A method for diagnosing a problematic noise source based on big data information include: measuring noise data of a powertrain of a vehicle by using a real-time noise measurement device, and converting the noise data into a signal that can be input to a portable device for diagnosing the problematic noise source through an interface device; analyzing a noise through a deep learning algorithm of an artificial intelligence on a converted signal, and diagnosing the problematic noise source as a cause of the noise; and displaying the cause of the noise by outputting a diagnostic result as the problematic noise source, and transmitting the diagnostic result to the portable device.

A computer-implemented method for defect analysis is provided. The computer-implemented method includes calculating a plurality of weight-of-evidence (WOE) scores respectively for a plurality of device operations with respect to detects occurred during a fabrication period, a higher WOE score indicating a higher correlation between a defect and a device operation; and ranking the plurality of WOE scores to obtain a list of selected device operations highly correlated with the defects occurred during the fabrication period, device operations in the list of selected device operations having a WOE score greater than a first threshold score. A respective one of the plurality of device operations is a respective device defined by a respective operation site at which the respective device perform a respective operation.

The present teachings relate to a method comprising a plurality of sensors, and one or more functionally connected processing units, the method comprising: providing, at any of the one or more processing units, time-series residual data of a sensor object; the sensor object being a group of at least some of the sensors from the plurality of sensors, and wherein the residual data comprises, for each of the sensors of the sensor object, a residue signal which is a difference between the sensor's measured output and the sensor's expected output, monitoring, via any of the one or more processing units, a level signal; wherein the level signal is indicative of a collective time-based variation of the time-series residual data, monitoring, via any of the one or more processing units, an association signal; wherein the association signal is indicative of the variation and/or association structure of the time-series residual data, generating, via any of the one or more processing units, an anomaly event signal when at a given time a value of the level signal and/or a value of the association signal changes from an expected value of the respective signal at or around that time. The present teachings also relate to a monitoring and/or control system for a plant comprising a plurality of sensors, wherein the system comprises one or more processing units configured to perform the method steps of any of the steps herein disclosed, and a computer software product.

A method and device for predicting an anomaly in a manufacturing process. The method includes receiving time-series equipment data including one or both of sensor data and specification data, converting the time-series equipment data into an image, dividing the image into a plurality of patch images, outputting a probability for each class associated with a sign of an anomaly in the time-series equipment data by inputting the plurality of patch images to a pretrained artificial neural network (ANN), and predicting the sign of the anomaly in the time-series equipment data by adjusting a probability weight for each class based on a preset standard.

A model construction support system supports searching for a feature used to construct a prediction model that outputs an objective variable related to a predicted event for a machine based on explanatory variables, and a division method for dividing the explanatory variables into groups to improve calculation accuracy of the objective variables based on the prediction model. The system divides the explanatory variables into a plurality of groups, calculates accuracy of the features set based on the explanatory variable in the groups, and calculates a score of the feature in the groups based on the accuracy and a support ratio of the explanatory variable to all of the explanatory variables before division. The system calculates accuracy of a group division feature used to divide the explanatory variables, and a score in the groups based on the score and the accuracy in the groups.

Methods, systems, and devices for equipment monitoring and fault prediction are described, including: receiving measurement data associated with a set of equipment; providing at least a portion of the measurement data to a machine learning network; receiving an output from the machine learning network in response to the machine learning network processing at least the portion of the measurement data; and outputting a notification based on the output from the machine learning network, the notification including an indication of the predicted status. The processing of at least the portion of the measurement data may be based on a predictive model associated with the set of equipment. The output from the machine learning network may include a predicted status of the set of equipment.

A method includes steps: 1) obtaining monitoring information of different monitoring points in normal state of power equipment; 2) setting faults and obtaining monitoring information of different fault types, positions, monitoring points of the equipment; 3) taking the monitoring information obtained in steps 1) to 2) as training dataset, taking the fault types and positions as labels, inputting the training dataset and the labels to deep CNN for training; 4) collecting monitoring data, performing verification and classification using step 3), obtaining probability values corresponding to each of the labels; 5) taking classification results of different labels as basic probability assignment values, with respect to a monitoring system composed of multiple sensors, taking different sensors as different evidences for decision fusion, performing fusion processing using the DS evidence theory to obtain fault diagnosis result. The invention can intelligently realize fault detection, fault type determination, and fault positioning of the power equipment.

Indexes having local features are automatically selected from sensor data of a plurality of sensors. Sensor data of the plurality of sensors, each associated with the plurality of indexes, is partitioned into a plurality of blocks. A principal component analysis is applied to the sensor data of each of the partitioned blocks and a plurality of principal components are extracted from each of the blocks. A migration distance evaluation unit extracts, from two different blocks, two principal components that form a principal component pair, and calculates a migration distance between each of the principal components regarding the extracted principal component pair. A migration factor index detection unit detects, as a migration factor index, an index among the plurality of indexes configuring the principal components having a large migration distance among the migration distances between each of the principal components calculated by the migration distance evaluation unit.

A system for monitoring, controlling and predicting required maintenance in fluid control circuits comprising: a plurality of local arrangements; each local arrangement further comprising: a plurality of elements characterized by monitorability, controllability and a combination thereof; a memory unit configured for storing data associated with monitoring said elements; a local data processing unit configured for at least one activity selected from the group consisting of: interrogating said memory unit, receiving said data associated with monitoring said elements, analyzing said data and predicting required maintenance of said elements and any combination thereof; a service provider server configured for duplicating at least one activity of said local data processing centers belonging to said local arrangements; said service provider server further configured for collecting detected operation patterns of said elements belonging to said local arrangements, statistically analyzing said collected operation patterns, indicating operation patterns referring to emergency and preventive maintenance.

An example embodiment involves rules related to repairing software programs, wherein the rules associate indications of software program failures with repair applications that are configured to correct the software program failures. One or more processors are configured to: (i) receive, by a predictive model, a representation of an execution history of a particular software program, wherein the predictive model has been trained on a corpus of execution histories of the software programs; (ii) generate, by the predictive model and from the execution history, a failure prediction for the particular software program; (iii) receive, by an automated repair controller application, the failure prediction from the predictive model; (iv) based on applying the rules to the failure prediction, determine, by the automated repair controller application, a repair application from the repair applications; and (v) cause, by the automated repair controller application, the repair application to be executed within the network.