Early fault detection of bearings based on adaptive variational mode decomposition and local tangent space alignment. Issue 2 (9th January 2019)

• Record Type: Journal Article
• Authors: Ma, Ping; Zhang, Hongli; Fan, Wenhui; Wang, Cong
• Is Part Of: Engineering computations. Volume 36:Issue 2(2019)
• Page Start: 509
• Page End: 532
• Publication Date: 2019-01-09
• Subjects: Bearings -- Adaptive variational mode decomposition -- Early fault detection -- Envelope entropy -- Local tangent space alignment; Computer-aided engineering -- Periodicals; Computer graphics -- Periodicals; 620.00285
• DOI: 10.1108/EC-05-2018-0206
• Languages: English
• ISSNs: 0264-4401

Abstract

Abstract : Purpose: Early fault detection of bearing plays an increasingly important role in the operation of rotating machinery. Based on the properties of early fault signal of bearing, this paper aims to describe a novel hybrid early fault detection method of bearings. Design/methodology/approach: In adaptive variational mode decomposition (AVMD), an adaptive strategy is proposed to select the optimal decomposition level K of variational mode decomposition. Then, a criterion based on envelope entropy is applied to select the optimal intrinsic mode functions (OIMF), which contains most useful fault information. Afterwards, local tangent space alignment (LTSA) is used to denoising of OIMF. The envelope spectrum of the OIMF is used to analyze the fault frequency, thereby detecting the fault. Experiments are conducted in a simulated signal and two experimental vibration signals of bearings to verify the effect of the new method. Findings: The results show that the proposed method yields a good capability of detecting bearing fault at an early stage. The new method can extract more useful information and can reduce noise, which can provide better detection accuracy compared with the other two methods. Originality/value: An adaptive strategy based on center frequency is proposed to select the optimal decomposition level of variational mode decomposition. Envelope entropy is used to fault feature selection. Combining the advantage of the AVMD-envelope entropy and LTSA, which suits the nature of the early fault signal. So, the proposed method has better detection accuracy, which provides a good alternative for early fault detection of bearings.