This paper proposes a wavelet-based statistical signal detection approach for monitoring and diagnosis of bearing compound faults at an early stage. The bearing vibration signal is decomposed by an orthonormal discrete wavelet transform to obtain its energy dispersions at multiple levels. We investigate the statistical properties of the decomposed signal energy under both the normal and faulty conditions, based on which a generalized likelihood ratio test is developed. An exponentially weighted moving average control chart is then constructed to detect faults at an early stage. Simulation studies and a real case study are conducted to demonstrate the effectiveness of the proposed method. Furthermore, the comparison studies show that the proposed method outperforms the empirical mode decomposition method and Hilbert envelope spectrum analysis method.

中文翻译：

---

基于小波的滚动轴承复合故障监测与诊断统计方法


本文提出了一种基于小波的统计信号检测方法，用于轴承复合故障的早期监测和诊断。通过正交离散小波变换对轴承振动信号进行分解，以获得其多个级别的能量色散。我们研究了正常和故障条件下分解信号能量的统计特性，并在此基础上开发了广义似然比检验。然后构建指数加权移动平均控制图以在早期阶段检测故障。进行了仿真研究和实际案例研究来证明所提出方法的有效性。此外，比较研究表明，该方法优于经验模态分解方法和希尔伯特包络谱分析方法。