A Fault Diagnosis Method of Bogie Axle Box Bearing Based on Spectrum Whitening Demodulation

Abstract

The axle box bearing of bogie is one of the key components of the rail transit train, which can ensure the rotary motion of wheelsets and make the wheelsets adapt to the conditions of uneven railways. At the same time, the axle box bearing also exposes most of the load of the car body. Long-time high-speed rotation and heavy load make the axle box bearing prone to failure. If the bearing failure occurs, it will greatly affect the safety of the train. Therefore, it is extremely important to monitor the health status of the axle box bearing. At present, the health status of the axle box bearing is mainly monitored by vibration information and temperature information. Compared with the temperature data, the vibration data can more easily detect the early fault of the bearing, and early warning of the bearing state can avoid the occurrence of serious fault in time. Therefore, this paper is based on the vibration data of the axle box bearing to carry out adaptive fault diagnosis of bearing. First, the AR model predictive filter is used to denoise the vibration signal of the bearing, and then the signal is whitened in the frequency domain. Finally, the characteristic value of vibration data is extracted by energy operator demodulation, and the fault type is determined by comparing with the theoretical value. Through the analysis of the constructed simulation signal data, the characteristic parameters of the data can be effectively extracted. The experimental data collected from the bearing testbed of high-speed train are analyzed and verified, which further proves the effectiveness of the feature extraction method proposed in this paper. Compared with other axle box bearing fault diagnosis methods, the innovation of the proposed method is that the signal is denoised twice by using AR filter and spectrum whitening, and the adaptive extraction of fault features is realized by using energy operator. At the same time, the steps of setting parameters in the process of feature extraction are avoided in other feature extraction methods, which improves the diagnostic efficiency and is conducive to use in online monitoring system.

Research on the application of artificial intelligence method in automobile engine fault diagnosis

“…When the fault signal is relatively weak and the fault characteristics are easily submerged in the noise, it is difficult to obtain an effective diagnosis result by pure time domain analysis. At this time, it is necessary to further combine the frequency spectrum analysis of the vibration signal [22], and use the frequency domain feature index as the original learning data for the SVM model to learn [23]. The frequency spectrum of the vibration acceleration signal of the engine in the four states is shown in figure 4.…”

Wheel fault diagnosis model based on multichannel attention and supervised contrastive learning

“…Ma et al 30 proposed a data-driven fault diagnosis method based on timefrequency analysis and a deep residual network method. However, the feature-learning capability of neural networks tends to decrease when dealing with noisy Time synchronous average (TSA) 18 Autoregressive moving average (ARMA) 19 Principal component analysis (PCA) 20 Correlation-based analysis 21 Frequency-domain analysis Fast Fourier transform (spectrum analysis) 22 Hilbert transform (envelope analysis) 8,23 Inverse Fourier Transform of logarithmic power spectrum (cepstrum analysis) 24 Time-Frequency analysis Short-time Fourier transform (STFT) 25 Wigner-Ville transform (WVT) 25 Wavelet transform (WT) 25,26 Hilbert-Huang transform (HHT) 27…”