Informative frequency band selection based on a new indicator: Accuracy rate

Jin, Yaqiang; Liu, Zhiliang; Peng, Dandan; Kang, Jinlong; Ding, Jianming

doi:10.3233/jifs-169528

Cited by 5 publications

(2 citation statements)

References 24 publications

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“…A meta-heuristics algorithm from the social hierarchy and hunting behavior of grey wolves named as MOGWO [35] is applied in this paper to obtain the P s and its corresponding P f of the multi-objective optimization in Equation (8). In the grey wolf optimizer (GWO) [37], the grey wolves have a strict social dominant hierarchy: the alpha (α) wolf who is the best in population, the beta (β) and delta (δ) wolves, who are the second and third best, and the rest are all classified as the omega (ω) wolves.…”

Section: Multi-objective Optimization and Multi-objective Grey Wolf Optimizermentioning

confidence: 99%

Multi-objective Informative Frequency Band Selection Based on Negentropy-induced Grey Wolf Optimizer for Fault Diagnosis of Rolling Element Bearings

Yang

Liu

et al. 2020

Sensors

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Informative frequency band (IFB) selection is a challenging task in envelope analysis for the localized fault detection of rolling element bearings. In previous studies, it was often conducted with a single indicator, such as kurtosis, etc., to guide the automatic selection. However, in some cases, it is difficult for that to fully depict and balance the fault characters from impulsiveness and cyclostationarity of the repetitive transients. To solve this problem, a novel negentropy-induced multi-objective optimized wavelet filter is proposed in this paper. The wavelet parameters are determined by a grey wolf optimizer with two independent objective functions i.e., maximizing the negentropy of squared envelope and squared envelope spectrum to capture impulsiveness and cyclostationarity, respectively. Subsequently, the average negentropy is utilized in identifying the IFB from the obtained Pareto set, which are non-dominated by other solutions to balance the impulsive and cyclostationary features and eliminate the background noise. Two cases of real vibration signals with slight bearing faults are applied in order to evaluate the performance of the proposed methodology, and the results demonstrate its effectiveness over some fast and optimal filtering methods. In addition, its stability in tracking the IFB is also tested by a case of condition monitoring data sets.

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Section: Multi-objective Optimization and Multi-objective Grey Wolf Optimizermentioning

confidence: 99%

Multi-objective Informative Frequency Band Selection Based on Negentropy-induced Grey Wolf Optimizer for Fault Diagnosis of Rolling Element Bearings

Yang

Liu

et al. 2020

Sensors

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“…The contributions of Z. Chen and Z. Li [4]; Peña et al [5]; Sánchez et al [6]; Xie et al [7]; Jin et al [8], and Luo et al [9] deal with feature selection. Feature fusion is the main subject of the contributions of Jiang et al [10] and X. Li et al [11] In the paper of Jiang et al a deep belief network is exploited as a feature fusion method for bearings diagnosis.…”

mentioning

confidence: 99%

Advances in intelligent computing for diagnostics, prognostics, and system health management

Oliveira

2018

IFS

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The guest editors have accepted 41 papers with the special issue. By diagnosis, prognostics and system health management we mean a set of activities including: fault detection, fault classification, fault prognosis, and system modeling. Informally, fault detection refers to the real-time signal processing required to know whether or not a given system is in its healthy normal operating state. Fault classification refers to determination of the type of fault an unhealthy system is suffering from and is a pattern recognition task. Fault prognosis refers to the forecast of the remaining useful life of a system and is based on dynamic modeling. In general, these activities require a sequence of operations such as data acquisition and conditioning, feature extraction, feature selection, and a final

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Local damage detection based on vibration data analysis in the presence of Gaussian and heavy-tailed impulsive noise

2021

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Informative frequency band selection based on a new indicator: Accuracy rate

Cited by 5 publications

References 24 publications

Multi-objective Informative Frequency Band Selection Based on Negentropy-induced Grey Wolf Optimizer for Fault Diagnosis of Rolling Element Bearings

Multi-objective Informative Frequency Band Selection Based on Negentropy-induced Grey Wolf Optimizer for Fault Diagnosis of Rolling Element Bearings

Advances in intelligent computing for diagnostics, prognostics, and system health management

Local damage detection based on vibration data analysis in the presence of Gaussian and heavy-tailed impulsive noise

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