Condition assessment for the performance degradation of bearing based on a combinatorial feature extraction method

Hong, Sheng; Zhou, Zheng; Zio, Enrico; Hong, Kan

doi:10.1016/j.dsp.2013.12.010

Cited by 208 publications

(116 citation statements)

References 29 publications

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“…The GPR is a kernel-based machine learning technique that can conveniently specify high dimensional and flexible nonlinear regression [4]. In this study, we assumed that the mean and covariance functions were zero and a linear function, respectively.…”

Section: Rul Prediction Resultsmentioning

confidence: 99%

“…Unexpected machine breakdown can result increased production downtime and maintenance costs and reduced productivity. Therefore, remaining useful lifetime (RUL) prediction is crucial for determining the condition of machines and establishing a maintenance strategy [1][2][3][4]. Vibration sensors are widely used to monitor the condition of the machine.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, researchers have proposed suitable HIs by selecting, extracting, and fusing multiple features. Hong et al [4] extracted time-frequency features using WPD and EMD techniques. Then, they used self-organizing maps (SOM) to construct a HI confidence value (CV).…”

Section: Introductionmentioning

confidence: 99%

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A Novel Image Feature for the Remaining Useful Lifetime Prediction of Bearings Based on Continuous Wavelet Transform and Convolutional Neural Network

Yoo¹,

Baek²

2018

Applied Sciences

137

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Abstract:In data-driven methods for prognostics, the remaining useful lifetime (RUL) is predicted based on the health indicator (HI). The HI detects the condition of equipment or components by monitoring sensor data such as vibration signals. To construct the HI, multiple features are extracted from signals using time domain, frequency domain, and time-frequency domain analyses, and which are then fused. However, the process of selecting and fusing features for the HI is very complex and labor-intensive. We propose a novel time-frequency image feature to construct HI and predict the RUL. To convert the one-dimensional vibration signals to a two-dimensional (2-D) image, the continuous wavelet transform (CWT) extracts the time-frequency image features, i.e., the wavelet power spectrum. Then, the obtained image features are fed into a 2-D convolutional neural network (CNN) to construct the HI. The estimated HI from the proposed model is used for the RUL prediction. The accuracy of the RUL prediction is improved by using the image features. The proposed method compresses the complex process including feature extraction, selection, and fusion into a single algorithm by adopting a deep learning approach. The proposed method is validated using a bearing dataset provided by PRONOSTIA. The results demonstrate that the proposed method is superior to related studies using the same dataset.

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Section: Rul Prediction Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Novel Image Feature for the Remaining Useful Lifetime Prediction of Bearings Based on Continuous Wavelet Transform and Convolutional Neural Network

Yoo¹,

Baek²

2018

Applied Sciences

137

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show abstract

“…Existing ANN methods predict the RUL by using failure history data, but suspended historical data are rarely utilized. Hong, et al [99], used a self-organizing map, which combined wavelet packet and EMD for feature extraction, to estimate bearing performance degradation. Javed, et al [100], used ELM and fuzzy clustering to predict the degradation state and the RUL of complex nonlinear systems.…”

Section: Annmentioning

confidence: 99%

Comprehensive Overview on Computational Intelligence Techniques for Machinery Condition Monitoring and Fault Diagnosis

Zhang

Jia

Zhu

et al. 2017

Chin. J. Mech. Eng.

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Computational intelligence is one of the most powerful data processing tools to solve complex nonlinear problems, and thus plays a significant role in intelligent fault diagnosis and prediction. However, only few comprehensive reviews have summarized the ongoing efforts of computational intelligence in machinery condition monitoring and fault diagnosis. The recent research and development of computational intelligence techniques in fault diagnosis, prediction and optimal sensor placement are reviewed. The advantages and limitations of computational intelligence techniques in practical applications are discussed. The characteristics of different algorithms are compared, and application situations of these methods are summarized. Computational intelligence methods need to be further studied in deep understanding algorithm mechanism, improving algorithm efficiency and enhancing engineering application. This review may be considered as a useful guidance for researchers in selecting a suitable method for a specific situation and pointing out potential research directions.

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“…Directly measuring deviations away from benchmark normal conditions, synthesized health indexes such as those based on logistic regression and support vector data description (SVDD) methods have also been constructed for machinery degradation evaluation [13,14]. Hong et al [15] combined wavelet packet and empirical mode decomposition (EMD) for feature extraction and bearing health states were assessed using a synthesized confidence value derived from self-organization mapping (SOM). For the degradation assessment aspect, many methods, mainly of the machine learning category, such as cerebellar model articulation controller (CMAC) and hidden Markov model (HMM) have been proposed [16][17][18][19] for machine degradation assessment.…”

Section: Introductionmentioning

confidence: 99%

Performance Degradation Assessment of Rolling Element Bearings Based on an Index Combining SVD and Information Exergy

Zhang

et al. 2014

Entropy

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Performance degradation assessment of rolling element bearings is vital for the reliable and cost-efficient operation and maintenance of rotating machines, especially for the implementation of condition-based maintenance (CBM). For robust degradation assessment of rolling element bearings, uncertainties such as those induced from usage variations or sensor errors must be taken into account. This paper presents an information exergy index for bearing performance degradation assessment that combines singular value decomposition (SVD) and the information exergy method. Information exergy integrates condition monitoring information of multiple instants and multiple sensors, and thus performance degradation assessment uncertainties are reduced and robust degradation assessment results can be obtained using the proposed index. The effectiveness and robustness of the proposed information exergy index are validated through experimental case studies.

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Condition assessment for the performance degradation of bearing based on a combinatorial feature extraction method

Cited by 208 publications

References 29 publications

A Novel Image Feature for the Remaining Useful Lifetime Prediction of Bearings Based on Continuous Wavelet Transform and Convolutional Neural Network

A Novel Image Feature for the Remaining Useful Lifetime Prediction of Bearings Based on Continuous Wavelet Transform and Convolutional Neural Network

Comprehensive Overview on Computational Intelligence Techniques for Machinery Condition Monitoring and Fault Diagnosis

Performance Degradation Assessment of Rolling Element Bearings Based on an Index Combining SVD and Information Exergy

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