The invention relates to a method for operating a textile machine (1) with a multiple number of similar work stations (2), whereas, with the assistance of work stations (2) during normal operation, yarn is produced or is rewound from a delivery coil onto a receiving coil, and whereas normal operation at the individual work stations (2) is interrupted at certain time intervals and is resumed with the assistance of a maintenance operation, and whereas the maintenance operations are carried out by one or more maintenance devices (3). In accordance with the invention, it proposed that for each work station (2), one or more production-related parameters are known or are determined, and that if more maintenance operations are to be carried out simultaneously than can be carried out by the maintenance devices(s) (3), the selection of the work station (2) to be serviced next will then be made under consideration of such production-related parameters, in such a manner that an upcoming batch change is completed as early as possible.

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.

Certain aspects of the present disclosure provide techniques for predicting a likelihood of future failure of components in an ophthalmic medical device and performing preventative maintenance on the ophthalmic medical device. An example method generally includes receiving, from an ophthalmic medical device, measurements of one or more operational parameters associated with the ophthalmic medical device. Using one or more models, a future failure of the ophthalmic medical is predicted. The predictions are generated based, at least in part, on the received measurements of the one or more operational parameters. One or more actions are taken to perform preventative maintenance on the ophthalmic medical device based on the predicted future failure of the ophthalmic medical device.

A cognitive assistant that allows a maintainer to speak to an application using natural language is disclosed. The maintainer can quickly interact with an application hands-free without the need to use complex user interfaces or memorized voice commands. The assistant provides instructions to the maintainer using augmented reality audio and visual cues. The assistant will walk the maintainer through maintenance tasks and verify proper execution using IoT sensors. If after completing a step, the IoT sensors are not as expected, the maintainer is notified on how to resolve the situation.

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 work assistance device that assists works for resolving a plurality of problems for simultaneously occurring in a system having a plurality of devices for producing products, the work assistance device including a processor configured to acquire, at occurrence of the plurality of problems, actual historical information on a processing time desired to previously produce the product in each of the devices; perform maximum a posteriori probability estimation of a processing time desired to produce the product in each of the devices, based on prior distribution stored in a storage unit and the acquired actual historical information on the processing time; and determine a work priority order of each of the devices based on the estimated processing times, and output the work priority order.

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.

Examples of the present disclosure include methods and/or systems for fault diagnosis and solution recommendation. A method may include: obtaining original data including fault problem of a target device; analyzing the original data including fault problem to obtain problem description information; analyzing the problem description information to obtain a diagnosis report; and, according to the diagnosis report, obtaining a video and/or document solution for the fault based on a cloud knowledge map, and recommending the solution to a user. The knowledge map comprises: nodes representing the fault, video solution and/or document solution, and multiple edges representing the relationship between nodes.

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.

Method for implementing event rules for maintenance relevant events in a plurality of machines, wherein the method utilizes a computer system containing a common central configurator, and wherein the machines are connected to the common central configurator to transfer data, where the method includes defining a maintenance relevant event for a certain class of machines by using the common central configurator, sending the event rule to an edge device of at least one machine of this class, preferably to edge devices of all machines of this class, where the edge device forms part of the computer system, and storing the event rule in the edge device and the common central configurator such that it is possible to implement event rules in a plurality of similar machines without having to implement those event rules on every single machine independently.