An abnormal irregularity cause identifying device includes a process data acquisition unit that reads, from a storage device storing process data and each associated with a management number of a processing target, the pieces of process data, an abnormality determination unit that continuously calculates an abnormality degree representing an extent of an irregularity of process data of the pieces of process data read by the process data acquisition unit, and a cause diagnosis unit that determines, for each of the pieces of process data and corresponding to the management number of the processing target, whether the abnormality degree calculated by the abnormality determination unit satisfies a predetermined criterion by using causal relation information defining a combination between a cause and the irregularity, which appears as an influence resulting from the cause, of the process data.

A method and system for reliability reporting for electrical hardware can involve analyzing stress data referenced to a predicted reliability of electrical hardware, the stress data including real-time stress information related to the electrical hardware. Reliability data associated with the electrical hardware based on the real-time information and the predicted reliability of the electrical hardware can be calculated. The reliability data can be presented in a graphical user interface that displays indicators of the reliability data including runtime data associated with the electrical hardware.

A method and computer program product including training a machine learning model by means of input data and score data, wherein the machine learning model is an artificial neural net, ANN; running the trained machine learning model by applying the first time-series to the trained machine learning model; and outputting, by the trained machine learning model, an output value, comprising at least a second criticality value of the at least one predicted observable process-value indicative of the abnormal behaviour of the industrial process in a predefined temporal distance.

The present disclosure relates to a task processing method and device based on defect detection, a computer readable storage medium, and a task processing apparatus . The method includes receiving a detection task; determining a task type of the detection task; storing the detection task in a task queue if the task type is a target task type; and executing the detection task in a preset order and generating a feedback signal when a processor is idle. The detection task of the target task type includes an inference task and a training task. Executing the training task includes modifying configuration information according to a preset rule based on product information in the detection task; acquiring training data and an initial model according to the product information; and using the training data to train the initial model according to the configuration information to obtain a target model and store it in memory.

A method for factory analysis and/or maintenance, preferably including receiving factory information and/or associating defects with factory components, and optionally including acting based on defect associations and/or operating factory machines. The method is preferably associated with one or more manufacturing systems and/or elements thereof.

Provided is a work machine maintenance management system capable of predicting replacement timing of a component of a work machine early. The work machine maintenance management system of this disclosure includes a maintenance management DB server 110 that accumulates maintenance management information of a plurality of work machines and a maintenance management control device 120 that predicts replacement timing of each component of each of the work machines based on the maintenance management information. The maintenance management information includes an actual durable period from start of use to replacement each component of each work machine. The maintenance management control device 120 includes a replacement-factor determining section 121, a service life model creator 122, a failure model creator 123, and a replacement time predictor 126. The replacement-factor determining section 121 determines whether a replacement factor of each component is a service life factor or a failure factor based on an actual durable period of each component of the plurality of work machines. The service life model creator 122 creates a service life model of the component whose replacement factor is determined to be the service life factor by the replacement-factor determining section 121. The failure model creator 123 creates a failure model of the component whose replacement factor is determined to be the failure factor by the replacement-factor determining section 121. The replacement time predictor 126 predicts the replacement timing of each component of each work machine based on the service life model and the failure model.

Fault tolerance for an automation controller for a machine is provided. A first portion of phases of the automation controller may be processed with fail operational protection, in which a failure of one of the computers used for the first portion still permits full operational functionality in the machine. The remaining portion of the phases are processed with fail degraded protection, in which a failure of a computer used for the remaining portion permits continued operation but with one or more constraints, as compared to the fail operational portions.

A method for factory analysis and/or maintenance, preferably including receiving factory information and/or associating defects with factory components, and optionally including acting based on defect associations and/or operating factory machines. The method is preferably associated with one or more manufacturing systems and/or elements thereof.

A communication apparatus includes a communication unit, a power supply unit, a temperature monitoring unit, and a control unit. The communication unit communicates with an additional apparatus. The power supply unit supplies power to components mounted in the communication apparatus. The temperature monitoring unit monitors a temperature in the communication apparatus to detect presence or absence of a temperature abnormality. In a case where the temperature monitoring unit detects the temperature abnormality, the control unit performs power supply stop processing of stopping the power supply from the power supply unit to at least some of the components. Furthermore, the control unit stops or decreases communication of the communication unit in a case where the temperature monitoring unit detects the temperature abnormality.

An AI-based anomaly signatures warning recommendation system is provided. The system includes a memory having computer-readable instructions stored therein and a processor configured to execute the computer-readable instructions to access a multi-asset connected system having a plurality of production and/or process lines. Each of the plurality of production lines includes a plurality of assets. The processor is configured to access production data corresponding to a plurality of products manufactured in each of the plurality of production lines and to access sensor signal data corresponding to each of the plurality of assets. The sensor signal data is indicative of health of each of the plurality of assets. The processor is further configured to process the production data and sensor signal data for each of the plurality of assets to identify one or more anomaly instances and to perform similarity analysis on the one or more anomaly instances to identify one or more anomaly signatures. The identified anomaly signatures, anomaly signature groups, anomaly signature group representative, and corresponding sensor signal data are stored in an anomaly signature repository. The anomaly signatures are representative of one or more substantially similar anomaly instances detected prior to unplanned downtime or critical process events in the connected system. The processor is configured to provide early warnings based on the occurrence of the identified anomaly signatures present in the anomaly signature repository to an end user and receive user-feedback from the end user on the warning severity and relevance of the early warnings. The processor is also configured to generate warning recommendations for anomaly signatures that are prioritized based on the end user-feedback.