A Model-Based Method for Remaining Useful Life Prediction of Machinery

Lei, Yaguo; Li, Naipeng; Gontarz, Szymon; Lin, Jing; Radkowski, S.; Dybała, Jacek

doi:10.1109/tr.2016.2570568

Cited by 468 publications

(187 citation statements)

References 39 publications

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“…This method demonstrated its superiority over SOM-based HIs. However, it obtained higher percent errors and lower scores than Lei et al [3].…”

Section: Comparing Related Workmentioning

confidence: 61%

“…The method shows improved errors over previous studies, but requires the extraction of approximately one hundred features to estimate bearing performance. The third study, published by Lei et al [3], proposed a new HI (i.e., WMQE) to predict the RUL of bearings. WMQE was constructed by fusing a select few weighted features based on correlation clustering among the 28 features extracted from the bearings.…”

Section: Comparing Related Workmentioning

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%

“…The model-based methods derive mathematical or physical models to estimate the degradation process of the machine. The commonly used models include Markov process, Wiener process, and Gaussian mixture model [3]. These methods rely on the expert's domain knowledge of the failure mechanism.…”

Section: Introductionmentioning

confidence: 99%

“…Zhao et al [20] extracted textual features of time-frequency representation and predicted RUL by transforming high-dimensional features into low-dimensional features using principal component analysis and linear discriminant analysis (LDA). Lei et al [3] proposed the use of the weighted minimum quantization error (WMQE) as a HI, which is a fusion of 10 time domain features, 16 time-frequency domain features, and two features based on trigonometric functions. While most of these studies relied on feature extraction, selection, and fusion techniques to construct the HI, these complex processes are labor-intensive.…”

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

139

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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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“…This method demonstrated its superiority over SOM-based HIs. However, it obtained higher percent errors and lower scores than Lei et al [3].…”

Section: Comparing Related Workmentioning

confidence: 61%

Section: Comparing Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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

139

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An Overview of Predictive Maintenance and Load Forecasting

Gupta,

Upadhyaya,

Gali

2024

Intelligent Techniques for Predictive Data Analytics

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Research on frequency band sparse optimization algorithm in health index of rolling bearing

Song

Yang

Chen

et al. 2022

Concurrency and Computation

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The construction of health indicators of rolling bearing is significant for the monitoring of its health condition and residual life prediction. The main work is based on the frequency band sparse optimization algorithm in the rolling bearing health index. On the one hand, the index construction method based on machine learning model has poor interpretability; on the other hand, there are many interference components in the original vibration signal, extracting statistical features directly based on the original signal as a poor health indicator. For the above problems, this paper proposes a rolling bearing health index construction method based on sparse frequency band characterization. First, the signal including each independent sub-band has been obtained by wavelet packet decomposition. Second, a sparse model is constructed by combining the sub-band amplitudes and matrices decomposed by the wavelet packet in the fault state and the normal state, so as to select the frequency band sensitive to the fault information. Thereafter, the spectral amplitude sum of sensitive frequency band (SASSFB) is calculated as a health indicator that reflects the degradation information during the complete life operation of the rolling bearing. Finally, the effectiveness of the method is verified by utilizing the published bearing accelerated life experimental data set.

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A Model-Based Method for Remaining Useful Life Prediction of Machinery

Cited by 468 publications

References 39 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

An Overview of Predictive Maintenance and Load Forecasting

Research on frequency band sparse optimization algorithm in health index of rolling bearing

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