Rotating Machinery Fault Diagnosis Based on Typical Resonance Demodulation Methods: A Review

Li, Hua; Liu, Tao; Li, Shaobo

doi:10.1109/jsen.2023.3235585

Cited by 21 publications

(3 citation statements)

References 165 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Due to the complex environmental noises and structural deformation, bearings are prone to failures. In order to reduce economic losses and prevent accidents, bearing fault diagnosis based on vibration analysis has become a crucial subject of study in academic and industrial fields for many years [1][2][3][4].…”

Section: Introductionmentioning

confidence: 99%

Bearings fault diagnosis under variable speed conditions by hypothesis-based FRFT technique

Cui,

Fan,

Zhao

et al. 2024

Eng. Res. Express

View full text Add to dashboard Cite

The vibration signals of faulty bearings under non-stationary conditions are inherently multi-component and time-varying, which presents a challenge for effective fault diagnosis. Considering the vibration characteristics of rolling bearings under non-stationary conditions and taking advantage of the FRFT, a novel diagnosis method based on the hypothesis-based FRFT has been proposed to separate the fault components. First, the fault characteristic frequencies (FCFs) are extracted from the time-frequency representation of the vibration signals, and the Vold-Kalman filtering is employed to eliminate the influence of noises and other interference components. Subsequently, the fractional feature model is constructed to obtain speed information by the hypothesis approach, whose central idea is that the rotational frequency (RF)-related frequencies under different fault types are estimated, based on the extracted FCFs and the fault characteristic orders. Finally, fault diagnosis is completed by the RF-related peaks in the final spectrum. The method eliminates the need for rotational speed measurement devices and angular resampling. Simulation and experiment estimation results show that the hypothesis-based FRFT method can accurately locate fault characteristic components of bearings under non-stationary conditions.

show abstract

Section: Introductionmentioning

confidence: 99%

Bearings fault diagnosis under variable speed conditions by hypothesis-based FRFT technique

Cui,

Fan,

Zhao

et al. 2024

Eng. Res. Express

View full text Add to dashboard Cite

show abstract

“…Meanwhile, with the continuous development of technology, higher reliability, stability, and safety of rotating machinery are required for high-quality production and life. Thus, it is of great significance to monitor the running status of rotating machinery in real time to predict and diagnose potential faults to ensure normal operation [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Real-Time Frequency-Tracking Method Based on Interpolated Kalman Filter Using Vibration and Surface Noise

Li,

Wan,

Pan

et al. 2024

Mathematical Problems in Engineering

View full text Add to dashboard Cite

The variation of frequency is a significant indicator of the operation status of rotating machinery. Generally, the frequency is extracted and tracked in real-time based on the vibration signal produced by the rotating machinery. However, various interferences generated from contact sampling and transmitting result in difficulty in obtaining the frequency correctly in real-time from the vibration. To solve this problem, this paper presents an interpolated Kalman filter (IKF) based on the vibration and surface noise signals for real-time frequency tracking. First, the cross-correlation operation is performed on the vibration and surface noise signals sampled synchronously to enhance the energy concentration. After that, a frequency search procedure is carried out to calculate the input of the tracking task. Finally, an IKF-based frequency lock procedure is applied to eliminate the interferences and track the frequency in real time. Besides, a correction procedure is added to prevent the measurement process from tracking the frequency incorrectly. The performance of the proposed method is verified by the experiments based on an ARM-based test bench using standard signals and actual test signals.

show abstract

“…Recently, several papers have been published to summarize the research on rotating machinery fault diagnosis, such as the publications on resonance demodulation methods [32] , modulation feature extraction methods [33] , machine learning in machinery fault diagnosis [26] , [34] , deep learning in planetary gearbox fault recognition [35] , data-driven methods in machinery fault diagnosis [36] , transfer learning in machinery fault diagnosis [37] , and health indicator construction of rotating machinery [38] , [39] , etc. However, these review papers mainly focus on constant speeds.…”

Section: Introductionmentioning

confidence: 99%

Rotating Machinery Fault Diagnosis Under Time-Varying Speeds: A Review

Liu,

Cui,

Wang

2023

IEEE Sensors J.

View full text Add to dashboard Cite

Rotating machinery often works under time-varying speeds, and nonstationary conditions as well as harsh environments make its key parts, such as rolling bearings and gears, prone to faults. Therefore, a number of fault diagnosis methods including nonstationary signal processing methods and data-driven methods have been developed. This paper presents a comprehensive review on the fault diagnosis of rotating machinery under time-varying speeds proposed mainly during the last five years. First, spectrum analysis-based methods, including order tracking, cyclic spectrum correlation and generalized demodulation, are reviewed. Second, the time-frequency analysis (TFA) methods in machinery fault diagnosis are divided into postprocessing methods and chirplet transform-based methods and are reviewed. Then, the artificial feature extraction-and deep learning-enabled intelligent diagnosis methods proposed specifically for time-varying speed conditions are reviewed. Finally, the research prospects are discussed.We not only review the relevant state-of-art methods and analyze how they overcome the problems caused by speed fluctuations but also discuss their advantages and disadvantages as well as the challenges that will be encountered when applying them to industrial applications. This paper is expected to provide new graduate students, institutions and companies with a preliminary understanding of the methods on this topic.

show abstract

Rotating Machinery Fault Diagnosis Based on Typical Resonance Demodulation Methods: A Review

Cited by 21 publications

References 165 publications

Bearings fault diagnosis under variable speed conditions by hypothesis-based FRFT technique

Bearings fault diagnosis under variable speed conditions by hypothesis-based FRFT technique

Real-Time Frequency-Tracking Method Based on Interpolated Kalman Filter Using Vibration and Surface Noise

Rotating Machinery Fault Diagnosis Under Time-Varying Speeds: A Review

Contact Info

Product

Resources

About