A monitoring device is disclosed that is configured to monitor conditions within an electrical enclosure containing electrical equipment. The monitoring device comprises a support configured to couple to an interior surface of the electrical enclosure. The support is configured to hold and electrically couple a plurality of sensors, at least two RF antennas, at least one processor in communication with the plurality of sensors and the at least two RF antennas, and a power connection configured to receive electrical and Ethernet input. The at least one processor is configured to receive and analyze data obtained from the plurality of sensors and the at least two RF antennas pertaining to a plurality of conditions inside the electrical enclosure. The at least one processor is configured to detect a potential electrical equipment failure based on the received an analyzed data.

The systems and methods described herein provide for a novel deep learning approach to estimating and predicting faulty mechanical system conditions before they occur without using any measurements from the system itself. Environmental data, such as temperature, humidity, occupancy, volatile organic compounds (VOC), equivalent carbon dioxide (eCO2) and particulate matter may be used in the estimation and prediction of faults, failures, and other inefficiencies within the HVAC system.

A method of adaptively controlling a brushless DC motor includes steps of: controlling the brushless DC motor rotating at a first speed according to an operation curve, accumulating a running time of the brushless DC motor, estimating a remaining used time of a bearing of the brushless DC motor according to the accumulated running time, executing an alarm operation when the remaining used time is less than a predetermined time, and decreasing the speed of the brushless DC motor to run at a second speed to prolong the used time of the bearing.

Operational planning of energy storage systems using batteries, e.g., Lithium-Ion batteries, is disclosed. A method of operating at least one server node includes: obtaining one or more load profiles associated with one or more interfacing modes of a battery energy storage system with an electrical utility distribution system, and predicting one or more degradations of the battery energy storage system, the one or more degradations being associated with operating the battery energy storage system in the one or more interfacing modes, the one or more degradations being predicted using an aging model of batteries of the battery energy storage system, the aging model being based on the one or more load profiles.

Techniques for geometric aging data reduction for machine learning applications are disclosed. In some embodiments, an artificial-intelligence powered system receives a first time-series dataset that tracks at least one metric value over time. The system then generates a second time-series dataset that includes a reduced version of a first portion of the time-series dataset and a non-reduced version of a second portion of the time-series dataset. The second portion of the time-series dataset may include metric values that are more recent than the first portion of the time-series dataset. The system further trains a machine learning model using the second time-series dataset that includes the reduced version of the first portion of the time-series dataset and the non-reduced version of the second portion of the time-series dataset. The trained model may be applied to reduced and/or non-reduced data to detect multivariate anomalies and/or provide other analytic insights.

A maintenance plan assistance system 100 configured to include: holding, in a storage device 101, remaining life information relating to devices covered by a maintenance plan, and a list of maintenance menus; and a computation device 104 identifying a maintenance effect when each maintenance menu is applied to each of the devices based on the maintenance menus and temporal changes in failure probabilities of each of the devices indicated by the remaining life information, identifying a variable range of a condition-based maintenance implementation timing, the variable range being dependent on the maintenance effect, and determining a maintenance menu including the variable range for a predetermined maintenance timing, and outputting, to a predetermined device, information including the maintenance menu identified by the determination and the condition-based maintenance implementation timing changed to the predetermined maintenance timing.

An apparatus management system determines whether temporary operation is possible or not when an abnormality of an apparatus occurs, and performs the temporary operation when possible. The apparatus management system includes a storage unit, an acquisition unit, and an operation content determination unit. The storage unit stores information on repair of the apparatus in association with the abnormality occurring in the apparatus. The acquisition unit acquires the information on the repair of the apparatus and writes the information in the storage unit. The operation content determination unit determines operation content of the temporary operation. The information on the repair of the apparatus includes information on a repair request for the abnormality of the apparatus. The operation content determination unit determines the operation content of the temporary operation in accordance with whether the repair request is present or not.

A rotational speed measurement system includes a video device configured to obtain a video of a pulley of a conveyor belt system. The rotational speed measurement system also includes a strobe configured to generate a stroboscopic effect on the obtained video. The rotational speed measurement system also includes a memory device. The rotational speed measurement system also includes a network interface. The rotational speed measurement system also includes one or more processors configured to: generate a stroboscopic window using the strobe and the obtained video, synchronize the strobe with the obtained video using the stroboscopic window, determine a measured rotational speed for the pulley based on the synchronized strobe, and determine belt slippage based on the measured rotational speed.

The progress of deterioration of a manufacturing facility is prevented while keeping KPI within an allowable range. A facility diagnosis device includes a deterioration prevention mode definition storage unit that stores information including an operation control method for preventing deterioration of a manufacturing facility or a portion thereof for each of predetermined deterioration prevention modes; a KPI calculation unit that calculates a predetermined KPI by using a deterioration degree predicted for each of the deterioration prevention modes for the manufacturing facility or the portion of the manufacturing facility, and determines whether the KPI satisfies a predetermined condition; a deterioration prevention determination unit that determines a deterioration prevention mode that should be executed from a satisfaction determination result; and a control information output unit that outputs control information on the manufacturing facility or the portion of the manufacturing facility according to the deterioration prevention mode that should be executed.

A fault prediction device capable of predicting an accurate deterioration state is provided. A fault prediction device for predicting fault of a target device whose deterioration state transitions with elapse of time includes autoencoders AED1 to AED4 respectively corresponding to deterioration states of the target device. The autoencoder AED2 corresponding to a first deterioration state determines whether the target device exists in the first deterioration state or not based on a state signal indicating a state of the target device. In a case where it is determined that the target device does not exist in the first deterioration state, the autoencoder AED3 corresponding to a second deterioration state determines whether the target device exists in the second deterioration state or not based on the state signal.