A method for online monitoring of device system health based on ν-gap metric

Abstract

The invention discloses a method for online monitoring of device system health based on ν-gap metric, which comprises the following steps: a sensor collects input and output data online, uses real-time learning to identify and update a system model, and maps the data to an operator space; The distance between the spatial metric online update model and the system reference model, where the ν‑gap metric is selected as the measurement method; finally, it is judged whether the system has a fault according to whether the distance between the models exceeds the set threshold, and the threshold is based on the reference obtained from offline identification Confidence intervals for the model were calculated. This method calculates the difference between the updated model and the reference model online, and is different from other data measurement methods. It innovatively proposes to measure the change of the dynamic characteristics of the system from the model level, and effectively monitors the system in all operating stages, including transient transition stages and steady states. The health of the running phase. The invention provides a brand-new idea for the dynamic monitoring of the health degree of the device system, and provides a strong guarantee for its safe operation.

Description

technical field [0001] The invention belongs to the field of process monitoring, and in particular relates to an online monitoring method for device system health based on a ν-gap metric. Background technique [0002] In recent years, with the development of my country's modern chemical industry, metallurgy, machinery, logistics and other industries and the progress of related technologies, the investment and scale of the device system are increasing day by day, the complexity is getting higher and higher, and there are also couplings between systems. For such a complex process, safety and reliability play a crucial role, so health monitoring and fault early warning technologies emerge as the times require. Due to the convenience of process data acquisition, data-based methods are widely used, which can be mainly divided into machine learning and multivariate statistical analysis methods. The two methods are used to monitor the state of the device system according to the ex...

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Problems solved by technology

[0004] However, the above examples only monitor the health of the device system in the steady state stage. In the actual production process, due to changes in working conditions or set points, there will often be a dynamic transition stage, that is, the operation of the system is determined by the transient state and the steady state. Composed of state processes, the full cycle work of the system should be completely monitored

In addition, feature extraction at the data and parameter level cannot fully obtain the dynamic information of the system, so the above methods are not effective in monitoring the transient phase.

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