Devices, methods, and systems for determining the cause of a fault in a heating, ventilation, and air conditioning (HVAC) system are described herein. One device includes a memory, and a processor configured to execute executable instructions stored in the memory to receive operational data associated with an HVAC system, receive control logic associated with a controller of the HVAC system, determine a cause of a fault occurring in the HVAC system based, at least in part, on the operational data associated with the HVAC system and the control logic of the controller of the HVAC system, and provide the cause of the fault occurring in the HVAC system to a user.

A type identification unit identifies a request type included in input information input from the outside of a control apparatus; and a state storage unit stores state information when the request type identified by the type identification unit is a preset request, the state information including device information and the input information associated with each other, the device information including device data stored in a storage area specified in specification information, the input information being input to the control apparatus.

Method for monitoring and/or predicting machining processes and/or machining outcomes in mechanical workpiece machining carried out by a workpiece processing machine operable by specifiable operating parameters and having at least one machining tool. The monitoring and/or predicting occurs via a computer program product evaluation algorithm executed on a computer on the basis of training data sets. The training data sets include, as training data, adjustment data relating to adjustment of operating parameters of the workpiece processing machine for carrying out a machining process to be monitored and/or predicted. The training data sets further include outcome data of workpieces finished in a machining process to be monitored and/or predicted, and state data of the processing machine, determined by sensor, during a machining process to be monitored and/or predicted. In use, capture of outcome data by prediction made on the basis of machine-learned knowledge is avoided or reduced.

An automotive sensor integration module including a plurality of sensors which differ in at least one of a sensing period or an output data format, and a signal processing unit, which simultaneously outputs, as sensing data, pieces of detection data respectively output from the plurality of sensors on the basis of the sensing period of any one of the plurality of sensors, determines whether each region of an outer cover corresponding to a location of each of the plurality of sensors is contaminated on the basis of the pieces of detection data, and outputs a determination result as contamination data.

Examples described herein provide a computer-implemented method that includes receiving training data indicative of incipient compressor surge for cabin air compressors. The method further includes generating, using the training data, a training spectrogram. The method further includes training, by a processing system, a machine learning model to detect incipient compressor surge events for the cabin air compressors using the spectrogram. The method further includes receiving, at a microcontroller associated with a cabin air compressor, operating data associated with the cabin air compressor. The method further includes generating, at the microcontroller and using the operating data, an operating spectrogram. The method further includes detecting, by the microcontroller associated with the cabin air compressor, an incipient compressor surge event by applying the machine learning model to the operating spectrogram. The method further includes implementing a corrective action to correct the incipient compressor surge event.

Provided are a diagnosing device, a diagnosing method, and a program with which it is possible to detect abnormalities accurately even if there is a small amount of data or the number of data points varies. This diagnosing device is provided with a Mahalanobis distance calculating unit which calculates the Mahalanobis distance (referred to as ‘MD value’ hereinbelow) of a detected value, and an abnormality determining unit which determines the presence or absence of an abnormality on the basis of the MD value, wherein the abnormality determining unit determines the presence or absence of an abnormality by arranging that a determination that there is no abnormality is more likely to occur if the number of samples per unit space is small than if the number of samples per unit space is large.

A building management system includes connected equipment configured to measure a plurality of monitored variables and a predictive diagnostics system configured to receive the monitored variables from the connected equipment; generate a probability distribution of the plurality of monitored variables; determine a boundary for the probability distribution using a supervised machine learning technique to separate normal conditions from faulty conditions indicated by the plurality of monitored variables; separate the faulty conditions into sub-patterns using an unsupervised machine learning technique to generate a fault prediction model, each sub-pattern corresponding with a fault, and each fault associated with a fault diagnosis; receive a current set of the monitored variables from the connected equipment; determine whether the current set of monitored variables correspond with one of the sub-patterns of the fault prediction model to facilitate predicting whether a corresponding fault will occur; and determining the fault diagnosis associated with the predicted fault.

Systems and methods described herein can involve management of a system having a plurality of sensors, the plurality of sensors observing a plurality of process steps, which can involve selecting a subset of the plurality of sensors for observation; executing anomaly detection from data provided from the subset of the plurality of sensors; for a detection of an anomaly from a sensor from the subset of sensors, selecting ones of the plurality of process steps based on the detected anomaly; estimating a probability of anomaly occurrence for the selected ones of the plurality of process steps; and for the estimated probability of anomaly occurrence meeting a predetermined criteria, selecting ones of the plurality of sensors associated with the selected ones of the plurality of process steps for observation.

Provided are an abnormality diagnosis system and an abnormality diagnosis method that can prevent wrongly diagnosing equipment as having an abnormality when the equipment actually does not have an abnormality. An abnormality diagnosis system 20 comprises a sampler 21 and a calculator 24. The calculator 24 is configured to: perform first abnormality determination of whether there is an abnormality based on a result of first principal component analysis; in the case where a result of the first abnormality determination is that there is an abnormality, and perform second abnormality determination of whether there is an abnormality based on a result of second principal component analysis; and in the case where a result of the second abnormality determination is that there is an abnormality, diagnose the equipment as having an abnormality.

Disclosed is a fault signal recovery system including a data processor configured to generate a signal subset U* by removing, from a signal set U for a plurality of tags, some tags including a fault signal, and a first learning signal subset X* by removing tags disposed at positions corresponding to the some tags from a learning signal set X containing only tags of normal signals, a modeling unit configured to generate feature information F extractable from the first learning signal subset X* and recovery information P on a plurality of recovery models usable for restoring the fault signal, and a recovery unit configured to estimate and recover normal signals for the some tags based on the signal subset U*, the first learning signal subset X*, the feature information F, the recovery information P on the plurality of recovery models, and similarity Z.