Prognostics and Health Management of Bearings Based on Logarithmic Linear Recursive Least-Squares and Recursive Maximum Likelihood Estimation

Liu, Xiongjun; Song, Ping; Yang, Cheng; Hao, Chuangbo; Peng, Wenjia

doi:10.1109/tie.2017.2733469

Cited by 61 publications

(27 citation statements)

References 34 publications

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“…(2) Initialize the local optimal position P i,j (0) for the current position H i,j (0). e mean optimal position Complexity m j (t) is computed by (11). Select a suitable valueÎ 2 and evaluate the objective function value f(H i,j (t)).…”

Section: Quantum-behaved Particle Swarm For the Hurst Exponent Optimimentioning

confidence: 99%

“…Moreover, the calculation of the remaining useful life for the bearing is crucial. For this reason, some methods were developed, such as the Gaussian process (GP) model [10] and the recursive maximum likelihood estimation (RMLE) [11]. When using the RMLE to solve the unknown noise in the system caused by nonstationary operating conditions, the noise value is relatively large.…”

Section: Introductionmentioning

confidence: 99%

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Multifractional Brownian Motion and Quantum-Behaved Partial Swarm Optimization for Bearing Degradation Forecasting

et al. 2020

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Gradual degradation of the bearing vibration signal is usually studied as a nonstationary stochastic time series. Roller bearings are working at high speed in a heavy load environment so that the combination of bearing faults gradually degraded during the rotation might lead to unpredicted catastrophic accidents. The degradation process has the property of long-range dependence (LRD), so that the fractional Brownian motion (fBm) is taken into account for a prediction model. Because of the dramatic changes in the bearing degradation process, the Hurst exponent that describes the fBm will change during the degradation process. A priori Hurst value of the conventional fBm in the prediction is fixed, thus inducing a minor accuracy of the prediction. To avoid this problem, we propose an improved prediction method. Based on the following steps, at the initial data processing, a skip-over factor is selected as the characteristics parameter of the bearing degradation process. A multifractional Brownian motion (mfBm) replaces the fBm for the degradation modeling. We will show that also our mfBm has the same property of long-range dependence as the fBm. Moreover, a time-varying Hurst exponent H(t) is taken to replace the constant H in fBm. Finally, we apply the quantum-behaved partial swarm optimization (QPSO) to optimize H(t) for a finite interval. Some tests and corresponding experimental results will show that our model QPSO + mfBm have a much better performance on the prediction effect than fBm.

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Section: Quantum-behaved Particle Swarm For the Hurst Exponent Optimimentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multifractional Brownian Motion and Quantum-Behaved Partial Swarm Optimization for Bearing Degradation Forecasting

et al. 2020

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“…3,4 The model-based methods usually require the analytical model that are derived from physical laws to describe the state evolution process of a machine. Liu et al 5 utilized the Paris' law to construct a physical model to predict the remaining useful life (RUL) of bearings. Paris' law 6 provides a method for estimating crack propagation, which has been used in many model-based articles.…”

Section: Introductionmentioning

confidence: 99%

State estimation for the electro-hydraulic actuator based on particle filter with an improved resampling technique

Guo

Wei

2019

Proceedings of the Institution of Mechanical Engineers, Part O:

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State estimation for the electro-hydraulic actuator of civil aircraft is one of the most valuable but intractable issues. Recently, the state estimation approach based on particle filters has widely attracted attention. We pursue the benefits of the data-driven approach when physical model is deficienct, and put forward some improvements that are triggered by the shortcomings of particle filters algorithm. In order to solve the particles’ degeneracy phenomenon in particle filters, a kernel function that integrates the information of probability distribution is constructed; then, the established probability kernel function is designed to represent the probability density function of resampling and the regularization form of probability density function in Hilbert space is defined. Consequently, the probability density function of resampling is obtained by solving the support vector regression model. The novel resampling method based on support vector regression-particle filters can keep the diversity of particles as well as relieve the degeneracy phenomenon and eventually make the estimated state more realistic. The approach is simulated and applied to an electro-hydraulic actuator model. The estimation results validate the effectiveness of the proposed algorithm.

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“…Therefore, it is urgent to timely detect/diagnosis the operating condition of rotating machines, and ultimately predict its durability and remaining useful life (RUL) to ensure the equipment runs effectively. However, prognostic and health management (PHM) is a perennial/long-term concern, an intractable issue, data storage and remote transmission, which comes up in PHM, brings more pressure on monitoring with data increasing daily [ 1 , 2 , 3 ].…”

Section: Introductionmentioning

confidence: 99%

Multichannel Signals Reconstruction Based on Tunable Q-Factor Wavelet Transform-Morphological Component Analysis and Sparse Bayesian Iteration for Rotating Machines

Hu²,

Peng³

2018

Entropy

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High-speed remote transmission and large-capacity data storage are difficult issues in signals acquisition of rotating machines condition monitoring. To address these concerns, a novel multichannel signals reconstruction approach based on tunable Q-factor wavelet transform-morphological component analysis (TQWT-MCA) and sparse Bayesian iteration algorithm combined with step-impulse dictionary is proposed under the frame of compressed sensing (CS). To begin with, to prevent the periodical impulses loss and effectively separate periodical impulses from the external noise and additive interference components, the TQWT-MCA method is introduced to divide the raw vibration signal into low-resonance component (LRC, i.e., periodical impulses) and high-resonance component (HRC), thus, the periodical impulses are preserved effectively. Then, according to the amplitude range of generated LRC, the step-impulse dictionary atom is designed to match the physical structure of periodical impulses. Furthermore, the periodical impulses and HRC are reconstructed by the sparse Bayesian iteration combined with step-impulse dictionary, respectively, finally, the final reconstructed raw signals are obtained by adding the LRC and HRC, meanwhile, the fidelity of the final reconstructed signals is tested by the envelop spectrum and error analysis, respectively. In this work, the proposed algorithm is applied to simulated signal and engineering multichannel signals of a gearbox with multiple faults. Experimental results demonstrate that the proposed approach significantly improves the reconstructive accuracy compared with the state-of-the-art methods such as non-convex Lq (q = 0.5) regularization, spatiotemporal sparse Bayesian learning (SSBL) and L1-norm, etc. Additionally, the processing time, i.e., speed of storage and transmission has increased dramatically, more importantly, the fault characteristics of the gearbox with multiple faults are detected and saved, i.e., the bearing outer race fault frequency at 170.7 Hz and its harmonics at 341.3 Hz, ball fault frequency at 7.344 Hz and its harmonics at 15.0 Hz, and the gear fault frequency at 23.36 Hz and its harmonics at 47.42 Hz are identified in the envelope spectrum.

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Prognostics and Health Management of Bearings Based on Logarithmic Linear Recursive Least-Squares and Recursive Maximum Likelihood Estimation

Cited by 61 publications

References 34 publications

Multifractional Brownian Motion and Quantum-Behaved Partial Swarm Optimization for Bearing Degradation Forecasting

Multifractional Brownian Motion and Quantum-Behaved Partial Swarm Optimization for Bearing Degradation Forecasting

State estimation for the electro-hydraulic actuator based on particle filter with an improved resampling technique

Multichannel Signals Reconstruction Based on Tunable Q-Factor Wavelet Transform-Morphological Component Analysis and Sparse Bayesian Iteration for Rotating Machines

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