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Characterisation of Major Fault Detection Features and Techniques for the Condition-Based Monitoring of High-speed Centrifugal Blowers
Author(s) -
Samer Gowid,
Roger Dixon,
Sаud Ghаni
Publication year - 2016
Publication title -
the international journal of acoustics and vibration
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.232
H-Index - 19
eISSN - 2415-1408
pISSN - 1027-5851
DOI - 10.20855/ijav.2016.21.2410
Subject(s) - fault (geology) , fault detection and isolation , computer science , centrifugal fan , reliability engineering , engineering , mechanical engineering , artificial intelligence , geology , seismology , inlet , actuator
This paper investigates and characterises the major fault detection signal features and techniques for the diagnostics of rotating element bearings and air leakage faults in high-speed centrifugal blowers. The investigation is based on time domain and frequency domain analysis, as well as on process information, vibration, and acoustic emission fault detection techniques.The results showed that the data analysis method applied in this study is effective, as it yielded a detection accuracy of 100%. A lookup table was compiled to provide an integrated solution for the developer of Condition-Based Monitoring (CBM) applications of centrifugal blowers. The major contribution of this paper is the integration and characterisation of the major fault detection features and techniques.

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