Application of Time-Frequency Analysis in Rotating Machinery Fault Diagnosis

Bai, Yihao; Cheng, Weidong; Wen, Weigang; Liu, Yang

doi:10.1155/2023/9878228

Cited by 6 publications

(3 citation statements)

References 109 publications

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“…The number of iterations can be set to be less than or equal to m. This scenario is applicable when extracting time-frequency ridges directly from the original signal, which often yields more accurate and suitable results for validation. Set the iteration count as j = j + 1, and continue with steps (3) to (7) as long as the number of separated components does not reach the predefined value of j. Otherwise, terminate the iteration process.…”

Section: Generalized Adaptive Singular Spectrum Decompositionmentioning

confidence: 99%

“…Timefrequency analysis unveils the instantaneous frequency composition of signals and captures the time-varying characteristics of each frequency component, making it a potent methodology for processing signals with variable speed dynamics. Classic time-frequency analysis methods include the short-time Fourier transform (STFT), wavelet transform (WT), Wigner-Ville distribution (WVD) [7]. However, these methods have some limitations.…”

Section: Introductionmentioning

confidence: 99%

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Generalized adaptive singular spectrum decomposition and its application in fault diagnosis of rotating machinery under varying speed

Pang,

Li,

Zhao

et al. 2023

Meas. Sci. Technol.

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Rotating machinery fault signals often consist of multiple components with time varying frequencies under variable speed conditions. Spectral overlap exists among these components, making it difficult to independently separate the features of the components. Singular spectrum decomposition (SSD), a singular spectrum analysis-based signal decomposition method, has shown its great potential in suppressing background noise and extracting fault-related components in complex background noise environments. However, SSD is a frequency domain decomposition method with equivalent filtering characteristics, and it is susceptible to the mode mixing when processing signals with spectral overlap. Moreover, the choice of a key parameter in the iteration decomposition process of SSD, the embedding dimension, is determined using an empirical formula, which might cause suboptimal decomposition outcomes. To address these issues, this paper proposes a generalized adaptive singular spectrum decomposition (GASSD) method, which combines generalized demodulation with improved embedding dimension selection for SSD. GASSD incorporates SSD into the framework of adaptive generalized demodulation to separate specific frequency domain features. Firstly, for an effective generalized demodulation analysis, a region block synchronous ridge extraction method is proposed to accurately estimate the instantaneous frequency ridges from the time-frequency plane, which helps construct proper demodulation phase functions. Secondly, to achieve optimal analysis of SSD, a Gini moderation decomposition index is designed to improve the construction of the trajectory matrix by determining an appropriate embedding dimension. Finally, the reliability of the proposed method is demonstrated by analyzing wind turbine generator bearing fault signals and rotor rubbing fault signals.

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Section: Generalized Adaptive Singular Spectrum Decompositionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Generalized adaptive singular spectrum decomposition and its application in fault diagnosis of rotating machinery under varying speed

Pang,

Li,

Zhao

et al. 2023

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…Fault diagnosis is an important task in industry where the goal is to detect and recognize faults in equipment or systems [1]. The importance of fault diagnosis lies not only in its ability to detect and recognize faults in equipment or systems, but also in its significant contribution to the prevention of equipment failures, the improvement of productivity, the reduction of maintenance costs, and the safety of personnel [2,3]. Bearings are key components in mechanical equipment, and their performance directly affects the reliability, efficiency, and life of the equipment.…”

Section: Introductionmentioning

confidence: 99%

Bearing fault diagnosis method based on angular domain resampling, relative position matrix and transfer learning

Zhang,

Xu,

Chen

et al. 2024

Meas. Sci. Technol.

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Bearings are key components in mechanical equipment, which are widely used in various fields such as automobiles and airplanes. Aiming at the analysis of vibration signal processing under the variable speed condition of bearings, this paper proposes a new bearing fault diagnosis method, which firstly resamples the vibration signals in the angular domain, and then converts the resampled signals into images by the relative position matrix method, and finally uses the transfer learning to automatically extract the features and classify them. To verify the effectiveness of the method, it is tested on the Case Western Reserve University bearing fault dataset and University of Ottawa bearing fault dataset respectively. Compared with other time series to image methods (Recurrence Plot, etc) and other pre-trained models (GoogLeNet, etc), the proposed method has some advantages in terms of accuracy, image generation time, training time, and testing time. The accuracy of the proposed method in this paper reaches more than 90%, which suggests its potential effectiveness in the classification of bearing faults under variable speed working conditions.

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Robust Fractional Low‐Order Multiple Window STFT for Infinite Variance Process Environment

Wang,

Deng,

Long

et al. 2024

IET Signal Processing

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Mechanical fault vibration signal is a typical non‐Gaussian process, they can be characterized by the infinite variance process, and the noise within these signals may also be the process in complex environments. The performance of the traditional cross‐term reduction algorithm is compromised, sometimes yielding incorrect results under the infinite variance process environment. Several robust fractional lower order time–frequency representation methods are proposed including fractional low‐order smoothed pseudo Wigner (FLOSPW), fractional low‐order multi‐windowed short‐time Fourier transform (FLOMWSTFT), and improved fractional low‐order multi‐windowed short‐time Fourier transform (IFLOMWSTFT) utilizing fractional low‐order statistics and short‐time Fourier transform (STFT) to mitigate cross‐terms, enhance time–frequency resolution, and accommodate the infinite variance process environment. When compared to traditional methods, simulation results indicate that they effectively suppress the pulse noise and function effectively in lower mixed signal noise ratio (MSNR) in an infinite variance process environment. The efficacy of the proposed time–frequency algorithm is validated through its application to mechanical bearing outer ring fault vibration signals contaminated with Gaussian noise and subjected to an α infinite variance process.

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Application of Time-Frequency Analysis in Rotating Machinery Fault Diagnosis

Cited by 6 publications

References 109 publications

Generalized adaptive singular spectrum decomposition and its application in fault diagnosis of rotating machinery under varying speed

Generalized adaptive singular spectrum decomposition and its application in fault diagnosis of rotating machinery under varying speed

Bearing fault diagnosis method based on angular domain resampling, relative position matrix and transfer learning

Robust Fractional Low‐Order Multiple Window STFT for Infinite Variance Process Environment

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