Sound Vibration Signal Enhancement for Bearing Fault Detection by Using Adaptive Filter: Adaptive Noise Canceling and Adaptive Line Enhancer

• Published in: Advanced Structured Materials
• Main Author: Sheikh Abdul Nasir S.M.F.; Abd Wahid K.A.; Farhan Saniman M.N.; Wan Muhammad W.M.; Abdul Rahim I.
• Format: Book chapter
• Language: English
• Published: Springer Science and Business Media Deutschland GmbH 2022
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85126711851&doi=10.1007%2f978-3-030-92964-0_14&partnerID=40&md5=cde323675c0ef4bc51364af22594c464
• ISSN: 18698433
• DOI: 10.1007/978-3-030-92964-0_14

This paper investigates the effectiveness of the adaptive filter, ANC, and ALE to improve vibration and sound signals. These signals have been used to detect the natural development of bearing defects for machine diagnosis applications. However, during measurement, these signals have been corrupted by the noise that was coming from other machine parts. In this work, the noise has been successfully removed by using an adaptive filter. Two types of adaptive filters will be compared which are the adaptive noise canceling (ANC) and the adaptive line enhancer (ALE). This investigation is carried out by collecting the vibration and sound signal from a bearing that has been loaded with 20 kg mass and rotated with fixed 1500 rpm. This bearing is continuously rotated for 40 h. It was shown that the ANC filter is more efficient compared to ALE with the least mean square error. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.