ECNN: Intelligent Fault Diagnosis Method Using Efficient Convolutional Neural Network

Article Engineering, Multidisciplinary

Fault diagnosis for small samples based on attention mechanism

Xin Zhang et al.

Summary: The study proposes a small-sample fault diagnosis method based on deep learning, which demonstrates exceptional capacities of generalization and robustness, enabling effective diagnosis under various complicated situations.

MEASUREMENT (2022)

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Article Engineering, Multidisciplinary

New transfer learning fault diagnosis method of rolling bearing based on ADC-CNN and LATL under variable conditions

Chunran Huo et al.

Summary: A modified ADC-CNN model and improved LATL training method were proposed for fault diagnosis of rolling bearings. Experimental results showed that the proposed method significantly outperformed the traditional 1D-CNN model on the PU dataset.

MEASUREMENT (2022)

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Article Engineering, Multidisciplinary

Intelligent fault diagnosis of rolling bearings under imbalanced data conditions using attention-based deep learning method

Jun Li et al.

Summary: The paper introduces a novel fault diagnosis approach for rolling bearings, combining DA-RNN and CBAM technologies, which enhances diagnostic accuracy by handling imbalanced datasets and utilizing convolutional neural networks.

MEASUREMENT (2022)

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Article Engineering, Electrical & Electronic

A novel multi-scale convolution model based on multi-dilation rates and multi-attention mechanism for mechanical fault diagnosis

Caiyuan Chu et al.

Summary: This study proposes a novel multiscale convolution model based on multi-dilation rates and multi-attention mechanism (MDRMA-MSCM) for mechanical fault diagnosis. Comprehensive experiments demonstrate that the proposed method achieves higher classification accuracy than other methods on bearing and gearbox datasets, and exhibits superior generalization capability under variable working conditions and different noise datasets.

DIGITAL SIGNAL PROCESSING (2022)

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Article Computer Science, Artificial Intelligence

A novel method based on deep transfer unsupervised learning network for bearing fault diagnosis under variable working condition of unequal quantity

Hao Su et al.

Summary: In this paper, a new method named dilated convolution deep belief network-dynamic multilayer perceptron (DCDBN-DMLP) is proposed for bearing fault recognition under alterable running states. The performance of this proposed DCDBN-DMLP model is validated by three transfer tasks with bearing fault dataset. Experimental results present that the abundant transferable features can be learned by this proposed method, and its classification accuracies significantly outperform other methods by comparison.

KNOWLEDGE-BASED SYSTEMS (2022)

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Article Computer Science, Artificial Intelligence

Deep sparse representation network for feature learning of vibration signals and its application in gearbox fault diagnosis

Mengqi Miao et al.

Summary: In this paper, a deep learning model called DSRNet is proposed to learn effective features and suppress noise from noisy signals. Experimental results show that DSRNet performs well in machinery fault diagnosis.

KNOWLEDGE-BASED SYSTEMS (2022)

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Article Automation & Control Systems

A Fast Anomaly Diagnosis Approach Based on Modified CNN and Multisensor Data Fusion

Wenfeng Gong et al.

Summary: This article proposes a modified convolutional neural network algorithm to address early anomaly diagnosis. By introducing 1D-CNN and nonlinear multiclass support vector machine, the method achieves superior performance in multisensor 1-D time-series measurements.

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2022)

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Article Computer Science, Artificial Intelligence

Neighbour feature attention-based pooling

Xiaosong Li et al.

Summary: In this paper, a simple and effective attention-based pooling method called Neighbour Feature Attention-based Pooling (NFP) is proposed. NFP integrates neighbour features of the pooling region to maintain semantic continuity and direct local spatial pooling for generating discriminative features. Experimental results demonstrate consistent performance improvement in image classification tasks.

NEUROCOMPUTING (2022)

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Article Computer Science, Artificial Intelligence

End-to-end CNN plus LSTM deep learning approach for bearing fault diagnosis

Amin Khorram et al.

Summary: Fault diagnostics and prognostics are crucial in reducing downtime and safety hazards in industrial plants. This study introduces a novel approach using raw data for bearing fault detection, achieving the highest accuracy in the shortest time without preprocessing or manipulation of input data.

APPLIED INTELLIGENCE (2021)

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Article Computer Science, Artificial Intelligence

A hybrid fine-tuned VMD and CNN scheme for untrained compound fault diagnosis of rotating machinery with unequal-severity faults

Ali Dibaj et al.

Summary: This study proposes a novel fault diagnosis method based on fine-tuned VMD and CNN, which can effectively extract and identify features of a minor fault in the presence of a more severe one. The high accuracy of the proposed method in compound fault diagnosis is confirmed through the analysis of simulated vibration signals and gearbox systems.

EXPERT SYSTEMS WITH APPLICATIONS (2021)

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Article Engineering, Multidisciplinary

Bearing fault diagnosis based on vibro-acoustic data fusion and 1D-CNN network

Xin Wang et al.

Summary: A new method utilizing fusion of multi-modal sensor signals has been proposed for more accurate and robust bearing-fault diagnosis. Experimental results show that this method achieves higher diagnosis accuracy compared to algorithms based on single-modal sensors.

MEASUREMENT (2021)

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Article Computer Science, Artificial Intelligence

Intelligent fault diagnosis of rotating machinery based on continuous wavelet transform-local binary convolutional neural network

Yiwei Cheng et al.

Summary: The proposed data-driven fault diagnosis approach based on CWTLBCNN model builds an end-to-end mechanism for fault diagnosis of rotating machinery, capturing features adaptively and diagnosing faults automatically. Compared to traditional methods, it has faster training speed and more reliable prediction accuracy.

KNOWLEDGE-BASED SYSTEMS (2021)

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Article Computer Science, Artificial Intelligence

Hierarchical diagnosis of bearing faults using branch convolutional neural network considering noise interference and variable working conditions

Kaige Su et al.

Summary: Rolling bearings are prone to malfunction and intelligent fault diagnosis based on convolutional neural networks is effective. A hierarchical branch CNN scheme is proposed to achieve hierarchical diagnosis of bearing faults with superior robustness.

KNOWLEDGE-BASED SYSTEMS (2021)

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Article Automation & Control Systems

A Novel Weighted Adversarial Transfer Network for Partial Domain Fault Diagnosis of Machinery

Weihua Li et al.

Summary: A novel weighted adversarial transfer network (WATN) is proposed for partial domain fault diagnosis, which reduces the distribution discrepancy of shared classes between domains and identifies and filters out irrelevant source examples by introducing adversarial training and a weighting learning strategy. Experiments show that WATN achieves satisfactory performance and outperforms state-of-the-art methods on two diagnosis data sets.

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2021)

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Article Chemistry, Analytical

Bearing Fault Diagnosis via Improved One-Dimensional Multi-Scale Dilated CNN

Jiajun He et al.

Summary: This paper proposes a novel bearing fault diagnosis method using an improved multi-scale convolutional neural network (IMSCNN) to extract multi-scale features and mitigate the effect of noise in vibration signals. Experimental results show the superiority of the proposed method compared to other related methods.

SENSORS (2021)

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Article Automation & Control Systems

Cascade Convolutional Neural Network With Progressive Optimization for Motor Fault Diagnosis Under Nonstationary Conditions

Fei Wang et al.

Summary: A cascade CNN (C-CNN) with progressive optimization is proposed for motor fault diagnosis in nonstationary conditions, addressing the limitations of traditional CNNs. Through physical characteristics of nonstationary vibration signals, the C-CNN achieves better performance in both constant and variable speed scenarios compared to existing methods.

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2021)

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Article Computer Science, Artificial Intelligence

A bearing fault diagnosis model based on CNN with wide convolution kernels

Xudong Song et al.

Summary: Intelligent fault diagnosis of bearings is essential in health management and prediction of rotating machinery systems. This paper proposes a convolutional neural network with wide convolution kernels (WKCNN) model to automatically extract features from vibration signals, improving accuracy, anti-noise, and timeliness of fault diagnosis.

JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING (2021)

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Article Nuclear Science & Technology

A data-driven adaptive fault diagnosis methodology for nuclear power systems based on NSGAII-CNN

Chen He et al.

Summary: This paper proposes a data-driven adaptive fault diagnosis approach NSGAII-CNN, which maps time-series data into two-dimensional images and utilizes NSGAII-CNN algorithm to enhance the accuracy and efficiency of fault diagnosis, showing significant advantages compared to classical CNN architecture models.

ANNALS OF NUCLEAR ENERGY (2021)

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Article Engineering, Multidisciplinary

A fault diagnosis method based on one-dimensional data enhancement and convolutional neural network

Yunyao Long et al.

Summary: A fault diagnosis method based on one-dimensional data enhancement and convolutional neural network is proposed in this study, which can improve training and testing accuracy in the presence of complex fault types and insufficient training samples.

MEASUREMENT (2021)

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Article Engineering, Multidisciplinary

A hybrid attention improved ResNet based fault diagnosis method of wind turbines gearbox

Kai Zhang et al.

Summary: This paper proposes a method for fault diagnosis of wind turbine gearboxes by highlighting the essential frequency bands of wavelet coefficients and the fault features of convolution channels. By performing wavelet packet transformation on the raw signal and improving the ResNet, the method effectively enhances the performance of fault diagnosis.

MEASUREMENT (2021)

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Article Engineering, Multidisciplinary

A fault diagnosis method based on improved convolutional neural network for bearings under variable working conditions

Ke Zhang et al.

Summary: An improved convolutional neural network (CNN) method is proposed in this paper to effectively extract fault features of rolling bearings and expand the labeled data set using pseudo-label learning method, thus improving the fault detection accuracy.

MEASUREMENT (2021)

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Article Green & Sustainable Science & Technology

Multichannel fault diagnosis of wind turbine driving system using multivariate singular spectrum decomposition and improved Kolmogorov complexity

Xiaoan Yan et al.

Summary: This paper proposes a new approach based on MSSD and IKC to effectively extract multichannel fault information of WTDS and automatically discriminate different fault patterns through PLS. Practical engineering data demonstrate the effectiveness of this method, which shows superiority in fault information extraction and health condition identification compared to other multichannel methods and traditional single-channel approaches.

RENEWABLE ENERGY (2021)

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Article Engineering, Electrical & Electronic

Deep Rational Attention Network With Threshold Strategy Embedded for Mechanical Fault Diagnosis

Dongfang Zhao et al.

Summary: In this study, a deep learning-based fault diagnosis method DRANet is proposed, which effectively addresses noise issues by developing a pseudo soft threshold function to modulate attention weights in convolutional neural networks. Experimental results demonstrate that this method achieves more outstanding diagnostic performance, especially in low SNR scenarios.

IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT (2021)

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Article Computer Science, Information Systems

Fault Diagnosis of Bearings Based on Multi-Sensor Information Fusion and 2D Convolutional Neural Network

Jiaxing Wang et al.

Summary: Intelligent operation and maintenance is crucial in Industry 4.0, utilizing artificial intelligence to evaluate equipment status. Multi-sensor information fusion and CNN technology play a significant role in fault diagnosis of industrial equipment, improving accuracy and efficiency.

IEEE ACCESS (2021)

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Article Computer Science, Interdisciplinary Applications