Detection and Diagnosis of Bearing Faults Under Fixed and Time-Varying Speed Conditions Using Persistence Spectrum and Multi-Scale Structural Similarity Index

Atta, Mohamed Esam El-Dine; Ibrahim, Doaa Khalil; Gilany, Mahmoud

doi:10.1109/jsen.2021.3139560

Cited by 9 publications

(4 citation statements)

References 46 publications

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“…In addition, expert knowledge is required for fault identification. The Persistence Spectrum may be a potential solution that we suggest to overcome the challenges associated with detecting fault related components in inverter-fed modes with a suitable calculation burden [138,139]. It is widely used to detect the hidden weak signals in other powerful signals.…”

Section: Discussion On Signal Processing-based Methods For Bbf Detect...mentioning

confidence: 99%

Broken Bar Fault Detection and Diagnosis Techniques for Induction Motors and Drives: State of the Art

2022

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Motors are the higher energy-conversion devices that consume around 40% of the global electrical generated energy. Induction motors are the most popular motor type due to their reliability, robustness, and low cost. Therefore, both condition monitoring and fault diagnosis of induction motor faults have motivated considerable research efforts. In this paper, a comprehensive review of the recent techniques proposed in the literature for broken bar faults detection and diagnosis is presented. This paper mainly investigates the fault detection methods in line-fed and inverter-fed motors proposed after 2015 and published in most relevant journals and conferences. The introduced review has deeply discussed the main features of the reported methods and compared them in many different aspects. Finally, the study has highlighted the main issues and the gaps that require more attention from researchers in this field.

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Section: Discussion On Signal Processing-based Methods For Bbf Detect...mentioning

confidence: 99%

Broken Bar Fault Detection and Diagnosis Techniques for Induction Motors and Drives: State of the Art

2022

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“…In [62] , flexible generalized demodulation was proposed to produce a demodulation frequency domain, and the features related to characteristic frequency harmonics were extracted for fault recognition. In [186] , the persistence spectra of the vibration signals were calculated, which were claimed to be more independent from the rotating speed, and then a multiscale structural similarity index was applied to differentiate the health conditions.…”

Section: Aartificial Feature Extraction-enabled Methodsmentioning

confidence: 99%

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

Liu,

Cui,

Wang

2023

IEEE Sensors J.

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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.

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“…Image features were extracted, model functions were established, and data and functions in different states were compared to realize fault diagnosis (Bin Liu et al, 2021) [ 97 ]. Analysis of vibration signals, the establishment of images with the persistent spectrum, comparison between images, bearing fault detection, and diagnosis under fixed and time-varying speed conditions were performed by Mohamed et al [ 98 ]. Vibration images with four different features generated by the original signal were tested by a CNN (Fan, Hongwei et al, 2021) [ 99 ].…”

Section: Detection Methods Based On Two-dimensional Signalsmentioning

confidence: 99%

A Review on Rolling Bearing Fault Signal Detection Methods Based on Different Sensors

Yan

Yang

et al. 2022

Sensors

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As a precision mechanical component to reduce friction between components, the rolling bearing is widely used in many fields because of its slight friction loss, strong bearing capacity, high precision, low power consumption, and high mechanical efficiency. This paper reviews several excellent kinds of study and their relevance to the fault detection of rolling bearings. We summarize the fault location, sensor types, bearing fault types, and fault signal analysis of rolling bearings. The fault signal types are divided into one-dimensional and two-dimensional images, which account for 40.14% and 31.69%, respectively, and their classification is clarified and discussed. We counted the proportions of various methods in the references cited in this paper. Among them, the method of one-dimensional signal detection with external sensors accounted for 3.52%, the method of one-dimensional signal detection with internal sensors accounted for 36.62%, and the method of two-dimensional signal detection with external sensors accounted for 19.72%. The method of two-dimensional signal detection with internal sensors accounted for 11.97%. Among these methods, the highest detection rate is 100%, and the lowest detection rate is more than 70%. The similarities between the different methods are compared. The research results summarized in this paper show that with the progress of the times, a variety of new and better research methods have emerged, which have sped up the detection and diagnosis of rolling bearing faults. For example, the technology using artificial intelligence is still developing rapidly, such as artificial neural networks, convolutional neural networks, and machine learning. Although there are still defects, such methods can quickly discover a fault and its cause, enrich the database, and accumulate experience. More and more advanced techniques are applied in this field, and the detection method has better robustness and superiority.

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Detection and Diagnosis of Bearing Faults Under Fixed and Time-Varying Speed Conditions Using Persistence Spectrum and Multi-Scale Structural Similarity Index

Cited by 9 publications

References 46 publications

Broken Bar Fault Detection and Diagnosis Techniques for Induction Motors and Drives: State of the Art

Broken Bar Fault Detection and Diagnosis Techniques for Induction Motors and Drives: State of the Art

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

A Review on Rolling Bearing Fault Signal Detection Methods Based on Different Sensors

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