Abstract
The health condition monitoring of rotating machinery can avoid the disastrous failure and guarantee the safe operation. The vibration-based fault diagnosis shows the most attractive character for fault diagnosis of rotating machinery (FDRM). Recently, Lempel-Ziv complexity (LZC) has been investigated as an effective tool for FDRM. However, the LZC only performs single-scale analysis, which is not suitable to extract the fault features embedded in vibrational signal over multiple scales. In this paper, a novel complexity analysis algorithm, called hierarchical Lempel-Ziv complexity (HLZC), was developed to extract the fault characteristics of rotating machinery. The proposed HLZC method considers the fault information hidden in both low-frequency and high-frequency components, resulting in a more accurate fault feature extraction. The superiority of the proposed HLZC method in detecting the periodical impulses was validated by using simulated signals. Meanwhile, two experimental signals were utilized to prove the effectiveness of the proposed HLZC method in extracting fault information. Results show that the proposed HLZC method had the best diagnosing performance compared with LZC and multi-scale Lempel-Ziv complexity methods.
Highlights
Rotating machinery is commonly used in modern industries, such as the aero-engine, vehicle, ship, and railway industries [1]
Experiment 1 consisted of one healthy condition and five single fault conditions, including inner race fault (IRF), ball fault (BF), grooving in the inner race (GIR), grooving in the outer race (GOR), race fault (IRF), ball fault (BF), grooving in the inner race (GIR), grooving in the outer race (GOR), and outer race fault (ORF)
For the MLZC method, a few features were mixed, resulting in difficulty for classification. This phenomenon indicated that the fault features extracted using hierarchical Lempel-Ziv complexity (HLZC) had more cluster ability than the MLZC method
Summary
Rotating machinery is commonly used in modern industries, such as the aero-engine, vehicle, ship, and railway industries [1]. [13], to extract fault features of rotating machinery, such as Lempel-Ziv complexity (LZC) [13], approximate approximate [14], fuzzy entropy [12,15], permutation entropy [16,17], symbolic dynamic entropy [14],entropy fuzzy entropy [12,15], permutation entropy [16,17], symbolic dynamic entropy [18], entropy [18], andentropy multi-scale. Results demonstrated demonstrated extraction that our proposed HLZC method is superior to LZC and MLZC in extracting fault characteristics with high stability. After the HLZC-based feature extraction, we combined the HLZC with support vector machine (SVM) classifier [27] to accomplish the intelligent fault diagnosis of rotating machinery.
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