A Feature Extraction Method for Vibration Signal of Bearing Incipient Degradation

Huang, Haifeng; Ouyang, Huajiang; Gao, Hongli; Guo, Lan-Ping; Li, Dan; Wen, Juan

doi:10.1515/msr-2016-0018

Cited by 13 publications

(8 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As a result, condition monitoring allows for the detection of incipient faulty mechanical elements, which is why this method is such a widely explored research field. [1][2][3][4][5][6][7][8] An important aspect of this work is that the experimental laboratory bench used to collect data includes a radial load due to the fact that this is the most important force for which rolling bearings are designed.…”

Section: Introductionmentioning

confidence: 99%

Wavelet Packets Transform processing and Genetic Neuro-Fuzzy classification to detect faulty bearings

Hernández

Castejón

García-Prada

et al. 2019

Advances in Mechanical Engineering

View full text Add to dashboard Cite

A great investment is made in maintenance of machinery in any industry. A big percentage of this is spent both in workers and in materials in order to prevent potential issues with said devices. In order to avoid unnecessary expenses, this article presents an intelligent method to detect incipient faults. Particularly, this study focuses on bearings due to the fact that they are the mechanical elements that are most likely to break down. In this article, the proposed method is tested with data collected from a quasi-real industrial machine, which allows for the measurement of the behaviour of faulty bearings with incipient defects. In a second phase, the vibrations obtained from healthy and defective pieces are processed with a multiresolution analysis with the purpose of extracting the most interesting characteristics. Particularly, a Wavelet Packets Transform processing is carried out. Finally, these parameters are used as Genetic Neuro-Fuzzy inputs; this way, once it has been trained, it will indicate whether the analyzed mechanical element is faulty or not.

show abstract

Section: Introductionmentioning

confidence: 99%

Wavelet Packets Transform processing and Genetic Neuro-Fuzzy classification to detect faulty bearings

Hernández

Castejón

García-Prada

et al. 2019

Advances in Mechanical Engineering

View full text Add to dashboard Cite

show abstract

“…PCA is convenient with signals that would require complicated models or their analytical model is unknown. The PCA is particularly prominent in biomedical applications [21]- [24], and has been utilized for basis extraction in various CS applications [3], [7], [10], [25]. The CS framework with both sensing and decoding side is summarized in Fig.1.…”

Section: B Reconstructionmentioning

confidence: 99%

Sparse Signal Acquisition via Compressed Sensing and Principal Component Analysis

Andráš

Dolinský

Michaeli

et al. 2018

Measurement Science Review

View full text Add to dashboard Cite

This paper presents a way of acquiring a sparse signal by taking only a limited number of samples; sampling and compression are performed in one step by the analog to information conversion. The signal is recovered with minimal information loss from the reduced data record via compressed sensing reconstruction. Several methods of analog to information conversion are described with focus on numerical complexity and implementation in existing embedded devices. Two novel analog to information conversion methods are proposed, distinctive by their computational simplicity - direct subsampling and subsampling with integration. Proposed sensing methods are intended for and evaluated with real water parameter signals measured by a wireless sensor network. Compressed sensing proves to reduce the data transfer rate by >80 % with very little signal processing performed at the sensing side and no appreciable distortion of the reconstructed signal.

show abstract

“…A change in the definition of the aforementioned FuzzyEn is the formation of the vector m i X , which is generalized by removing a baseline defined by (2). However, this implementation of removing the baseline focuses only on the local characteristics of the signal and neglects its global trend [25].…”

Section: The Improved Fuzzy Entropymentioning

confidence: 99%

“…Machinery condition monitoring has received considerable attention for many years because a reliable condition monitoring system can significantly reduce the huge cost due to the unplanned downtime [1]- [2]. Rolling element bearings are the most widely used component in the rotating machines, and they are also one of the most easily damaged mechanical parts.…”

Section: Introductionmentioning

confidence: 99%

Performance Degradation Assessment of Rolling Element Bearings using Improved Fuzzy Entropy

Zhu

Jiang

Chen

et al. 2017

Measurement Science Review

View full text Add to dashboard Cite

Rolling element bearings are an important unit in the rotating machines, and their performance degradation assessment is the basis of condition-based maintenance. Targeting the non-linear dynamic characteristics of faulty signals of rolling element bearings, a bearing performance degradation assessment approach based on improved fuzzy entropy (FuzzyEn) is proposed in this paper. FuzzyEn has less dependence on data length and achieves more freedom of parameter selection and more robustness to noise. However, it neglects the global trend of the signal when calculating similarity degree of two vectors, and thus cannot reflect the running state of the rolling element bearings accurately. Based on this consideration, the algorithm of FuzzyEn is improved in this paper and the improved FuzzyEn is utilized as an indicator for bearing performance degradation evaluation. The vibration data from run-to-failure test of rolling element bearings are used to validate the proposed method. The experimental results demonstrate that, compared with the traditional kurtosis and root mean square, the proposed method can detect the incipient fault in advance and can reflect the whole performance degradation process more clearly.

show abstract

A Feature Extraction Method for Vibration Signal of Bearing Incipient Degradation

Cited by 13 publications

References 28 publications

Wavelet Packets Transform processing and Genetic Neuro-Fuzzy classification to detect faulty bearings

Wavelet Packets Transform processing and Genetic Neuro-Fuzzy classification to detect faulty bearings

Sparse Signal Acquisition via Compressed Sensing and Principal Component Analysis

Performance Degradation Assessment of Rolling Element Bearings using Improved Fuzzy Entropy

Contact Info

Product

Resources

About