Diagnosing Rotating Machines With Weakly Supervised Data Using Deep Transfer Learning

Li, Xiang; Zhang, Wei; Ding, Qian; Xu, Li

doi:10.1109/tii.2019.2927590

Cited by 227 publications

(81 citation statements)

References 28 publications

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“…The domain adaptation module maximizes domain recognition errors and minimizes probability distribution distance to help 1-D CNN learning domain invariant features. Li et al [ 37 ] proposed a weakly supervised transfer learning method with domain adversarial training. This method aims to improve the diagnostic performance on the target domain by knowledge transferation from multiple different but related source domain.…”

Section: Related Workmentioning

confidence: 99%

Frequency-Domain Fusing Convolutional Neural Network: A Unified Architecture Improving Effect of Domain Adaptation for Fault Diagnosis

Zheng

et al. 2021

Sensors

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In recent years, transfer learning has been widely applied in fault diagnosis for solving the problem of inconsistent distribution of the original training dataset and the online-collecting testing dataset. In particular, the domain adaptation method can solve the problem of the unlabeled testing dataset in transfer learning. Moreover, Convolutional Neural Network (CNN) is the most widely used network among existing domain adaptation approaches due to its powerful feature extraction capability. However, network designing is too empirical, and there is no network designing principle from the frequency domain. In this paper, we propose a unified convolutional neural network architecture from a frequency domain perspective for a domain adaptation named Frequency-domain Fusing Convolutional Neural Network (FFCNN). The method of FFCNN contains two parts, frequency-domain fusing layer and feature extractor. The frequency-domain fusing layer uses convolution operations to filter signals at different frequency bands and combines them into new input signals. These signals are input to the feature extractor to extract features and make domain adaptation. We apply FFCNN for three domain adaptation methods, and the diagnosis accuracy is improved compared to the typical CNN.

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Section: Related Workmentioning

confidence: 99%

Frequency-Domain Fusing Convolutional Neural Network: A Unified Architecture Improving Effect of Domain Adaptation for Fault Diagnosis

Zheng

et al. 2021

Sensors

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“…Many ML methods have been applied on the quality prediction in various fields. Scime and Beuth 8 used Decision Tree, Support Vector Machine for additive manufacturing quality identification; El Mazgualdi et al 9 applied Random Forest, XGBoost, and Deep Learning for prediction of efficiency in manufacturing industry; Li et al 10 and Zhang et al 11 applied and showed that data-driven algorithms are highly effective tool for automatic feature extraction and quality monitoring performance.…”

Section: Literature Reviewmentioning

confidence: 99%

A new automatic machine learning based hyperparameter optimization for workpiece quality prediction

Wen

Gao

2020

Measurement and Control

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Workpiece quality prediction is very important in modern manufacturing industry. However, traditional machine learning methods are very sensitive to their hyperparameters, making the tuning of the machine learning methods essential to improve the prediction performance. Hyperparameter optimization (HPO) approaches are applied attempting to tune hyperparameters, such as grid search and random search. However, the hyperparameters space for workpiece quality prediction model is high dimension and it consists with continuous, combinational and conditional types of hyperparameters, which is difficult to be tuned. In this article, a new automatic machine learning based HPO, named adaptive Tree Pazen Estimator (ATPE), is proposed for workpiece quality prediction in high dimension. In the proposed method, it can iteratively search the best combination of hyperparameters in the automatic way. During the warm-up process for ATPE, it can adaptively adjust the hyperparameter interval to guide the search. The proposed ATPE is tested on sparse stack autoencoder based MNIST and XGBoost based WorkpieceQuality dataset, and the results show that ATPE provides the state-of-the-art performances in high-dimensional space and can search the hyperparameters in reasonable range by comparing with Tree Pazen Estimator, annealing, and random search, showing its potential in the field of workpiece quality prediction.

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“…Some researchers have begun to explore this issue. Li et al [34] designed a deep transfer learning based on CNN, where the diagnostics ability trained on sufficient supervised data of different rotating machines is transferred to target equipment with domain adversarial training. Guo et al [35] developed a deep convolutional transfer learning network consisting of two modules of condition recognition and domain adaption.…”

Section: Introductionmentioning

confidence: 99%

A Multistage Deep Transfer Learning Method for Machinery Fault Diagnostics Across Diverse Working Conditions and Devices

et al. 2020

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Deep learning methods have promoted the vibration-based machinery fault diagnostics from manual feature extraction to an end-to-end solution in the past few years and exhibited great success on various diagnostics tasks. However, this success is based on the assumptions that sufficient labeled data are available, and that the training and testing data are from the same distribution, which is normally difficult to satisfy in practice. To overcome this issue, we propose a multistage deep convolutional transfer learning method (MSDCTL) aimed at transferring vibration-based fault diagnostics capabilities to new working conditions, experimental protocols and instrumented devices while avoiding the requirement for new labeled fault data. MSDCTL is constructed as a one-dimensional convolutional neural network (CNN) with double-input structure that accepts raw data from different domains as input. The features from different domains are automatically learned and a customized layer is designed to compute the distribution discrepancy of the features. This discrepancy is further minimized such that the features learned from different domains are domain-invariant. A multistage training strategy including pre-train and fine-tuning is proposed to transfer the weight of a pre-trained model to new diagnostics tasks, which drastically reduces the requirement on the amount of data in the new task. The proposed model is validated on three bearing fault datasets from three institutes, including one from our own. We designed nine transfer tasks covering fault diagnostics transfer across diverse working conditions and devices to test the effectiveness and robustness of our model. The results show high diagnostics accuracies on all the designed transfer tasks with strong robustness. Especially for transfer to new devices the improvement over state of the art is very significant. INDEX TERMS Fault diagnostics, transfer learning, convolutional neural network, maximum mean difference, multistage training.

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Diagnosing Rotating Machines With Weakly Supervised Data Using Deep Transfer Learning

Cited by 227 publications

References 28 publications

Frequency-Domain Fusing Convolutional Neural Network: A Unified Architecture Improving Effect of Domain Adaptation for Fault Diagnosis

Frequency-Domain Fusing Convolutional Neural Network: A Unified Architecture Improving Effect of Domain Adaptation for Fault Diagnosis

A new automatic machine learning based hyperparameter optimization for workpiece quality prediction

A Multistage Deep Transfer Learning Method for Machinery Fault Diagnostics Across Diverse Working Conditions and Devices

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