A new method of health condition detection for hydraulic pump using enhanced whale optimization-resonance-based sparse signal decomposition and modified hierarchical amplitude-aware permutation entropy

2021 ◽  
pp. 014233122110195
Author(s):  
Fuming Zhou ◽  
Wuqiang Liu ◽  
Xiaoqiang Yang ◽  
Jinxing Shen ◽  
Peiping Gong
Keyword(s):  
Detection Method ◽  
Health Condition ◽  
Signal Decomposition ◽  
Normal Operation ◽  
Permutation Entropy ◽  
Sparse Signal ◽  
Hydraulic Pump ◽  
Whale Optimization ◽  
Hydraulic Equipment ◽  
Sparse Signal Decomposition

The normal operation of the hydraulic pump is the significant premise for the stable and dependable working of hydraulic equipment. Consequently, this research comes up with a health condition detection method of hydraulic pump. First of all, this approach selects resonance-based sparse signal decomposition (RSDD) to adaptively disintegrate vibration signals. The biggest problem of the RSDD algorithm is the requirement to artificially set a large number of key parameters, such as quality factor Q, weight coefficient A, and Lagrange operator u. The improper parameter settings will seriously affect the decomposition performance. To overcome this shortcoming, an enhanced whale optimization algorithm is presented to search the best parameter combination of the RSDD. The algorithm takes the correlation kurtosis as the optimization objective function to adaptively disintegrate the signal into low and high resonance components. Moreover, on the basis of the modified analytic hierarchy process and the amplitude-aware permutation entropy, the modified hierarchical amplitude-aware permutation entropy is raised for measuring the complexity of the measured time series more accurately and comprehensively. After that, a health condition detection method for hydraulic pump based on enhanced whale optimization-resonance-based sparse signal decomposition and modified hierarchical amplitude-aware permutation entropy is raised. Finally, through the usage of the hydraulic pump vibration data, this method is compared with other approaches. According to the experimental results, the raised method can identify the fault type more effectively, which is capable of offering a feasible idea for the health condition detection of hydraulic equipment.

scholarly journals Fault diagnosis of rolling bearing based on resonance-based sparse signal decomposition with optimal Q-factor

2019 ◽  
Vol 52 (7-8) ◽  
pp. 1111-1121 ◽  
Author(s):  
Yan Lu ◽  
Juan Du ◽  
Xian Tao
Keyword(s):  
Energy Operator ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Signal Decomposition ◽  
Sparse Signal ◽  
Q Factor ◽  
Component Mixture ◽  
Model Decomposition ◽  
Periodic Impulse ◽  
Sparse Signal Decomposition

When a localized defect is induced, the vibration signal of rolling bearing always consists periodic impulse component accompanying with other components such as harmonic interference and noise. However, the incipient impulse component is often submerged under harmonic interference and background noise. To address the aforementioned issue, an improved method based on resonance-based sparse signal decomposition with optimal quality factor ( Q-factor) is proposed in this paper. In this method, the optimal Q-factor is obtained first by genetic algorithm aiming at maximizing kurtosis value of low-resonance component of vibration signal. Then, the vibration signal is decomposed based on resonance-based sparse signal decomposition with optimal Q-factor. Finally, the low-resonance component is analyzed by empirical model decomposition combination with energy operator demodulating; the fault frequency can be achieved evidently. Simulation and application examples show that the proposed method is effective on extracting periodic impulse component from multi-component mixture vibration signal.

scholarly journals Ship Radiated Noise Recognition Using Resonance-Based Sparse Signal Decomposition

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Jiaquan Yan ◽  
Haixin Sun ◽  
En Cheng ◽  
Xiaoyan Kuai ◽  
Xiaoliang Zhang
Keyword(s):  
Feature Extraction ◽  
Low Frequency ◽  
Signal Decomposition ◽  
Sparse Signal ◽  
Support Vector ◽  
Analysis Method ◽  
Radiated Noise ◽  
High Resonance ◽  
Residual Component ◽  
Sparse Signal Decomposition

Under the complex oceanic environment, robust and effective feature extraction is the key issue of ship radiated noise recognition. Since traditional feature extraction methods are susceptible to the inevitable environmental noise, the type of vessels, and the speed of ships, the recognition accuracy will degrade significantly. Hence, we propose a robust time-frequency analysis method which combines resonance-based sparse signal decomposition (RSSD) and Hilbert marginal spectrum (HMS) analysis. First, the observed signals are decomposed into high resonance component, low resonance component, and residual component by RSSD, which is a nonlinear signal analysis method based not on frequency or scale but on resonance. High resonance component is multiple simultaneous sustained oscillations, low resonance component is nonoscillatory transients, and residual component is white Gaussian noises. According to the low-frequency periodic oscillatory characteristic of ship radiated noise, high resonance component is the purified ship radiated noise. RSSD is suited to noise suppression for low-frequency oscillation signals. Second, HMS of high resonance component is extracted by Hilbert-Huang transform (HHT) as the feature vector. Finally, support vector machine (SVM) is adopted as a classifier. Real audio recordings are employed in the experiments under different signal-to-noise ratios (SNRs). The experimental results indicate that the proposed method has a better recognition performance than the traditional method under different SNRs.

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