A method for detecting a fault includes: receiving a plurality of time-series sensor data obtained in one or more manufacturing processes of an electronic device; arranging the plurality of time-series sensor data in a two-dimensional (2D) data array; providing the 2D data array to a convolutional neural network model; identifying a pattern in the 2D data array that correlates to a fault condition using the convolutional neural network model; providing a fault indicator of the fault condition in the one or more manufacturing processes of the electronic device; and determining that the electronic device includes a fault based on the fault indicator. The 2D data array has a dimension of an input data to the convolutional neural network model.

An information processing apparatus acquires detection information of a physical quantity that changes according to an operation state of a target device, transmits an acquisition request to the target device at predetermined time, to acquire context information relating to an operation status of the target device in response to the acquisition request, specifies a processing period in which the target device is in the middle of processing of the target object based on the predetermined time and the context information, extracts processing period detection information of the specified processing period from the detection information, and determines an occurrence of a defect relating to processing by the target device during the processing period based on the processing period detection information and the context information used for specifying the processing period.

An apparatus and method for monitoring a quality of an object of a 3D-print job series of identical objects, each object built from a multitude of stacked 2D-layers printed by a 3D-printer in an additive manufacturing process, including: determining a layer quality indicator of a currently printed layer of an object, comparing the determined layer quality indicator of the currently printed layer with a predetermined lower confidence limit of the layer, the predetermined lower confidence limit being calculated depending on layer quality indicators of previously completely manufactured objects complying with predefined quality requirements, and generating a warning signal, if the layer quality indicator of the currently printed layer has a value equal or lower than the lower quality limit is provided.

A plant control system includes a plant system and a control system controlling the plant system. Runtime conditions of an operating point of the plant control system are received. The runtime conditions include a runtime state of the plant system, a runtime output of the plant system, and a runtime control action applied to the plant system. Reference conditions of a reference point corresponding to the operating point are determined. Stability radius measures of a state difference, an output difference, and a control action difference are computed. One or more of an observability anomaly indicator, health observability indicator, tracking performance anomaly indicator, tracking performance health indicator, controllability anomaly indicator, and controllability health indicator are determined based on respective spectral correlations between two of the stability radius measure of the output difference, the stability radius measure of the state difference, and the stability radius measure of the control action difference.

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 plant control system includes a plant system and a control system controlling the plant system. Runtime conditions of an operating point of the plant control system are received. The runtime conditions include a runtime state of the plant system, a runtime output of the plant system, and a runtime control action applied to the plant system. Reference conditions of a reference point corresponding to the operating point are determined. Stability radius measures of a state difference, an output difference, and a control action difference are computed. One or more of an observability anomaly indicator, health observability indicator, tracking performance anomaly indicator, tracking performance health indicator, controllability anomaly indicator, and controllability health indicator are determined based on respective spectral correlations between two of the stability radius measure of the output difference, the stability radius measure of the state difference, and the stability radius measure of the control action difference.

Targeted distributing of reports containing historical process control information to particular user devices via a communications network. A curating service permits assigning a score to each report based on an interest level value of the historical process control information to a user associated with each user device and/or an urgency value of the historical process control information. Routing reports to user devices based on the score raises visibility of the historical process control information without overburdening the communications network.

Example implementations described herein are directed to systems and methods for defect rate analytics to reduce defectiveness in manufacturing. In an example implementation, a method include determining, from data associated with each feature for a manufacturing process, the data feature indicative of process defects detected based on the feature, an estimated condition for the feature that reduces a defect rate of the process defects, the estimated condition indicating the data into a first group and second group; calculating the rate reduction of the defect rate based on a difference in defects between the first group and the second group; for the rate reduction meeting a target confidence level for a target defect rate, applying the estimated condition to the manufacturing process associated with each of the features. In example implementations, the defect rate analytics reduce defectiveness in manufacturing with independent processes and/or dependent processes.

This invention discloses an automatic magnetic core sorting system based on machine vision, which comprises an image acquisition and detection mechanism, a digital conversion module, an image processing module, and a workpiece sorting mechanism. The digital conversion module communicates with the image acquisition and detection mechanism. The image processing module receives the digital signals of images of each workpiece taken from various angles and compares the digital signals with a preset value. Afterwards, the results of comparison are sent to the central control module which judges and controls the operations of the workpiece sorting mechanism according to the results sent from the image processing module. This invention adopts the machine vision-based automatic identification technology to automatically identify color and appearance defects of magnetic sheets on conveyor line. With advantages of real-time and accurate detection with high precision, it greatly improves the efficiency and the degree of automation of production.

Systems and methods for monitoring stability of a wafer inspection recipe over time are provided. One method includes collecting inspection results over time. The inspection results are generated by at least one wafer inspection tool while performing the wafer inspection recipe on wafers at different points in time. The method also includes identifying abnormal variation in the inspection results by comparing the inspection results generated at different times to each other. In addition, the method includes determining if the abnormal variation is attributable to the wafers, the wafer inspection recipe, or one or more of the at least one wafer inspection tool thereby determining if the wafer inspection recipe is stable over time.