Abstract
Envelope analysis is a widely used tool for fault detection in rotating machines. In envelope analysis, impulsive noise contaminates the measured signal, making it difficult to extract the features of defects. This paper proposes a time–frequency envelope analysis that overcomes the effects of impulsive noises. Envelope analysis is performed by dividing the signal into several sections through a time window. The effect of impulsive noises is eliminated by using the frequency characteristics of the short time rectangular wave. The proposed method was verified through simulation and experimental data. The simulation was conducted by mathematically modeling a cyclo-stationary process that characterizes rotating machinery signals. In addition, the effectiveness of the method was verified by the measured data of normal and defective air-conditioners produced on the actual assembly line. This simple proposed method is effective enough to detect the faults. In the future, the approaches of big data and deep learning will be required for the development of the prognostic health-management framework.
Highlights
In order to prevent greater losses from malfunctions, unnecessary overhaul, and human loss, the fault detection of rotating machinery has been studied in various ways [1,2]
The simple time–frequency envelope analysis has been proposed to improve the degradation of the detection performance when impulsive noises contaminate the measured signals for condition monitoring and fault detection of the rotating machinery
When the impulsive noise is mixed in one time, the baseline of the envelope spectrum slightly increases, and the sharpness of the peak related to the defect becomes dull
Summary
In order to prevent greater losses from malfunctions, unnecessary overhaul, and human loss, the fault detection of rotating machinery has been studied in various ways [1,2]. There can be many unpredictable noises on the factory floor and industrial application, such as noises generated by other technical processes around the inspection sites or by the activities of workers [18,25,26] To address this issue that degrades the performance of the CS-approach-based method in impulsive noise environment, various methods have been proposed, such as cyclic correntropy [13,27,28], gini-index guided bearing fault diagnosis to replace kurtosis [18], and infogram using both periodicity and impulsiveness of fault signature [14]. To mitigate the effects of impulsive noises independent of the fault signal of a rotating machinery, envelope analysis using a time window was first performed and the frequency response characteristics of a single short time rectangular wave was used. Equation (3) represents the Hilbert transform in the frequency domain, with the phase of positive frequencies shifting by −90 degrees and those of negative frequencies by +90 degrees [19,41]
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
