Machinery fault diagnosis with imbalanced data using deep generative adversarial networks

Zhang, Wei; Li, Xiang; Jia, Xiaodong; Ma, Hui; Zhang, Luo; Xu, Li

doi:10.1016/j.measurement.2019.107377

Cited by 270 publications

(80 citation statements)

References 33 publications

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“…Synthetic data generation using Generative Adversarial Networks is preferable as compared to other augmentation techniques [459] as RL agents can be exposed to a broad range of extreme conditions by combining both real and synthetic data that allows the model to deal with unpredicted rare events [454], [460] and [461]. Unlike other augmentation techniques, GANs also eliminate data set biases in generated data for proper training of a machine learning model and make synthetic data indistinguishable from real data [462] and [463].…”

Section: K Lesson Learnedmentioning

confidence: 99%

A Gentle Introduction to Reinforcement Learning and its Application in Different Fields

Naeem

Rizvi

Coronato³

2020

IEEE Access

126

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Due to the recent progress in Deep Neural Networks, Reinforcement Learning (RL) has become one of the most important and useful technology. It is a learning method where a software agent interacts with an unknown environment, selects actions, and progressively discovers the environment dynamics. RL has been effectively applied in many important areas of real life. This article intends to provide an in-depth introduction of the Markov Decision Process, RL and its algorithms. Moreover, we present a literature review of the application of RL to a variety of fields, including robotics and autonomous control, communication and networking, natural language processing, games and self-organized system, scheduling management and configuration of resources, and computer vision.

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Section: K Lesson Learnedmentioning

confidence: 99%

A Gentle Introduction to Reinforcement Learning and its Application in Different Fields

Naeem

Rizvi

Coronato³

2020

IEEE Access

126

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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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“…While the results suggest that deep learning-based fault diagnosis methods can largely benefit from an expanded dataset with more generated labeled instances, but the new samples are generally similar to the real samples and lack sufficient diversity of data, so improvement in model generalization are limited [21][22][23]. In addition, generative adversarial networks (GANs) have been recently used to learn the distributions of the machinery vibration data and generate additional realistic fake samples to expand the training dataset, despite the improved testing performance, the main drawback of GANs-based bearing fault diagnostics is its reliance on a relatively large model consisting of multiple GANs for the minority classes [24]. Instead of using data augmentation methods described above, transfer learning was employed in this study.…”

Section: Introductionmentioning

confidence: 99%

A Generic Intelligent Bearing Fault Diagnosis System Using Convolutional Neural Networks With Transfer Learning

Lü

Han

et al. 2020

IEEE Access

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It is very important and necessary to diagnose bearing faults timely, quickly, and accurately in practical applications, because the operation status of the bearings is directly related to the performance and reliability of the rotating machinery. Therefore, a generic intelligent bearing fault diagnosis system based on AlexNet with transfer learning was proposed to automatically identify and classify different bearing faults. Transfer learning was used to avoid overfitting problem of deep network. Five bearing faults at two different motor loads and speeds were selected from the Case Western Reserve University (CWRU) bearing dataset to validate the performance of the proposed method. Results showed that compared to previous methods, the proposed method achieved excellent performance, with overall classification accuracy over 99.7%, and fast training and testing times. Feature visualization displayed the common and high-level features of spectrograms of vibration signals learned by the trained classification model. And strongest activations demonstrated the classification model had learned the correct features of each bearing fault. Importantly, there were two testing datasets employed in this study, where the training dataset and testing dataset (2) were completely independent and the number of subspectrograms used for testing was 3-5 times greater than those used for training. Thus, all the results suggest that the proposed method is stable, reliable, suitable for diagnosing different bearing faults and has a great potential in practical applications.

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Machinery fault diagnosis with imbalanced data using deep generative adversarial networks

Cited by 270 publications

References 33 publications

A Gentle Introduction to Reinforcement Learning and its Application in Different Fields

A Gentle Introduction to Reinforcement Learning and its Application in Different Fields

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

A Generic Intelligent Bearing Fault Diagnosis System Using Convolutional Neural Networks With Transfer Learning

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