Imbalanced Fault Diagnosis of Rolling Bearing Based on Generative Adversarial Network: A Comparative Study

Mao, Wentao; Liu, Yamin; Ding, Ling; Li, Yuan

doi:10.1109/access.2018.2890693

Cited by 214 publications

(102 citation statements)

References 23 publications

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“…To get a more robust detection result, it is necessary to eliminate the abnormal points in the common feature space. Traditional state assessment methods are mostly based on singular value decomposition [26], or work directly on RMS value [27]. However, these methods do not consider the negative influence of fluctuations in normal state, so it is easy to generate false alarms.…”

Section: Robust State Assessment Methodsmentioning

confidence: 99%

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Robust Detection of Bearing Early Fault Based on Deep Transfer Learning

et al. 2020

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In recent years, machine learning techniques have been proven to be a promising tool for early fault detection of rolling bearings. In many actual applications, however, bearing whole-life data are not easy to be historically accumulated, while insufficient data may result in training a detection model that is not good enough. If utilizing the available data under different working conditions to facilitate model training, the data distribution of different bearings are usually quite different, which does not meet the precondition of i n d e p e n d e n t a n d i d e n t i c a l d i s t r i b u t i o n ( i . i . d ) and tends to cause performance reduction. In addition, disturbed by the unstable noise under complex conditions, most of the current detection methods are inclined to raise false alarms, so that the reliability of detection results needs to be improved. To solve these problems, a robust detection method for bearings early fault is proposed based on deep transfer learning. The method includes offline stage and online stage. In the offline stage, by introducing a deep auto-encoder network with domain adaptation, the distribution inconsistency of normal state data among different bearings can be weakened, then the common feature representation of the normal state is obtained. With the extracted common features, a new state assessment method based on the robust deep auto-encoder network is proposed to evaluate the boundary between normal state and early fault state in the low-rank feature space. By training a support vector machine classifier, the detection model is established. In the online stage, along with the data batch arriving sequentially, the features of target bearing are extracted using the common representation learnt in the offline stage, and online detection is conducted by feeding them into the SVM model. Experimental results on IEEE PHM Challenge 2012 bearing dataset and XJTU-SY dataset show that the proposed approach outperforms several state-of-the-art detection methods in terms of detection accuracy and false alarm rate.

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Section: Robust State Assessment Methodsmentioning

confidence: 99%

“…Different from the current state assessment methods [26,27], the proposed method conduct assessment in a low-rank space which is optimized by RDA. Therefore, the proposed method has good anti-interference ability against irregular fluctuation in normal state data.…”

Section: Robust State Assessment Methodsmentioning

confidence: 99%

Robust Detection of Bearing Early Fault Based on Deep Transfer Learning

et al. 2020

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“…In this paper, the number of assigned leaves is similar to the number of signal classes presented in the dataset, which is equal to four. Equations (35) and (36) are used to formulate the respective fault detection and fault diagnosis based on the DT algorithm.…”

Section: Decision Tree-based Fault Diagnosismentioning

confidence: 99%

“…This shift is primarily enabled by the significant increase of computational capabilities of modern computer systems. The most popular DL-based solutions used for solving different problems in condition monitoring are convolutional neural networks [34] (fault diagnosis), autoencoders [19] (fault diagnosis, feature extraction, and data augmentation), generative adversarial networks [35] (data augmentation), and recurrent neural networks [36] (fault prediction). The principles of DL-based solutions are similar to those of the ANN; they adapt the weights of neurons and tune the hyperparameters to meet the requirements according to the task.…”

Section: Introductionmentioning

confidence: 99%

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Rolling-Element Bearing Fault Diagnosis Using Advanced Machine Learning-Based Observer

et al. 2019

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Rotating machines represent a class of nonlinear, uncertain, and multiple-degrees-of-freedom systems that are used in various applications. The complexity of the system’s dynamic behavior and uncertainty result in substantial challenges for fault estimation, detection, and identification in rotating machines. To address the aforementioned challenges, this paper proposes a novel technique for fault diagnosis of a rolling-element bearing (REB), founded on a machine-learning-based advanced fuzzy sliding mode observer. First, an ARX-Laguerre algorithm is presented to model the bearing in the presence of noise and uncertainty. In addition, a fuzzy algorithm is applied to the ARX-Laguerre technique to increase the system’s modeling accuracy. Next, the conventional sliding mode observer is applied to resolve the problems of fault estimation in a complex system with a high degree of uncertainty, such as rotating machinery. To address the problem of chattering that is inherent in the conventional sliding mode observer, the higher-order super-twisting (advanced) technique is introduced in this study. In addition, the fuzzy method is applied to the advanced sliding mode observer to improve the accuracy of fault estimation in uncertain conditions. As a result, the advanced fuzzy sliding mode observer adaptively improves the reliability, robustness, and estimation accuracy of rolling-element bearing fault estimation. Then, the residual signal delivered by the proposed methodology is split in the windows and each window is characterized by a numerical parameter. Finally, a machine learning technique, called a decision tree, adaptively derives the threshold values that are used for problems of fault detection and fault identification in this study. The effectiveness of the proposed algorithm is validated using a publicly available vibration dataset of Case Western Reverse University. The experimental results show that the machine learning-based advanced fuzzy sliding mode observation methodology significantly improves the reliability and accuracy of the fault estimation, detection, and identification of rolling element bearing faults under variable crack sizes and load conditions.

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A novel fault diagnosis method for imbalanced datasets based on MCNN‐Transformer model in industrial processes

2024

Adaptive Control & Signal

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SummaryFault diagnosis methods based on deep learning have been extensively applied to the fault classification of rolling bearings, yielding favorable results. However, many of these methods still have substantial room for improvement in practical industrial scenarios. This article addresses the issue of imbalanced fault data categories commonly encountered in real‐world contexts and discusses the characteristics of long time series data in fault signals. To tackle these challenges, a model based on multi‐scale convolutional neural networks and transformer (MCNNT) is proposed. First, in the data processing stage, a diffusion model is introduced to handle the problem of data imbalance. This model learns the distribution of minority samples and generates new samples. Second, the proposed model incorporates an attention mechanism, enabling it to capture the global information of the data during the feature learning stage and effectively utilize the relationships between preceding and subsequent elements in long sequential data. This allows the model to accurately focus on key features. Experimental results demonstrate the exceptional performance of the proposed method, which is capable of generating high‐quality samples and providing a solution to address challenges in practical industrial scenarios. Consequently, the proposed method exhibits significant potential for further development.

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Imbalanced Fault Diagnosis of Rolling Bearing Based on Generative Adversarial Network: A Comparative Study

Cited by 214 publications

References 23 publications

Robust Detection of Bearing Early Fault Based on Deep Transfer Learning

Robust Detection of Bearing Early Fault Based on Deep Transfer Learning

Rolling-Element Bearing Fault Diagnosis Using Advanced Machine Learning-Based Observer

A novel fault diagnosis method for imbalanced datasets based on MCNN‐Transformer model in industrial processes

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