A Fault Diagnosis Method Based on Deep Learning and Signal Analysis

Abstract

The invention discloses a fault diagnosis method based on deep learning and signal analysis. The method includes: acquiring data in normal and faulty states of an industrial process in advance, and dividing the data into a training set and a test set; and training model parameters offline based on the training set, detecting a model through the test set, wherein a performance index refers to the precision of fault diagnosis, and a value thereof represents the generalization performance of the model, namely the online diagnosis capability of faults. According to the method, as a variant of a neural network, physical information of a process operation variable in a time domain can be obtained, and frequency domain information of a process measurement variable can be obtained through introduction of a wavelet analysis method; besides, a depth structure adopted by the method adapts to big, fast, various, and uncertain characteristics of industrial big data, the physical information of theprocess operation variable and frequency characteristics of the process measurement variable are combined, a complex mode of a deep grade of the faults is learned, fault diagnosis can be effectively realized, and excellent generalization capability is displayed in an online diagnosis test.

Description

technical field [0001] The invention belongs to the technical field of industrial process monitoring, and more specifically relates to a fault diagnosis method based on deep learning and signal analysis. Background technique [0002] Fault detection and identification technology plays an important role in a safe and stable industrial production process. It has developed into an interdisciplinary subject, involving system integration, control engineering, artificial intelligence, applied mathematics and statistics, and various application fields. With the development of electronic industry and computer technology, many experts and scholars have applied different fault detection methods, such as worker on-site monitoring, programmed self-testing, model modeling, and data-driven. [0003] In the data-driven diagnostic technology, it is a prerequisite and a key role to make appropriate method selection and improvement for the application scenario. In the prior art, there are th...

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Problems solved by technology

However, in the application scenarios of complex industrial processes, it is difficult to analyze the industrial process in terms of specific research objects and the applicability of theories by directly applying the above methods without integrating the theories involved in the application scenarios.

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