A Novel Faults Diagnosis Method for Rolling Element Bearings Based on ELCD and Extreme Learning Machine

Liang, Mingliang; Dong-min, SU; Daidi, Hu; Ge, Mingtao

doi:10.1155/2018/1891453

Cited by 14 publications

(6 citation statements)

References 23 publications

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“…A more recent work that was just published in the current year, 2018, proposed a novel FDD Method for REB [85], [86], [88][89][90], [92], [93], [101], [103] Fault prognosis [87] based on ensemble local characteristic-scale decomposition (ELCD) and the ELM (ELCD-ELM) algorithm [131]. First, numerous intrinsic scale components (ISCs) were obtained by decomposing the vibration signals using ELCD, and then different ISCs (in the time domain, energy, and relative entropy) were calculated to be the inputs to the ELM-based REB FDD.…”

Section: Combined Methods For Reb Phmmentioning

confidence: 99%

“…The complexity of combined method May be difficult to interpret Merging two or methods may result in a time-consuming and/or power-consuming issue FDD [127][128][129][130][131][132][133][134][135][136][137][138][139], [141][142][143][144][145][146][147][148][149][150][151][152] Fault prognosis [126], [140] Although the deep learning is not a new concept, it has only recently started to gain more attention and to be successfully applied in different fields, such as computer vision, language and audio processing, and (automatic) recognition [153], [154]. It is only in the last few years that deep learning started to be applied to the PHM field [155][156][157].…”

Section: Deep Learning For Reb Phmmentioning

confidence: 99%

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A comprehensive review of artificial intelligence-based approaches for rolling element bearing PHM: shallow and deep learning

et al. 2019

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The objective of this paper is to present a comprehensive review of the contemporary techniques for fault detection, diagnosis, and prognosis of rolling element bearings (REBs). Data-driven approaches, as opposed to model-based approaches, are gaining in popularity due to the availability of low-cost sensors and big data. This paper first reviews the fundamentals of prognostics and health management (PHM) techniques for REBs. A brief description of the different bearing-failure modes is given, then, the paper presents a comprehensive representation of the different health features (indexes, criteria) used for REB fault diagnostics and prognostics. Thus, the paper provides an overall platform for researchers, system engineers, and experts to select and adopt the best fit for their applications. Second, the paper provides overviews of contemporary REB PHM techniques with a specific focus on modern artificial intelligence (AI) techniques (i.e., shallow learning algorithms). Finally, deep-learning approaches for fault detection, diagnosis, and prognosis for REB are comprehensively reviewed.

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Section: Combined Methods For Reb Phmmentioning

confidence: 99%

Section: Deep Learning For Reb Phmmentioning

confidence: 99%

A comprehensive review of artificial intelligence-based approaches for rolling element bearing PHM: shallow and deep learning

et al. 2019

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“…For further comparison, an extreme learning machine (ELM) [29][30][31] classifier is adopted to estimate the proposed method. Considering that the energy at the fault characteristic frequencies will increase if the bearing is faulty, the proportion of fault energy (PFE) is firstly introduced.…”

Section: Incipient Rolling Element Faultmentioning

confidence: 99%

Parameter-Adaptive VMD Method Based on BAS Optimization Algorithm for Incipient Bearing Fault Diagnosis

Wang

Deng

Jianxi

et al. 2020

Mathematical Problems in Engineering

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In view of the incipient fault characteristics are difficult to be extracted from the raw bearing fault signals, an incipient bearing fault diagnosis method based on parameter-adaptive variational mode decomposition (VMD) is proposed. The beetle antennae search (BAS) algorithm is adopted to seek for the optimal combination of the VMD parameters. The reciprocals of the calculated kurtosis values of intrinsic mode functions (IMFs) decomposed via VMD are employed as a fitness function in the searching process. The optimal mode number and the quadratic penalty term of VMD are adaptively set after the search. Afterwards, a vibration signal is decomposed into a set of IMFs using the parameter-adaptive VMD, and the IMF with the maximal kurtosis value is selected as the sensitive one. The selected IMF is further analyzed by Hilbert envelope demodulation. The resulting envelope spectrum can show the significant fault impulse characteristics which are highly helpful to diagnose incipient bearing faults. The kurtosis and the proportion of fault energy are introduced as the input vector of the extreme learning machine (ELM). Comparisons have been conducted via ELM to evaluate the performance by using EMD and the fixed-parameter VMD. The experimental results demonstrate that the proposed method is more effective in extracting the incipient bearing fault characteristics.

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“…Liang et al, [20] presented a rolling bearing fault diagnosis method based on ensemble local characteristic-scale decomposition (ELCD) and extreme learning machine (ELM)is proposed. Vibration signals were decomposed using ELCD, and numerous intrinsic scale components (ISCs) were obtained.…”

Section: Introductionmentioning

confidence: 99%

Hasty Fault Diagnosis of a Rotating Machinery Hinge on Stalwart Trippy Classifier with Robust Harmonized Swan Machine

2020

International Journal of Mathematics and Computers in Simulation

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Monitoring with fault diagnosis of machineries are critically important for production efficiency and plant safety in modern enterprises. Along the process of fault diagnosis due to the addition of faulty signals, it is not an easy task to extract the exact representative features from the original signal. Accordingly, for making the vibration signal analysis more effective, there is a need to have the proper faulty feature extraction and moreover to have the proper estimation of spectral density for eminently producing stable decomposition results even if the signal contains missing values. Moreover, there is a difficulty to measure the correlation between the features with the existing fault diagnosis researches and also it considers more learning time as well as memory constraints which makes the learned concept difficult to understand for classifying the faulty features prominently. Thus to commensurate a perfect diagnosis, in this research a “Robust Harmonised Swan Machine (RHSM) with Stalwart Trippy classifier” is formulated in which the iterative estimation of each mode satisfying a self-consistency nature in decomposition method of RHSM which in turn resolves the missing sample problem eminently and aids reinforcement learning precisely which measures the correlation between the features to classify the faulty features extremely thereby it takes only less memory constraint with less learning time.

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A Novel Faults Diagnosis Method for Rolling Element Bearings Based on ELCD and Extreme Learning Machine

Cited by 14 publications

References 23 publications

A comprehensive review of artificial intelligence-based approaches for rolling element bearing PHM: shallow and deep learning

A comprehensive review of artificial intelligence-based approaches for rolling element bearing PHM: shallow and deep learning

Parameter-Adaptive VMD Method Based on BAS Optimization Algorithm for Incipient Bearing Fault Diagnosis

Hasty Fault Diagnosis of a Rotating Machinery Hinge on Stalwart Trippy Classifier with Robust Harmonized Swan Machine

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