Adaptive MOMEDA based on improved advance-retreat algorithm for fault features extraction of axial piston pump

Xiao, Chaoang; Tang, Hesheng; Ren, Yan; Xiang, Jiawei; Kumar, Anil

doi:10.1016/j.isatra.2021.10.033

Cited by 20 publications

(15 citation statements)

References 29 publications

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“…Two particles, m and n (other than particle i (m ̸ = n ̸ = i)), are selected randomly from the current particle swarm to expand the searching range of particles and enhance the global optimisation capability for the optimal particle. The location difference between particles m and n is defined as 17) is substituted with equation (18). gbest 1, 2 (t) is multiplied by the random weight (1 − ϕ) to diminish the constraint and reinforce the randomness of the global optimal particle location in the last generation.…”

Section: De Operatormentioning

confidence: 99%

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Fault feature extraction for planetary bearing of CRF pump in nuclear power plant based on TFDC-QPSO-optimised MOMEDA

Zhang¹,

Xiong²,

He³

et al. 2022

Meas. Sci. Technol.

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To address the issue of weak fault features and difficulty in feature extraction for planetary gearbox bearings of the circulation water pump (CRF pump) unit in the conventional island of a nuclear power plant, a deconvolution method, named the Multipoint Optimal Minimum Entropy Deconvolution Adjusted (MOMEDA), is optimised by a mixing operator improved Quantum behaviour Particle Swarm Optimisation (QPSO) algorithm, to extract the periodic fault impulse of planetary gearbox bearings. In the mixing operator, a Differential Evolution (DE) operator is introduced to improve particle swarms' diversity and enhance the algorithm's global optimisation capability. Meanwhile, a proposed adaptive CrossOver (CO) operator is incorporated into the algorithm to increase its convergence speed. Then, combining these two types of operators can construct a parameter optimisation algorithm displaying both global optimisation capability and high algorithm execution efficiency. Consider capturing the periodic impulsive features relating to bearing faults. A fitness function based on the characteristic information of the deconvolution signal in the time and frequency domain is proposed. This function serves as the objective function to facilitate the parameter optimisation on MOMEDA. Thereby, it enhances the optimisation capability of our feature extraction method. Experiments were conducted by adopting the proposed method on the signals collected from the inner- and outer-race faulty planetary bearing in a test bed, in which favourable feature extraction results are obtained. After the comparative analysis, we observed that both significances of features extracted by this method and the execution efficiency of the algorithm are superior compared to other methods. The on-site feature extraction results of the input shaft bearing in the planetary gearbox of a CRF pump unit in a nuclear power plant also demonstrated the engineering practicability of the method proposed in this work.

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Section: De Operatormentioning

confidence: 99%

“…MOMEDA also avoids the iterative operations to find the optimal value of L. To this end, researchers adopted several natural computing algorithms, e.g. grid search algorithm [16], sparrow search algorithm [17], and forward-backward algorithm [18], to optimise the MOMEDA parameters for extracting weak features of bearing faults.…”

Section: Introductionmentioning

confidence: 99%

Fault feature extraction for planetary bearing of CRF pump in nuclear power plant based on TFDC-QPSO-optimised MOMEDA

Zhang¹,

Xiong²,

He³

et al. 2022

Meas. Sci. Technol.

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show abstract

“…To date, numerous vibration-based procedures have yielded fruitful results in fault feature extraction [6,7], such as variational mode decomposition [8], multipoint optimal minimum entropy deconvolution adjusted [9], wavelet analysis [10], and sparse representation [11]. Especially wavelet transform shows promising performance in fault feature extraction owing to its excellent local time-frequency resolution.…”

Section: Introductionmentioning

confidence: 99%

Bearing fault-induced feature enhancement via adaptive multi-band denoising model

Zhao¹,

Zhang²,

Zhang³

et al. 2023

Meas. Sci. Technol.

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To accurately extract the bearing fault-induced impulse features from the vibration signals corrupted by heavy noise and large-amplitude random impulses, an adaptive multi-band denoising model based on the Morlet wavelet filter and sparse representation is put forward. First, to locate the desired frequency band associated with fault components, the Morlet wavelet filter is employed to band-pass the signal from the perspective of the frequency-domain. Herein, an improved Protrugram-based index, termed as windowed envelope spectral kurtosis, is designed as the objective function to choose the optimal center frequency and the bandwidth of the Morlet wavelet filter. Furthermore, benefitting from the time-domain characteristics of the vibration signal, the in-band noise is eliminated by sparse representation. One of the critical parameters (resonance frequency) of the wavelet atom used in the sparse representation dictionary is directly taken as the center frequency of the Morlet wavelet filter, which makes full use of the information derived from the filter, and thus significantly improves the calculation efficiency. Finally, the recovery signal is demodulated by the Hilbert transform to extract the fault characteristic frequency. The effectiveness and superiority of the proposed method are demonstrated through a complete analysis of the simulated, experimental, and engineering signals, as well as a comparison with such prevalent methods as Kurtogram, individual sparse representation, and Morlet wavelet filter combined with the maximum correlation kurtosis deconvolution.

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“…However, the deconvolution results of MOMEDA depend on the a priori knowledge of the fault period. Xiao [ 11 ] determined the fault period by using MKurt and calculated the filter size of MOMEDA by using the advance–retreat algorithm. Cheng [ 12 , 13 ] optimized MED, MCKD, OMEDA, and MOMEDA by solving the filter coefficients with a standard particle swarm optimization algorithm based on generalized spherical coordinate transformation.…”

Section: Introductionmentioning

confidence: 99%

Blind Deconvolution Based on Correlation Spectral Negentropy for Bearing Fault

Tian

Tang

Tian

et al. 2023

Entropy

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Blind deconvolution is a method that can effectively improve the fault characteristics of rolling bearings. However, the existing blind deconvolution methods have shortcomings in practical applications. The minimum entropy deconvolution (MED) and the optimal minimum entropy deconvolution adjusted (OMEDA) are susceptible to extreme values. Furthermore, maximum correlated kurtosis deconvolution (MCKD) and multipoint optimal minimum entropy deconvolution adjusted (MOMEDA) are required prior knowledge of faults. On the basis of the periodicity and impact of bearing fault signals, a new deconvolution algorithm, namely one based on maximum correlation spectral negentropy (CSNE), which adopts the particle swarm optimization (PSO) algorithm to solve the filter coefficients, is proposed in this paper. Verified by the simulated vibration model signal and the experimental simulation signal, the PSO–CSNE algorithm proposed in this paper overcomes the influence of harmonic signals and random pulse signals more effectively than other blind deconvolution algorithms when prior knowledge of the fault is unknown.

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Adaptive MOMEDA based on improved advance-retreat algorithm for fault features extraction of axial piston pump

Cited by 20 publications

References 29 publications

Fault feature extraction for planetary bearing of CRF pump in nuclear power plant based on TFDC-QPSO-optimised MOMEDA

Fault feature extraction for planetary bearing of CRF pump in nuclear power plant based on TFDC-QPSO-optimised MOMEDA

Bearing fault-induced feature enhancement via adaptive multi-band denoising model

Blind Deconvolution Based on Correlation Spectral Negentropy for Bearing Fault

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