Multiple Time-Frequency Curve Classification for Tacho-Less and Resampling-Less Compound Bearing Fault Detection Under Time-Varying Speed Conditions

Tang, Gang; Wang, Yatao; Huang, Yujing; Wang, Han

doi:10.1109/jsen.2020.3035623

Cited by 32 publications

(7 citation statements)

References 24 publications

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“…It should be highlighted that there are still many other CFD methods that have been developed based on signal processing algorithms that are not included in the four subcategories mentioned above, which will not be discussed here [87][88][89][90].…”

Section: ) Sparse Representation-based Methodsmentioning

confidence: 99%

Compound Fault Diagnosis for Rotating Machinery: State-of-the-Art, Challenges, and Opportunities

Huang

Xia

Zhang

et al. 2023

JDMD

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Compound fault, as a primary failure leading to unexpected downtime of rotating machinery, dramatically increases the difficulty in fault diagnosis. To deal with the difficulty encountered in implementing compound fault diagnosis (CFD), researchers and engineers from industry and academia have made numerous significant breakthroughs in recent years. Admittedly, many systematic surveys focused on fault diagnosis have been conducted by reputable researchers. Nevertheless, previous review articles paid more attention to fault diagnosis with several single or independent faults, resulting in that there is still lacking a comprehensive survey on CFD. Therefore, to fulfill the above requirements, it is necessary to provide an in-depth overview of fault diagnosis methods or algorithms for compound faults of rotating machinery and uncover potential challenges or opportunities that would guide and inspire readers to devote their efforts to promoting fault diagnosis technology more effective and practical. Specifically, the backgrounds including the related definitions and a new taxonomy of CFD methods are detailed according to the way of implementing compound fault recognition. Then, the state-of-the-art applications of CFD are overviewed based on relevant publications in the past decades. Finally, the challenges and opportunities associated with implementing CFD are concluded and followed by a conclusion for ending this survey. We believe that this review article can provide a systematic guideline of CFD from different aspects for potential readers and seasoned researchers.

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Section: ) Sparse Representation-based Methodsmentioning

confidence: 99%

Compound Fault Diagnosis for Rotating Machinery: State-of-the-Art, Challenges, and Opportunities

Huang

Xia

Zhang

et al. 2023

JDMD

View full text Add to dashboard Cite

show abstract

“…The time-frequency ridge extraction in [22][23][24] adopted the fast path optimization algorithm. Similarly, Wang et al [25] and Tang et al [26] used the respective cost function ridge detection algorithm and the local peak search method to obtain ridges from TFR. The TFA method with good energy concentration and less susceptibility to noise interference is the prerequisite for the accurate extraction of time-varying ISRF and IFCF curves.…”

Section: Introductionmentioning

confidence: 99%

A Novel Method for Bearing Fault Diagnosis under Variable Speed Based on Envelope Spectrum Fault Characteristic Frequency Band Identification

Yue

Jiao

2023

Sensors

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Rolling element bearing (REB) vibration signals under variable speed (VS) have non-stationary characteristics. Order tracking (OT) and time-frequency analysis (TFA) are two widely used methods for REB fault diagnosis under VS. However, the effect of OT methods is affected by resampling errors and close-order harmonic interference, while the accuracy of TFA methods is mainly limited by time-frequency resolution and ridge extraction algorithms. To address this issue, a novel method based on envelope spectrum fault characteristic frequency band identification (FCFBI) is proposed. Firstly, the characteristics of the bearing fault vibration signal’s envelope spectrum under VS are analyzed in detail and the fault characteristic frequency band (FCFB) is introduced as a new and effective representation of faults. Then, fault templates based on FCFB are constructed as reference for fault identification. Finally, based on the calculation of the correlation coefficients between the envelope spectrum and fault templates in the extended FCFB, the bearing fault can be diagnosed automatically according to the preset correlation coefficient criterion. Two bearing VS experiments indicate that the proposed method can achieve satisfactory diagnostic accuracy. The comparison of OT and TFA methods further demonstrates the comprehensive superiority of the proposed method in the overall consideration of accuracy, diagnostic time, tachometer dependency, and automatic degree.

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“…In addition, many studies have focused on extracting time-varying fault characteristics directly through time-frequency analysis. Several methods have been proposed to obtain high-quality time-frequency representations with fine resolution and better energy concentration [ 8 , 9 , 10 ].…”

Section: Introductionmentioning

confidence: 99%

A Sound and Vibration Fusion Method for Fault Diagnosis of Rolling Bearings under Speed-Varying Conditions

Wan

Yang

et al. 2023

Sensors

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The fault diagnosis of rolling bearings is critical for the reliability assurance of mechanical systems. The operating speeds of the rolling bearings in industrial applications are usually time-varying, and the monitoring data available are difficult to cover all the speeds. Though deep learning techniques have been well developed, the generalization capacity under different working speeds is still challenging. In this paper, a sound and vibration fusion method, named the fusion multiscale convolutional neural network (F-MSCNN), was developed with strong adaptation performance under speed-varying conditions. The F-MSCNN works directly on raw sound and vibration signals. A fusion layer and a multiscale convolutional layer were added at the beginning of the model. With comprehensive information, such as the input, multiscale features are learned for subsequent classification. An experiment on the rolling bearing test bed was carried out, and six datasets under various working speeds were constructed. The results show that the proposed F-MSCNN can achieve high accuracy with stable performance when the speeds of the testing set are the same as or different from the training set. A comparison with other methods on the same datasets also proves the superiority of F-MSCNN in speed generalization. The diagnosis accuracy improves by sound and vibration fusion and multiscale feature learning.

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Multiple Time-Frequency Curve Classification for Tacho-Less and Resampling-Less Compound Bearing Fault Detection Under Time-Varying Speed Conditions

Cited by 32 publications

References 24 publications

Compound Fault Diagnosis for Rotating Machinery: State-of-the-Art, Challenges, and Opportunities

Compound Fault Diagnosis for Rotating Machinery: State-of-the-Art, Challenges, and Opportunities

A Novel Method for Bearing Fault Diagnosis under Variable Speed Based on Envelope Spectrum Fault Characteristic Frequency Band Identification

A Sound and Vibration Fusion Method for Fault Diagnosis of Rolling Bearings under Speed-Varying Conditions

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