{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T14:50:36Z","timestamp":1773154236508,"version":"3.50.1"},"reference-count":50,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2023,11,8]],"date-time":"2023-11-08T00:00:00Z","timestamp":1699401600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"University of Southampton","award":["I2BS: 717174"],"award-info":[{"award-number":["I2BS: 717174"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Rolling element bearings (REBs) are an essential part of rotating machinery. A localised defect in a REB typically results in periodic impulses in vibration signals at bearing characteristic frequencies (BCFs), and these are widely used for bearing fault detection and diagnosis. One of the most powerful methods for BCF detection in noisy signals is envelope analysis. However, the selection of an effective band-pass filtering region presents significant challenges in moving towards automated bearing fault diagnosis due to the variable nature of the resonant frequencies present in bearing systems and rotating machinery. Cepstrum Pre-Whitening (CPW) is a technique that can effectively eliminate discrete frequency components in the signal whilst detecting the impulsive features related to the bearing defect(s). Nevertheless, CPW is ineffective for detecting incipient bearing defects with weak signatures. In this study, a novel hybrid method based on an improved CPW (ICPW) and high-pass filtering (ICPW-HPF) is developed that shows improved detection of BCFs under a wide range of conditions when compared with existing BCF detection methods, such as Fast Kurtogram (FK). Combined with machine learning techniques, this novel hybrid method provides the capability for automated bearing defect detection and diagnosis without the need for manual selection of the resonant frequencies. The results from this novel hybrid method are compared with a number of established BCF detection methods, including Fast Kurtogram (FK), on vibration signals collected from the project I2BS (An EU Clean Sky 2 project \u2018Integrated Intelligent Bearing Systems\u2019 collaboration between Schaeffler Technologies and the University of Southampton. Safran Aero Engines was the topic manager for this project) and those from three databases available in the public domain\u2014Case Western Reserve University (CWRU), Intelligent Maintenance Systems (IMS) datasets, and Safran jet engine data\u2014all of which have been widely used in studies of this kind. By calculating the Signal-to-Noise Ratio (SNR) of each case, the new method is shown to be effective for a much lower SNR (with an average of 30.21) compared with that achieved using the FK method (average of 14.4) and thus is much more effective in detecting incipient bearing faults. The results also show that it is effective in detecting a combination of several bearing faults that occur simultaneously under a wide range of bearing configurations and test conditions and without the requirement of further human intervention such as extra screening or manual selection of filters.<\/jats:p>","DOI":"10.3390\/s23229048","type":"journal-article","created":{"date-parts":[[2023,11,9]],"date-time":"2023-11-09T03:42:40Z","timestamp":1699501360000},"page":"9048","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["A Novel Hybrid Technique Combining Improved Cepstrum Pre-Whitening and High-Pass Filtering for Effective Bearing Fault Diagnosis Using Vibration Data"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5978-1970","authenticated-orcid":false,"given":"Amirmasoud","family":"Kiakojouri","sequence":"first","affiliation":[{"name":"National Centre for Advanced Tribology at Southampton (nCATS), School of Engineering, University of Southampton, Southampton SO17 1BJ, UK"}]},{"given":"Zudi","family":"Lu","sequence":"additional","affiliation":[{"name":"Southampton Statistical Sciences Research Institute (S3RI), School of Mathematical Sciences, University of Southampton, Southampton SO17 1BJ, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1222-4381","authenticated-orcid":false,"given":"Patrick","family":"Mirring","sequence":"additional","affiliation":[{"name":"Schaeffler Technologies AG & Co. KG, Georg-Schaefer-Str. 30, 97421 Schweinfurt, Germany"}]},{"given":"Honor","family":"Powrie","sequence":"additional","affiliation":[{"name":"GE Aerospace, School Lane, Chandlers Ford, Eastleigh SO53 4YG, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2894-6784","authenticated-orcid":false,"given":"Ling","family":"Wang","sequence":"additional","affiliation":[{"name":"National Centre for Advanced Tribology at Southampton (nCATS), School of Engineering, University of Southampton, Southampton SO17 1BJ, UK"}]}],"member":"1968","published-online":{"date-parts":[[2023,11,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1765","DOI":"10.1007\/s11071-018-4314-y","article-title":"Overview of Dynamic Modelling and Analysis of Rolling Element Bearings with Localized and Distributed Faults","volume":"93","author":"Liu","year":"2018","journal-title":"Nonlinear Dyn."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1131","DOI":"10.1007\/s11668-017-0351-y","article-title":"Analysis of Oil Debris in an Aero Gas Turbine Engine","volume":"17","author":"Mishra","year":"2017","journal-title":"J. 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