An object of the present subject matter is to appropriately acquire and analyze a camera image for monitoring and to constantly monitor a monitoring target with high accuracy. A programmable logic controller (PLC) includes: an image processing section for sequentially acquiring the image data of the camera image from the camera sensor and generating characteristic amount data indicating a characteristic amount of image data in a preset monitoring region; a time-series data acquisition section for sequentially collecting characteristic amount data from the image processing section and acquiring time-series data of a characteristic amount; and a monitoring section for monitoring time-series data of a current characteristic amount acquired by the time-series data acquisition section in accordance with a monitoring timing defined by the device of the device memory.

Industrial quality control is challenging for artificial neural networks (ANNs) and deep neural networks (DNNs) because of the nature of the processed data: there is an abundance of consistent data representing good products, but little data representing bad products. In quality control, the task is changed from conventional DNN task of “recognize what I learned best” to “recognize what I have never seen before.” Lifelong DNN (L-DNN) technology is a hybrid semi-supervised neural architecture that combines the ability of DNNs to be trained, with high precision, on known classes, while being sensitive to any number of unknown classes or class variations. When used for industrial inspection, L-DNN exploits its ability to learn with little and highly unbalanced data. L-DNN's real-time learning capability takes advantage of rare cases of poor-quality products that L-DNN encounters after deployment. L-DNN can be applied to industrial inspections and manufacturing quality control.

Systems and methods for data collection for an industrial heating process are disclosed. The system according to one embodiment can include a plurality of data collectors, including a swarm of self-organized data collector members, wherein the swarm of self-organized data collector members organize to enhance data collection based on at least one of capabilities and conditions of the data collector members of the swarm, and wherein the plurality of data collectors is coupled to a plurality of input channels for acquiring collected data relating to the industrial heating process, and a data acquisition and analysis circuit for receiving the collected data via the plurality of input channels and structured to analyze the received collected data using a neural network to monitor a plurality of conditions relating to the industrial heating process.

The present disclosure relates to computer-implemented methods, software, and systems for predicting failure event occurrence for a machine asset. Run-to-failure sequences of time series data that include an occurrence of a failure event for the machine asset are received. One or more candidate cut-off values are determined based on iterative evaluation of a plurality of potential cut-off points. A candidate cut-off value is identified as substantially corresponding to a local peak point for calculated distances between relative frequency distributions of positive and negative sub-sequences. A failure prediction model is iteratively trained to iteratively extract sets of relevant features to determine a prediction horizon for an occurrence of the failure event for the machine asset. A candidate cut-off value associated with a model of highest quality from a set of failure prediction models determined during the iterations is selected to determine the prediction horizon for the machine asset.

Methods, systems, and non-transitory computer readable medium are described for sensor metrology data integration. A method includes receiving sets of sensor data and sets of metrology data. Each set of sensor data includes corresponding sensor values associated with producing corresponding product by manufacturing equipment and a corresponding sensor data identifier. Each set of metrology data includes corresponding metrology values associated with the corresponding product manufactured by the manufacturing equipment and a corresponding metrology data identifier. The method further includes determining common portions between each corresponding sensor data identifier and each corresponding metrology data identifier. The method further includes, for each of the sensor-metrology matches, generating a corresponding set of aggregated sensor-metrology data and storing the sets of aggregated sensor-metrology data to train a machine learning model. The trained machine learning model is capable of generating one or more outputs for performing a corrective action associated with the manufacturing equipment.

The present invention discloses a method for controlling a process in a process plant using a controller. The method comprises receivable associated with the process, determining a first value of at least one key performance indicator associated with the transition from the process data of the first process variable between the first steady state and the second steady state, comparing the determined first value of the at least one key performance indicator against a threshold value of the at least one key performance indicator; and determining a correction factor for modifying a set point of the process variable based on the comparison, for controlling the process.

A system for generating and using a predictive model to control building equipment includes building equipment operable to affect one or more variables in a building and an operating data aggregator that collects a set of operating data for the building equipment. The system includes an autocorrelation corrector that removes an autocorrelated model error from the set of operating data by determining a residual error representing a difference between an actual output of the building equipment and an output predicted by the predictive model, using the residual error to calculate an autocorrelation for the model error, and transforming the set of operating data using the autocorrelation. The system includes a model generator module that generates a set of model coefficients for the predictive model using the transformed set of operating data and a controller that controls the building equipment by executing a model-based control strategy that uses the predictive model.

A method and apparatus for detecting an abnormality of a manufacturing facility is disclosed. According to an example embodiment of the present disclosure, a learning model generating method for manufacturing facility abnormality detection may include receiving a measured value for a normal state of a manufacturing facility collected through a multi-sensor on a time-by-time basis, generating a learning model including a predetermined weight set and training the learning model using the measured value, and determining, using the learning model, a threshold corresponding to a boundary between the normal state and an abnormal state of the manufacturing facility and a criterion for determining the abnormal state in a local window representing a predetermined time interval.

A control method includes: monitoring a statistic obtained by performing a multivariate analysis on a plurality of parameters; extracting, from the plurality of parameters, a predetermined number of higher-order parameters in terms of an influence degree on fluctuation of the statistic; generating a plurality of experimental patterns according to an experimental design method; acquiring a measurement result of a specific parameter indicating quality of a product when at least one device is controlled according to each of the plurality of experimental patterns; setting a new target value of the predetermined number of higher-order parameters in order to stabilize a value of the specific parameter within a management range based on the measurement result; and controlling the at least one device such that the predetermined number of higher-order parameters approaches the new target value.

Provided are a diagnosing device, a diagnosing method, and a program with which it is possible to detect abnormalities accurately even if there is a small amount of data or the number of data points varies. This diagnosing device is provided with a Mahalanobis distance calculating unit which calculates the Mahalanobis distance (referred to as ‘MD value’ hereinbelow) of a detected value, and an abnormality determining unit which determines the presence or absence of an abnormality on the basis of the MD value, wherein the abnormality determining unit determines the presence or absence of an abnormality by arranging that a determination that there is no abnormality is more likely to occur if the number of samples per unit space is small than if the number of samples per unit space is large.