Abstract
When the rotary machinery is running, the vibration signals measured with sensors are mixed with all vibration sources and contain very strong noises. It is difficult to separate mixed signals with conventional methods of signal processing, so there are difficulties in machine health monitoring and fault diagnosis. The principle and method of blind source separation were introduced, and it was pointed out that the blind source separation algorithm was invalid in strong pulse noise environment. In these environments, the vibration signals are first denoised with the synchronous cumulative average noise reduction (SCA) method, and the denoised signals were separated with the improved fast independent component analysis (FastICA) algorithm. The results of simulation test and rotor fault experiments demonstrate that the novel method can effectively extract fault features, certifying its superiority in comparison with previous methods. Therefore, it is likely to be useful and practical in the fault detection area, especially under the condition of strong noise and vibration interferences.
Highlights
During the operation of rotating machinery, the vibration signal measured by the sensor is usually superimposed by the vibration of multiple components [1,2,3]
In order to solve the problem of fault feature extraction of rotating machinery under strong noise, a fault separation method combining synchronous cumulative average noise reduction (SCA) algorithm and improved fast independent component analysis (FastICA) algorithm (SCAFastICA) is proposed
According to the abovementioned analysis, this paper proposes a comprehensive sorting algorithm, aiming at the disadvantage that FastICA algorithm is sensitive to noise impact and cannot sort low SNR noisy signals, the FastICA algorithm is optimized. e specific algorithm steps are as follows: (1) e observed signal is a low SNR signal s(t), the model of blind source separation with noise is established, and the signal is processed by q times accumulation
Summary
During the operation of rotating machinery, the vibration signal measured by the sensor is usually superimposed by the vibration of multiple components [1,2,3]. Erefore, when blind source separation algorithm is used to separate the overlapped vibration signals directly, it may cause great errors or draw wrong conclusions. Erefore, it is very important to reduce noise before blind separation of measured mechanical vibration signals, so as to improve the signal-to-noise ratio. E synchronous cumulative average algorithm [28, 29] is based on the characteristic of periodic repetition of vibration signal It can improve the signal-to-noise ratio through the cumulative average processing of multiple periodic sampling points, without losing weak signal. In order to solve the problem of fault feature extraction of rotating machinery under strong noise, a fault separation method combining synchronous cumulative average noise reduction (SCA) algorithm and improved FastICA algorithm (SCAFastICA) is proposed. Accumulated average algorithm is used to reduce the noise of the mixed vibration signals, and the improved FastICA algorithm is used to separate the noise reduced signals, so as to achieve the extraction of fault feature signals
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