Health monitoring of cooling fan bearings based on wavelet filter. (December 2015)

• Record Type: Journal Article
• Authors: He, Wei; Miao, Qiang; Azarian, Michael; Pecht, Michael
• Is Part Of: Mechanical systems and signal processing. Volume 64/65(2015)
• Page Start: 149
• Page End: 161
• Publication Date: 2015-12
• Subjects: Health monitoring -- Cooling fan bearing -- Wavelet -- Degradation assessment -- Fault diagnosis -- Time domain index; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2015.04.002
• Languages: English
• ISSNs: 0888-3270

Abstract

Abstract: In this paper, a vibration-based health monitoring approach for cooling fans is proposed using a wavelet filter for early detection of faults in fan bearings and for the assessment of fault severity. To match the wavelet filter to the fault characteristic signal, a fuzzy rule is introduced to maximize the amplitudes of bearing characteristic frequencies (BCFs), which are an indicator of bearing faults. The sum of the amplitudes of BCFs and their harmonics (SABCF) is used as an index to capture the bearing degradation trend. A comparative study is conducted with commonly used time-domain indices in the degradation assessment, and performance is quantified by three measures, i.e., monotonicity, prognosability, and trendability. The analysis results of the experimental data show that the proposed method can effectively detect incipient defects and can better capture the degradation trend of fan bearings than traditional time-domain indices in vibration analysis. Abstract : Highlights: We present a health monitoring approach for cooling fan bearings in electronic products. This approach can be used for fault detection, degradation trending, as well as the low reliable bearing identification in the product qualification. A fuzzy rule is proposed to find the optimal wavelet parameters that can maximize the characteristic feature of bearing faults. The sum of the amplitudes of BCFs and their harmonics (SABCF) is used as an index to detect the incipient bearing fault and track the bearing degradation. The proposed approach is more effective than the traditional CWT, DWT and time-domain analysis in the bearing health monitoring.