A self-organizing data marketplace includes a plurality of data collectors and a corresponding plurality of industrial environments, wherein each of the plurality of data collectors is structured to collect detection values from at least one sensor of the corresponding industrial environment, a data storage structured to store a data pool comprising at least a portion of the detection values, a data marketplace structured to self-organize the data pool, and a transaction system structured to interpret a user data request, and to selectively provide a portion of the self-organized data pool to the user in response to the user data request.

The disclosure provides an environment in which it is possible to switch an algorithm involved in an abnormality detection process. A control device: calculates a feature quantity from a state value acquired from a monitored object; uses a learning model on the basis of the calculated feature quantity to execute one of a plurality of types of algorithms for calculating a value indicating the probability that an abnormality is occurring in the monitored object; determines, on the basis of the calculated value, whether the abnormality is occurring; and switches, in accordance with a condition defined in advance, the one algorithm that is executed.

The disclosed embodiments relate to a system that automatically adapts a prognostic-surveillance system to account for aging phenomena in a monitored system. During operation, the prognostic-surveillance system is operated in a surveillance mode, wherein a trained inferential model is used to analyze time-series signals from the monitored system to detect incipient anomalies. During the surveillance mode, the system periodically calculates a reward/cost metric associated with updating the trained inferential model. When the reward/cost metric exceeds a threshold, the system swaps the trained inferential model with an updated inferential model, which is trained to account for aging phenomena in the monitored system.

A system includes an expert graphical user interface configured to: present a list of reliability measures of an industrial machine, facilitate a selection by a user of a reliability measure from the list of reliability measures, present a representation of a smart band data collection template associated with the reliability measure selected by the user, and a data routing and collection system configured to, in response to a user indication of acceptance of the smart band data collection template, collect data from a plurality of sensors in an industrial environment in response to a data value from one of the plurality of sensors being detected outside of an acceptable range of data values.

Multi-metric artificial intelligence (AI)/machine learning (ML) models for detection of anomalous behavior of a machine/system are disclosed. The multi-metric AI/ML models are configured to detect anomalous behavior of systems having multiple sensors that measure correlated sensor metrics such as coolant distribution units (CDUs). The multi-metric AI/ML models perform the anomalous system behavior detection in a manner that enables both a reduction in the amount of sensor instrumentation needed to monitor the system's operational behavior as well as a corresponding reduction in the complexity of the firmware that controls the sensor instrumentation. As such, AI-enabled systems and corresponding methods for anomalous behavior detection disclosed herein offer a technical solution to the technical problem of increased failure rates of existing multi-sensor systems, which is caused by the presence of redundant sensor instrumentation that necessitates complex firmware for controlling the sensor instrumentation.

The gas supply system of this invention is furnished with a cylinder apparatus having a pneumatic valve that supplies process gas to a process chamber, and a solenoid valve that opens or closes said pneumatic valve by supplying or stopping the flow of valve actuating gas to said pneumatic valve; and a gas supply control apparatus that controls the actuation of the solenoid valve. In addition, said gas supply control apparatus comprises a main controller that controls the actuation of said solenoid valve during normal operation, and a sub-controller that senses an abnormal state of said main controller and if an abnormality is sensed, controls the actuation of said solenoid valve instead of said main controller.

Systems and methods for data collection in an industrial environment are disclosed. A system can include a plurality of analog sensors, wherein each of the plurality of analog sensors is operationally coupled to a respective data collection point of a machine component, and generates a respective stream of detection values. A data acquisition and analysis circuit can receive the respective stream of detection values and analyze the respective stream of detection values using an expert system analysis circuit, wherein the expert system analysis circuit determines an occurrence of an anomalous condition based on an analysis of the respective stream of detection values, wherein the expert system analysis circuit utilizes a neural network including one of a probabilistic, a time delay, and a convolutional neural network.

Systems for self-organizing collection and storage in a distribution environment are disclosed. A system may include a data collector for handling a plurality of sensor inputs from sensors in the distribution environment, wherein the sensor inputs sense at least one of an operational mode, a fault mode, a maintenance mode, or a health status of at least one target system selected from a group consisting of a power system, a conveyor system, a robotic transport system, a robotic handling system, a packing system, a cold storage system, a hot storage system, a refrigeration system, a vacuum system, a hauling system, a lifting system, an inspection system, and a suspension system. A system may further include a self-organizing system for: a storage operation of the data, a data collection operation, or a selection operation.

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.

An apparatus, methods and systems for data collection in an industrial environment are disclosed. A monitoring system can include a data collector communicatively coupled to each one of a plurality of input channels utilizing one of a plurality of collector routes, wherein each input channel includes data corresponding to an element of a first industrial machine, and wherein each of the plurality of collector routes includes a distinct data collection routine, a data storage circuit structured to store a plurality of detection values that corresponds to the plurality of input channels, and a data marketplace circuit structured to communicate at least a portion of the detection values to a data marketplace, wherein the data marketplace circuit performs at least one of self-organizing the data marketplace and automating the data marketplace.