A new rotating machinery fault diagnosis method based on improved local mean decomposition

Li, Yongbo; Xu, Min; Zhao, Honglin; Yu, Wei; Huang, Wenhu

doi:10.1016/j.dsp.2015.07.001

Cited by 46 publications

(25 citation statements)

References 29 publications

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“…It should be noted that the boundary conditions of the signal have been handled by the mirror-symmetric extension [11,27]. Actually, the iteration stop condition in LMD method a 1(n+1) (t) = 1 is too strict to fulfill it.…”

Section: Lmd Methodsmentioning

confidence: 99%

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A new rolling bearing fault diagnosis method based on multiscale permutation entropy and improved support vector machine based binary tree

et al. 2016

Measurement

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251

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Section: Lmd Methodsmentioning

confidence: 99%

“…Also, the comparisons of LMD and EMD have been done, and the merits of the LMD have been verified [3,11]. It is generally accepted that the measured vibration signal of rolling bearing often exhibits AM-FM feature under fault conditions.…”

Section: Introductionmentioning

confidence: 99%

A new rolling bearing fault diagnosis method based on multiscale permutation entropy and improved support vector machine based binary tree

et al. 2016

Measurement

Self Cite

251

144

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“…The damage degree of key components of RM, such as bearings, gears, rotors and impellers, determines whether the machine can operate safely and reliably for a long time [3,4]. Therefore, it is very important to monitor the operating condition of RM and predict its residual useful life (RUL) [5,6].…”

Section: Introductionmentioning

confidence: 99%

Quantum Gene Chain Coding Bidirectional Neural Network for Residual Useful Life Prediction of Rotating Machinery

Cheng

Tang

et al. 2020

Preprint

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In classical recurrent neural networks, the pre- and post-relationships of time series tend to be neglected so that long-term overall memory is generally inaccessible; meanwhile, the weights are transferred and updated mainly by the gradient descent method, which leads to their low prediction accuracy and high computation cost in the application of residual useful life (RUL) prediction of rotating machinery (RM). In view of this, a quantum gene chain coding bidirectional neural network (QGCCBNN) is proposed to predict RUL of RM in this paper. In our proposed QGCCBNN, the quantum bidirectional transmission mechanism is designed to establish the pre- and post-relationships of time series for readjusting the weight parameters according to the feedback from the output layer, so that higher consistency between the input information and the overall memory of the network can be realized, thus endowing QGCCBNN with better nonlinear approximation ability. Moreover, in order to improve the global optimization ability and convergence speed, the quantum gene chain coding instead of gradient descent method is constructed to transmit and update data, in which the qubit probability amplitude real number coding is adopted and the cosine and sinusoidal qubit probability amplitudes corresponding to the minimum loss function are compared with those of the current time by the phase selection matrix for the directional parallel updating of the weight parameters. On this basis, a new RUL prediction method for RM is proposed, and higher prediction accuracy as well as desirable efficiency can be obtained due to the advantages of QGCCBNN in nonlinear approximation ability and convergence speed. The experimental example for RUL prediction of a double-row roller bearing demonstrates the effectiveness of our proposed method.

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“…However, early fault features are far weak and submerged in heavy noise, so it is quite challenging to effectively extract them. Traditionally, in order to obtain the fault features, various signal processing techniques, such as wavelet transform [6,7], local mean decomposition [8][9][10], empirical mode decomposition [11][12][13][14], etc., aim at eliminating and suppressing the noise because it is always considered an undesirable disturbance contaminating the useful signals. Although these traditional denoising methods exhibit an excellent effect, the useful signal components may inevitably be weakened or even destroyed during this process resulting in not effectively extracting the fault features.…”

Section: Introductionmentioning

confidence: 99%

Improving the weak feature extraction by adaptive stochastic resonance in cascaded piecewise-linear system and its application in bearing fault detection

Liu¹,

Han²,

Yang³

et al. 2017

J. vibroeng.

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In mechanical engineering field, early fault features are extremely weak and submerged in heavy noise, and the weak feature extraction is quite challenging. In this work, we apply the adaptive stochastic resonance in cascaded piecewise-linear system to extract the weak features. The adaptive stochastic resonance is realized by the quantum particle swarm algorithm. By optimizing system parameters, the efficiency of the feature extraction is improved greatly. As a result, the weak features can be easily extracted eventually. The effectiveness and the high-performance of the proposed method are verified by the numerical simulation and experimental data of rolling element bearings. The bearing fault under different motor loads is detected effectively, consequently confirming the robustness of the proposed method.

show abstract

A new rotating machinery fault diagnosis method based on improved local mean decomposition

Cited by 46 publications

References 29 publications

A new rolling bearing fault diagnosis method based on multiscale permutation entropy and improved support vector machine based binary tree

A new rolling bearing fault diagnosis method based on multiscale permutation entropy and improved support vector machine based binary tree

Quantum Gene Chain Coding Bidirectional Neural Network for Residual Useful Life Prediction of Rotating Machinery

Improving the weak feature extraction by adaptive stochastic resonance in cascaded piecewise-linear system and its application in bearing fault detection

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