Systems and methods are provided to retrieve or analyze usage data collected from a device or a facility where the device, optionally with devices are located, and identify useful features for making a diagnosis of the device. The diagnosis can be made before a system failure to reduce down time and inefficient use of the device, or after the system failure to expedite and facilitate diagnosis and repair. In addition to the usage data, such as energy and resource consumption, the system can also obtain information relating to the facility and the device's external environment which can be used for normalizing the usage data. Further, based on the diagnosis, the system can make suitable recommendations for repair, replacement, maintenance and upgrade.

The invent relates to an intelligent fault monitoring method based on high-order information enhanced recurrent neural network, for real-time fault monitoring of sewage treatment process. The invent includes two phases of offline modeling and online monitoring. In offline phase, the original data is extracted into high-dimensional high-order information features using OCIA, which can effectively deal with the non Gaussian feature of the data and solve the correlation between variables. Then the extracted features are trained by DRNN. In the online phase, the data are directly mapped to new high-order feature components, and to be discriminated in category by the DRNN network after trained offline. If there is no fault, then the results get into the monitoring model composed of simple OICA for unsupervised monitoring. If no fault is detected, it is determined that there is no fault in the process. On the contrary, the process fault is determined, and the fault information will be added to the training data of the network for training, so as to continuously improve the monitoring accuracy of DRNN.

A method of operating a domestic appliance or electronic assembly may include receiving one or more component input signals vectors of the electronic assembly. The method may also include generating a plurality of discrete signal matrices based on the one or more input signals. The method may further include joining the plurality of discrete signal matrices together as a combined matrix. The method may still further include analyzing the combined matrix for appliance performance.

An anomalous scenario synthesizer apparatus includes a rotatable shaft configured to be rotationally driven about a rotation axis, a data acquisition system operably associated with the rotatable shaft and configured to measure attributes of the rotatable shaft, and a dynamic anomaly generator operably connected to the rotatable shaft. The dynamic anomaly generator is configured to generate at least one anomaly in the rotatable shaft while the rotatable shaft is rotating, and is configured to generate at least one dynamic label for each anomaly while the rotatable shaft is rotating. The dynamic label for each anomaly includes at least one descriptor corresponding to the anomaly that describes the anomaly such that a machine learning method may utilize the descriptor for machine learning.

Systems, methods, and computer readable storage mediums for performing sensor health monitoring are described. The method includes verifying data quality and suppressing alert generation using machine learning techniques to identify whether two anomalies generated by an asset monitoring system are related. The method can include receiving data characterizing measurement data acquired by a sensor coupled to an industrial asset. An anomalous data sample within the received data can be identified and removed from the anomalous data sample. A new sample of the removed data sample can be estimated using interpolation and the new sample can be assessed. Maintenance analysis can be performed based on the assessed, estimated new sample.

A storage device of a noise inspection apparatus is configured to store a neural network machine-learned to receive, as inputs, an original sound characteristic value indicating a characteristic of sound generated by a transmission and an evaluation sound characteristic value indicating a characteristic of sound that reaches a vehicle cabin, and output a route part characteristic value that is a value indicating a characteristic of a vibration transfer of a vehicle part positioned on a vibration transfer route from the transmission to the vehicle cabin. An execution device of the noise inspection apparatus is configured to calculate, as an estimated value of the route part characteristic value, an output of the neural network that has received, as inputs, measured values of the original sound characteristic value and the evaluation sound characteristic value.

A system and method for automatically detecting anomalies in an industrial control system (ICS) is provided. A behavioral model is provided, the model comprising groups of learned sets of interdependent ICS signals of parameters associated with an operation of the ICS. For each of the groups, the learned sets in the respective group include at least one independent signal and one or more dependent signals that are dependent on the independent signal in accordance with a common type of dependency. Monitoring signals of given parameters are obtained, the monitoring signals corresponding to a given learned set of the learned sets in one of the groups. Upon determining a nonconformance of an observed interdependency of the monitoring signals with a predicted interdependency of the monitoring signals, the predicted interdependency being in accordance with the type of dependency associated with the given learned set, an anomaly is automatically detected.

A sensor unit, a control method, and a recording medium are provided for reducing data amount of failure diagnosis data while detecting a failure of a device performing work while moving more reliably. The disclosure includes an output limiting part outputting the failure diagnosis data only for a period in which an absolute value of the acceleration is equal to or less than a predetermined threshold.

Device and method for analyzing time series data monitored on a machine, wherein the device segments the time series data into multiple time segments, determines a cluster of time segments estimated to have the same dynamics of the time series data, then classifies the cluster based on label information associated with at least one of the time segments, presents at least a part of the time series data of the cluster to a user if none of the time segments of the cluster has associated label information, classifies the cluster and generates label information associated with the time segments of the cluster based on a user input received in response to presentation of the time series data, where the generated label information indicates a result of classifying the cluster.

The present disclosure relates to a system, an apparatus, and a method for managing health condition of at least one rotating system. The method includes receiving, by a processing unit, operational data associated with the rotating system in real-time, from one or more sensing units. The operational data includes parameter values corresponding to an operation of the rotating system. Further, a virtual replica of the rotating system is configured using the operational data. A behavior of the rotating system is simulated on a simulation instance of the rotating system based on the configured virtual replica. The simulation results are analyzed to determine an abnormality in the health condition of the rotating system. The abnormality corresponds to a health status of an internal component of the rotating system. Further, a notification indicating the abnormality is generated, on a Graphical User Interface.