Related references
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Article
Engineering, Multidisciplinary
Liang Meng et al.
Summary: To address the difficulty of fault feature extraction and low accuracy of pattern recognition in gearbox fault diagnosis, a differential continuous wavelet transform-parallel multi-block fusion residual network method is proposed. The method improves time-frequency feature resolution by applying continuous wavelet transform after the first-order difference. It utilizes parallel fusion residual blocks and an attentional feature fusion layer to enhance feature learning and achieve effective fault information fusion. Experimental results demonstrate superior diagnostic performance compared to other methods in bearing and gearbox gear faults.
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Automation & Control Systems
Xiaoli Tang et al.
Summary: Helical gearboxes are crucial for power transmission in industrial applications, but they are prone to various faults due to long-term and heavy-duty operations. Conventional measurements for gearbox fault diagnosis include lubricant analysis, vibration, airborne acoustics, thermal images, and electrical signals. However, relying on a single measurement domain may lead to unreliable diagnosis, especially in harsh environments. This article proposes a Compressive Sensing-based Dual-Channel Convolutional Neural Network method that utilizes non-contact measurements (thermal images and acoustic signals) to accurately diagnose gearbox faults.
Article
Engineering, Mechanical
Surinder Kumar et al.
Summary: In order to detect gear faults in a worm gearbox, a fault diagnosis scheme has been proposed. The scheme utilizes L-moment ratios as condition indicators and extracts information about the fault location from the frequency sidebands at higher harmonics of the gear meshing frequency in the vibration frequency spectrums. The results show that the proposed scheme can effectively classify different health conditions of the worm gearbox and achieve a higher fault classification accuracy compared to conventional indicators.
JOURNAL OF VIBRATION ENGINEERING & TECHNOLOGIES
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Linshan Jia et al.
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Engineering, Mechanical
Zhenghong Wu et al.
Summary: This paper proposes a knowledge dynamic matching unit-guided multi-source domain adaptation network for bearing fault diagnosis, which can dynamically adapt its model parameters to input samples. The network consists of a feature extractor with the knowledge dynamic matching unit and two classifiers with attention mechanism. Experimental results demonstrate that KDMUMDAN achieves superior bearing fault diagnosis ability across multiple domains.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
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Josef Koutsoupakis et al.
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Computer Science, Artificial Intelligence
Haoran Wen et al.
Summary: This paper presents a novel deep clustering network, c-GCN-MAL, for intelligent fault diagnosis of various bearings. The network utilizes autoencoder and graph convolutional network to extract multiple representations of data, and defines new loss functions to enhance its clustering and transfer ability. Experimental results show that the proposed network achieves higher accuracy and stable results for cross-domain fault diagnosis of bearings.
EXPERT SYSTEMS WITH APPLICATIONS
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Onur Surucu et al.
Summary: In modern industry, the quality of maintenance directly influences equipment's operational uptime and efficiency. Predictive maintenance, based on monitoring the condition of machinery, can minimize machine downtime and potential losses. However, the efficacy of predictive maintenance relies on selecting the appropriate data processing method and machine learning model. Existing surveys do not comprehensively inform users or evaluate the quality of proposed monitoring systems. This survey reviews the recent literature on machine learning-driven condition monitoring systems and provides insights into successful intelligent monitoring systems.
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Engineering, Mechanical
Hongchuang Tan et al.
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INTERNATIONAL JOURNAL OF MECHANICAL SCIENCES
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ENERGY CONVERSION AND MANAGEMENT
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Shengnan Tang et al.
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Article
Engineering, Multidisciplinary
Govind Vashishtha et al.
Summary: A deep learning-based defect identification scheme for the Pelton wheel was developed, achieving 100% accuracy using TVF-EMD optimization and CNN model. The proposed AGWO algorithm was tested on benchmark functions and Wilcoxon test, showing efficiency and superiority. The CNN classifier outperformed other learning models in the comparison.
Article
Acoustics
Yong Zhu et al.
Summary: Hydraulic piston pumps are critical components in fluid power systems and their health status is crucial for the safety and reliability of mechanical equipment. This research introduces the particle swarm optimization algorithm to automatically select the hyperparameters of a diagnosis model and constructs a convolutional neural network model optimized by PSO. The proposed PSO-LeNet model, based on acoustic signals, identifies five common states of a hydraulic piston pump with high accuracy and stability compared to other CNN models.
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Govind Vashishtha et al.
Summary: A novel bearing fault identification scheme using deep learning is proposed in this work. The raw vibration signal is processed through a time-varying filter-based empirical mode decomposition (TVF-EMD) to extract sensitive features representing different bearing conditions. These features are used to build fuzzy-based classification models. The results show that the developed method outperforms other classification models in terms of performance and computational time.
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Yun Gao et al.
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IEEE-ASME TRANSACTIONS ON MECHATRONICS
(2022)
Article
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Govind Vashishtha et al.
Summary: This paper proposes a novel unsupervised learning method called general normalized sparse filtering (GNSF) based on Wasserstein distance with maximum mean discrepancy (MMD) for fault diagnosis. Experimental results demonstrate that the proposed method achieves high accuracy and efficiency in fault diagnosis of centrifugal pump and Pelton wheel vibration data.
JOURNAL OF VIBRATION ENGINEERING & TECHNOLOGIES
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Yunyang Zhang et al.
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Acoustics
Yong Zhu et al.
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Article
Engineering, Multidisciplinary
Surinder Kumar et al.
Summary: A worm gearbox is a slow speed gear arrangement that can greatly reduce velocity. Signal processing techniques can be used to extract weak fault features and identify fault frequencies with high accuracy, up to 99.27%.
MEASUREMENT SCIENCE AND TECHNOLOGY
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MEASUREMENT SCIENCE AND TECHNOLOGY
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Qian Shi et al.
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Anil Kumar et al.
Summary: This study introduces a novel convolutional neural network (NCNN) for effectively identifying bearing defects. By modifying the cost function of the convolutional neural network to include additional sparsity cost, the network is able to effectively learn features from small samples. Experimental results validate the effectiveness of the proposed method.
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Jie Wu et al.
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Zhiyu Zhu et al.
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Anil Kumar et al.
JOURNAL OF NONDESTRUCTIVE EVALUATION
(2019)
Review
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Mohammadreza Tahan et al.