Abstract
In this paper, a new method was introduced for feature extraction and fault diagnosis in bearings based on wavelet packet decomposition and analysis of the energy in different frequency bands. This method decomposes a signal into different frequency bands using different types of wavelets and performs multi-resolution analysis to extract different features of the signals by choosing energy levels in different frequency bands. The support vector machines (SVM) technique was used for faults classifications. Daubechies, biorthogonal, coiflet, symlet, Meyer, and reverse Meyer wavelets were used for feature extraction. The most appropriate decomposition level and frequency band were selected by analyzing the variation in the signal’s energy level. The proposed approach was applied to the fault diagnosis of rolling bearings, and testing results showed that the proposed approach can reliably identify different fault categories and their severities. Moreover, the effectiveness of the proposed feature selection and fault diagnosis method was significant based on the similarity between the wavelet packet and the signal, and effectively reduced the influence of the signal noise on the classification results.
Highlights
Machine condition monitoring and fault diagnosis as a part of maintenance systems became global due to the potential advantages of reduced maintenance costs, improved productivity, and increased machine availability
Various methods have been used for feature extraction such as statistical analysis [3,4,5], cyclic spectral analysis [6], wavelet analysis [7,8,9,10,11,12,13], correlation [14], Hilbert–Huang transform, and acoustic techniques [15,16]
Due to the unique features of the wavelet analysis, it is a useful technique for fault diagnosis in bearings and gears [18,19], as well as the detection of the location and size of the cracks in structures and components [20,21]
Summary
Machine condition monitoring and fault diagnosis as a part of maintenance systems became global due to the potential advantages of reduced maintenance costs, improved productivity, and increased machine availability. Pattern recognition techniques can be used to classify objects into distinctive classes using four basic steps: data importation, data preprocessing, feature extraction, and classification. Due to the unique features of the wavelet analysis, it is a useful technique for fault diagnosis in bearings and gears [18,19], as well as the detection of the location and size of the cracks in structures and components [20,21]. The support vector machine classification technique is used to classify the data with high accuracy and reduce the effect of signal noise on classifying data of energy-based feature extraction in different frequency bands.
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