A method and a system are disclosed for detecting, isolating and estimating a degradation of a corresponding component of an actuator controlled by an actuator command signal, the method comprising obtaining a control signal of the actuator; obtaining a signal indicative of a displacement of the actuator; computing an envelope of admissible values for the displacement of the actuator; determining if the displacement of the actuator is outside the computed envelope and in the case where the displacement of the actuator is outside the computed enveloped computing an estimation of each parameter and state of the actuator; identifying at least one corresponding parameter responsible for causing the actuator displacement to be outside the computer envelope and providing an indication of the at least one corresponding parameter.

In order to provide a method for fault analysis in an industrial-method plant, for example a painting plant, by means of which fault situations are analysable simply and reliably, it is proposed according to the invention that the method should comprise the following: in particular automatic recognition of a fault situation in the industrial-method plant (101); storage of a fault situation data set for the respective recognised fault situation, in a fault database (136); automatic determination of a cause of the fault for the fault situation and/or automatic determination of process values that are relevant to the fault situation, on the basis of the fault data set of a respective recognised fault situation.

Aspects of the disclosed technology encompass solutions for automatically managing autonomous vehicle (AV) operating and maintenance tasks, such as implementing an alternative operating mode or ordering replacement parts for an AV components. In some aspects, a process of the disclosed technology can include steps for receiving diagnostic data corresponding with an AV component, determining an estimated life cycle of the AV component, and determining whether to generate an action to implement an alternative operating mode or an order request for one or more replacement parts of the AV component, based on the estimated life cycle of the AV component. Systems and machine-readable media are also provided.

Provided is an apparatus for evaluating a response time of a plant protection system. The apparatus includes: a classification module classifying a design process related to a response time requirement of each channel performing a safety function into four operations of a safety analysis operation, a system design operation, a response time analysis operation, and a response time test operation; and an integrated evaluation module determining whether a response time evaluation is proper based on a time t1 derived in the safety analysis operation, a time t2 derived in the system design operation, a time t3 derived in the response time analysis operation, and a time t4 derived in the response time test operation.

There is provided a monitoring device including: an input unit that receives input of process data related to a plant; a model generation unit that generates a model representing a relationship between the process data based on the input process data; a determination unit that determines an operation state of the plant in a first determination mode using the model generated based on a forecast value of the process data input by a person or in a second determination mode using the model generated based on an actually measured value of the process data; and a display unit that displays a determination result by the determination unit.

A diagnostic device includes a storage unit that stores first information including a first abnormal event which occurred in the past in a plant, a first attribution event that is a cause of the first abnormal event, and a first occurrence probability of the first attribution event, in which a causal relationship between the first abnormal and attribution events is indicated by a tree structure, and second information including a second abnormal event which is supposed to occur in the plant but has not yet occurred, a second attribution event that is a cause of the second abnormal event, and a second occurrence probability of the second attribution event, in which a causal relationship between the second abnormal and attribution events is indicated by a tree structure; and an estimation unit that estimates the cause of the sign of the abnormality, based on the first and second information.

A method for generating a multi-layer predictive model includes collecting historical observable data from one or more pieces of equipment of a same type, wherein the historical observable data is collected at different hierarchical levels of the one or more pieces of equipment; collecting operational state indications of the pieces of equipment corresponding to the collected historical observable data; generating, from the collected historical observable data, a set of operational state models, wherein each operational state model corresponds to one of the different hierarchical levels; and generating, from outputs of the set of operational state models, a top-level operational model for the piece of equipment. The top-level operational model is operable to determine maintenance and replacement timing for the piece of equipment.

Aspects of the present disclosure provide a method and apparatus for predicting noncompliance conditions of rotating machinery in aerial vehicles. Embodiments include receiving records related to historical noncompliance conditions of a plurality of rotating machines. The records include deceleration time values related to the plurality of rotating machines. Embodiments include generating, based at least on the deceleration time values and the historical noncompliance conditions, a predictive model. Embodiments include receiving sensor data related to a rotating machine of an aerial vehicle. Embodiments include determining, based on the sensor data, a series of deceleration time values for the rotating machine. Embodiments include using the predictive model to determine whether a noncompliance condition is predicted to occur for the rotating machine based on the series of deceleration time values.

The present invention relates to a power plant early warning device and method which employ a multiple prediction model, and includes: a plurality of prediction models which receive information about the operation states of equipment in a power plant, and output prediction values and the reliabilities of the prediction values; a reliability analysis module which quantizes and analyzes the reliabilities output from the plurality of prediction models to determine the ranks of the plurality of prediction models, and calculates a final prediction value; a comparison module which compares the final prediction value with an actual measurement value, and outputs a residual; and a determination module which analyzes the residual to determine whether there are defects in the operating states of the equipment in the power plant.

A gas delivery apparatus is provided, comprising a system controller configured to collect valve position information and sensor information from at least a plurality of the sensors and valves, store the valve position information and sensor information into the monitored gas delivery process data, and execute the discriminative classifier including a first artificial intelligence (AI) model configured to extract features in a first input image of the monitored gas delivery process; a second AI model configured to extract features in a second input image of a golden gas delivery process; and a contrastive loss function configured to calculate a similarity between the first input image and the second input image based on outputs of the first AI model and the second AI model, and output a repeatability confidence value based on a similarity index between the first input image and the second input image.