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

Du, Wenliao; Wang, Zhiyang; Gong, X. G.; Wang, Liangwen; Luo, Guofu

doi:10.1155/2016/1248626

Cited by 11 publications

(9 citation statements)

References 14 publications

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“…e purpose of this type of method is to narrow the analyzed frequency band and to help the further processes to perform better. Another kind of approaches, such as envelope analysis [10], squared envelope analysis [11], fast spectral correlation [12], and cyclostationary analysis [13], focus on demodulating or revealing the faulty signal pattern based on the assumption that the impulses excited by the defect are generally considered as an amplitude modulated and quasiperiodic signal.…”

Section: Introductionmentioning

confidence: 99%

A Morphological Filtering Method Based on Particle Swarm Optimization for Railway Vehicle Bearing Fault Diagnosis

Huang

Lin

Liu

et al. 2019

Shock and Vibration

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With the rapid development of high-speed railway, the fault diagnosis of railway vehicles has become more and more important for ensuring the operating safety. The MF is a nonlinear signal processing method which can extract the modulated faulty information via reshaping the analyzed signal. However, the choices of operators and structure elements (SE) are numerous and complicated to determine the best MF solution for different bearing faulty signals. In this paper, the particle swarm optimization (PSO) was introduced to optimize the effect of MF among several classical MF operators and different SE parameters. The proposed method applied PSO to select the best MF result with respect to the fitness function adopting kurtosis. A set of bearing signals with additional interference of wheel-track excitement are analyzed to verify the effectiveness of the proposed method. The results demonstrated that the proposed method is capable of obtaining the optimized solution and accurately extracting the fault information. Furthermore, the shaft rotation frequency and wheel-track interference were reduced by the proposed method.

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Section: Introductionmentioning

confidence: 99%

A Morphological Filtering Method Based on Particle Swarm Optimization for Railway Vehicle Bearing Fault Diagnosis

Huang

Lin

Liu

et al. 2019

Shock and Vibration

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“…Lu et al [12] created an EEMD paving and optimized support vector regression method to detect faults and estimate the fault sizes of a piston pump based upon discharge pressure signals. On the other hand, vibration signal also reflects the performance of the piston pumps and the faults of piston pumps are generally accompanied by changes in vibration signal [14][15][16][17][18]. Jiang et al [14] found the fault vibration signals of hydraulic pump were often modulated by the impact and the fault features were concealed when it became fault.…”

Section: Introductionmentioning

confidence: 99%

“…Based on vibration signal, Wang et al [16] created a method using deep belief networks to detect five classes of commonly occurred faults of in piston pumps. An optimum intrinsic mode functions (IMFs) selection based envelope analysis was proposed by Du [17] to detect bearing failure in piston pump.…”

Section: Introductionmentioning

confidence: 99%

A multi-fault diagnosis method for piston pump in construction machinery based on information fusion and PSO-SVM

Tang¹,

Wang²,

Wu³

2019

J. vibroeng.

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Piston pumps are key components in construction machinery, the failure of which may cause long delay of the construction work and even lead to serious accident. Because construction machines are exposed to poor working conditions, multiple faults of piston pumps are most likely to occur simultaneously. When multiple faults occur together, it is difficult to detect. A multi-fault diagnosis method for piston pump based on information fusion and PSO-SVM is proposed in this thesis. Information fusion is used as fault feature extraction and PSO-SVM is applied as the fault mode classifier. According to the method, vibration signal and pressure signal of piston pump in normal state, single fault state and multi-fault state are collected at first. Then the empirical mode decomposition (EMD) is used to decompose vibration signals into different frequency band and energy features are extracted. These energy features extracted from vibration signals and time-domain features extracted from pressure signal are information fused at the feature layer and constitute the eigenvectors. Finally, these eigenvectors are put into support vector machine (SVM) and the working conditions of piston pump were classified. Particle swarm optimization (PSO) is applied to optimize two parameters of SVM. The experimental results show that the recognition accuracy of the normal state, three single failure modes and multi-fault modes are 98.3 %, 97.6 % and 94 % respectively. These recognition accuracies are higher than which using vibration signal or pressure signal alone. So, the proposed method can not only identify the single fault, but also effectively identify the multi-fault of piston pump.

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“…The state of pump is usually analyzed by vibration signal which is acquired by acceleration sensor. [34][35][36] However, fault is a dynamic development process, and early weak fault vibration signal is so complex, often mixed with large amount of background noise, that the fault diagnosis gets more difficult. 37 Compared to vibration signal, AE elastic wave is not confined to the audible domain, and the effective spectral range can extend to several or even tens of megahertz frequency range.…”

Section: Introductionmentioning

confidence: 99%

Research on fault diagnosis of mud pump fluid end based on acoustic emission

Deng

Pei

Wang

et al. 2017

Advances in Mechanical Engineering

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A mud pump is one of the three key components of a drilling site, and its lifetime and reliability are related to safety and cost. The fluid end is the most easily damaged part of the mud pump. To ensure normal operation, the fault modes of the fluid end need to be effectively identified. This study proposes to employ acoustic emission technique to identify the fault modes of mud pump fluid end, including valve disk leakage, spring break, and piston wear. The analysis method of parameters and waveforms of the acoustic emission signals are both used in this article. The characteristic parameters of the acoustic emission signals are combined with grey relational analysis to identify the fault modes with small samples; wavelet packet signal processing technique is applied to decompose and reconstruct the obtained acoustic emission signal waveforms and extract the energy of each frequency range to construct the eigenvectors which are input into the genetic algorithm with back propagation neural network for fault diagnosis. The results show that the fault modes under small samples can be effectively identified by combining the characteristic parameters and the grey relational analysis; the frequency range energy eigenvalue can be extracted using the method of wavelet packet signal processing, and the genetic algorithm with back propagation neural network is of better convergence than that of the back propagation neural network.

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

Cited by 11 publications

References 14 publications

A Morphological Filtering Method Based on Particle Swarm Optimization for Railway Vehicle Bearing Fault Diagnosis

A Morphological Filtering Method Based on Particle Swarm Optimization for Railway Vehicle Bearing Fault Diagnosis

A multi-fault diagnosis method for piston pump in construction machinery based on information fusion and PSO-SVM

Research on fault diagnosis of mud pump fluid end based on acoustic emission

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