Abstract
Intelligent fault diagnosis of complex machinery is crucial for industries to reduce the maintenance cost and to improve fault prediction performance. Acoustic signal is an ideal source for diagnosis because of its inherent characteristics in terms of being non-directional and insensitive to structural resonances. However, there are also two main drawbacks of acoustic signal, one of which is the low signal to noise ratio (SNR) caused by its high sensitivity and the other one is the low computational efficiency caused by the huge data size. These would decrease the performance of the fault diagnosis system. Therefore, it is significant to develop a proper feature extraction method to improve computational efficiency and performance in both periodic and irregular fault diagnosis. To enhance SNR of the acquired acoustic signal, the correlation coefficient (CC) method is employed to eliminate the redundant intrinsic mode functions (IMF), which comes from the decomposition procedure of pre-processing known as ensemble empirical mode decomposition (EEMD), because the higher the correlated coefficient of an IMF is, the more significant fault signatures it would contain, and the redundant IMF would compromise both the SNR and the computational cost performance. Singular value decomposition (SVD) and sample Entropy (SampEn) are subsequently used to extract the fault feature, by exploiting their sensitivities to irregular and periodic fault signals, respectively. In addition, the proposed feature extraction method using sparse Bayesian based pairwise coupled extreme learning machine (PC-SBELM) outperforms the existing pairwise-coupling probabilistic neural network (PC-PNN) and pairwise-coupling relevance vector machine (PC-RVM) by 1.8% and 2%, respectively, to achieve an accuracy of 93.9%. The experiments conducted on the periodic and irregular faults in the gears and bearings have demonstrated that the proposed hybrid fault diagnosis system is effective.
Highlights
Nowadays, many efforts have been devoted to revealing the hidden patterns in fault-related signals
As the faults of rotating machinery can usually be classified into two types as periodic and irregular faults, this study proposed a proper feature selection method via the combination of Singular value decomposition (SVD) and sample Entropy (SampEn) to generate a feature matrix that could identify both irregular and periodic faults adequately
To verify that the SampEn is sensitive to the irregular faults in rotating machinery, the values of elements has the largest value of SampEn
Summary
Many efforts have been devoted to revealing the hidden patterns in fault-related signals. Vibration signal is adopted in most fault diagnosis cases, acoustic signal is more desirable in some implementations due to its unique merits [2]. One acoustic sensor is required to achieve the same functionality as that of vibration signal. It is easier for acoustic sensor to capture incipient fault information than the vibration sensor as it is more sensitive [4]. These advantages, in two aspects, fulfill the functional requirements of fault diagnosis. Incipient faults would likely generate weak vibration energy, which could hardly cause any significant physical degradation within the solid structure, which makes it impossible for the vibration sensor to capture any useful pattern
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