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Article

Fault Diagnosis of Rolling Bearings in Rail Train Based on Exponential Smoothing Predictive Segmentation and Improved Ensemble Learning Algorithm

1
School of Computer and Information Engineering, Beijing Technology and Business University, Beijing 100048, China
2
State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, China
*
Author to whom correspondence should be addressed.
Appl. Sci. 2019, 9(15), 3143; https://doi.org/10.3390/app9153143
Received: 30 June 2019 / Revised: 29 July 2019 / Accepted: 31 July 2019 / Published: 2 August 2019
(This article belongs to the Special Issue Fault Diagnosis of Rotating Machine)
The rolling bearing is a key component of the bogie of the rail train. The working environment is complex, and it is easy to cause cracks and other faults. Effective rolling bearing fault diagnosis can provide an important guarantee for the safe operation of the track while improving the resource utilization of the rolling bearing and greatly reducing the cost of operation. Aiming at the problem that the characteristics of the vibration data of the rolling bearing components of the railway train and the vibration mechanism model are difficult to establish, a method for long-term faults diagnosis of the rolling bearing of rail trains based on Exponential Smoothing Predictive Segmentation and Improved Ensemble Learning Algorithm is proposed. Firstly, the sliding time window segmentation algorithm of exponential smoothing is used to segment the rolling bearing vibration data, and then the segmentation points are used to construct the localized features of the data. Finally, an Improved AdaBoost Algorithm (IAA) is proposed to enhance the anti-noise ability. IAA, Back Propagation (BP) neural network, Support Vector Machine (SVM), and AdaBoost are used to classify the same dataset, and the evaluation indexes show that the IAA has the best classification effect. The article selects the raw data of the bearing experiment platform provided by the State Key Laboratory of Rail Traffic Control and Safety of Beijing Jiaotong University and the standard dataset of the American Case Western Reserve University for the experiment. Theoretical analysis and experimental results show the effectiveness and practicability of the proposed method. View Full-Text
Keywords: rolling bearing; time-series segmentation; local spectrum; improved AdaBoost algorithm; fault classification rolling bearing; time-series segmentation; local spectrum; improved AdaBoost algorithm; fault classification
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MDPI and ACS Style

Han, L.; Yu, C.; Liu, C.; Qin, Y.; Cui, S. Fault Diagnosis of Rolling Bearings in Rail Train Based on Exponential Smoothing Predictive Segmentation and Improved Ensemble Learning Algorithm. Appl. Sci. 2019, 9, 3143. https://doi.org/10.3390/app9153143

AMA Style

Han L, Yu C, Liu C, Qin Y, Cui S. Fault Diagnosis of Rolling Bearings in Rail Train Based on Exponential Smoothing Predictive Segmentation and Improved Ensemble Learning Algorithm. Applied Sciences. 2019; 9(15):3143. https://doi.org/10.3390/app9153143

Chicago/Turabian Style

Han, Lu, Chongchong Yu, Cuiling Liu, Yong Qin, and Shijie Cui. 2019. "Fault Diagnosis of Rolling Bearings in Rail Train Based on Exponential Smoothing Predictive Segmentation and Improved Ensemble Learning Algorithm" Applied Sciences 9, no. 15: 3143. https://doi.org/10.3390/app9153143

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