Composite fault diagnosis method for rotary machine based on improved deep sparse auto-encoder network

Abstract

The invention discloses a composite fault diagnosis method for a rotary machine based on an improved deep sparse auto-encoder network. The method comprises the following steps: 1) acquiring vibrationsignals of the rotary machine under c conditions in a normal state and different fault states respectively to obtain d groups of time domain vibration signal samples respectively; 2) carrying out Fourier transform on each sample to obtain preprocessed signal samples; 3) constructing a diagnosis sample set to serve as a training sample set; 4) building a rotary machine composite fault diagnosis model to obtain the connection weight and bias parameter of the deep sparse auto-encoder network; 5) acquiring a softmax classifier model; 6) carrying out fast Fourier transform, and selecting a test sample; 7) carrying out deep learning on the test sample by using the test sample as the input of a trained improved deep sparse auto-encoder network, and carrying out feature extraction to obtain a testsample feature signal; and 8) using test feature information as the matching feature of the test sample to obtain the composite fault diagnosis result of the rotary machine to be tested. Through theadoption of the method, the diagnosis accuracy and efficiency are improved.

Description

technical field [0001] The invention belongs to the technical fields of mechanical fault diagnosis and computer artificial intelligence, and in particular relates to an improved diagnosis method for compound faults of rotating machinery in deep sparse autoencoder networks. Background technique [0002] In recent years, the equipment in the industrial system has become increasingly large-scale, continuous, complex, high-speed and automated, which has also become the main feature of modern large-scale enterprise production. In today's era, the development of industrial technology also puts forward higher requirements for the safety and reliability of industrial production processes, especially in the pillar industries of the national economy. If the failure of production equipment cannot be prevented in time, once a production accident occurs, it will cause great economic losses, and even cause casualties and environmental pollution. [0003] The common features of these indu...

AI Technical Summary

Problems solved by technology

These processes need to make full use of human experience and knowledge in signal processing and fault diagnosis. The recognition accuracy of fault diagnosis depends on the quality of feature extraction, which greatly reduces the adaptive ability of diagnostic methods.

In addition, the existing diagnostic methods for single components, subsystems, and subunits are difficult to find out the correlation between monitoring data, which inevitably increases the risk of misdiagnosis and missed diagnosis in composite fault diagnosis

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