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.

A building management system includes building equipment operable to affect a physical state or condition of a building, a system manager coupled to equipment via a system bus, and a cloud-based data platform. The system manager is configured to obtain timeseries data from the building equipment and generate a request for a timeseries identifier based on the timeseries data. The cloud-based data platform includes a timeseries service configured to receive the request for the timeseries identifier from the system manager, create a timeseries for the building equipment, assign the timeseries identifier to the timeseries, and send the timeseries identifier to the system manager. The system manager is configured to generate a timeseries sample comprising the timeseries data and send the timeseries sample to the timeseries service along with the timeseries identifier which identifies the timeseries sample as a sample of the timeseries.

To provide a diagnostic service system and diagnostic method using a network. A factory monitoring system (100) comprises a factory monitoring system (100) which includes: a data acquisition unit (1011) that acquires data related to at least one machine, including time information; and a stored data management unit (1012) that stores data related to each machine acquired by the data acquisition unit in a storage unit (1002) together with identification information of each machine, wherein, based on past history data related to the machine and current data related to the machine, the diagnostic service system (1) predicts a possibility of abnormality occurrence in the machine, and provides preventative maintenance information related to the machine.

A system for monitoring a condition of a mobility system includes a number of sensors coupled to the mobility system, the number of sensors being structured and configured to generate data indicative of use of the mobility system during use, and a controller implementing a trained machine learning system. The controller is structured and configured to receive the data, characterize a lifecycle stage of the mobility system using the trained machine learning system based on at least the received data, and generate an alert for required maintenance for and/or predicted breakdown of the mobility system based on the characterized lifecycle stage.

Systems and methods for monitoring an operational system. An initial set of sensor data is accumulated from a system over a substantially shorter time than is required to collect data to characterize a regression model for an operating parameter of the system. An initial regression model is created based on the initial set of sensor data. A subsequent set of sensor data is received from the at least one sensor after creating the initial regression model. An expected dependent value for the subsequent independent value is determined using the initial regression model. An operator is prompted to update the initial regression model based on a difference between a subsequent dependent value and the expected dependent value. The initial regression model is updated to incorporate the subsequent set of sensor data. A notification is provided based on a difference between presently received sensor data and the updated regression model.

A plant monitoring apparatus according to an embodiment includes a determiner and a display processor. The determiner compares a first process value acquired in time series from a first point of a monitoring target in a plant and a limit value corresponding to the first process value to determine a time when the first process value exceeds the limit value. The display processor causes a display device to display, in time series, the first process value within a time range decided on the basis of the time obtained by the determiner and the limit value corresponding to the first process value.

A cell controller of the present application includes: a machine information reception part that receives at least one of alarm information on manufacturing machines and status information on the manufacturing machines, and receives physical layout information on the manufacturing machines; a classification part that classifies the received physical layout information into a plurality of groups; and a display part that displays an abnormality status of the manufacturing machines for each of the groups of the classified physical layout information.

The present invention relates to an universal integrated environmental monitoring and management technique for operating underground storage sites of gaseous substances including CO.sub.2 capture and storage (CCS) site. According to the present invention, Firstly, a base dataset for observed soil gases and related surrounding environmental variables by a step of configuring/refining procedures. Next, extracting the time varying characteristics of the base dataset using wavelet-based multiresolution state-space modeling, and identifying the driving forces that governing the soil gases dynamics and evaluating their contributions through multiscale time-frequency domain correlation analysis. And finally, predicting and forecasting future scenarios with deep leaning models which intensively trained by the key driving forces. Furthermore, the present invention can provide quantitative based for analyzing the causation between driving forces and observed soil gases. In addition, the present invention can effectively be used to detect early leakage signs and to assess environmental impacts of leakage based on the identification, evaluation, and prediction results.

An equipment monitoring system includes a control unit that switches a detection operation mode of a detector between a simple detection mode where the detector periodically performs a momentary detection operation, and a detailed detection mode where the detector performs a continuous detection operation. In the simple detection mode, a diagnosis unit diagnoses whether an operating state of monitored equipment is a normal state or a state requiring caution based on results of detection by the detector. In the simple detection mode, the control unit maintains the simple detection mode when the diagnosis unit has diagnosed that the operating state of the monitored equipment is a normal state, and switches the detection operation mode of the detector from the simple detection mode to the detailed detection mode when the diagnosis unit has diagnosed that the operating state of the monitored equipment is a state requiring caution.

A sensor device is coupled to a mechanical machine. The sensor device detects vibrations of the mechanical machine and transmits the vibration data to a remote processing device. The vibration data may be compressed prior to transmission. The remote processing device receives the data and generates a reconstructed version of the vibration data. The remote processing device includes a machine learning model trained to examine vibration data and to identify a motion pattern associated with an error condition. The machine learning model is applied to the reconstructed vibration data and detects an occurrence of an error condition in the mechanical machine. An alert indicating that an error condition has been detected is transmitted to a human operator. The human operator verifies the status of the mechanical machine and confirms that an error condition has occurred. In response to receipt of the confirmation, the machine learning model is further trained on training data updated to include the vibration data generated by the mechanical machine.