The present invention concerns a method for estimating a level of damage of an electric device. The method comprises the steps of: forming a histogram of operating cycles related to the electric device for which the level of damage estimation is performed, comparing the formed histogram to histograms of a collection of histograms or to combinations of histograms of the collection of histograms, each histogram of the collection of histogram being associated to a level of damage, in order to determine the histogram of the collection of histograms or the combination of histograms which is the closest from the formed histogram, determining an estimate of the level of damage of the electric device from the level of damage of the closest histogram or from the levels of damages of the histograms of the closest combination of histograms.

A CPU unit is a life prediction device for a fan. The CPU unit includes a temperature calculation unit to calculate internal temperature of the CPU unit on the basis of at least one of the utilization of the CPU unit, the temperature a CPU, and the rotation speed of the fan; and a storage unit to store fan life data that indicates the life of the fan relative to temperature. A control unit includes a life prediction unit to calculate remaining life expectancy of the fan on the basis of the fan life data and the internal temperature calculated by the temperature calculation unit.

A multi-platform industrial search system facilitates indexing and searching of plant-wide data residing on multiple different data platforms. The industrial search system automatically inventories industrial devices and other data sources located throughout a plant, and identifies available data items on each data source. The search system indexes the discovered data items in a federated data model that can subsequently be searched to locate data items or tags of interest. The federated data model records references to data items found on different types of data sources, including but not limited to industrial controller programs, human-machine interface applications, data historians, device documentation stores, inventory tracking systems, and other such data sources. The search system can also initiate automated searches of the federated data model in response to detected performance issues and deliver notifications and contextual information to plant personnel.

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.

Disclosed is a method of determining the remaining time interval until a measurement characteristic of a field device will have drifted outside of a predetermined tolerance range and a service action is required. The method includes predetermining a maximum tolerance of the measurement characteristic correlated/related to the measuring performance of the field device; registering continuously the measurement characteristic of the field device; estimating a lag time interval wherein the estimated lag time interval depends on the drift of the measurement characteristic of the field device in the process specific application; using a method of Artificial Intelligence to determine, at the end of the estimated lag time interval, the remaining time interval until the measurement characteristic of a field device will have drifted outside the predetermined maximum tolerance; and generating a message informing of the remaining time interval until the service action is required.

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.

The example embodiments are directed to a system and method for sharing machine learning model parameters among edge devices in a clustered group of edge devices sensing data about an industrial asset. In one example, the method may include one or more of storing unique parameters of a machine learning (ML) model associated with an industrial asset which are unique with respect to unique parameters of other edge systems in the group of edge systems, receiving common parameter information from the group of edge systems which is shared among the group of edge systems, generating updated parameter values for an ML model based on a combination of the unique parameters and the received common parameter information, and executing the updated ML model based on incoming data from the industrial asset to generate predictive information about the industrial asset.

An information processing device according to one aspect of the present invention includes a first acquirer configured to acquire measured data of a sensor, a second acquirer configured to acquire characteristic data of the sensor, the characteristic data having been acquired by maintaining the sensor, and a generator configured to generate teacher data in which the acquired characteristic data is associated as label information with the acquired measured data.

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.

An industrial machine predictive maintenance system may include an industrial machine data analysis facility that generates streams of industrial machine health monitoring data by applying machine learning to data representative of conditions of portions of industrial machines received via a data collection network. The system may include an industrial machine predictive maintenance facility that produces industrial machine service recommendations responsive to the health monitoring data by applying machine fault detection and classification algorithms thereto. A computerized maintenance management system (CMMS) that produces orders and/or requests for service and parts responsive to the industrial machine service recommendations can be included. The system may include a service and delivery coordination facility that processes information regarding services performed on industrial machines responsive to the orders and/or requests for service and parts, thereby validating the services performed while producing a ledger of service activity and results for individual industrial machines.