scholarly journals Optimum IMFs Selection Based Envelope Analysis of Bearing Fault Diagnosis in Plunger Pump

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Wenliao Du ◽  
Zhiyang Wang ◽  
Xiaoyun Gong ◽  
Liangwen Wang ◽  
Guofu Luo
Keyword(s):  
Fault Diagnosis ◽  
Promising Result ◽  
Rolling Element Bearing ◽  
Band Pass Filter ◽  
Intrinsic Mode Functions ◽  
Pass Filter ◽  
Plunger Pump ◽  
Filter Bandwidth ◽  
Envelope Analysis ◽  
Inner Race

As the plunger pump always works in a complicated environment and the hydraulic cycle has an intrinsic fluid-structure interaction character, the fault information is submerged in the noise and the disturbance impact signals. For the fault diagnosis of the bearings in plunger pump, an optimum intrinsic mode functions (IMFs) selection based envelope analysis was proposed. Firstly, the Wigner-Ville distribution was calculated for the acquired vibration signals, and the resonance frequency brought on by fault was obtained. Secondly, the empirical mode decomposition (EMD) was employed for the vibration signal, and the optimum IMFs and the filter bandwidth were selected according to the Wigner-Ville distribution. Finally, the envelope analysis was utilized for the selected IMFs filtered by the band pass filter, and the fault type was recognized by compared with the bearing character frequencies. For the two modes, inner race fault and compound fault in the inner race and roller of rolling element bearing in plunger pump, the experiments show that a promising result is achieved.

Envelope Analysis Based on the Combination of Morlet Wavelet and Kurtogram

2012 ◽  
Vol 490-495 ◽  
pp. 305-308
Author(s):  
Yu Liang ◽  
Yu Guo ◽  
Chuan Hui Wu ◽  
Yan Gao
Keyword(s):  
Frequency Band ◽  
Filter Banks ◽  
Morlet Wavelet ◽  
Center Frequency ◽  
Band Pass Filter ◽  
Pass Filter ◽  
Filter Bandwidth ◽  
Envelope Analysis ◽  
Band Pass ◽  
Complex Morlet Wavelet

Envelope analysis based on the combination of complex Morlet wavelet and Kurtogram have advantages of automatic calculation of the center frequency and bandwidth of required band-pass filter. However, there are some drawbacks in the traditional algorithm, which include that the filter bandwidth is not -3dB bandwidth and the analysis frequency band covered by the filter-banks are inconsistent at different levels. A new algorithm is introduced in this paper. Through it, both optimal center frequency and bandwidth of band-pass filter in the envelop analysis can be obtained adaptively. Meanwhile, it ensures that the filters in the filter-banks are overlapped at the point of -3dB bandwidth and the consistency of frequency band that the filter-banks covered.

Application of SVM Classifier and Fractal Feature in Circuit Fault Diagnosis

2012 ◽  
Vol 490-495 ◽  
pp. 942-945
Author(s):  
Jing Kui Mao ◽  
Xian Bai Mao
Keyword(s):  
Fault Diagnosis ◽  
Analog Circuits ◽  
Fractal Theory ◽  
Svm Classifier ◽  
Band Pass Filter ◽  
Pass Filter ◽  
Diagnosis Method ◽  
Diagnosis Accuracy ◽  
Band Pass ◽  
Circuit Fault Diagnosis

Combining SVM and fractal theory, a novel fault diagnosis method for analog circuits based on SVM using fractal dimension is developed in this paper. Simulation results of diagnosing the Sallen-Key band pass filter circuit have confirmed that the proposed approach increases the fault diagnosis accuracy, thereby it may be considered as an alternative for the analog fault diagnosis.

Adaptive Meyer wavelet filters for machinery fault diagnosis based on harmonic infinite-taxicab norm and grasshopper optimization algorithm

2020 ◽  
pp. 095440622097166
Author(s):  
Zhenling Mo ◽  
Heng Zhang ◽  
Jinglin Wang ◽  
Jianyu Wang ◽  
Hongyong Fu ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Optimization Algorithm ◽  
Band Pass Filter ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Wavelet Filters ◽  
Empirical Wavelet Transform ◽  
Grasshopper Optimization Algorithm ◽  
Grasshopper Optimization ◽  
Meyer Wavelet

Meyer wavelet filters are the key building blocks of empirical wavelet transform. In mechanical fault diagnosis, however, the boundaries of Meyer wavelet filters are usually defined empirically. In order to solve the problems, this paper proposes a new index called harmonic infinite-taxicab norm to guide grasshopper optimization algorithm to primarily optimize a band-pass filter and thus, concurrently and secondarily optimize a low-pass filter and a high-pass filter of Meyer wavelet. The proposed index is inspired by spectral Lp/Lq norm and it is closely related to fault characteristic frequency of rotating machinery. In addition, only three Meyer wavelet filters are demanded in each iteration of optimization. The effectiveness of the proposed method is validated by comparing with fast kurtogram method on analyzing faulty bearing data and gearbox data.

scholarly journals A wavelet cluster-based band-pass filtering and envelope demodulation approach with application to fault diagnosis in a dry vacuum pump

2007 ◽  
Vol 221 (11) ◽  
pp. 1279-1286 ◽  
Author(s):  
W Jiang ◽  
S K Spurgeon ◽  
J A Twiddle ◽  
F S Schlindwein ◽  
Y Feng ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
High Frequency ◽  
Experimental Studies ◽  
Vacuum Pump ◽  
Pass Band ◽  
Band Pass Filter ◽  
Pass Filter ◽  
Bearing Defects ◽  
Resonance Band ◽  
Band Pass

A Morlet-like wavelet cluster-based method for band-pass filtering and envelope demodulation is described. Via appropriate choice of wavelet parameters, a wavelet cluster combined with multi-Morlet-like wavelets can be used as a band-pass filter with zero phase shift, flat topped pass-band and rapid attenuation in the transition band. It can be used to extract high frequency natural vibration components. The imaginary part of the Morlet-like wavelet cluster is the Hilbert transformation of its real part. This can be used to implement envelope demodulation and extract the envelope component of the high frequency resonance band. The method is applied for fault diagnosis relating to bearing defects in a dry vacuum pump. It is shown that the fault characteristic frequencies can be extracted effectively. The efficacy of the method is demonstrated in experimental studies.

Selecting effective intrinsic mode functions of empirical mode decomposition and variational mode decomposition using dynamic time warping algorithm for rolling element bearing fault diagnosis

2018 ◽  
Vol 41 (7) ◽  
pp. 1923-1932 ◽  
Author(s):  
Prem Shankar Kumar ◽  
Lakshmi Annamalai Kumaraswamidhas ◽  
Swarup Kumar Laha
Keyword(s):  
Fault Diagnosis ◽  
Dynamic Time Warping ◽  
Rolling Element Bearing ◽  
Variational Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Time Warping ◽  
Mode Decomposition ◽  
Rolling Element ◽  
Element Bearing ◽  
Dynamic Time

Empirical Mode Decomposition (EMD) and Variational Mode Decomposition (VMD) are data-driven self-adaptive signal processing methods to decompose a complex signal into different modes of separate spectral bands, in to a number of Intrinsic Mode Functions (IMFs). While the EMD extracts modes recursively and empirically, the VMD extracts modes non-recursively and concurrently. In this paper, both the EMD and the VMD have been applied to examine their efficacy in fault diagnosis of rolling element bearing. However, all the IMFs do not contain necessary information regarding fault characteristic signature of the bearing. In order to select the effective IMF, the Dynamic Time Warping (DTW) algorithm has been employed here, which gives a measurement of similarity index between two signals. Also, correlation analysis has been carried out to select the appropriate IMFs. Finally, out of the selected IMFs, bearing characteristic fault frequencies have been determined with the envelope spectrum.

scholarly journals A Spectrum Detection Approach for Bearing Fault Signal Based on Spectral Kurtosis

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Yunfeng Li ◽  
Liqin Wang ◽  
Jian Guan
Keyword(s):  
Wavelet Spectrum ◽  
Rolling Element Bearing ◽  
Band Pass Filter ◽  
Pass Filter ◽  
Time Frequency ◽  
Bearing Fault ◽  
Spectral Kurtosis ◽  
Spectrum Detection ◽  
Detection Approach ◽  
The Impact

According to the similarity between Morlet wavelet and fault signal and the sensitive characteristics of spectral kurtosis for the impact signal, a new wavelet spectrum detection approach based on spectral kurtosis for bearing fault signal is proposed. This method decreased the band-pass filter range and reduced the wavelet window width significantly. As a consequence, the bearing fault signal was detected adaptively, and time-frequency characteristics of the fault signal can be extracted accurately. The validity of this method was verified by the identifications of simulated shock signal and test bearing fault signal. The method provides a new understanding of wavelet spectrum detection based on spectral kurtosis for rolling element bearing fault signal.

scholarly journals Application of DBN and GWO-SVM in analog circuit fault diagnosis

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Xiyuan Su ◽  
Changqing Cao ◽  
Xiaodong Zeng ◽  
Zhejun Feng ◽  
Jingshi Shen ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Analog Circuit ◽  
Experimental Results ◽  
Support Vector ◽  
Band Pass Filter ◽  
Pass Filter ◽  
Highpass Filter ◽  
Diagnosis Method ◽  
Circuit Fault Diagnosis ◽  
Factor C

AbstractFor large-scale integrated electronic equipment, the complex operating mechanisms make fault detection very difficult. Therefore, it is important to accurately identify analog circuit faults in a timely manner. To overcome this problem, this paper proposes a novel fault diagnosis method based on the deep belief network (DBN) and restricted Boltzmann machine (RBM) optimized by the gray wolf optimization (GWO) algorithm. First, DBN is used to extract the deep features of the analog circuit output signal. Then, GWO is used to optimize the penalty factor c and kernel parameter g of support vector machine (SVM). Finally, GWO-SVM is used to diagnose the signal features extracted by the DBN. Fault diagnosis simulation was conducted for the Sallen–Key band-pass filter and a four-opamp biquad highpass filter. The experimental results show that compared with the existing algorithms, the algorithm proposed in this paper improves the accuracy of Sallen–Key bandpass filter circuit to 100% and shortens the fault diagnosis time by about 90%; for four-opamp biquad highpass filter, the accuracy rate has increased to 99.68%, and the fault diagnosis time has been shortened by approximately 75%, and reduce hundreds of iterations. Moreover, the experimental results reveal that the proposed fault diagnosis method greatly improves the accuracy of analog circuit fault diagnosis, which solves a major problem in analog circuitry and has great significance for the future development of relevant applications.

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