A sensor management system includes a historical data repository, a building management system (BMS) controller, and a sensor diagnostics system. The historical data repository may store historical data by a plurality of sensors. The BMS controller may control one or more components of a building subsystem based on data provided by one or more sensors. The sensor diagnostic system may receive, from a sensor, sensor data. The sensor diagnostic system may determine at least one fault in the sensor data. The sensor diagnostic system may select, from the historical data repository, substitute sensor data for the sensor based on a comparison of one or more attributes of the sensor data and one or more attributes of the historical data in the data repository. The sensor diagnostic system may provide, in replacement of the sensor data from the sensor, the supplemental sensor data to the BMS controller.

An anomaly determination device and an anomaly determination method determine an anomaly of a device based on state data of the device, by using a first determination model configured to determine whether a predetermined anomaly has occurred in the device, and a second determination model configured to classify state of the device, and output the determined anomaly of the device as an unknown anomaly in a case where the anomaly of the device is not the predetermined anomaly.

A computer implemented method for the maintenance optimization of a fleet or group of turbomachinery assets is disclosed. The method comprises the step of model training and setup, aiming at setting configurations parameters, that can be executed offline, and the step of online calculation on new input data, which is based on detected data and extracted statistical features. An anomaly identification and classification follow, thus calculating a risk assessment, for estimating the risk that an anomaly might cause any event that requires a maintenance task to be executed on one or more assets of the fleet.

The present invention relates to a system for coordinating operation control and operation maintenance for an urban rail transit and a method using the same, where the system includes: an intelligent operation maintenance subsystem and an intelligent operation control subsystem, the intelligent operation maintenance subsystem and the intelligent operation control subsystem include coordination linkage engine modules respectively, and the intelligent operation maintenance subsystem synchronizes, by using the coordination linkage engine modules, a fault handling plan to the intelligent operation control subsystem. Compared with the prior art, the present invention has the advantages of scientific and reasonable dispatching decision-making, high efficiency and high intelligence.

Methods may comprise: identifying a fault indicator associated with a physical system; collecting first data related to a state of the physical system; applying a surrogate model to the first data to produce a plurality of potential fault modes; applying an optimization algorithm to the plurality of potential fault modes using a similarity metric to produce an input and a plurality of outputs, wherein each of the plurality of outputs corresponds to one of the plurality of potential fault modes, wherein the input provides differentiation between each of the plurality of outputs; applying the input to the physical system; collecting second data from physical system in response to applying the input; identifying a true mode of the physical system based on a comparison of the second data and the plurality of outputs; and diagnosing a fault of the physical system based on the true mode.

A system includes a diagnostic step module and an optimal diagnostic procedure module. The diagnostic step module is configured to identify diagnostic steps of a service procedure to be performed to diagnose a root cause of a fault on a vehicle based on a diagnostic trouble code identifying the fault, and identify a cost associated with performing each of the diagnostic steps. The optimal diagnostic procedure module is configured to determine an order in which to perform the diagnostic steps that minimizes the cost of diagnosing the fault, and output an optimal diagnostic procedure that indicates the diagnostic steps and the order in which to perform the diagnostic steps.

Systems and methods of managing a building are disclosed. In some embodiments, a method includes receiving, by a processing circuit, an indication to execute a digital twin, the digital twin including one or more fault detection or diagnostics functions and a virtual representation of a piece of equipment, the virtual representation including one or more entities of a building and relationships between the entities of the building, executing the digital twin based on the virtual representation of the piece of building equipment to generate an indication of a fault or a diagnosis of the fault for the one or more pieces of building equipment, and storing an indication of the fault or a diagnosis of the fault, or a link to the fault or the diagnosis of the fault, in the virtual representation of the piece of equipment.

An abnormal irregularity cause display device includes a process data acquisition unit that reads, from a storage device, the pieces of process data, an abnormality determination unit that calculates an abnormality degree representing an extent of an irregularity of process data of the pieces of process data read by the process data acquisition unit, a cause diagnosis unit that determines, for each of the pieces of process data, whether the abnormality degree calculated by the abnormality determination unit satisfies a predetermined criterion by using causal relation information defining a combination between a cause and the irregularity, which appears as an influence resulting from the cause, of the process data output by each of the plurality of sensors, and an output control unit that reads, from the storage device, the information indicating the handling and makes an output device output the information.

A system processes historical facility data that relate to facility states and modes of operation. The historical facility data are clustered into groups representing the facility states and the modes of operation. The groups are used to determine a current state and mode of the facility. When the facility is in a normal state, the system determines whether an event in the facility is an abnormality. If an abnormality is identified, the system transmits a signal indicating the abnormality.

Methods and systems for fault identification and mitigation in an engine system. A state observer obtains current state information from the engine system, and a feature calculator uses data obtained from the state observer to calculate one or more feature indicators, which are monitored by a health estimator for the occurrence of a change using one or more change probability models. When the health estimator identifies a change, a fault isolator determines a component of the engine system that is subject to fault or health deterioration.