MULTIPLE BAND-PASS AUTOREGRESSIVE DEMODULATION FOR ROLLING-ELEMENT BEARING FAULT DIAGNOSIS

✍ Scribed by J. ALTMANN; J. MATHEW

• Publisher: Elsevier Science
• Year: 2001
• Tongue: English
• Weight: 526 KB
• Volume: 15
• Category: Article
• ISSN: 0888-3270
• DOI: 10.1006/mssp.2001.1410

✦ Synopsis

This paper presents a novel method to enhance the detection and diagnosis of low-speed rolling-element bearing faults based on discrete wavelet packet analysis (DWPA). The method involves the automatic extraction of wavelet packets containing bearing faultrelated features from the discrete wavelet packet analysis representation of machine vibrations. Automated selection of the wavelet packets of interest is achieved via an adaptive network-based fuzzy inference system (ANFIS), which can be implemented on-line. The resultant signal extracted by this technique is essentially an optimal multiple band-pass "lter of the high-frequency bearing impact transients. Used in conjunction with the autoregressive (AR) spectrum of the envelope signal, a sensitive diagnosis of the bearing condition can be made. The discrete wavelet packet analysis multiple band-pass "ltering of the signal results in a signi"cantly improved signal-to-noise ratio compared to its high-pass counterpart, with an exceptional capacity to exclude contaminating sources of vibration. A more modest increase in the signal-to-noise ratio is achieved when compared to digital band-pass "ltering, with the "lter range adjusted to obtain the best possible isolation of the bearing transients.