A method includes receiving, by a processing device, first data indicative of a processing recipe. The method further includes receiving second data. The second data includes operational data associated with the processing recipe. The method further includes receiving third data. The third data includes historical data associated with the processing recipe. The method further includes performing analysis indicative of performance of a processing chamber based on the first, second, and third data. The method further includes causing performance of a corrective action in view of the analysis.

The present invention discloses an industrial equipment operation, maintenance and optimization method and system based on a complex network model. The method includes the following steps: obtaining data of all sensors of industrial equipment, and calculating a Spearman correlation coefficient between data of every two of the sensors within the same time period; using each sensor as a node, and using the Spearman correlation coefficient as a weight of a network edge, to construct a fully connected weighted network; and obtaining, when an adjustment instruction for a target feature is received, a currently optimal parameter adjustment path of the target feature based on the fully connected weighted network. In the present invention, production equipment in reality is digitized to construct a complex network oriented to industrial big data. An optimal path for equipment parameter tuning may be found by using the network, thereby reducing dependence of an enterprise on a domain expert.

Assistance device for automatically generating training data of a time series of sensor data, further on called temporal sensor data, applied to train an Artificial Intelligence system used for detecting anomalous behavior of a technical system, including a processor configured to perform - obtaining historical temporal sensor data, dividing the historical temporal sensor data into a temporal sequence of segments and assigning one segment type out of several different segment types to each segment, iteratively for each segment, determining a neighborhood pattern of segment types, determining the most frequently occurring neighborhood pattern from all determined neighborhood patterns as reference pattern for normal operation of the technical system, -selecting a subsequence of segments out of the historical temporal sensor data, which is ordered according to the reference pattern, and - outputting the subsequence of segments for applying as training data.

A process capability index warning system and a warning method for the same are provided. The system includes a process capability index calculation device, which determines whether a quantity of test samples of the test device within a cycle of a warning interval is less than a quantity threshold. When the quantity of the test samples is less than the quantity threshold, the quantity of the test samples is accumulated until an end of a next cycle of the warning interval. When the quantity of the test samples is not less than the quantity threshold, the process capability index calculation device calculates and obtains process capability index values, and determines whether or not the process capability index values are less than an index threshold. When any of the process capability index values is less than the index threshold, the process capability index calculation device sends a warning message.

A method includes identifying trace data including a plurality of data points, the trace data being associated with production, via a substrate processing system, of substrates having property values that meet threshold values. The method further includes determining, based on a guardband, guardband violation data points of the plurality of data points of the trace data. The method further includes determining, based on the guardband violation data points, guardband violation shape characterization. Classification of additional guardband violation data points of additional trace data is to be based on the guardband violation shape characterization. Performance of a corrective action associated with the substrate processing system is based on the classification.

A method for detecting an abnormal event performed by a computing device according to an embodiment of the present disclosure includes analyzing log data to identify sequentially executed activities and generating a process model comprising a node indicating each of the activities and an edge indicating an execution predecessor relationship between the activities, and outputting a result of analyzing log data generated in real time based on the process model.

A system and method for monitoring the condition of a travelling grate machine including univocally identifying each pallet car; collecting a plurality of condition indicating parameters for the wheels, the grate bars, the car body and/or the side walls of the pellet car; attributing the collected condition indicating parameters to an individual pallet car; storing the collected condition indicating parameters for each pallet car in a database; evaluating the condition of the travelling grate machine; comparing the different condition indicating parameters collected by the different sensor means of each pallet car to reference parameters and/or to previously collected condition indicating parameters of that same pallet car; identifying the faults in each pallet car based on this comparison; classifying each pallet car according to its need of maintenance based on the severity of different identified faults; and determining the pallet car in most need of maintenance based on this classification.

A method for learning and detecting an abnormal part of a device through artificial intelligence comprises: an information collection step for collecting a current waveform of a current value that changes over time in a driving state of at least one device and collecting information about a faulty part of the device, together with current waveform information before a fault occurs in the device; a model setting step for learning, by a control unit, information collected in the information collection step and setting a reference model of a current waveform for each faulty part of the device; and a detection step for, when an abnormal symptom of the device is detected in a real-time driving state, comparing, by the control unit, a real-time current waveform of the device and the reference model, and detecting and providing an abnormal part regarding the abnormal symptom of the device.

A method for detecting events includes receiving measurements from a plurality of sensors, and executing a machine learning model trained to identify events based on the measurements. The machine learning model identifies the events based on the measurements. The method also includes determining based on the measurements and the identified events that training applied to the machine learning model is to be modified. A modified training data set is generated based on the measurements and an initial training data set used to train the machine learning model to identify the events. The modified training data set is applied to retrain the machine learning model.

In order to provide a method for anomaly and/or fault recognition in an industrial-method plant, for example a painting plant, wherein anomalies and/or fault situations are recognisable simply and reliably by means of the method, it is proposed according to the invention that the method should comprise the following: automatic generation of an anomaly and/or fault model of the industrial-method plant that comprises information on the occurrence probability of process values; automatic input of process values of the industrial-method plant during operation thereof; automatic recognition of an anomaly and/or fault situation by determining an occurrence probability by means of the anomaly and/or fault model on the basis of the process values of the industrial-method plant that have been input and by checking the occurrence probability for a limit value,