Research on Remaining Useful Life Prediction Method of Rolling Bearing Based on Health Indicator Extraction and Trajectory Enhanced Particle Filter

Luo, Peng; Hu, Jiao; Zhang, Lun; Hu, Niaoqing; Yin, Zhengyang

doi:10.37965/jdmd.2022.64

Cited by 10 publications

(3 citation statements)

References 20 publications

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“…However, when the power exponent is too large, PFGI1s, PFGI2s, and PFGI3s are too sensitive to transient features and are easily affected by interference noise, resulting in relatively large fluctuations in the normal state of the bearing. Bearing degradation is irreversible and an approximately monotonic degradation trend is expected (e.g., remaining useful life prediction 47 ), therefore, in this case, PFGI1s with p = 3 and p = 4, PFGI2s with p = 3 and p = 4 and PFGI3 with p = 3 deliver excellent capability to characterize bearing degradation state compared with other explored sparsity measures.…”

Section: Rolling Bearing Condition Monitoringmentioning

confidence: 99%

Power function-based Gini indices: New sparsity measures using power function-based quasi-arithmetic means for bearing condition monitoring

Chen

Zhang

et al. 2023

Structural Health Monitoring

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The Gini index (GI), GI II, and GI III are proven to be effective sparsity measures in the fields of machine condition monitoring and fault diagnosis, and they can be reformulated as the ratio of different quasi-arithmetic means (RQAM). Under this framework, generalized Gini indices (GGIs) have been developed for sparse quantification by applying nonlinear weights to GI, and another generalized form of GI, referred to here as power function-based Gini indices I (PFGI1s), has been introduced by using power function as the generator of quasi-arithmetic means. The GGIs with different weight parameters exhibit reliable sparse quantization capability for repetitive transient features, while their repetitive transient discriminability is lower than kurtosis and negentropy under noise contamination. PFGI1 achieves enhanced repetitive transient discriminability with increasing power exponent, showing the advantage of the generalization approach. In this paper, based on RQAM, a single-parameter generalization method for generating PFGI1s is introduced into GI II and GI III from the perspective of the quasi-arithmetic mean generator, which leads to the power function-based Gini indices II and III (PFGI2s and PFGI3s) constructed from GI II and GI III, respectively. Mathematical derivation proves that PFGI2s and PFGI3s satisfy at least five of six typical attributes of sparsity measures and are two new families of sparsity measures. Simulation analysis shows that, similar to PFGI1s, PFGI2s and PFGI3s can monotonically estimate the sparsity of the data sequence and can simultaneously achieve strong random transient resistibility and high repetitive transient discriminability compared with traditional sparsity measures. The experimental results of bearing run-to-failure demonstrate that PFGI1s, PFGI2s, and PFGI3s with appropriate power exponents can effectively quantify the repetitive transient features caused by bearing faults and can accurately characterize the bearing degradation status compared with the state-of-the-art sparsity measures.

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Section: Rolling Bearing Condition Monitoringmentioning

confidence: 99%

Power function-based Gini indices: New sparsity measures using power function-based quasi-arithmetic means for bearing condition monitoring

Chen

Zhang

et al. 2023

Structural Health Monitoring

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“…But bearings are affected by a variety of factors, including friction, shock and extreme temperatures under harsh conditions such as high speeds, heavy loads, and prolonged use. As a result, the occurrence of faults in bearings lead to equipment damage, compromised economic benefits, and even casualties [1,2]. Hence, it is imperative to conduct research on effective early fault diagnosis methods for bearings to ensure industrial production safety [3,4].…”

Section: Introductionmentioning

confidence: 99%

Improved multiscale coded dispersion entropy: a novel quadratic-coded health indicator of rolling bearings

Fan,

Yuan,

et al. 2024

Meas. Sci. Technol.

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Dispersion entropy (DE) is widely used to quantify the complexity of nonlinear time series. In order to improve the ability to capture fault characteristics, a novel approach called coded dispersion entropy (CDE) has been introduced in recent years. CDE aims to expand the number of possible dispersion patterns and enhance the encoding of similar dispersion patterns. However, the coding method of CDE ignores the amplitude differences between dispersion elements and average elements, resulting in the inaccurate assignment of samples. Additionally, CDE is unable to extract effective information from other time scales. These limitations hinder the ability of CDE to effectively characterize faults. This paper proposes an improved multiscale coded dispersion entropy (IMCDE) to solve these limitations. The method introduces the interval scaling factor "R" and utilizes the sum and difference between the mean element and R as new coding boundaries. This approach addresses the sensitivity issue of the CDE coding mode towards smaller amplitude differences by the reasonable quadratic coding mode. Additionally, a composite coarse-graining process is introduced to rearrange the first coarse-grained point in sequence, resulting in multiple sets of sequences. The average probability of the same dispersion pattern at each scale is calculated to correct the entropy error. The experimental results from two sets of bearing faults demonstrate the effectiveness of this method in extracting critical features associated with faulty bearings. Furthermore, the method showed higher classification accuracy compared to multiscale dispersion entropy (MDE) and multiscale coded dispersion entropy (MCDE). Additionally, it exhibited smaller classification error than MDE and MCDE.

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“…Sparse measures are also used to construct health indicators for mechanical health condition monitoring. 30 There are generally two classical algorithms for solving inverse filters, the objective function method (OFM) and the eigenvalue algorithm (EVA). Miao et al 31 provided an exhaustive review of BD methods applied to mechanical fault diagnostics.…”

Section: Introductionmentioning

confidence: 99%

Maximum negative entropy deconvolution and its application to bearing condition monitoring

Zhou

Chen

Huang

et al. 2023

Structural Health Monitoring

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Blind deconvolution (BD) has proven to be an effective approach to detecting repetitive transients caused by bearing faults. However, BD suffers from instability issues including excessive sensitivity of kurtosis-guided BD methods to the single impulse and high computational time cost of the eigenvector algorithm-aided BD methods. To address these critical issues, this paper proposed a novel BD method maximizing negative entropy (NE), shortened as maximum negative entropy deconvolution (MNED). MNED utilizes NE instead of kurtosis as the optimization metric and optimizes the filter coefficients through the objective function method. The effectiveness of MNED in enhancing repetitive transients is illustrated through a simulation case and two experimental cases. A quantitative comparison with three existing BD methods demonstrates the advantages of MNED in fault detection and computational efficiency. In addition, the performance of the four methods under different filter lengths and external shocks is compared. MNED exhibits lower sensitivity to random impulse noise than the kurtosis-guided BD methods and higher computational efficiency than the BD methods based on the eigenvalue algorithm. The simulation and experimental results demonstrate that MNED is a robust and cost-effective method for bearing fault diagnosis and condition monitoring.

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Research on Remaining Useful Life Prediction Method of Rolling Bearing Based on Health Indicator Extraction and Trajectory Enhanced Particle Filter

Cited by 10 publications

References 20 publications

Power function-based Gini indices: New sparsity measures using power function-based quasi-arithmetic means for bearing condition monitoring

Power function-based Gini indices: New sparsity measures using power function-based quasi-arithmetic means for bearing condition monitoring

Improved multiscale coded dispersion entropy: a novel quadratic-coded health indicator of rolling bearings

Maximum negative entropy deconvolution and its application to bearing condition monitoring

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