Online sequential prediction of bearings imbalanced fault diagnosis by extreme learning machine. (15th January 2017)

• Record Type: Journal Article
• Authors: Mao, Wentao; He, Ling; Yan, Yunju; Wang, Jinwan
• Is Part Of: Mechanical systems and signal processing. Volume 83(2017)
• Page Start: 450
• Page End: 473
• Publication Date: 2017-01-15
• Subjects: Fault diagnosis -- Extreme learning machine -- Imbalance problem -- Incipient fault -- Online sequential Learning; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2016.06.024
• Languages: English
• ISSNs: 0888-3270

Abstract

Abstract: Diagnosis of bearings generally plays an important role in fault diagnosis of mechanical system, and machine learning has been a promising tool in this field. In many real applications of bearings fault diagnosis, the data tend to be online imbalanced, which means, the number of fault data is much less than the normal data while they are all collected in online sequential way. Suffering from this problem, many traditional diagnosis methods will get low accuracy of fault data which acts as the minority class in the collected bearing data. To address this problem, an online sequential prediction method for imbalanced fault diagnosis problem is proposed based on extreme learning machine. This method introduces the principal curve and granulation division to simulate the flow distribution and overall distribution characteristics of fault data, respectively. Then a confident over-sampling and under-sampling process is proposed to establish the initial offline diagnosis model. In online stage, the obtained granules and principal curves are rebuilt on the bearing data which are arrived in sequence, and after the over-sampling and under-sampling process, the balanced sample set is formed to update the diagnosis model dynamically. A theoretical analysis is provided and proves that, even existing information loss, the proposed method has lower bound of the model reliability. Simulation experiments are conducted on IMS bearing data and CWRU bearing data. The comparative results demonstrate that the proposed method can improve the fault diagnosis accuracy with better effectiveness and robustness than other algorithms. Abstract : Highlights: We propose a new OS-ELM method for solving online sequential data imbalance problem. We propose a hybrid sampling strategy for various data distribution characteristics. We prove theoretically the rationality and validity of this strategy. We conduct a number of experiments on IMS bearing and CRWU bearing data.