A method and an automation component for identifying a process-disrupting automation component in an industrial automation assembly, wherein a process disruption is determined in a first automation component and examined by a first local analysis device, where an automation component arranged upstream and/or an automation component arranged downstream is first determined by each automation component, an interrogation message is sent from a first automation component to a second automation component and the same interrogation message or a further interrogation message is recursively sent by the second automation component to a third automation component arranged upstream or downstream of the second automation component and processed, where in the event of a locally determined disruption, the relevant automation component sends a response message, which is back-propagated to the origin and signaled such that a decentralized error analysis becomes possible, even with a changing system topology, without the need for any redesign work.

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 computer implemented method of administering a complex system includes receiving multivariate data from a plurality of sensors of the system in an ambient state. Event sequences in the received multivariate data are identified. The multivariate event sequences are projected to a lower stochastic latent embedding. A temporal structure of the sequences is learned in a lower latent space. A probabilistic prediction in the lower latent space is provided. The probabilistic prediction in the lower stochastic latent space is decoded to an event prediction in the ambient state.

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.

There is described a system and method for active fault detection of an HVAC system and its associated mechanical equipment comprising building automation controllers and a remote device. A request for active fault detection of controllers of a building automation system (“BAS”) network is received. A passive test associated with each controller is executed by analyzing the controller via read-only access to operations of the controller. The passive test includes identifying a fault condition and a work item associated with the controller or a mechanical device connected to the controller. A full range full range active test based on the fault condition and the work item associated with each controller is executed by analyzing the controller via direct command access to the operations of the controller. A controller function associated with the request for active fault detection of the controllers is performed in response to executing the full range active test.

A distributed multi-node control system (100) and method, relating to the field of control technology. The distributed multi-node control system (100) comprises: a first control node (11), a second control node (12), a plurality of servo nodes (20) and a plurality of execution devices (30), the first control node (11) and the second control node (12) being respectively communicationally connected to the plurality of servo nodes (20), the servo nodes (20) being electrically connected to the execution devices (30) and configured to control operating states of the corresponding execution devices (30), the first control node (11) being configured to control an operating state of at least one first servo node (21) among the plurality of servo nodes (20), the second control node (12) being configured to control an operating state of at least one second servo node (22) among the plurality of servo nodes (20).

A valve abnormality detecting device includes an opening acquiring portion to acquire a valve opening value; a pressure acquiring portion to acquire a pressure value of operating device air of an operating device for a valve; a stability-time detecting portion configured to detect a stable-opening state in which the valve opening value acquired by the opening acquiring portion 1 is substantially constant; a frictional force detecting portion configured to detect a difference between a maximum pressure value and a minimum pressure value of the operating device air in the stable-opening state as an index value indicating a frictional force at a sliding portion of the valve; and an abnormality determining portion configured to determine that an abnormality may have occurred in the valve in a case where a frequency of occurrence of reduction in which the index value falls below a specified value is an abnormal frequency.

Systems and methods for detecting hardware faults in computer-based feedback control systems. Multiple instances of the system control program(s) are run on system processors. System sensor data are input to each instance, and the control commands output by each instance are compared. As instantiations of the same programs receive largely the same sensor data, differences between output commands may indicate the presence of one or more hardware faults.

A system and method is disclosed which monitors factors in order to prevent impending component failure within a mechanical system, such as an aircraft. The monitoring system monitors the health and condition of system components, and utilizes proprietary algorithms to predict impending failures in monitored components before failure occurs. The system can shut down a component, send an alert, or adjust component thresholds as required.

A learning diagnosis apparatus performs learning from failure data to create a diagnostic model, and stores a model, a failure cause part, and sensor data of the equipment in a rare case data table when the number of cases of the failure cause part of the equipment is less than a predetermined number. Then, based on the diagnostic model created by a learning unit, an estimated probability of causing a failure is calculated for each part of the equipment in which a failure has occurred. Based on the rare case data table, a sensor data match rate between sensor data of the equipment in which the failure has occurred and past sensor data of the model of the equipment is calculated. Then, the calculated sensor data match rate for each part of the equipment in which the failure has occurred is displayed.