Provided is a maintenance operation assistance system comprising: a failure knowledge database wherein failure knowledge data is recorded; a failure knowledge coupling unit for reconstructing partial failure knowledge data into failure knowledge data; and an inspection procedure generation unit for presenting an inspection procedure using the reconstructed failure knowledge data. The failure knowledge coupling unit evaluates and adjusts the relatedness of nodes among different instances of partial failure knowledge data, and connects the different instances of partial failure knowledge data. On the basis of the reconstructed failure knowledge data, the inspection procedure generation unit sets priorities for when presenting the inspection procedure, and presents the inspection procedure to a diagnostic interface unit in accordance with the priorities.

Provided is a prediction device, to which one or more wire electrical discharge machines are connected, the one or more wire electrical discharge machines each performing machining on an object to be machined according to a predetermined machining condition while moving a wire electrode relatively with respect to the object to be machined along a machining path, the prediction device including a prediction unit that predicts, for a predetermined component constituting the wire electrical discharge machine, a predicted rate of consumption indicative of a rate of consumption based on the machining condition when the wire electrode is moved relatively by a unit distance, and a display unit that displays the predicted rate of consumption of the component.

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 substrate processing system having a plurality of substrate processing apparatuses, a host control apparatus, and a management apparatus is provided. The management apparatus determines a substrate processing apparatus that needs maintenance processing, based on operation information collected from each of the substrate processing apparatuses, and notifies a determination result to the substrate processing apparatus determined to need the maintenance processing. Each of the substrate processing apparatuses transmits information for executing the maintenance processing to the host control apparatuses upon receiving the determination result. The host control apparatus monitors an operation state of each of the substrate processing apparatuses, and, based on the operation state of the substrate processing apparatus that transmitted the information for executing the maintenance processing, controls a timing of performance of the maintenance processing with respect to the substrate processing apparatus.

A control apparatus that controls a plurality of robots, includes a failure prediction part that predicts a time of failure with respect to each component of the robots, a maintenance time adjustment part that adjusts maintenance times of the plurality of robots based on the components for which the times of failure are predicted, and a load adjustment part that adjust workloads of the robots according to the predicted times of failure for activation until the maintenance times.

Embodiments of systems and methods for supporting weld quality across a manufacturing environment are disclosed. One embodiment includes a manufacturing cell supporting welding of a sequence of welds to manufacture a workpiece. The manufacturing cell includes robotic welding equipment to make robotic welds as at least a portion of manufacturing a workpiece. The manufacturing cell also includes non-robotic welding equipment configured to allow a human operator to make non-robotic welds as at least a portion of manufacturing the workpiece. The manufacturing cell further includes a weld sequence controller configured to control timing associated with making the robotic welds and the non-robotic welds as a sequence of welds to manufacture the workpiece.

The condition of a technical installation is automatically monitored. A measured value of at least one component of the installation is sensed by at least one sensor device on the installation, and the at least one measured value is used, in a data processing arrangement specific to the installation, for determining a current operating state of the installation. By way of the data processing arrangement, including component-specific aging functions, a future chronological course of the operating condition is determined from the current operating condition, and a maintenance interval for the installation is adjusted on the basis of the future chronological course of the operating condition, in order to determine a next maintenance date of the installation. A corresponding arrangement and a computer program product for condition monitoring are also described.

A machine tool management system connects an external server and a large number of NC devices controlling the external server and respective machine tools through a network. The system collects several kinds of signal data from the NC device of each machine tool to the external server. In the system, the external server collects an estimated thermal distortion from the NC device and compares the estimated thermal distortion with pre-stored data to determine whether the tool is machining favorably.

A method for maintaining industrial equipment through analyzing data from multiple sources associated with the industrial equipment is based on a predetermined list of fault categories. The method includes acquiring the data associated with the industrial equipment, analyzing the data according to the predetermined list of fault categories, and outputting a fault report of the industrial equipment according to the analysis. Information as to fault processing of the industrial equipment is updated to the predetermined list, the fault processing information being adopted according to the fault report. An equipment maintenance device and a storage medium are also provided.

In a management system which manages user equipment installed in a prescribed space managed by a user, the user equipment including a plurality of motion guidance devices each including a track member, a moving member, and a plurality of displacement sensors which detect displacements of the moving member in a prescribed number of displacement directions in the moving member, on the basis of lifespan-related information corresponding to each of the plurality of motion guidance devices, a lifespan exhaustion ratio of each motion guidance device is calculated and, on the basis of the calculated lifespan exhaustion ratio, replacement object devices to be replaced within a prescribed period which constitute a part of or all of the plurality of motion guidance devices are determined. In addition, a user is notified of a replacement timing at which all of the determined replacement object devices in the user equipment are to be replaced.