A computer-implemented method of providing troubleshooting support and maintenance instructions for servicing assets including plant, equipment, and systems.

Aggregate processing systems, methods and apparatus are described. Some embodiments include a plurality of equipment criteria sensors, product quantity sensors, product characteristic sensors, and/or actuators. Some embodiments include a control system incorporating algorithm logic. Some embodiments include a control system incorporating artificial intelligence logic.

A compressor controller for operating a compressor within an industrial automation environment is provided. The compressor controller includes a control module, configured to control the compressor via control settings, and a machine learning module, coupled with the control module. The machine learning module is configured to receive a set of supervised data related to the compressor, and to train with the supervised data to produce a Newtonian physics model representing the inputs and outputs of the compressor within the industrial automation environment. The machine learning module is also configured to receive performance data related to the compressor, receive environment data related to the compressor, and to process the performance data and environment data to produce predicted future performance data for the compressor, and to produce control settings for the compressor.

A mobile panel maintenance system including a mobile panel maintenance unit having a base supported for translational motion over a surface within a panel array and a carriage movably mounted to the base to position a panel maintenance assembly in relation to a panel surface for panel maintenance.

Systems, methods, and apparatus for prediction of maintenance service for machines. In one example, one or more sensors are configured to generate a sensor data stream during operation of a machine. An artificial neural network (ANN) is configured to receive the sensor data stream and predict a maintenance service for the machine based on the sensor data stream. For example, the ANN can be trained using the sensor data stream collected within a predetermined time period of a machine being newly-installed in an assembly line or other industrial automation facility. The machine can be considered to be operating in a normal condition during the predetermined time period such that the ANN can be trained to detect anomaly that deviates from the normal patterns of the sensor data stream. For example, the ANN can be a spiking neural network (SNN).

In one embodiment, a computing device includes interface circuitry and processing circuitry. The processing circuitry receives, via the interface circuitry, a data stream captured at least partially by sensor(s), which contains feature values corresponding to an unlabeled instance of a feature set. The processing circuitry then groups the data stream into a data stream group, which is assigned from a set of data stream groups based on the feature values in the data stream. The processing circuitry then selects a predictive model for the data stream group from a set of predictive models, which are each trained to predict a target variable for a corresponding data stream group. The processing circuitry then predicts the target variable for the data stream using the predictive model, which infers the target variable based on the set of feature values in the data stream.

System, equipment, and procedure for monitoring, predictive maintenance, and operational optimization of vibrating screeners represented by an inventive solution in the industry and trade of vibrating equipment, with mechanically-driven vibration technology, with particular application to vibrating screening (Pe) equipment (Eq), aiming to monitor operational parameters, foresee the deterioration of the structural conditions of the equipment (Eq), so as to increase the interaction between maintenance and production engineering of the company, where, for such purpose, a system has been conceived whose architecture is composed of the following modules: hardware module (GHW), intelligence generation module (GI), data persistent layer module (CPD), and event management module (Ge), resulting in the conversion of the equipment (Eq)'s operational needs into a description of the performance parameters with functional analysis, synthesis, modeling, simulation, optimization, design, testing, and evaluation, integrating the performance parameters with the other requirements in the modeling process.

Disclosed is an information processing device including: a first communication unit configured to permit communication with a remote maintenance device that remotely operates machine tools; a second communication unit configured to permit communication with the machine tools; an operation unit including a remote maintenance switch; and a control unit configured to, according to whether the remote maintenance switch is in an ON state or an OFF state, determine whether or not to permit communication for remotely operating the machine tools by the remote maintenance device, and control connection and disconnection between the remote maintenance device and the machine tools. Accordingly, it is possible to perform remote maintenance on machine tools while maintaining the confidentiality of information indicating the operation state of the machine tools.

A mobile panel maintenance system including a mobile panel maintenance unit having a base supported for translational motion over a surface within a panel array and a carriage movably mounted to the base to position a panel maintenance assembly in relation to a panel surface for panel maintenance.

An operation plan creation device includes: an operation information creation unit configured to receive an input of a simple setting including information with which an operation period of a device is identifiable and to create operation information including an inspection period and a replacement scheduled component in the operation period of the device; a component information acquisition unit configured to acquire component information including lifespan information and stock quantity information about each of the components, based on use history information about each of the plurality of components; a dummy information creation unit configured to create, when the acquired component information includes missing information, dummy information to be used instead of the missing information; and a plan creation unit configured to create the operation plan for the components related to the device based on the operation information and the component information or the dummy information.