SDA: Regularization with Cut-Flip and Mix-Normal for machinery fault diagnosis under small dataset

Lv, Haixin; Chen, Jinglong; Zhang, Tianci; Hou, Rujie; Zhou, Zitong

doi:10.1016/j.isatra.2020.11.005

Cited by 22 publications

(2 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Further, the distribution of sensor measurements, even for the same equipment, varies depending on the operating conditions. Moreover, conventional augmentation methods make generating realistic training samples difficult due to the complex non-linear operations of industrial machines [16].…”

Section: Related Workmentioning

confidence: 99%

Deep Transfer Learning-Based Fault Diagnosis Using Wavelet Transform for Limited Data

et al. 2022

View full text Add to dashboard Cite

Although various deep learning techniques have been proposed to diagnose industrial faults, it is still challenging to obtain sufficient training samples to build the fault diagnosis model in practice. This paper presents a framework that combines wavelet transformation and transfer learning (TL) for fault diagnosis with limited target samples. The wavelet transform converts a time-series sample to a time-frequency representative image based on the extracted hidden time and frequency features of various faults. On the other hand, the TL technique leverages the existing neural networks, called GoogLeNet, which were trained using a sufficient source data set for different target tasks. Since the data distributions between the source and the target domains are considerably different in industrial practice, we partially retrain the pre-trained model of the source domain using intermediate samples that are conceptually related to the target domain. We use a reciprocating pump model to generate various combinations of faults with different severity levels and evaluate the effectiveness of the proposed method. The results show that the proposed method provides higher diagnostic accuracy than the support vector machine and the convolutional neural network under wide variations in the training data size and the fault severity. In particular, we show that the severity level of the fault condition heavily affects the diagnostic performance.

show abstract

Section: Related Workmentioning

confidence: 99%

Deep Transfer Learning-Based Fault Diagnosis Using Wavelet Transform for Limited Data

et al. 2022

View full text Add to dashboard Cite

show abstract

“…The CWRU dataset is a remarkable and representative rolling bearing fault diagnostic dataset that has been utilized in many studies to validate condition monitoring and fault diagnosis methods for rotating motors [ 42 , 44 ]. It is used as a benchmark in this work for experimental investigations to verify the advantages of the proposed EDCNN-based fault diagnosis method.…”

Section: Experimental Studymentioning

confidence: 99%

Ensemble Dilated Convolutional Neural Network and Its Application in Rotating Machinery Fault Diagnosis

Cai

Wang

Yao

et al. 2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

Fault diagnosis of rotating machinery is an attractive yet challenging task. This paper presents a novel intelligent fault diagnosis scheme for rotating machinery based on ensemble dilated convolutional neural networks. The novel fault diagnosis framework employs a model training strategy based on early stopping optimization to ensemble several one-dimensional dilated convolutional neural networks (1D-DCNNs). By varying the dilation rate of the 1D-DCNN, different receptive fields can be obtained to extract different vibration signal features. The early stopping strategy is used as a model update threshold to prevent overfitting and save computational resources. Ensemble learning uses a weighted mechanism to combine the outputs of multiple 1D-DCNN subclassifiers with different dilation rates to obtain the final fault diagnosis. The proposed method outperforms existing state-of-the-art classical machine learning and deep learning methods in simulation studies and diagnostic experiments, demonstrating that it can thoroughly mine fault features in vibration signals. The classification results further show that the EDCNN model can effectively and accurately identify multiple faults and outperform existing fault detection techniques.

show abstract

Adaptive variational autoencoding generative adversarial networks for rolling bearing fault diagnosis

Wang,

Jiang,

et al. 2023

Advanced Engineering Informatics

View full text Add to dashboard Cite

SDA: Regularization with Cut-Flip and Mix-Normal for machinery fault diagnosis under small dataset

Cited by 22 publications

References 21 publications

Deep Transfer Learning-Based Fault Diagnosis Using Wavelet Transform for Limited Data

Deep Transfer Learning-Based Fault Diagnosis Using Wavelet Transform for Limited Data

Ensemble Dilated Convolutional Neural Network and Its Application in Rotating Machinery Fault Diagnosis

Adaptive variational autoencoding generative adversarial networks for rolling bearing fault diagnosis

Contact Info

Product

Resources

About