A New Deep Transfer Learning Based on Sparse Auto-Encoder for Fault Diagnosis

Wen, Long; Gao, Liang; Li, Xinyu

doi:10.1109/tsmc.2017.2754287

Cited by 784 publications

(287 citation statements)

References 32 publications

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“…Second, some parameters are transferred from source task to target task to assist model training on a small amount of target data. Lu et al [16] used a three-layer SAE network with maximum mean discrepancy (MMD) regularizer to extract features from a raw vibration signal. Here, the MMD regularizer is used to punish the feature difference between training data and test data.…”

Section: Preliminary Workmentioning

confidence: 99%

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: Preliminary Workmentioning

confidence: 99%

Robust Detection of Bearing Early Fault Based on Deep Transfer Learning

et al. 2020

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“…Correlation distances [6] and maximum mean discrepancy [7] are the most commonly used measurement distance. For bearing diagnosis under different working conditions, Maximum mean discrepancy (MMD) is utilized to reduce the domain discrepancy between feature representations extracted by deep neural network (DNN) [8] or sparse auto-encoder (SAE) [9]. In [10], the domain discrepancy is further reduced by multi-kernel MMD in multi layers of deep convolutional neural network.…”

Section: Introductionmentioning

confidence: 99%

Triplet Loss Guided Adversarial Domain Adaptation for Bearing Fault Diagnosis

Wang

Liu

2020

Sensors

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Recently, deep learning methods are becomingincreasingly popular in the field of fault diagnosis and achieve great success. However, since the rotation speeds and load conditions of rotating machines are subject to change during operations, the distribution of labeled training dataset for intelligent fault diagnosis model is different from the distribution of unlabeled testing dataset, where domain shift occurs. The performance of the fault diagnosis may significantly degrade due to this domain shift problem. Unsupervised domain adaptation has been proposed to alleviate this problem by aligning the distribution between labeled source domain and unlabeled target domain. In this paper, we propose triplet loss guided adversarial domain adaptation method (TLADA) for bearing fault diagnosis by jointly aligning the data-level and class-level distribution. Data-level alignment is achieved using Wasserstein distance-based adversarial approach, and the discrepancy of distributions in feature space is further minimized at class level by the triplet loss. Unlike other center loss-based class-level alignment approaches, which hasto compute the class centers for each class and minimize the distance of same class center from different domain, the proposed TLADA method concatenates 2 mini-batches from source and target domain into a single mini-batch and imposes triplet loss to the whole mini-batch ignoring the domains. Therefore, the overhead of updating the class center is eliminated. The effectiveness of the proposed method is validated on CWRU dataset and Paderborn dataset through extensive transfer fault diagnosis experiments.

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“…In powerful deep structure applications, preprocess is not required. However, for practical interpretation of engineering machine learning, some preprocessing treatments are needed during detection model building, such as time-frequency transformation [23], autocorrelation power spectrum [24], FFT [25], etc.…”

Section: Introductionmentioning

confidence: 99%

Generative Transfer Learning for Intelligent Fault Diagnosis of the Wind Turbine Gearbox

Guo

Zhang

et al. 2020

Sensors

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Intelligent fault diagnosis algorithms based on machine learning and deep learning techniques have been widely used in industrial applications and have obtained much attention as well as achievements. In real industrial applications, working loads of machines are always changing. Hence, directly applying the traditional algorithms will cause significant degradation of performance with changing conditions. In this paper, a novel domain adaptation method, named generative transfer learning (GTL), is proposed to tackle this problem. First, raw datasets were transformed to time–frequency domain based on short-time Fourier transformation. A domain discriminator was then built to distinguish whether the data came from the source or the target domain. A target domain classification model was finally acquired by the feature extractor and the classifier. Experiments were carried out for the fault diagnosis of a wind turbine gearbox. The t-distributed stochastic neighbor embedding technique was used to visualize the output features for checking the effectiveness of the proposed algorithm in feature extraction. The results showed that the proposed GTL could improve classification rates under various working loads. Compared with other domain adaptation algorithms, the proposed method exhibited not only higher accuracy but faster convergence speed as well.

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A New Deep Transfer Learning Based on Sparse Auto-Encoder for Fault Diagnosis

Cited by 784 publications

References 32 publications

Robust Detection of Bearing Early Fault Based on Deep Transfer Learning

Robust Detection of Bearing Early Fault Based on Deep Transfer Learning

Triplet Loss Guided Adversarial Domain Adaptation for Bearing Fault Diagnosis

Generative Transfer Learning for Intelligent Fault Diagnosis of the Wind Turbine Gearbox

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