The anomalous and smoothed anomalous envelope spectra for rotating machine fault diagnosis. (September 2021)

• Record Type: Journal Article
• Authors: Schmidt, Stephan; Gryllias, Konstantinos C.
• Is Part Of: Mechanical systems and signal processing. Volume 158(2021)
• Publication Date: 2021-09
• Subjects: Anomalous envelope spectrum -- Cyclostationary analysis -- Novelty detection -- Blind fault detection -- Time-varying operating conditions; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2021.107770
• Languages: English
• ISSNs: 0888-3270

Abstract

Highlights: The spectral coherence is combined with healthy historical data for fault diagnosis. The anomalous envelope spectrum enhances the anomalous components in the data. The smoothed anomalous envelope spectrum removes spurious components. The method is tested on numerical and experimental datasets. The method is well-suited for applications under time-varying operating conditions. Abstract: The order-frequency spectral coherence and its integrated spectra (e.g. improved envelope spectrum, squared envelope spectrum) are some of the most powerful methods for performing fault diagnosis under time-varying operating conditions. However, it may require much work to interrogate the order-frequency spectral coherence for symptoms of damage. Hence, in this work we propose a methodology that combines the order-frequency spectral coherence with historical data that were acquired from a healthy machine to obtain an anomalous envelope spectrum, which is further processed for fault diagnosis. This anomalous envelope spectrum is further processed with a smoothing operation to not only perform automatic fault detection, but it is also possible to identify the damaged component if the kinematics of the gearbox are known. The proposed method is investigated on one numerical gearbox dataset and three experimental datasets, where its potential for performing automatic fault detection under time-varying operating conditions is highlighted.