Abstract: In some examples, a computer system may receive sensor data and failure data for equipment. The system may determine, for the equipment, a plurality of time between failure (TBF) durations that are longer than other TBF durations for the equipment. The system may determine, from the sensor data corresponding to operation of the equipment during the plurality of TBF durations, a plurality of measured sensor values for the equipment. Additionally, the system may determine a subset of the measured sensor values corresponding to a largest number of the TBF durations of the plurality of TBF durations. The system may further determine at least one operating parameter value for the equipment based on the subset of the measured sensor values. The system may send a control signal for operating the equipment based on the operating parameter value and/or a communication based on the operating parameter value.

Abstract: Example implementations involve fault detection and isolation in industrial networks through defining a component as a combination of measurements and parameters and define an industrial network as a set of components connected with different degrees of connections (weights). Faults in industrial network are defined as unpermitted changes in component parameters. Further, the fault detection and isolation in industrial networks are formulated as a node classification problem in graph theory. Example implementations detect and isolate faults in industrial networks through 1) uploading/learning network structure, 2) detecting component communities in the network, 3) extracting features for each community, 4) using the extracted features for each community to detect and isolate faults, 5) at each time step, based on the faulty components provide maintenance recommendation for the network.

Abstract: Example implementations described herein involve a new data-driven analytical redundancy relationship (ARR) generation for fault detection and isolation. The proposed solution uses historical data during normal operation to extract the data-driven ARRs among sensor measurements, and then uses them for fault detection and isolation. The proposed solution thereby does not need to rely on the system model, can detect and isolate more faults than traditional data-driven methods, can work when the system is not fully observable, and does not rely on a vast amount of historical fault data, which can save on memory storage or database storage. The proposed solution can thereby be practical in many real cases where there are data limitations.

Abstract: In some examples, a computer system may receive historical repair data and may extract features from the historical repair data for use as training data. The computer system may determine, from the historical repair data, a repair hierarchy including a plurality of repair levels which includes repair actions as one of the repair levels. Furthermore, the computer system may train the machine learning model, which performs multiple tasks for predicting values of individual levels of the repair hierarchy, by tuning parameters of the machine learning model using the training data.

Abstract: Example implementations involve a system for Predictive Maintenance using Generative Adversarial Networks for Failure Prediction. Through utilizing three processes concurrently and training them iteratively with data-label pairs, example implementations described herein can thereby generate a more accurate predictive maintenance model than that of the related art. Example implementations further involve shared networks so that the three processes can be trained concurrently while sharing parameters with each other.

Abstract: Example implementations described herein involve a new data-driven analytical redundancy relationship (ARR) generation for fault detection and isolation. The proposed solution uses historical data during normal operation to extract the data-driven ARRs among sensor measurements, and then uses them for fault detection and isolation. The proposed solution thereby does not need to rely on the system model, can detect and isolate more faults than traditional data-driven methods, can work when the system is not fully observable, and does not rely on a vast amount of historical fault data, which can save on memory storage or database storage. The proposed solution can thereby be practical in many real cases where there are data limitations.

Abstract: Example implementations described herein involve, for data having incomplete labeling to generate a plurality of predictive maintenance models, processing the data through a multi-task learning (MTL) architecture including generic layers and task specific layers for the plurality of predictive maintenance models configured to conduct tasks to determine outcomes for one or more components associated with the data, each task specific layer corresponding to one of the plurality of predictive maintenance models; the generic layers configured to provide, to the task specific layers, associated data to construct each of the plurality of predictive maintenance models; and executing the predictive maintenance models on subsequently recorded data.

Abstract: Example implementations described herein involve systems and methods for conducting feature extraction on a plurality of templates associated with vibration sensor data for a moving equipment configured to conduct a plurality of tasks, to generate a predictive maintenance model for the plurality of tasks, the predictive maintenance model configured to provide one or more of fault detection, failure prediction, and remaining useful life (RUL) estimation.

Abstract: Example implementations described herein are directed to systems and methods for predictive maintenance with health indicators using reinforcement learning. An example implementation includes a method to receive sensor data, operational condition data, and failure event data and generate a model to determine health indicators that indicate equipment performance based on learned policies, state values, and rewards. The model is applied to external sensor readings and operating data for a piece of equipment to output a recommendation based on the model.

Abstract: In some examples, a system may receive from a device, speech sound patterns corresponding to a voice input related to equipment. Further, the system may determine an identity of a person associated with the device, and may identify the equipment related to the voice input. Using at least one of the received speech sound patterns or a text conversion of the speech sound patterns, along with an equipment history of the identified equipment, as input to one or more machine learning models, the system may determine, at least partially, an instruction related to the equipment. Additionally, the system may send, to the device, the instruction related to the equipment as an audio file for playback on the device.

Abstract: Example implementations described herein involve systems and methods for conducting feature extraction on a plurality of templates associated with vibration sensor data for a moving equipment configured to conduct a plurality of tasks, to generate a predictive maintenance model for the plurality of tasks, the predictive maintenance model configured to provide one or more of fault detection, failure prediction, and remaining useful life (RUL) estimation.

Abstract: Example implementations described herein involve an anomaly detection method for robotic apparatuses such as robotic arms using vibration data. Such example implementations can involve fluctuation-based anomaly detection (e.g., based on their fluctuations in the vibration measurements) and/or frequency spectrum-based anomaly detection (e.g., based on their natural fluctuations in the vibration measurements).

Abstract: Example implementations involve a system for Predictive Maintenance using Generative Adversarial Networks for Failure Prediction. Through utilizing three processes concurrently and training them iteratively with data-label pairs, example implementations described herein can thereby generate a more accurate predictive maintenance model than that of the related art. Example implementations further involve shared networks so that the three processes can be trained concurrently while sharing parameters with each other.

Abstract: Example implementations described herein involve, for data having incomplete labeling to generate a plurality of predictive maintenance models, processing the data through a multi-task learning (MTL) architecture including generic layers and task specific layers for the plurality of predictive maintenance models configured to conduct tasks to determine outcomes for one or more components associated with the data, each task specific layer corresponding to one of the plurality of predictive maintenance models; the generic layers configured to provide, to the task specific layers, associated data to construct each of the plurality of predictive maintenance models; and executing the predictive maintenance models on subsequently recorded data.

Abstract: Example implementations described herein involve a system for maintenance recommendation based on data-driven failure prediction. The example implementations can involve estimating the probability of having a failure event in the near future given sensor measurements and events from the equipment, and then alerts the system user or maintenance staff if the probability of failure exceeds a certain threshold. The example implementations utilize historical failure cases along with the associated sensor measurements and events to learn a group of classification models that differentiate between failure and non-failure cases. In example implementations, the system then chooses the optimal model for failure prediction such that the overall cost of the maintenance process is minimized.

Abstract: Example implementations involve fault detection and isolation in industrial networks through defining a component as a combination of measurements and parameters and define an industrial network as a set of components connected with different degrees of connections (weights). Faults in industrial network are defined as unpermitted changes in component parameters. Further, the fault detection and isolation in industrial networks are formulated as a node classification problem in graph theory. Example implementations detect and isolate faults in industrial networks through 1) uploading/learning network structure, 2) detecting component communities in the network, 3) extracting features for each community, 4) using the extracted features for each community to detect and isolate faults, 5) at each time step, based on the faulty components provide maintenance recommendation for the network.

Abstract: In some examples, a computer system may receive historical repair data for equipment and/or domain knowledge related to the equipment. The system may construct a hierarchical data structure for the equipment including a first hierarchy and a second hierarchy, the first hierarchy including a plurality of equipment nodes corresponding to different equipment types, and the second hierarchy including a plurality of repair category nodes corresponding to different repair categories. The system may generate a plurality of machine learning models corresponding to the plurality of repair category nodes, respectively. When the system receives a repair request associated with the equipment, the system determines a certain one of the equipment nodes associated with the equipment, and based on determining that a certain repair category node is associated with the certain equipment node, uses the machine learning model associated with the certain repair category node to determine one or more repair actions.

Abstract: In some examples, a computer system may receive historical repair data and may extract features from the historical repair data for use as training data. The computer system may determine, from the historical repair data, a repair hierarchy including a plurality of repair levels which includes repair actions as one of the repair levels. Furthermore, the computer system may train the machine learning model, which performs multiple tasks for predicting values of individual levels of the repair hierarchy, by tuning parameters of the machine learning model using the training data.

Abstract: In some examples, a computer system may receive historical repair data for first equipment, and may extract features from the historical repair data for the first equipment as training data including one or more of: free-text variables associated with comments related to the first equipment; usage attributes associated with the first equipment; equipment attributes associated with the first equipment; sensor data associated with the first equipment; or event data associated with the first equipment. The system may determine a repair hierarchy including a plurality of repair levels for the equipment. The system may use the training data to train a machine learning model as a multilayer model trained to perform multiple tasks for predicting individual levels of the repair hierarchy. The system may receive a repair request associated with second equipment and uses the machine learning model to determine at least one repair action based on the received repair request.

Abstract: In some examples, a system may receive from a device, speech sound patterns corresponding to a voice input related to equipment. Further, the system may determine an identity of a person associated with the device, and may identify the equipment related to the voice input. Using at least one of the received speech sound patterns or a text conversion of the speech sound patterns, along with an equipment history of the identified equipment, as input to one or more machine learning models, the system may determine, at least partially, an instruction related to the equipment. Additionally, the system may send, to the device, the instruction related to the equipment as an audio file for playback on the device.