Reviews of bearing vibration measurement using fast Fourier transform and enhanced fast Fourier transform algorithms

Abstract

The rolling element bearing is one of the most critical components in a machine. Vibration signals resulting from these bearings imply important bearing defect information related to the machinery faults. Any defect in a bearing may cause a certain vibration with specific frequencies and amplitudes depending on the nature of the defect. Therefore, the vibration analysis plays a key role for fault detection, diagnosis, and prognosis to reach the reliability of the machines. Although fast Fourier transform for time-frequency analysis is still widely used in industry, it cannot extract enough frequencies without enough samples. If the real frequency does not match fast Fourier transform frequency grid exactly, the spectrum is spreading mostly among neighboring frequency bins. To resolve this drawback, the recent proposed enhanced fast Fourier transform algorithm was reported to improve this situation. This article reviews and compares both fast Fourier transform and enhanced fast Fourier transform for vibration signal analysis in both simulation and practical work. The comparative results verify that the enhanced fast Fourier transform can provide a better solution than traditional fast Fourier transform.

A self-Adaptive CNN with PSO for bearing fault diagnosis

“…In signal-based methods, there are many kinds of signals such as vibration and acoustic signals (Liu et al, 2016), electric current (Boudinar et al, 2016), temperature (Yuan et al, 2018), and wear debris. Among these, the vibration signal is the most widely used approach (Lin & Ye, 2019;Sohaib et al, 2017). The signal processing methods generally include Fourier Transform (FT), Short-Time Fourier Transform (STFT), Wavelet Transform (WT), Wavelet Package Transform (WPT), Empirical Wavelet Transform (EWT), Hilbert-Huang Transform CONTACT Jungan Chen friendcen21@hotmail.com (HHT), Empirical Mode Decomposition (EMD), Variation Mode Decomposition (VMD), and Ensemble Empirical Mode Decomposition (EEMD).…”

“…The signal processing methods generally include Fourier Transform (FT), Short-Time Fourier Transform (STFT), Wavelet Transform (WT), Wavelet Package Transform (WPT), Empirical Wavelet Transform (EWT), Hilbert-Huang Transform CONTACT Jungan Chen friendcen21@hotmail.com (HHT), Empirical Mode Decomposition (EMD), Variation Mode Decomposition (VMD), and Ensemble Empirical Mode Decomposition (EEMD). For more details on the advantages and disadvantages of these methods can be seen in Refs (Lin & Ye, 2019;Ma et al, 2018;Yi et al, 2016). Nowadays, a signal-based method is always integrated with a knowledge-based method, which can be used to optimize the parameters in the signal-based method.…”

Fault Diagnosis of Rotating Machinery Based on Convolutional Neural Network and Singular Value Decomposition

“…In order to obtain useful information from the vibration signal that can reflect the operating status of mechanical equipment, various signal processing methods have been proposed, such as Fourier analysis [1,2], empirical mode decomposition (EMD) [3,4], and wavelet transform [5,6]. On vibration signal processing, Guan et al [7] used EMD to decompose the signal into several intrinsic mode functions (IMFs) with different signal-to-noise ratios.…”

In Situ Diagnosis of Industrial Motors by Using Vision-Based Smart Sensing Technology

“…The monitoring of vibrations in industrial motors is a proven method for preventative maintenance aimed at reducing downtime [1]. Fault diagnosis depends on vibration detection using a variety of sensors [2], such as laser displacement sensors and fiber Bragg gratings to measure displacement [3,4], accelerometers to inspect acceleration [5], and radio frequency identification systems to record cycle times [6]. In many instances, the sensing point must be prearranged, since the expanding number of sensors raises the cost of mechanical systems.…”