An industrial asset may have monitoring nodes that generate current monitoring node values representing a current operation of the industrial asset. An abnormality detection computer may detect when a monitoring node is currently being attacked or experiencing a fault based on a current feature vector, calculated in accordance with current monitoring node values, and a detection model that includes a decision boundary. A model updater (e.g., a continuous learning model updater) may determine an update time-frame (e.g., short-term, mid-term, long-term, etc.) associated with the system based on trigger occurrence detection (e.g., associated with a time-based trigger, a performance-based trigger, an event-based trigger, etc.). The model updater may then update the detection model in accordance with the determined update time-frame (and, in some embodiments, continuous learning).

The objective of the present invention is to acquire maintenance information easily when an alarm is generated. This control device for controlling an industrial machine is provided with: a monitoring unit which monitors the industrial machine to detect an abnormality in the industrial machine; an information acquiring unit which acquires alarm information relating to an alarm pertaining to the abnormality detected by the monitoring unit, and maintenance information relating to maintenance for dealing with the abnormality; and a display control unit which causes the acquired alarm information and maintenance information to be displayed on a display device.

A fault identification may be triggered by a component of a power generation system (PGS), such as a hardware component, a controller of a hardware component, a device of the PGS, a computer connected to the PGS, a computer configured to monitor the PGS, and/or the like. The fault identification may be the result of a failure of a component of the PGS, a future failure of a component of the PGS, a routine maintenance of the PGS, and/or the like. The fault is converted to a notification on a user interface using a mapping of faults, root-causes, notification rules, and/or the like. The conversion may use one or more lookup tables and/or formulas for determining the impact of the fault on the PGS, and/or the like.

An abnormality detection apparatus is provided, comprising a target data generation unit configured to generate, based on operation-related data resulting from an operation of a movable apparatus, a plurality of target data that are temporally separated, and a detection processing execution unit configured to execute change detection processing on the plurality of target data. An abnormality detection method that is executed by a computer for detecting an abnormality in a movable apparatus is provided, the method comprising generating, based on operation-related data resulting from an operation of the movable apparatus, a plurality of target data that are temporally separated, and executing change detection processing on the plurality of target data.

A method for using sensor data and operational data of an industrial process to identify problems includes gathering sensor data from one or more sensors sensing conditions on equipment of an industrial process, receiving command information about operational commands issued to the equipment of the industrial process, and for each sensor of the one or more sensors, comparing the sensor data with signature information for the sensor. The signature information for the sensor is relevant for operational commands issued to the equipment. The method includes determining if the sensor data of a sensor of the one or more sensors exceeds the signature information corresponding to the sensor, locating a problem with a piece of equipment of the industrial process monitored by the sensor of the one or more sensors based on the sensor data exceeding the signature information for the sensor and issuing an alert reporting the problem.

Apparatus and associated methods relate to a vibrational sensing system (VSS) including an accelerometer and a data processor, which determines an “operational state” of a mechanical drive unit, the processor further employing the “operational state” to gate learning of long-term vibrational data to exclude collection of non-operational data, the long-term data collected to calculate alarm thresholds. For example, vibrations from a target motor are sensed by a coupled accelerometer. Vibrational data from the accelerometer is fed into a data processor which determines the operational state of the motor. The operational state (e.g., on/off indication) may gate data collection such that data is only acquired during on-time, which may advantageously create accurate baselines from which alarm thresholds may be generated, and nuisance alarms may be avoided.

An apparatus may include a processor that may be caused to access a plurality of measurements of a device. The processor may provide the plurality of measurements as an input to an autoencoder, the autoencoder being trained based on measurements of devices in working condition and access an output of the autoencoder, the output comprising a reconstruction of the input based on decoding an encoded version of the input. The processor may further be caused to determine whether the device will fail based on the output.

A method for correlating data from different sensors for product lifecycle management includes receiving sensor information from an additional sensor of a plurality of sensors of an industrial operation. The additional sensor is different from component sensors used for functionality of a component of the industrial operation. Sensor information from the additional sensor monitors conditions of a portion of the industrial operation different from sensor information of the component sensors used for the functionality of the component. The method includes deriving, using the sensor information, a correlation between an operational parameter of the component and sensor information of the additional sensor. The operational parameter is related to a predicted operational lifetime of the component. The method includes identifying an abnormal operating condition of the component based on a comparison between additional sensor information from the additional sensor and the operational parameter, and sending an alert with the abnormal operating condition.

A normal model is acquired from a memory. A monitoring target data for each of a plurality of systems is acquired. A distance between the normal model and a value based on the monitoring target data for each of the plurality of systems is calculated. A system that provides an anomaly factor is identified from among the plurality of systems based on the distance.

A method for visualizing process data in which a process control system controls and monitors an industrial technology plant, wherein the process control system automatically triggers a process alarm if the process data fulfills a trigger condition such a corresponding alarm message is transferred to an alarm system for output to an operator, triggered process data alarms are archived as a history, such that by selecting a process data item and specify a display period by the operator the alarm system simultaneously requests the history of the selected process data item process alarms assigned to a process object for the display period, where the alarm system outputs a time sequence of the process data item as a graphic and presents process data points in the graphic in an encoding that specifies for each process data point the highest priority with which process alarms have occurred during the acquisition period.