A power distribution unit and a fault detecting method applied in the power distribution unit are disclosed herein. The power distribution unit includes an input terminal, an insulation fault detection circuit and a processing circuit. The input terminal is electrically coupled to a positive power line and a negative power line, and configured to receive a high voltage direct current (HVDC) voltage. The insulation fault detection circuit is configured to detect an insulation resistance value between a ground terminal and the positive power line or the negative power line. The processing circuit is configured to output a warning signal according to the insulation resistance value.

A cell control apparatus comprises an operation information acquisition unit which acquires operation information of manufacturing machine, a failure prediction unit which predicts a failure time of the manufacturing machine based on the operation information of the manufacturing machine, and a replacement time acquisition unit which acquires a replacement time of the component from a component management apparatus. When the failure time is earlier than the replacement time, the cell control apparatus performs a control for reducing an operation load of the manufacturing machine on which a failure of the component is predicted so that the failure time is later than the replacement time.

Described are systems and methods for optimizing process control or monitoring of manufacturing processes in manufacturing environments. Systems and methods can generate predictions or recommendations for process variables, target properties, or root causes of anomalies. Systems can include data and machine learning layers that can include: a data collector configured to receive data from the client application layer; a dataset generator configured to enable a user to create customized datasets from the data; a model management module configured to enable the user to build, train and/or update machine learning models; and an inference module configured to use the machine learning models for generating predictions or recommendations. Machine learning models can include aggregated adaptive online models (AggAOM) for generating predictions with scarce or sparse data.

The present disclosure provides methods, systems, and computer-readable media for the fault detection and identification in an aircraft that may occur in real time during a flight, or any time the aircraft is operating. For example, a controller may receive and calculate various parameter values at various times during an aircraft flight, and compare those values to baseline values in order to determine if a fault has occurred. Additionally, the controller may identify a fault that has occurred by comparing a calculated fault signature value with a fault signature database comprising fault signatures and their associated faults.

A technique is provided for providing early fault detection using process control data generated by control devices in a process plant. The technique determines a leading indicator of a condition within the process plant, such as a fault, abnormality, or decrease in performance. The leading indicator may be determined using principal component analysis. A process signal indicating a process variable corresponding to the leading indicator is then obtained and analyzed. A rolling fast Fourier transform (FFT) may be performed on the process signal to generate time-series data with which to monitor the process plant. When the presence of the leading indicator is detected in the time-series data, an alert or other prediction of the condition may be generated. Thus, process faults may be identified using fluctuations and abnormalities as leading predictors.

A behavior recognition device for recognizing behaviors of a semiconductor manufacturing apparatus includes a storage device and a control unit. The storage device is configured to store log data of the semiconductor manufacturing apparatus. The control unit is cooperatively connected to the storage device, and configured to build a transition state model based on the log data to analyze behaviors related to wafer transfer sequences and manufacturing operations of the semiconductor manufacturing apparatus.

A method for fault isolation includes: acquiring a thermal imaging picture of a surface of a to-be-tested chip, the thermal imaging picture being obtained by scanning the to-be-tested chip to which a test signal is applied through an infrared thermal imaging device, and analyzing the thermal imaging picture to obtain a phase angle of each point on the surface of the to-be-tested chip; acquiring a three-dimensional image of the surface of the to-be-tested chip, the three-dimensional image being obtained by scanning the to-be-tested chip to which the test signal is applied through an image scanning device, and analyzing the three-dimensional image to obtain a three-dimensional coordinate of each point on the surface of the to-be-tested chip; calculating a three-dimensional coordinate of the fault in the to-be-tested chip according to the phase angle and the three-dimensional coordinate of each point on the surface of the to-be-tested chip.

A method for supporting troubleshooting software and hardware issues in a distributed control system (DCS) associated with an automation equipment in industrial plant includes monitoring data; detecting an anomaly in the monitored data based on predetermined anomaly detection rules; based on a result of the detecting, performing, for a detected anomaly, a similarity search on historic anomaly data associated with the DCS and/or the automation equipment; based on a result of the performed similarity search, querying a large language model (LLM) for diagnosis and/or recommendation for troubleshooting the detected anomaly; based on the querying, obtaining an output from the LLM, wherein the output is indicative of a diagnosis and/or recommendation for troubleshooting the detected anomaly; and providing the output to a user.