Abstract
When a compound fault occurs, the randomness and ambiguity of the gearbox will cause uncertainty in the collected signal and reduce the accuracy of signal feature extraction. To improve accuracy, this research proposes a gearbox compound fault feature extraction method, which uses the inverse cloud model to obtain the signal feature value. First, EEMD is used to decompose the collected vibration signals of gearbox faults in normal and fault states. Then, the mutual information method is used to select the sensitive eigenmode function that can reflect the characteristics of the signal. Subsequently, the inverse cloud generator is used to extract cloud digital features and construct sample feature sets. On this basis, the concept of synthetic cloud is introduced, and the cloud-based distance measurement principle is used to synthesize new clouds, reduce the feature dimension, and extract relevant features. Finally, a simulation experiment on a rotating machinery unit with a certain type of equipment verifies that the proposed method can effectively extract the feature of gearbox multiple faults with less feature dimension. And comparing with the feature set extracted by the single cloud model, the results show that the method can better represent the fault characteristic information of the signal.
Highlights
Gear transmission is one of the commonly used transmission methods in mechanical equipment and is often used in high-speed trains, wind power generation, aviation, shipping, petrochemical, mining, lifting, and transportation industries
Empirical mode decomposition (EMD) has no fixed basis, so compared with wavelet analysis methods, it solves the problem of difficult selection of wavelet basis, and it has a better processing effect on nonstationary signals than wavelet, but there is a problem with model confusion
To solve the above problems, Wu et al [5] proposed the ensemble empirical mode decomposition (EEMD) to denoise the original signal, which overcomes the inherent mode confusion problem compared with the original EMD method
Summary
Gear transmission is one of the commonly used transmission methods in mechanical equipment and is often used in high-speed trains, wind power generation, aviation, shipping, petrochemical, mining, lifting, and transportation industries. To reduce the noise in the signal, some researchers applied the wavelet denoising method to feature extraction and achieved good results [2,3,4]. This method has difficulties in selecting wavelet bases and determining thresholds in practical applications. Han et al [18] proposed that EEMD can be combined with the cloud model to perform feature extraction of bearing faults and achieved good results, but there is a problem of more fault feature dimensions. By comparing with the feature sets extracted by the single cloud model, the result shows that this method can better represent the feature information of the fault signal
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