Abstract
The empirical mode decomposition (EMD) method is a technique that recursively decomposes an input signal into intrinsic mode functions (IMFs) by residual signals, primarily for identifying desirable features. The suggested algorithm observes the residual signal instead of the IMF, which lowers the computing load. The study introduces a new method for detecting bearing faults by enhancing signal extraction from sensor data using EMD and multi-axis feature extraction. This method streamlines the process by filtering out high-frequency noise and correlating residual signal information with analysis. The approach also enhances the signal-to-noise ratio (SNR) and feature signature identification using digital signal processing (DSP) techniques. The algorithm for vibration data analysis is tested for bearing failures, identifying shaft frequency and inner race bearing faults, which can be implemented in parallel. For the inner race fault bearing analysis, two-level EMD with a residual signal generates output similar to five-iteration EMD, saving 60% of computations. The use of spectral multiplication to multi-axis data processing produced a rise in the SNR of 18.32 dB to 20.92 dB for Y-axis and X-axis input, respectively. When compared to the single-axis IMF data computation, 20% fewer iterations are needed overall. A single-level EMD is adequate for calculating the rotational frequency of a healthy bearing. For the Y- and X-axis input, multi-axis analysis increases SNR by 10.68 dB and 13.14 dB, accordingly. This comprehensive strategy reduces computational complexity, improves fault detection accuracy, and minimizes noise impact, making it a promising solution for bearing fault detection.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: 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.