Gas compressor vibration fault detection method based on recurrence plot and deep convolutional network

Abstract

The invention discloses a gas compressor vibration fault detection method based on a recurrence plot and a deep convolutional network. The method comprises the steps of firstly, constructing a compressor aerodynamic instability vibration fault database, selecting existing experimental data, and obtaining a large number of fault databases through data preprocessing; secondly, selecting a two-dimensional image conversion method; converting the one-dimensional vibration signal into a two-dimensional image, finally, selecting a deep convolutional neural network Inception V3 model, keeping a modelfeature extraction link unchanged by adopting transfer learning, and performing fault feature extraction and detection on a two-dimensional image recursive graph by adjusting a feature integration link structure. According to the method, the two-dimensional image reflects the phase space manifold in the time sequence internal dynamic system, the dynamic characteristics of the system are disclosed,and the method is suitable for carrying out characteristic analysis on the non-stationary nonlinear time sequence. Advantages in machine vision are applied to the field of non-vision, and mechanicalvibration fault detection is performed by adopting a deep convolutional neural network Inception V3 model so that huge workload of manual feature design can be reduced.

Description

technical field [0001] The invention relates to the field of fault diagnosis of rotating machinery vibration signals, in particular to a compressor vibration fault detection method based on a recursive graph and a deep convolutional neural network. Background technique [0002] Compressor is the power source of large civil and military aero-engines, and its aerodynamic instability belongs to a kind of vibration fault phenomenon. This fault not only greatly deteriorates the engine performance (thrust, economy), limits the working range of the engine, but more seriously, they will cause the engine to suddenly shut down, or cause the compressor blades to vibrate violently so that the blades break and cause the entire engine to fail. damage. The research on the fault detection method of the aerodynamic instability and vibration of the compressor has important scientific research significance and engineering application value for improving the stability and reliability of the en...

AI Technical Summary

Problems solved by technology

However, the window added to the signal by the short-time Fourier transform is fixed, which cannot meet the frequency requirements of non-stationary signal changes; the empirical mode decomposition has serious endpoint effects and modal aliasing phenomena, which will affect the analysis results. Correctness and precision; wavelet decomposition has a strong dependence on prior knowledge of the signal in the choice of wavelet basis

More importantly, for mechanical vibration fault signals, traditional signal processing methods can only diagnose faults by capturing the instantaneous fault frequency, and cannot visually see the type and degree of faults

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