A comprehensive review on convolutional neural network in machine fault diagnosis

A systematic review of deep transfer learning for machinery fault diagnosis

For the past three decades, robotic machining has attracted a large amount of research interest owning to the benefit of cost efficiency, high flexibility and multi-functionality of industrial robot. Covering articles published on the subjects of robotic machining in the past 30 years or so; this paper aims to provide an up-to-date review of robotic machining research works, a critical analysis of publications that publish the research works, and an understanding of the future directions in the field. The research works are organised into two operation categories, low material removal rate (MRR) and high MRR, according their machining properties, and the research topics are reviewed and highlighted separately. Then, a set of statistical analysis is carried out in terms of published years and countries. Towards an applicable robotic machining, the future trends and key research points are identified at the end of this paper.

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Industrial robotic machining: a review

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A perspective survey on deep transfer learning for fault diagnosis in industrial scenarios: Theories, applications and challenges

Motor fault diagnosis based on deep learning frameworks has gained much attention from academic research and industry to guarantee motor reliability. Those methods are commonly under two default assumptions: 1) massive labeled training samples and 2) the training and test data share a similar distribution under unvarying working conditions. Unfortunately, these assumptions are nearly invalid in a real-world scenario, where the signals are unlabeled and the working condition changes constantly, resulting in the diagnosis models of the previous studies that always fail in classifying the unlabeled data in real applications. To deal with those issues, in this paper, we propose a novel feature adaptive motor fault diagnosis using deep transfer learning to improve the performance by transferring the knowledge learned from labeled data under invariant working conditions to the unlabeled data under constantly changing working conditions. A convolutional neural network (CNN) is adopted as the base framework to extract multi-level features from raw vibration signals. Then, the regularization term of maximum mean discrepancy (MMD) is incorporated in the training process to impose constraints on the CNN parameters to reduce the distribution mismatch between the features in the source and target domains. To verify the effectiveness of our proposal, data from the motor tests of European driving cycle (NEDC) for simulating the real working scenario and the motor tests under invariant working conditions are, respectively, conducted as the target domain and the source domain. The results show that the proposal presents higher diagnosis accuracy for the unlabeled target data than other methods, and it is of applicability to bridge the discrepancy between different domains.

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Domain Adaptive Motor Fault Diagnosis Using Deep Transfer Learning

A hierarchical method based on weighted extreme gradient boosting in ECG heartbeat classification.

Actual bearing compound fault diagnosis based on active learning and decoupling attentional residual network

Precise cutterhead torque prediction for shield tunneling machines using a novel hybrid deep neural network

Currently, semi-analytical stability analysis methods for milling processes focus on improving prediction accuracy and simultaneously reducing computing time. This paper presents a Chebyshev-wavelet-based method for improved milling stability prediction. When including regenerative effect, the milling dynamics model can be concluded as periodic delay differential equations, and is re-presented as state equation forms via matrix transformation. After divide the period of the coefficient matrix into two subintervals, the forced vibration time interval is mapped equivalently to the definition interval of the second kind Chebyshev wavelets. Thereafter, the explicit Chebyshev–Gauss–Lobatto points are utilized for discretization. To construct the Floquet transition matrix, the state term is approximated by finite series second kind Chebyshev wavelets, while its derivative is acquired with a simple and explicit operational matrix of derivative. Finally, the milling stability can be semi-analytically predicted using Floquet theory. The effectiveness and superiority of the presented approach are verified by two benchmark milling models and comparisons with the representative existing methods. The results demonstrate that the presented approach is highly accurate, fast and easy to implement. Meanwhile, it is shown that the presented approach achieves high stability prediction accuracy and efficiency for both large and low radial-immersion milling operations.

Chengjin Qin

Papers

Domain Adaptive Motor Fault Diagnosis Using Deep Transfer Learning

A hierarchical method based on weighted extreme gradient boosting in ECG heartbeat classification.

Actual bearing compound fault diagnosis based on active learning and decoupling attentional residual network

Precise cutterhead torque prediction for shield tunneling machines using a novel hybrid deep neural network

A novel Chebyshev-wavelet-based approach for accurate and fast prediction of milling stability