Abstract
A combination of spectral kurtosis (SK), based on Choi–Williams distribution (CWD) and hidden Markov models (HMM), accurately identifies initial gearbox failures and diagnoses fault types of gearboxes. First, using the LMD algorithm, five types of gearbox vibration signals are collected and decomposed into several product function (PF) components and the multicomponent signals are decomposed into single-component signals. Then, the kurtosis value of each component is calculated, and the component with the largest kurtosis value is selected for the CWD-SK analysis. According to the calculated CWD-SK value, the characteristics of the initial failure of the gearbox are extracted. This method not only avoids the difficulty of selecting the window function, but also provides original eigenvalues for fault feature classification. In the end, from the CWD-SK characteristic parameters at each characteristic frequency, the characteristic sequence based on CWD-SK is obtained with HMM training and diagnosis. The experimental results show that this method can effectively identify the initial fault characteristics of the gearbox, and also accurately classify the fault characteristics of different degrees.
Highlights
In rotating machinery, gearboxes are widely used in various industries as a universal component for changing speed and transmitting power
From the Choi–Williams distribution (CWD)-spectral kurtosis (SK) characteristic parameters at each characteristic frequency, the characteristic sequence based on Choi–Williams distribution and SK (CWD-SK) is obtained with hidden Markov models (HMM)
CWD-SK can identify initial gearbox failures there is a slight failure of the gearbox, Figure 4b, which is in line with the actual situation
Summary
Gearboxes are widely used in various industries as a universal component for changing speed and transmitting power. In 2011, a real-time gear fault feature extraction method was proposed, which combined a one-dimensional map and a band-pass filter, they were inherently slow and not suitable for real-time applications [18]. It showed that the proposed method outperformed empirical mode decomposition and SK in extracting fault features of gearboxes [20]. The HMM model has strong feature classification capabilities based on these two stochastic processes It is especially suitable for statistical analysis of nonstationary, repetitively poor dynamic signals. This paper is organized as follows: In Section 2, the main calculation steps of CWD-SK and the impact of window functions are described; in Section 3 the basic principles of HMM are outlined; in Section 4, the application of this method to gearbox fault diagnosis is introduced; and, the summary is stated This paper is organized as follows: In Section 2, the main calculation steps of CWD-SK and the impact of window functions are described; in Section 3 the basic principles of HMM are outlined; in Section 4, the application of this method to gearbox fault diagnosis is introduced; and in Section 5, the summary is stated
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