Classifier-guided neural blind deconvolution: A physics-informed denoising module for bearing fault diagnosis under noisy conditions

Liao, Jing-Xiao; He, Chao; Li, Jipu; Sun, Jinwei; Zhang, Shiping; Zhang, Xiaoge

doi:10.1016/j.ymssp.2024.111750

Mechanical Systems and Signal Processing

2025

DOI: 10.1016/j.ymssp.2024.111750

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Classifier-guided neural blind deconvolution: A physics-informed denoising module for bearing fault diagnosis under noisy conditions

Jing-Xiao Liao,

Chao He,

Jipu Li

et al.

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Cited by 3 publications

References 68 publications

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M-IPISincNet: An explainable multi-source physics-informed neural network based on improved SincNet for rolling bearings fault diagnosis

Zhong,

Zheng,

Ruan

et al. 2025

Information Fusion

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M-IPISincNet: An explainable multi-source physics-informed neural network based on improved SincNet for rolling bearings fault diagnosis

Zhong,

Zheng,

Ruan

et al. 2025

Information Fusion

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Advancing machine fault diagnosis: a detailed examination of convolutional neural networks

Vashishtha,

Chauhan,

Sehri

et al. 2024

Meas. Sci. Technol.

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The growing complexity of machinery and the increasing demand for operational efficiency and safety have driven the development of advanced fault diagnosis techniques. Among these, convolutional neural networks (CNNs) have emerged as a powerful tool, offering robust and accurate fault detection and classification capabilities. This comprehensive review delves into the application of CNNs in machine fault diagnosis, covering its theoretical foundation, architectural variations, and practical implementations. The strengths and limitations of CNNs are analyzed in this domain, discussing their effectiveness in handling various fault types, data complexities, and operational environments. Furthermore, we explore the evolving landscape of CNN-based fault diagnosis, examining recent advancements in data augmentation, transfer learning, and hybrid architectures. Finally, the future research directions and potential challenges to further enhance the application of CNNs for reliable and proactive machine fault diagnosis is highlighted.

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A Real-Time Downhole Fluid Identification System Empowered by Efficient Quadratic Neural Network

Chen,

Ni,

Pei

et al. 2024

Electronics

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In the petroleum industry, accurately identifying downhole fluid is crucial for understanding fluid composition and estimating crude oil contamination and other properties. Near-infrared (NIR) spectrum analysis technology has achieved successful fluid identification applications due to its non-destructive nature and high efficiency. However, for real-time downhole fluid analysis, the NIR spectrometer faces challenges such as miniaturization and cost effectiveness. To address these issues, we construct a real-time downhole fluid identification system in this work. First, we propose a lightweight and deployable fluid identification model by integrating the successive projections algorithm (SPA) and a quadratic neural network (QNN). The SPA allows for spectral feature selection, and the QNN acts as an efficient identification model. Consequently, we use only four specific wavelengths with a one-hidden-layer QNN to achieve high identification accuracy. Second, we devise a hardware deployment scheme for real-time identification. We use four laser diodes to replace conventional light sources, further saving space. The QNN is then deployed to the STM32 MCU to implement real-time identification. Computational and online experiments demonstrate that our system functions well in real-time fluid identification and can further estimate the oil contamination rate with acceptable error.

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Classifier-guided neural blind deconvolution: A physics-informed denoising module for bearing fault diagnosis under noisy conditions

Cited by 3 publications

References 68 publications

M-IPISincNet: An explainable multi-source physics-informed neural network based on improved SincNet for rolling bearings fault diagnosis

M-IPISincNet: An explainable multi-source physics-informed neural network based on improved SincNet for rolling bearings fault diagnosis

Advancing machine fault diagnosis: a detailed examination of convolutional neural networks

A Real-Time Downhole Fluid Identification System Empowered by Efficient Quadratic Neural Network

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