Systems and methods are provided for improving a high-volume manufacturing (HVM) line by assessing robustness and performance of an early fault detection machine learning (EFD ML) models. Learning curve(s) may be constructed from a received amount of data from the electronics' production line, the learning curve representing a relation between a performance of the EFD ML model and a sample size of the data on which the EFD ML model is based. Learning curve(s) may be used to derive estimation(s) of model robustness by (i) fitting the learning curve to a power law function and (ii) estimating a tightness of the fitting and/or by (iii) applying a machine learning algorithm that is trained on a given plurality of learning curves and related normalized performance values.

Systems and methods are provided for improving a high-volume manufacturing (HVM) line that has a test pass ratio of at least 90%, by constructing a genetic neural architecture search (GNAS) network that detects anomalies in the HVM line at a detection rate of at least 85%. Disclosed systems and methods combine data balancing of the highly skewed raw data with a network construction that is based on building blocks that reflect technical knowledge related to the HVM line. The GNAS network construction is made thereby both simpler and manageable and provides meaningful insights for improving the production process.

A petroleum pipeline safety system for preventing contamination of an environmentally sensitive area close to a pipeline is provided. The system includes a first portion of the pipeline including an upstream portion of the pipeline supplying a flow of fluid material and a flow restriction downstream of the first portion of the pipeline. The system further includes a second portion of the pipeline downstream of the flow restriction, receiving the flow of fluid material from the first portion and conveying the flow of fluid material through the environmentally sensitive area to a downstream portion of the pipeline. The flow restriction is configured to create a lower pipeline internal pressure within the second portion as compared to a pipeline internal pressure within the first portion. The system further includes a third portion of the pipeline downstream of the environmentally sensitive area and including the downstream portion of the pipeline.

Disclosed herein are systems and methods for automating production intelligence across value streams using interconnected machine-learning models. An embodiment of a system includes an upstream machine-learning model corresponding to each of one or more upstream entity in a production value stream of a product; a final-assembly machine-learning model corresponding to a final-assembly process in the production value stream of the product; a causal-analysis machine-learning model for the production value stream of the product; an action-and-alert process for the production value stream of the product; and an implementation interface for the production value stream of the product. The upstream machine-learning models and the final-assembly machine-learning model are interconnected to provide product-throughput prediction for the product. The causal-analysis machine-learning model infers causal factor for the product-throughput prediction, and alerts and/or recommended actions are issued to the implementation interface.

According to the embodiments of the present disclosure, there is provided a method and device of detecting fault in production, and a computer readable storage medium. The method includes: determining whether a plurality of production paths in a production line are faultless in one or more production batches, based on production record data; and determining at least one of the plurality of production paths to be faulty, at least partially based on whether the plurality of production paths are faultless in the one or more production batches.

A state determination device acquires data related to an injection molding machine, stores a learning model obtained by learning an operation state of the injection molding machine with respect to the data, and performs estimation using the learning model based on the data. Further, the state determination device acquires a correction coefficient, which is associated with a type of the injection molding machine and equipment attached to the injection molding machine and numerically converts and corrects the estimation result with a predetermined correction function to which the acquired correction coefficient is applied.

Power systems are disclosed. The power system may include at least one computing device in communication with a control circuit including a plurality of electrical loops. The computing device(s) may be configured to test each of the plurality of electrical loops of the control circuit by performing processes including configuring a first electrical loop in a first electrical setting by adjusting an operational characteristic of one or more electrical switch(s) of the first electrical loop. The processes may also include determining an actual electrical status of the first electrical loop in the first electrical setting based on whether a relay of a return line in the first electrical loop detects a supplied voltage. Additionally, the computing device(s) may detect a fault in the first electrical loop in response to the determined actual electrical status of the first electrical loop differing from an expected electrical status of the first electrical loop.

A monitoring device monitors a status of a process in a production line in separate unit tasks. The production line includes a plurality of processes. The unit tasks form the process. The monitoring device includes an operational condition acquisition section, a determination section, and an output section. The operational condition acquisition section acquires, on a per-unit task basis, operational conditions of a device being used in the tasks. The determination section determines whether or not the acquired operational conditions are appropriate based on preset reference values. The output section outputs, on a per-unit task basis, information as to whether or not the process is being appropriately performed, based on results of the determination.

A building system for detecting faults in an operation of building equipment. The building system comprising one or more memory devices configured to store instructions thereon that cause one or more processors to perform a cumulative sum (CUSUM) analysis on actual building data and corresponding predicted building data to obtain cumulative sum values for a plurality of times within a first time period; determine a first time at which a first cumulative sum value is at a first maximum; identify a second cumulative sum value at a second maximum at a second time occurring after the first time; compare the identified second cumulative sum value to a threshold; and based on determining that the identified second cumulative sum value does not exceed the threshold, determine that a first fault ended at the first time.

A petroleum pipeline safety system for preventing contamination of an environmentally sensitive area close to a pipeline includes an upstream portion of the pipeline supplying a flow of fluid material, a crossing portion of the pipeline receiving the flow of fluid material from the upstream portion and conveying the flow of fluid material through the environmentally sensitive area to a downstream portion of the pipeline, the downstream portion, a pipeline pressure activated valve selectively capable of blocking the flow of fluid material from entering the crossing portion based upon a change in pressure within the crossing portion, and a fluid capacitor connected to the upstream portion configured to filter out a pressure spike in the upstream portion associated with the valve blocking the flow of fluid material.