Rolling Bearing Fault Diagnosis Using a New Resonance Demodulation Method

doi:10.3969/j.issn.1004-132X.2025.09.014

Abstract

Abstract:

To extract weak features and diagnose rolling bearing faults， a new resonance demodulation method was proposed based on subbands-reconstructed and-rearranged dual-tree complex wavelet packet transform（SRR-DTCWPT） and peak frequency extraction. The SRR-DTCWPT-based frequency band division method was fine， maintained the advantages of DTCWPT in approximate translation invariance and less spectral energy leakage， and solved the problem of band misalignment. The resonance demodulation method based on SRR-DTCWPT and peak frequency extraction did not require the participation of any indicator， might extract frequency bands at any position， avoided the influences of strong impact interference， and automated the calculation processes. The proposed method was compared with Fast Kurtogram and Autogram in simulation and case studies， and the results demonstrate the effectiveness and efficiency of the proposed method.

Key words: bearing fault diagnosis, resonance demodulation, dual-tree complex wavelet packet transform（DTCWPT）, subbands reconstruction and rearrangement（SRR）, peak frequency extraction

摘要：

为实现滚动轴承微弱特征提取与故障诊断，提出了一种基于子带重构重排-双树复小波包变换（SRR-DTCWPT）与峰值频率提取的共振解调新方法。基于SRR-DTCWPT的频带划分方法较为精细，并且在保持DTCWPT近似平移不变性和谱能量泄漏少的优点的同时解决了频带错乱的问题。基于SRR-DTCWPT与峰值频率提取的共振解调方法不需要任何指标参与，能提取任意位置的频带，避免了强冲击干扰的影响，且计算过程自动化。将所提方法与Fast Kurtogram和Autogram算法进行比较，验证了该方法在滚动轴承故障诊断中的有效性与高效性。

关键词: 轴承故障诊断, 共振解调, 双树复小波包变换, 子带重构重排, 峰值频率提取

CLC Number:

TH17

Siqian FENG, Jiaxu WANG, Xin ZHANG, Xinyue HUANG. Rolling Bearing Fault Diagnosis Using a New Resonance Demodulation Method[J]. China Mechanical Engineering, 2025, 36(9): 2022-2031.

冯思茜, 王家序, 张新, 黄欣玥. 基于共振解调新方法的滚动轴承故障诊断[J]. 中国机械工程, 2025, 36(9): 2022-2031.

Figures/Tables 18

Fig.1 Spectral cliff map based on binary ternary wavelet packet decomposition

Fig.2 Spectral cliff plot of Autogram

Fig.3 DTCWPT's parsing structure and use of filters

Fig.4 The Fourier spectra of 3rd level wavelet subbands before and after rearrangement

Fig.5 Flowchart of the fault band selection based on peak frequency extraction

Fig.6 Simulated signals Ⅰ

Fig.7 Analysis results of the proposed method for sinulated signal Ⅰ

Fig.8 Analysis results of the FK for sinulated signal Ⅰ

Fig.9 Analysis results of the Autogram for simulated signal Ⅰ

Tab.1 The specific parameters of b2(t)

l	$α l$	$β l$	$ν l$	$t l$
$1$	3.2	200	0.005	0.13
$2$	3.6	240	0.02	0.26
$3$	3.0	260	0.02	0.63

Tab.1 The specific parameters of b2(t)

l	$α l$	$β l$	$ν l$	$t l$
$1$	3.2	200	0.005	0.13
$2$	3.6	240	0.02	0.26
$3$	3.0	260	0.02	0.63

Fig.10 Simulated signals Ⅱ

Fig.11 Analysis results of the proposed method for simulated signal Ⅱ

Fig.12 Analysis results of the FK for simulated signal Ⅱ

Fig.13 Analysis results of the Autogram for simulated signal Ⅱ

Fig.14 Bearing failure implantation test bench

Fig.15 Analysis results of the proposed method for test signal

Fig.16 Analysis results of the FK for test signal

Fig.17 Analysis results of the Autogram for test signal

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