A Hybrid Prognostics Approach for Estimating Remaining Useful Life of Rolling Element Bearings

Wang, Biao; Lei, Yaguo; Li, Naipeng; Li, Ningbo

doi:10.1109/tr.2018.2882682

Cited by 1,068 publications

(377 citation statements)

References 34 publications

Supporting

Mentioning

375

Contrasting

Order By: Relevance

“…In this case, an appropriate mother wavelet was selected using the maximum energy to ShanEn ratio criterion. A bearing with wear damage on the inner race was studied with vibration data contributed by the Xi'an Jiaotong University [116]. Fig.…”

Section: B Entropy Criterion For Parameter Selectionmentioning

confidence: 99%

See 1 more Smart Citation

Entropy Measures in Machine Fault Diagnosis: Insights and Applications

Huo

Martínez-García

Zhang

et al. 2020

IEEE Trans. Instrum. Meas.

135

View full text Add to dashboard Cite

Entropy, as a complexity measure, has been widely applied for time series analysis. One preeminent example is the design of machine condition monitoring and industrial fault diagnostic systems. The occurrence of failures in a machine will typically lead to non-linear characteristics in the measurements, caused by instantaneous variations, which can increase the complexity in the system response. Entropy measures are suitable to quantify such dynamic changes in the underlying process, distinguishing between different system conditions. However, notions of entropy are defined differently in various contexts (e.g., information theory and dynamical systems theory), which may confound researchers in the applied sciences. In this paper, we have systematically reviewed the theoretical development of some fundamental entropy measures and clarified the relations among them. Then, typical entropy-based applications of machine fault diagnostic systems are summarized. Further, insights into possible applications of the entropy measures are explained, as to where and how these measures can be useful towards future datadriven fault diagnosis methodologies. Finally, potential research trends in this area are discussed, with the intent of improving online entropy estimation and expanding its applicability to a wider range of intelligent fault diagnostic systems.

show abstract

Section: B Entropy Criterion For Parameter Selectionmentioning

confidence: 99%

“…(a) Faulty bearing vibration presenting inner race wear[116], (b) resulting raw vibration signal, and (c) obtained wavelet coefficients through CWT analysis using maximum energy to ShanEn ratio criterion.…”

mentioning

confidence: 99%

Entropy Measures in Machine Fault Diagnosis: Insights and Applications

Huo

Martínez-García

Zhang

et al. 2020

IEEE Trans. Instrum. Meas.

135

View full text Add to dashboard Cite

show abstract

“…The rolling bearing dataset generated by the Institute of Design Science and Basic Components at Xi'an Jiaotong University (XJTU) [27], is analyzed. The experimental setup is shown in Figure 2.…”

Section: Experimental Setup and Data Descriptionmentioning

confidence: 99%

Rolling Bearing Fault Diagnosis Based on Wavelet Packet Transform and Convolutional Neural Network

Deng

et al. 2020

Applied Sciences

View full text Add to dashboard Cite

Timely sensing the abnormal condition of the bearings plays a crucial role in ensuring the normal and safe operation of the rotating machine. Most traditional bearing fault diagnosis methods are developed from machine learning, which might rely on the manual design features and prior knowledge of the faults. In this paper, based on the advantages of CNN model, a two-step fault diagnosis method developed from wavelet packet transform (WPT) and convolutional neural network (CNN) is proposed for fault diagnosis of bearings without any manual work. In the first step, the WPT is designed to obtain the wavelet packet coefficients from raw signals, which then are converted into the gray scale images by a designed data-to-image conversion method. In the second step, a CNN model is built to automatically extract the representative features from gray images and implement the fault classification. The performance of the proposed method is evaluated by a real rolling-bearing dataset. From the experimental study, it can be seen the proposed method presents a more superior fault diagnosis capability than other machine-learning-based methods.

show abstract

“…As shown in Figure 5, a total of 32,768 data points (i.e., 1.28 s) were recorded for each sampling, and the sampling period was 1 min. Detailed information about the platform and experiments can be found in [41].…”

Section: Data Descriptionmentioning

confidence: 99%

An Ensemble Learning and RUL Prediction Method Based on Bearings Degradation Indicator Construction

Tian

Wang

2020

Applied Sciences

View full text Add to dashboard Cite

The prediction of the remaining life of a bearing plays a vital role in reducing the accident-related maintenance costs of machinery and in improving the reliability of machinery and equipment. To predict bearing remaining useful life (RUL), the abilities of statistical characteristics to reflect the bearing degradation state differ, and the single prediction model has low generalization ability and a poor prediction effect. An ensemble robust prediction method is proposed here to predict bearing RUL based on the construction of a bearing degradation indicator set: the initial bearing degradation indicator subsets were constructed using the Fast Correlation-Based Filter with Approximate Markov Blankets (FCBF-AMB) and Maximal Information Coefficient (MIC) selection methods. Through the cross-operation of the obtained subsets, we obtained a set of robust degradation indicators. These selected degradation indicators were fed into the long short-term memory (LSTM) neural network prediction model enhanced by the AdaBoost algorithm. We found through calculation that the average prediction accuracy of the proposed method is 91.40%, 92.04%, and 93.25% at 2100, 2250, and 2400 rpm, respectively. Compared with other methods, the proposed method improves the prediction accuracy by 1.8% to 14.87% at most. Therefore, the method proposed in this paper is more accurate than the other methods in terms of RUL prediction.

show abstract

A Hybrid Prognostics Approach for Estimating Remaining Useful Life of Rolling Element Bearings

Cited by 1,068 publications

References 34 publications

Entropy Measures in Machine Fault Diagnosis: Insights and Applications

Entropy Measures in Machine Fault Diagnosis: Insights and Applications

Rolling Bearing Fault Diagnosis Based on Wavelet Packet Transform and Convolutional Neural Network

An Ensemble Learning and RUL Prediction Method Based on Bearings Degradation Indicator Construction

Contact Info

Product

Resources

About