Abstract: A method and system of a predictive maintenance IoT system comprises receiving a plurality of sensor data over a communications network and determining one or more clusters from the sensor data based on a pre-determined rule set. Further, the sensor data is classified through a machine learning engine and the sensor data is further base-lined through a combination of database architecture, data training architecture, and a base-lining algorithm. Intensity or degree of fault state is mapped to a fuel gauge to be depicted on a user interface and a predictive maintenance state is predicted through a regression model and appropriate alarm is raised for user action.

Abstract: A machine learning method and system for predictive maintenance of a dryer. The method includes obtaining over a communication network, an information associated with the dryer and receiving measurements of a vibration level of one of a process blower, a cassette motor and a regeneration blower associated with the dryer. Further, an anomaly is determined based on at least one of a back pressure and a fault and balance of at least one of the process blower and the regeneration blower is tracked. An alarm for maintenance is raised when one of an anomaly and an off-balance is detected.

Abstract: A machine learning method and system for predictive maintenance of a dryer. The method includes obtaining over a communication network, an information associated with the dryer and receiving measurements of a vibration level of one of a process blower, a cassette motor and a regeneration blower associated with the dryer. Further, an anomaly is determined based on at least one of a back pressure and a fault and balance of at least one of the process blower and the regeneration blower is tracked. An alarm for maintenance is raised when one of an anomaly and an off-balance is detected.

Abstract: Disclosed is an IoT-based system for overseeing process control and predictive maintenance of a machine or a network of machines by employing machine wearable sensors. The system comprises a plurality of IR temperature sensors, each of which secured to the exterior of the machine; each IR sensor capable of transmitting captured temperature data wirelessly over a communications network, an algorithm engine capable of receiving data from the IR sensors, the algorithm engine for further processing the received data to recognize real-time temperature patterns, deviations, etc., and based on the same issuing control commands pertaining to the machine, and one or more control modules disposed in operative communication with the control panel of the machine, the control module capable of receiving, over a communications network, the control commands and executing the same resulting in accomplishing process control or predictive maintenance of the machine or both.

Abstract: An automated earth fault testing system and early warning system designed to be used with mobile towers for real-time monitoring of the earthing values. The automated earth fault testing system comprises an earth fault testing device powered by a low voltage direct current battery, a plurality of terminals, at least one calibration switch including a calibration pad for calibrating the earth fault testing device, a plurality of visual indication means for providing indication of a variety of conditions including high and/or normal value of the earth resistance value and for indicating a charge level of the earth fault testing device and a liquid crystal display for displaying the earthing values. The earth fault testing device is connected to an alarm system of a base transceiver station utilizing a relay for informing the mobile signal station and operator with information regarding a status of the earth fault testing device.

Abstract: Disclosed is an IoT-based system for overseeing process control and predictive maintenance of a machine or a network of machines by employing machine wearable sensors. The system comprises a plurality of IR temperature sensors, each of which secured to the exterior of the machine; each IR sensor capable of transmitting captured temperature data wirelessly over a communications network, an algorithm engine capable of receiving data from the IR sensors, the algorithm engine for further processing the received data to recognize real-time temperature patterns, deviations, etc., and based on the same issuing control commands pertaining to the machine, and one or more control modules disposed in operative communication with the control panel of the machine, the control module capable of receiving, over a communications network, the control commands and executing the same resulting in accomplishing process control or predictive maintenance of the machine or both.

Abstract: A method and system of on spot diagnosis of one or more issues associated with a machine includes collecting sound data and image data associated with a machine through a mobile device and transmitting them to a cloud server over a communications network. The on spot diagnosis includes analyzing the sound data and image data in combination with an on spot feedback system through the cloud server. The on spot feedback system is communicatively coupled to a machine-learning engine and a Big Data architecture. The machine issue condition is indicated through a user interface dynamic such a circular gauge. An alarm is set, through a mobile device, for the machine issue.

Abstract: A field learning system comprising a system of feedback uses a user interface in web based and mobile applications to overcome the difficulty and infeasibility of supervised machine learning systems used for modeling failure states of machines

Abstract: A method and system of a distributed power line diagnosis includes one or more electrical readings received at a firmware board computation engine and an output of firmware board engine computation being transmitted through a wide communication network to a data hub computation engine. An output of the data hub computation engine is transmitted through the communication network to a big data server. One or more electrical line issues are visualized based on an analysis through the big data server and the same can be indicated through a user interface dynamic and an alarm can be set as well through the processor for the one or more electrical line issues.

Abstract: A machine learning method and system for predictive maintenance of a dryer. The method includes obtaining over a communication network, an information associated with the dryer and receiving measurements of a vibration level of one of a process blower, a cassette motor and a regeneration blower associated with the dryer. Further, an anomaly is determined based on at least one of a back pressure and a fault and balance of at least one of the process blower and the regeneration blower is tracked. An alarm for maintenance is raised when one of an anomaly and an off-balance is detected.

Abstract: A computer implemented method and system for determining reliability of a machine includes receiving one of a machine data from one or more locations through an internet of things (IOT) based machine wearable sensor network. The method further includes storing the data in a distributed computer database communicatively coupled to an enterprise resource planning (ERP) system and extracting, through a computer server, one or more entity information from the data to compare against a pre-defined baseline. Further, mapping, though a big data machine learning engine, the extracted one or more entity information into a multi-classification model. The method includes indicating, through a machine learning engine coupled to a predictive analytics engine, on a user interface a set of analytical predictions for machine maintenance, repair and operation.

Abstract: An automated earth fault testing system and early warning system designed to be used with mobile towers for real-time monitoring of the earthing values. The automated earth fault testing system comprises an earth fault testing device powered by a low voltage direct current battery, a plurality of terminals, at least one calibration switch including a calibration pad for calibrating the earth fault testing device, a plurality of visual indication means for providing indication of a variety of conditions including high and/or normal value of the earth resistance value and for indicating a charge level of the earth fault testing device and a liquid crystal display for displaying the earthing values. The earth fault testing device is connected to an alarm system of a base transceiver station utilizing a relay for informing the mobile signal station and operator with information regarding a status of the earth fault testing device.

Abstract: A method and system of a predictive maintenance IoT system comprises receiving a plurality of sensor data over a communications network and determining one or more clusters from the sensor data based on a pre-determined rule set. Further, the sensor data is classified through a machine learning engine and the sensor data is further base-lined through a combination of database architecture, data training architecture, and a base-lining algorithm. Intensity or degree of fault state is mapped to a fuel gauge to be depicted on a user interface and a predictive maintenance state is predicted through a regression model and appropriate alarm is raised for user action.

Abstract: A system for rule management, predictive maintenance and quality assurance of a process using automatic rule formation comprising a plurality of sensors capable of being attached to at least one machine for measuring at least one information about the process and machine operation. The system comprises a server connected to the sensors over a wireless communication network and running a reconfigurable rule management program for identifying and processing the particular process and machine information related to at least one process received from the plurality of sensors. A controller in communication with the server capable of controlling the process based on a rule set by the rule engine. The rule engine automatically detects the normal process data, classifies the received data based on the dynamic rule formed by the rule engine and finds anomalies in the process or machine operation for predictive maintenance and process quality assurance.

Abstract: A system and method for accurately measuring the real-time valid dew-point value of a material and determining the total moisture content of the material within the valid dew-point value by using an algorithm during the material drying process. The algorithm estimates the valid dew-point value of the material and the total moisture content of the material by analyzing the sensor data received on a server. The algorithm determines a valid dew-point value by estimating an inflection point for the material, and the total moisture content of the material is determined within the valid dew-point value.

Abstract: A method and system of detecting faults in rotor driven equipment includes generating data from one or more vibration sensors communicatively coupled to the rotor driven equipment. The data from the one or more machine wearable sensors is collected onto a mobile data collector. The data is sampled at random to estimate a maximum value. Further, a sampling error may be controlled under a predefined value. The data may be analyzed through a combination of Cartesian to Spherical transformation, statistics of the entity extraction (such as variance of azimuthal angle), big data analytics engine and a machine learning engine. A fault is displayed on a user interface associated with the rotor driven equipment.

Abstract: A method and system of a machine learning architecture for predictive and preventive maintenance of vacuum pumps. The method includes receiving one of a motor sensor data and a blower sensor data over a communications network. The motor sensor data is classified into one of a vacuum state sensor data and break state sensor data. The vacuum state sensor data is analyzed to detect an operating vacuum level and an alarm is raised when the vacuum state sensor data exceeds a pre-defined safety range. Vacuum break data is classified into one of a clean filter category and clogged filter category and an alarm is raised if an entry under the clogged filter category is detected. The blower sensor data in association with the motor sensor data is analyzed based on machine learning to detect one of a deficient oil level and a deficient oil structure.

Abstract: Disclosed is an IoT-based system for overseeing process control and predictive maintenance of a machine or a network of machines by employing machine wearable sensors. The system comprises a plurality of IR temperature sensors, each of which secured to the exterior of the machine; each IR sensor capable of transmitting captured temperature data wirelessly over a communications network, an algorithm engine capable of receiving data from the IR sensors, the algorithm engine for further processing the received data to recognize real-time temperature patterns, deviations, etc., and based on the same issuing control commands pertaining to the machine, and one or more control modules disposed in operative communication with the control panel of the machine, the control module capable of receiving, over a communications network, the control commands and executing the same resulting in accomplishing process control or predictive maintenance of the machine or both.