An apparatus for assisting maintenance work, a method of assisting maintenance work, and a program for assisting maintenance work that assists maintenance work, while reducing the number of onside services by a maintenance operator, are provided. An apparatus for assisting maintenance work includes a first acquirement unit configured to acquire a data set including a combination of device information of a target device and operation information of the target device, or a data set including a combination of the device information of the target device and event information indicating an event for the target device. The apparatus for assisting maintenance work includes a second acquirement unit configured to acquire work content information indicating a content of maintenance work, for the target device, performed through a maintenance operator, the work content information indicating a replaced or repaired part, or a new part after replacement. The apparatus for assisting maintenance work includes a learning unit configured to perform learning based on the data set acquired by the first acquirement unit, the data set being associated with the replaced or repaired part, or the new part after replacement that is indicated by the work content information acquired by the second acquirement unit.

A predictive maintenance system for an irrigation system includes one or more sensors configured to generate a signal indicative of abnormal operation within the irrigation system, the sensors electrically coupled to a drive system, a processor, and a memory. The memory includes instructions stored thereon, which when executed by the processor cause the predictive maintenance system to receive the generated signal, determine abnormal operation of the drive system based on the generated signal, and predict, by a machine learning model, a maintenance requirement of the drive system based on the determined abnormal operation.

Systems and methods include monitoring a health of at least one asset. A remaining useful life (RUL) of the at least one asset is estimated based on the monitoring. The RUL of the asset is categorized into categories comprising shorter than a time to complete a current mission and longer than the time to complete the current mission. One or more remedial actions are automatically performed during the current mission if the RUL is categorized as being less than the time to complete the current mission. The remedial actions comprise one or more of initiating a fail-safe mode, adapting a controller of the one or more assets, reconfiguration of the system, and adjusting the current mission of the one or more assets. Maintenance is scheduled for after the current mission of the at least one asset if the RUL is categorized as being greater than or equal to the time to complete the current mission.

A remote work support system includes a wearable device worn by a site worker, a mobile terminal carried by the site worker separately from the wearable device, and a support operator terminal used by a support operator who remotely supports the site worker. The wearable device includes an image capturing unit, a voice input unit, and a voice output unit. The wearable device transmits an image. The wearable device transmits and receives voice. The mobile terminal includes a display unit to display a received image. The support operator terminal includes a voice input unit, a voice output unit, and a display unit. The support operator terminal transmits and receive voice to and from the wearable device. The support operator terminal receives an image from the wearable device. The support operator terminal transmits an image to the mobile terminal.

A method of performing fault isolation for an aircraft comprises identifying a fault that occurs during a flight of the aircraft; identifying a first set of parameters associated with the aircraft based on the identification of the fault; automatically determining values of the first set of parameters to obtain a first set of measured values; determining whether the first set of measured values are within acceptable ranges; and identifying a source of the fault based on the determination of whether the first set of measured values are within the acceptable ranges.

An apparatus includes memory to store computer-readable program code for a knowledge-based system including an inference engine and a knowledge base, and processing circuitry configured to access the memory, and execute the code. The apparatus is caused to at least receive a time series of measurements of operating conditions of a machine recorded during an operation of the machine. The apparatus is also caused to cluster the time series of measurements into one or more respective clusters and identify a pattern across the clusters. The apparatus is also caused to define a current state of the machine that includes the pattern across the clusters, access and search a knowledge base for a historical case describing a respective solution to a historical problem state similar to the current state, the respective solution including a repair action, and generate an output display indicating the repair action to address the current state.

Systems and methods are provided for maintaining an air conditioning system. A system can include one or more sensors positioned inside of the air conditioning system configured to transmit current sensor data to a remote location. A data repository contains historic sensor data and corresponding air conditioning system status data. A neural network is trained using the historic sensor data and the corresponding air conditioning system status data to predict a future air conditioning system status based on the transmitted current sensor data. A server computer system is configured to predict the future air conditioning system status based on the current sensor data using the neural network, and a graphical user interface is configured to display the predicted future air conditioning system status to a remote client. The current sensor data is stored in the data repository and the neural network is further trained based on the current sensor data.

Aspects of the present disclosure provide systems, methods, and computer-readable storage media that leverage artificial intelligence and machine learning to analyze and categorize events associated with an equipment asset, such as industrial machinery, to determine a status (e.g., insight) associated with the equipment asset, and to determine maintenance actions to be performed with respect to the equipment asset to prevent, or reduce the likelihood or severity of, occurrence of a fault at the equipment asset. Machine learning (ML) models may be trained to categorize events that are detected based on operating characteristics data associated with the equipment asset, to determine a status of the equipment asset, and to recommend one or more maintenance actions (or other actions). Output that indicates the maintenance actions may be displayed to a user or used to automatically initiate performance of one or more of the maintenance actions.

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.

Systems and methods are provided for maintaining an air conditioning system. A system can include one or more sensors positioned inside of the air conditioning system configured to transmit current sensor data to a remote location. A data repository contains historic sensor data and corresponding air conditioning system status data. A neural network is trained using the historic sensor data and the corresponding air conditioning system status data to predict a future air conditioning system status based on the transmitted current sensor data. A server computer system is configured to predict the future air conditioning system status based on the current sensor data using the neural network, and a graphical user interface is configured to display the predicted future air conditioning system status to a remote client. The current sensor data is stored in the data repository and the neural network is further trained based on the current sensor data.