A Recursive Denoising Learning for Gear Fault Diagnosis Based on Acoustic Signal in Real Industrial Noise Condition

Yao, Yong; Gui, Gui; Yang, Suixian; Zhang, Sen

doi:10.1109/tim.2021.3108216

Cited by 14 publications

(3 citation statements)

References 34 publications

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“…There are typically two approaches to addressing this problem: one is to filter out noise through data preprocessing. Yao et al [99] developed a mechanical fault detection method that employs a recursive learning strategy to effectively mitigate noise. Their approach introduces a novel multilevel attention mechanism, which recursively tracks the noise components and gradually denoises them in a coarse-to-fine manner, leading to satisfied noise suppression performance.…”

Section: A Methods For Iiot Multisource Heterogeneous Time-series Fusionmentioning

confidence: 99%

Deep Learning for Time-Series Prediction in IIoT: Progress, Challenges, and Prospects

Ren

Jia

Laili

et al. 2024

IEEE Trans. Neural Netw. Learning Syst.

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Time-series prediction plays a crucial role in the Industrial Internet of Things (IIoT) to enable intelligent process control, analysis, and management, such as complex equipment maintenance, product quality management, and dynamic process monitoring. Traditional methods face challenges in obtaining latent insights due to the growing complexity of IIoT. Recently, the latest development of deep learning provides innovative solutions for IIoT time-series prediction. In this survey, we analyze the existing deep learning-based time-series prediction methods and present the main challenges of time-series prediction in IIoT. Furthermore, we propose a framework of state-of-the-art solutions to overcome the challenges of time-series prediction in IIoT and summarize its application in practical scenarios, such as predictive maintenance, product quality prediction, and supply chain management. Finally, we conclude with comments on possible future directions for the development of time-series prediction to enable extensible knowledge mining for complex tasks in IIoT.

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Section: A Methods For Iiot Multisource Heterogeneous Time-series Fusionmentioning

confidence: 99%

Deep Learning for Time-Series Prediction in IIoT: Progress, Challenges, and Prospects

Ren

Jia

Laili

et al. 2024

IEEE Trans. Neural Netw. Learning Syst.

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“…These factors lead to inconspicuous vibration fault characteristics of the gearbox. The vibro-acoustic signal-based diagnosis is a promising method for machinery fault detection due to its ability to overcome the limitation of vibration measurement through non-contact measurement by air couple [10]. Thus, the method of fault diagnosis using vibro-acoustic signal has the advantages of high efficiency, non-invasive technique, instant measurement and low cost [11,12].…”

Section: Introductionmentioning

confidence: 99%

A novel vibro-acoustic fault diagnosis approach of planetary gearbox using intrinsic wavelet integrated GE-EfficientNet

Hu,

Lv,

Yuan

et al. 2023

Meas. Sci. Technol.

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Planetary gearbox operates under complex working conditions involving high speed, heavy load, and corrosion. When the planetary gearbox is in tight spaces, it is difficult to measure its signal by conventional methods. In this case, acoustic sensors can measure signal with the noncontact method. This paper proposes a vibro-acoustic fault diagnosis method with respect to planetary gearbox. The method addresses challenges related to weak vibro-acoustic signal, difficulty in extracting fault features, and low diagnostic accuracy and efficiency. Firstly, vibro-acoustic signal is captured by a unidirectional microphone. Next, intrinsic wavelet analysis extracts intrinsic features of the planetary gears. The band-limited intrinsic mode functions (BLIMFs) of the acoustic signal are obtained by optimized variational mode decomposition, and the BLIMFs are then transformed into time-frequency map features. Finally, these time-frequency map features are utilized as the inputs for Ghost module and Efficient channel attention module (GE)-improved EfficientNet model, namely GE-EfficientNet model, to achieve fault diagnosis of planetary gearbox. The superiority of the proposed method is verified by the experimental results which show that the diagnostic accuracy of GE-EfficientNet reached 100%, and the floating-point operations and parameter numbers are only 5.1 G and 0.4 MB, respectively. The results demonstrate that the proposed vibro-acoustic fault diagnosis method achieves good diagnostic accuracy and efficiency.

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“…Secondly, more and more research has been conducted on fault identification and the diagnosis of mechanical equipment by acoustic signal features, from the traditional spectral amplitude feature clustering comparison diagnosis method [27,28] and further acoustic field diagnosis techniques [29,30] to the popular machine learning [31,32] and deep learning techniques [33][34][35] today. These methods include Acoustic Imaging technology [36,37], Recursive Denoising diagnosis [38], Sparse Representation [39] and One-shot Learning [40]. However, at present, compared to traditional methods that require a significant amount of manual labor, acoustic length techniques that require a significant amount of equipment and space as well as deep learning techniques that require a significant amount of updated data, the development of mature and easily controlled machine learning methods has become a more dominant research method for processing acoustic signals.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis of Train Wheelset Bearing Roadside Acoustics Considering Sparse Operation with GA-RBF

et al. 2023

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Trackside acoustic signals are useful for non-contact measurements as well as early warnings in the diagnosis of train wheelset bearing faults. However, there are two important problems when using roadside acoustic signals to diagnose wheel-to-wheel bearing faults; one is the presence of strong interference from strong noise and high harmonics in the signal, and the other is the low efficiency of bearing fault identification caused by it. Therefore, from the viewpoint of solving the two problems, a sparse operation method is proposed for denoising and detuning the modulation of the roadside acoustic signal, and a machine learning classifier with a Genetic Algorithm (GA)-optimized Radial Basis Neural Network (RBFNN) is proposed to improve the rate at which the features of roadside acoustic signal faults are recognized. Firstly, the background noise is filtered out from the Doppler-corrected acoustic signal using the Sparse Representation method, and the inverse wavelet transform is reconstructed into a noiseless signal. Secondly, the interference high-harmonic signal in the signal is filtered out using the Resonant Sparse Signal Decomposition (RSSD) method. Then, the GA is selected to optimize the parameters of the RBF neural network and build a fault diagnosis model. Finally, the extracted acoustic signal feature set is trained on the network model, and the trained model is used for testing. In summary, the sparse operation on the roadside acoustic signal processing and the GA-RBFNN diagnosis model were verified as being very effective in the diagnosis of roadside acoustic train wheel pair faults through the simulation experiment.

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A Recursive Denoising Learning for Gear Fault Diagnosis Based on Acoustic Signal in Real Industrial Noise Condition

Cited by 14 publications

References 34 publications

Deep Learning for Time-Series Prediction in IIoT: Progress, Challenges, and Prospects

Deep Learning for Time-Series Prediction in IIoT: Progress, Challenges, and Prospects

A novel vibro-acoustic fault diagnosis approach of planetary gearbox using intrinsic wavelet integrated GE-EfficientNet

Fault Diagnosis of Train Wheelset Bearing Roadside Acoustics Considering Sparse Operation with GA-RBF

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