Large-scale equipment online monitoring and fault prediction system

Abstract

The invention discloses a large-scale equipment online monitoring and fault prediction system. The system comprises a data acquisition assembly, a field control end, a central control end and an equipment monitoring end; the data acquisition assembly acquires one or more feature data of the large-scale equipment in real time; the central control end traces and analyzes historical data of the large-scale equipment and constructs a fault prediction model; the invention includes inputting the feature data received in real time into the fault prediction model, performing feature extraction, identification and classified learning on the feature data of the large-scale equipment by using an artificial intelligence algorithm including a convolutional neural network, and performing fault prediction and diagnosis on the large-scale equipment; and the equipment monitoring end receives and displays the characteristic data of the large-scale equipment sent by the field control end in real time, and receives and displays the fault prediction and diagnosis result of the central control end at the same time. According to the invention, fault prediction and timely maintenance of the large-scale equipment are effectively realized, the purpose of nipping in advance is achieved, and the large-scale equipment is ensured to be in a safe and reliable operation state.

Description

technical field [0001] The invention relates to the technical field of monitoring and early warning of large equipment, in particular to an online monitoring and fault prediction system of large equipment. Background technique [0002] With the vigorous construction and rapid development of large-scale equipment in the country, the research on large-scale equipment security technology has also become a hot spot in the field. In an industrial environment, the continuous operation of large-scale equipment is likely to lead to failures. Therefore, by collecting, processing and predicting data on the operation status of large-scale equipment, monitoring its operation trend, and predicting risks in advance, the probability of failure can be effectively reduced. [0003] In recent years, with the widespread application of artificial intelligence algorithms, the field of fault prediction has become more mature. For example, Patent No. CN202010184204.0 discloses a fan fault predict...

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Problems solved by technology

[0005] This application provides an online monitoring and fault prediction system for large-scale equipment, which solves the technical problem of inaccurate prediction results caused by one-sided fault monitoring of equipment in the prior art, and realizes real-time online monitoring of various operating parameters of equipment. , to complete the beneficial effect of failure prediction

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