine capable of executing instructions (sequential or otherwise) that specify actions to be taken by that machine. Further, while only a single machine is illustrated, the term “machine” shall also be taken to include any collection of machines that individually or jointly execute a set (or multiple sets) of instructions to perform any one or more of the methodologies discussed herein, such as cloud computing, software as a service (SaaS), other computer cluster configurations.

Examples, as described herein, may include, or may operate on, logic or a number of components, modules, or mechanisms. Modules are tangible entities (e.g., hardware) capable of performing specified operations and may be configured or arranged in a certain manner. In an example, circuits may be arranged (e.g., internally or with respect to external entities such as other circuits) in a specified manner as a module. In an example, the whole or part of one or more computer systems (e.g., a standalone, client or server computer system) or one or more hardware processors may be configured by firmware or software (e.g., instructions, an application portion, or an application) as a module that operates to perform specified operations. In an example, the software may reside on a machine readable medium. In an example, the software, when executed by the underlying hardware of the module, causes the hardware to perform the specified operations.

Accordingly, the term “module” is understood to encompass a tangible entity, be that an entity that is physically constructed, specifically configured (e.g., hardwired), or temporarily (e.g., transitorily) configured (e.g., programmed) to operate in a specified manner or to perform part or all of any operation described herein. Considering examples in which modules are temporarily configured, each of the modules need not be instantiated at any one moment in time. For example, where the modules comprise a general-purpose hardware processor configured using software, the general-purpose hardware processor may be configured as respective different modules at different times. Software may accordingly configure a hardware processor, for example, to constitute a particular module at one instance of time and to constitute a different module at a different instance of time.

The processor 502 may be a digital signal processor (DSP), microprocessor, microcontroller, application-specific integrated circuit (ASIC), field-programmable gate array (FPGA), combinational logic, other digital logic, or combinations thereof. The processing may be done by a single processor, or may be distributed over different devices. The processing of signals referenced in this application may be performed using the processor or over different devices. Processing may be done in the digital domain, the analog domain, or combinations thereof. Processing may be done using subband processing techniques. Processing may be done using frequency domain or time domain approaches. Some processing may involve both frequency and time domain aspects. For brevity, in some examples, drawings may omit certain blocks that perform frequency synthesis, frequency analysis, analog-to-digital conversion, digital-to-analog conversion, signal transmission, amplification, buffering, and certain types of filtering and processing. In various examples of the present subject matter the processor is adapted to perform instructions stored in one or more memories, which may or may not be explicitly shown. Various types of memory may be used, including volatile and nonvolatile forms of memory. In various examples, the processor or other processing devices execute instructions to perform a number of processing tasks. In various examples of the present subject matter, different realizations of the block diagrams, circuits, and processes set forth herein may be created by one of skill in the art without departing from the scope of the present subject matter.

Machine (e.g., computer system) 500 may include a hardware processor 502 (e.g., a central processing unit (CPU), a graphics processing unit (GPU), a hardware processor core, or any combination thereof), a controller, a microcontroller, a microprocessor, a main memory 504 and a static memory 506, some or all of which may communicate with each other via an interlink (e.g., bus) 508. The machine 500 may further include a display unit 510, an alphanumeric input device 512 (e.g., a keyboard), and a user interface (UI) navigation device 514 (e.g., a mouse). In an example, the display unit 510, input device 512 and UI navigation device 514 may be a touch screen display. The machine 500 may additionally include a storage device (e.g., drive unit) 516, a signal generation device 518 (e.g., a speaker), a network interface device 520, and one or more sensors 521, such as a global positioning system (GPS) sensor, compass, accelerometer, or other sensor. The machine 500 may include an output controller 528, such as a serial (e.g., universal serial bus (USB), parallel, or other wired or wireless (e.g., infrared (IR), near field communication (NFC), etc.) connection to communicate or control one or more peripheral devices (e.g., a printer, card reader, etc.).

The storage device 516 may include a machine readable medium 522 on which is stored one or more sets of data structures or instructions 524 (e.g., software) embodying or utilized by any one or more of the techniques or functions described herein. The instructions 524 may also reside, completely or at least partially, within the main memory 504, within static memory 506, or within the hardware processor 502 during execution thereof by the machine 500. In an example, one or any combination of the hardware processor 502, the main memory 504, the static memory 506, or the storage device 516 may constitute machine readable media.

While the machine readable medium 522 is illustrated as a single medium, the term “machine readable medium” may include a single medium or multiple media (e.g., a centralized or distributed database, and/or associated caches and servers) configured to store the one or more instructions 524.

The term “machine readable medium” may include any medium that is capable of storing, encoding, or carrying instructions for execution by the machine 500 and that cause the machine 500 to perform any one or more of the techniques of the present disclosure, or that is capable of storing, encoding or carrying data structures used by or associated with such instructions. Non-limiting machine-readable medium examples may include solid-state memories, and optical and magnetic media. Specific examples of machine-readable media may include: non-volatile memory, such as semiconductor memory devices (e.g., Electrically Programmable Read-Only Memory (EPROM), Electrically Erasable Programmable Read-Only Memory (EEPROM)) and flash memory devices; magnetic disks, such as internal hard disks and removable disks; magneto-optical disks; Random Access Memory (RAM); Solid State Drives (SSD); and CD-ROM and DVD-ROM disks. In some examples, machine readable media may include non-transitory machine-readable media. In some examples, machine readable media may include machine readable media that is not a transitory propagating signal.

The instructions 524 may further be transmitted or received over a communications network 526 using a transmission medium via the network interface device 520. The Machine 500 may communicate with one or more other machines utilizing any one of a number of transfer protocols (e.g., frame relay, internet protocol (IP), transmission control protocol (TCP), user datagram protocol (UDP), hypertext transfer protocol (HTTP), etc.). Example communication networks may include wired and wireless communications, such as Ethernet, Bluetooth, Bluetooth Low Energy, other Personal Area Networks (PANs), LoRa, NFC, Wi-Fi, WiMAX, 3G, 4G, LTE, 5G, the unlicensed 915 MHz Industrial, Scientific, and Medical (ISM) frequency band, ZigBee, among others. Some standards may support mesh networks. The networks include, but are not limited to, a local area network (LAN), a low-power wide-area network (LPWAN), a wide area network (WAN), a packet data network (e.g., the Internet), mobile telephone networks (e.g., cellular networks), Plain Old Telephone (POTS) networks, and wireless data networks, e.g., Institute of Electrical and Electronics Engineers (IEEE) 802.11 family of standards known as Wi-Fi®, IEEE 802.16 family of standards known as WiMax®, NFC, IEEE 802.15.4 family of standards, a Long Term Evolution (LTE) family of standards, a Universal Mobile Telecommunications System (UMTS) family of standards, peer-to-peer (P2P) networks, among others. The NFC circuitry may be embodied as relatively short-range, high frequency wireless communication circuitry and may implement standards such as ECMA-340/ISO/IEC 18092 and/or ECMA-352/ISO/IEC 21481 to communicate with other devices. In an example, the network interface device 520 may include one or more physical jacks (e.g., Ethernet, coaxial, or phone jacks) or one or more antennas to connect to the communications network 526. In an example, the network interface device 520 may include a plurality of antennas to wirelessly communicate using at least one of single-input multiple-output (SIMO), multiple-input multiple-output (MIMO), or multiple-input single-output (MISO) techniques. In some examples, the network interface device 520 may wirelessly communicate using Multiple User MIMO techniques.

Other Notes and Examples

Example 1 is a method including monitoring, using a control board, a dishwasher during operation of the dishwasher, detecting, using the control board, an error condition of the dishwasher using signals from one or more sensors, reporting, using a wireless communication transceiver, failure information of the error condition, diagnosing, using the failure information and machine learning, a component failure of the dishwasher, generating, based on the diagnosed component failure, service instructions for the component failure of the dishwasher, and displaying, for a user of the dishwasher, the service instructions including identifying information of replacement components for the dishwasher.

In Example 2, the subject matter of Example 1 optionally includes wherein the one or more sensors include embedded sensors of the control board.

In Example 3, the subject matter of any of the preceding Examples optionally includes wherein the one or more sensors include a sensor on or in the dishwasher.

In Example 4, the subject matter of any of the preceding Examples optionally includes wherein the one or more sensors include a sensor remote from the control board and remote from the dishwasher.

In Example 5, the subject matter of any of the preceding Examples optionally further includes logging the replacement components for the dishwasher in a memory.

In Example 6, the subject matter of any of the preceding Examples optionally further includes predicting the component failure using the failure information and the machine learning.

In Example 7, the subject matter of any of the preceding Examples optionally includes wherein reporting failure information of the error condition includes reporting failure information to a mobile device application.

In Example 8, the subject matter of any of the preceding Examples optionally includes wherein reporting failure information of the error condition includes reporting failure information to a cloud-based system.

In Example 9, the subject matter of any of the preceding Examples optionally includes wherein displaying the service instructions includes using a mobile application configured to execute on a user device.

In Example 10, the subject matter of any of the preceding Examples optionally includes wherein displaying the service instructions includes displaying the service instructions on a display on the dishwasher.

In Example 11, the subject matter of any of the preceding Examples optionally includes wherein displaying the service instructions includes displaying a diagnostic summary on a user device.

In Example 12, the subject matter of any of the preceding Examples optionally includes wherein displaying the service instructions includes displaying text with step-by-step repair instructions on a user device.

In Example 13, the subject matter of any of the preceding Examples optionally includes wherein displaying the service instructions includes displaying a video including step-by-step repair instructions on a user device.

Example 14 is a system including an electronic control board including embedded sensors configured to monitor a dishwasher during operation of the dishwasher, a microprocessor configured to detect an error condition of the dishwasher using signals from one or more sensors, and a wireless communication transceiver configured to transmit failure information of the error condition, and one or more processors and a data storage system in communication with the one or more processors, wherein the data storage system comprises instructions thereon that, when executed by the one or more processors, causes the one or more processors to: diagnose, using the failure information and machine learning, a component failure of the dishwasher, generate, based on the diagnosed component failure, service instructions for the component failure of the dishwasher, and display, for a user of the dishwasher, the service instructions including identifying information of replacement components for the dishwasher.

In Example 15, the subject matter of Example 14 optionally includes wherein the one or more sensors include embedded sensors of the control board.

In Example 16, the subject matter of any of Examples 14-15 optionally includes wherein the one or more sensors include a sensor on or in the dishwasher.

In Example 17, the subject matter of any of Examples 14-16 optionally includes wherein the one or more sensors include a sensor remote from the control board and remote from the dishwasher.

In Example 18, the subject matter of any of Examples 14-17 optionally includes wherein the instructions further cause the one or more processors to log the replacement components for the dishwasher in a memory.

In Example 19, the subject matter of any of Examples 14-18 optionally includes wherein the instructions further cause the one or more processors to predict the component failure using the failure information and machine learning.

In Example 20, the subject matter of any of Examples 14-19 optionally includes wherein the one or more processors include the microprocessor of the electronic control board.

In Example 21, the subject matter of any of Examples 14-20 optionally includes wherein the one or more processors include a processor of a mobile device.

In Example 22, the subject matter of any of Examples 14-21 optionally includes wherein the one or more processors include a processor of a cloud-based system.

In Example 23, the subject matter of any of Examples 14-22 optionally includes wherein the wireless communication transceiver includes a Bluetooth communication transceiver.

In Example 24, the subject matter of any of Examples 14-23 optionally includes wherein the wireless communication transceiver includes a cellular communication transceiver.

In Example 25, the subject matter of any of Examples 14-24 optionally includes wherein the embedded sensors are configured to monitor electrical current used by a component of the dishwasher.

In Example 26, the subject matter of any of Examples 14-25 optionally includes wherein displaying the service instructions includes using a mobile application configured to execute on a user device.

Example 27 is at least one machine-readable medium including instructions that, when executed by processing circuitry, cause the processing circuitry to perform operations to implement of any of Examples 1-26.

Example 28 is an apparatus comprising means to implement of any of Examples 1-26.

Example 29 is a system to implement of any of Examples 1-26.

Example 30 is a method to implement of any of Examples 1-26.

The foregoing examples are not intended to be an exhaustive or exclusive list of examples and variations of the present subject matter. The above description is intended to be illustrative, and not restrictive. Those of skill in the art will appreciate additional variations of the embodiments that can be used within the scope of the teachings set forth herein. The scope of the invention should be determined with reference to the appended claims, along with the full scope of equivalents to which such claims are entitled.

Claims

1. A method, comprising:

monitoring, using a control board, a dishwasher during operation of the dishwasher;

detecting, using the control board, an error condition of the dishwasher using signals from one or more sensors;

reporting, using a wireless communication transceiver, failure information of the error condition;

diagnosing, using the failure information and machine learning, a component failure of the dishwasher;

generating, based on the diagnosed component failure, service instructions for the component failure of the dishwasher; and

displaying, for a user of the dishwasher, the service instructions including identifying information of replacement components for the dishwasher.

2. The method of claim 1, wherein the one or more sensors include embedded sensors of the control board.

3. The method of claim 1, wherein the one or more sensors include a sensor on or in the dishwasher.

4. The method of claim 1, wherein the one or more sensors include a sensor remote from the control board and remote from the dishwasher.

5. The method of claim 1, further comprising logging the replacement components for the dishwasher in a memory.

6. The method of claim 1, further comprising predicting the component failure using the failure information and the machine learning.

7. The method of claim 1, wherein reporting failure information of the error condition includes reporting failure information to a mobile device application.

8. The method of claim 1, wherein reporting failure information of the error condition includes reporting failure information to a cloud-based system.

9. The method of claim 1, wherein displaying the service instructions includes using a mobile application configured to execute on a user device.

10. The method of claim 1, wherein displaying the service instructions includes displaying the service instructions on a display on the dishwasher.

11. The method of claim 1, wherein displaying the service instructions includes displaying a diagnostic summary on a user device.

12. The method of claim 1, wherein displaying the service instructions includes displaying text with step-by-step repair instructions on a user device.

13. The method of claim 1, wherein displaying the service instructions includes displaying a video including step-by-step repair instructions on a user device.

14. A system, comprising:

an electronic control board, including: embedded sensors configured to monitor a dishwasher during operation of the dishwasher; a microprocessor configured to detect an error condition of the dishwasher using signals from one or more sensors; and a wireless communication transceiver configured to transmit failure information of the error condition; and

one or more processors and a data storage system in communication with the one or more processors, wherein the data storage system comprises instructions thereon that, when executed by the one or more processors, causes the one or more processors to: diagnose, using the failure information and machine learning, a component failure of the dishwasher; generate, based on the diagnosed component failure, service instructions for the component failure of the dishwasher; and display, for a user of the dishwasher, the service instructions including identifying information of replacement components for the dishwasher.

15. The system of claim 14, wherein the one or more sensors include the embedded sensors of the control board.

16. The system of claim 14, wherein the one or more sensors include a sensor on or in the dishwasher.

17. The system of claim 14, wherein the one or more sensors include a sensor remote from the control board and remote from the dishwasher.

18. The system of claim 14, wherein the instructions further cause the one or more processors to log the replacement components for the dishwasher in a memory.

19. The system of claim 14, wherein the instructions further cause the one or more processors to predict the component failure using the failure information and machine learning.

20. The system of claim 14, wherein the one or more processors include the microprocessor of the electronic control board.

21. The system of claim 14, wherein the one or more processors include a processor of a mobile device.

22. The system of claim 14, wherein the one or more processors include a processor of a cloud-based system.

23. The system of claim 14, wherein the wireless communication transceiver includes a Bluetooth communication transceiver.

24. The system of claim 14, wherein the wireless communication transceiver includes a cellular communication transceiver.

25. The system of claim 14, wherein the embedded sensors are configured to monitor electrical current used by a component of the dishwasher.

26. The system of claim 14, wherein displaying the service instructions includes using a mobile application configured to execute on a user device.