An inspection system and method of operation may include receiving sensor data for a component of a powered system, and determining an expected failure time of the component at which the component is expected to fail based at least in part on the sensor data. The expected failure time occurring on a timeline. A window start time may be determined on the timeline that is after a current time but is prior to the expected failure time. The window start time and the expected failure time may define a window time range that extends between the window start time and the expected failure time on the timeline. One or both of a repair action or a maintenance action of the powered system may be scheduled at a scheduled time occurring during the window time range and at a time prior to the expected failure time on the timeline.

A method for predicting power converter health is provided. The method comprises receiving a plurality of parameter measurements associated with a power converter system comprising a power converter. The plurality of parameter measurements comprises a first set of system measurements and a second set of failure precursor measurements. The method further comprises inputting the first set of system measurements into a first machine learning algorithm to generate expected failure precursor measurement information and inputting the expected failure precursor measurement information and the second set of failure precursor measurements into a second machine learning algorithm to generate component failure prediction information. The method also comprises performing one or more actions based on the generated component failure prediction information.

The technology disclosed in the present specification is embodied as a support device for supporting maintenance on an apparatus including an expendable component. The support device includes at least one computer. The at least one computer executes: a process of acquiring an index indicative of a consumption level of the expendable component; a process of, at a time point when the index indicative the consumption level reaches a predetermined threshold, specifying a necessary period of time required for replacement of the expendable component at the time point; and a process of restricting an operation of the apparatus when a remaining period of time of the expendable component, the remaining period of time being associated with the threshold, is shorter than the necessary period of time required for the replacement.

A method for predicting and compensating frictions of a feed system includes following steps: constantly obtaining current signals and angle-position signals of a motor by a motor driver of a feed system after being activated; calculating frictions of the motor upon each rotating position according to the obtained current signals and angle-position signals and generating multiple records of friction data; creating a friction model according to the multiple records of friction data and the angle-position signals each respectively corresponding to each record of friction data with respect to each rotating position; importing current angle-position signal of the motor to the friction model for predicting a predicted friction; calculating a compensation current based on the predicted friction; and, controlling the motor driver to additionally provide the compensation current to the motor for conquering an upcoming friction of the feed system approximate to the predicted friction.

A monitoring and/or control device for an environmental control unit such as a heat pump determines the performance status and whether maintenance is required of a component of the unit for example a compressor during operation of the component. The device includes sensors configured to be situated relative to the compressor so as to receive and signal data from the compressor during operation of the component. In some embodiments, the device includes a vibration detector and a controller coupled to the vibration detector. The controller is configured to (i) receive electrical signals from the vibration detector, (ii) compare the electrical signals to a reference signal, (iii) determine the performance characteristic of the component based on the results of the comparison, and (iv) output a signal corresponding to the performance characteristic of the component to a user display. The controller may also request maintenance and/or order parts automatically.

An abnormality predicting system includes a processor and a memory having instructions. The instructions, when executed by the at least one processor, cause the at least one processor to execute operations including: inputting processing target data acquired from a target device; storing information related to an abnormality prediction of the processing target data; calculating an abnormality degree of the processing target data; executing processing related to the abnormality prediction including a failure occurrence prediction using a latest abnormality degree transition and a past abnormality degree transition of the processing target data; and generating a display screen for displaying a processing result including an abnormality degree transition and a result of the failure occurrence prediction.

Systems and methods for operating an energy plant are disclosed herein. A time series of performance variable associated with a device in the energy plant is obtained. An auto-correlation function data of the device is obtained based on the time series of the performance variable associated with the device. An electronic model of the device is generated based on the auto-correlation function data. Time, at which a future event of the device is predicted to occur, is predicted based on the electronic model. A report indicating the future event of the device and the predicted time may be generated. The device may be automatically configured, according to the future event and the predicted time.

A valve maintenance assistance device includes: a valve ID storing portion configured to store IDs of an ON-OFF valve which may be a candidate for maintenance; an opening acquiring portion configured to acquire opening measurement data from an opening sensor provided on the ON-OFF valve; a pressure acquiring portion configured to acquire pressure measurement data on operating device air supplied to an operating device of the ON-OFF valve; a storing portion configured to store the opening measurement data and the pressure measurement data; and a diagnosis index calculating portion configured to calculate a dead zone time from when an operating device air pressure is changed until an opening degree of the ON-OFF valve is changed as a diagnosis index based on the pressure measurement data and the opening measurement data; and a diagnosis index presenting portion configured to present a numerical value of the diagnosis index calculated by the diagnosis index calculating portion.

A control apparatus for controlling an industrial machine includes a deterioration diagnostic unit that has a diagnostic function, which is repeatedly executed at predetermined timing, for diagnosing deterioration of each component of the industrial machine and outputs an alarm signal in response to deterioration equal to or more than a predetermined level in each component, an alarm control unit that outputs a stop command to the industrial machine based on the alarm signal and informs that the industrial machine is in an alarm stop state, and a cancellation operation receiving unit that receives the cancellation operation for cancelling the alarm stop state regardless of deterioration of each component. The deterioration diagnostic unit cancels the alarm stop state brought on by the diagnostic function based on the cancellation operation, and allows the industrial machine to operate until the diagnostic function is executed next.

Systems, methods, and other embodiments associated with long-term predictions for specific maintenance within a planned maintenance schedule. In one embodiment, a method includes updating a failure probability curve for a component part of a device based at least in part on data obtained from a sensor associated with the component part; determining based at least in part on the updated failure probability curve that a likelihood of failure for the component part following a first upcoming planned maintenance and before a second upcoming planned maintenance exceeds a threshold; and transmitting a work order for specific maintenance to reduce the likelihood of failure of the component part to be performed during the first upcoming planned maintenance.