scholarly journals Generative Adversarial Learning Enhanced Fault Diagnosis for Planetary Gearbox under Varying Working Conditions

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1685 ◽  
Author(s):  
Weigang Wen ◽  
Yihao Bai ◽  
Weidong Cheng
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Fault Diagnosis ◽  
Working Conditions ◽  
Planetary Gear ◽  
Adversarial Learning ◽  
Planetary Gearbox ◽  
Deep Convolution Neural Network ◽  
Fault Diagnostic ◽  
And Performance

Planetary gearbox is a critical component for many mechanical systems. It is essential to monitor the planetary gearbox health and performance in order to maintain the whole machine works well. The methodology of mechanical fault diagnosis is increasingly intelligent with the extensive application of deep learning. However, the cross-domain issue caused by varying working conditions becomes an enormous encumbrance to fault diagnosis based on deep learning. In this paper, in order to fully excavate potentialities of deep neural network architectures, a novel generative adversarial learning method was introduced for a completely new fault diagnosis based on a deep convolution neural network. In addition, the intelligent fault diagnostic scheme for planetary gearbox under varying speed conditions was developed. After that, some experiments on measured vibration signals of planetary gearbox were conducted to verify the validity and efficiency of the fault diagnostic scheme. The results showed that the proposed method enhanced the capability of the intelligent diagnosis for planetary gear faults under varying speed conditions.

Research on fault diagnosis of automobile engines based on the deep learning 1D-CNN method

2022 ◽  
Author(s):  
Canyi Du ◽  
Rui Zhong ◽  
Yishen Zhuo ◽  
Xinyu Zhang ◽  
Feifei Yu ◽  
...  
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Deep Learning ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Pattern Recognition Method ◽  
Recognition Method ◽  
Vibration Signals ◽  
One Dimensional ◽  
Sample Set

Abstract Traditional engine fault diagnosis methods usually need to extract the features manually before classifying them by the pattern recognition method, which makes it difficult to solve the end-to-end fault diagnosis problem. In recent years, deep learning has been applied in different fields, bringing considerable convenience to technological change, and its application in the automotive field also has many applications, such as image recognition, language processing, and assisted driving. In this paper, a one-dimensional convolutional neural network (1D-CNN) in deep learning is used to process vibration signals to achieve fault diagnosis and classification. By collecting the vibration signal data of different engine working conditions, the collected data are organized into several sets of data in a working cycle, which are divided into a training sample set and a test sample set. Then, a one-dimensional convolutional neural network model is built in Python to allow the feature filter (convolution kernel) to learn the data from the training set and these convolution checks process the input data of the test set. Convolution and pooling extract features to output to a new space, which is characterized by learning features directly from the original vibration signals and completing fault diagnosis. The experimental results show that the pattern recognition method based on a one-dimensional convolutional neural network can be effectively applied to engine fault diagnosis and has higher diagnostic accuracy than traditional methods.

scholarly journals Defect Detection in CK45 Steel Structures through C-scan Images Using Deep Learning Method

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Navid Moshtaghi Yazdani
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Measurement System ◽  
Processing System ◽  
Steel Structures ◽  
Learning Method ◽  
Metallic Structures ◽  
Defect Profile ◽  
Network Methods ◽  
Deep Convolution Neural Network

In the present paper, a method for reliable estimation of defect profile in CK45 steel structures is presented using an eddy current testing based measurement system and post-processing system based on deep learning technique. So a deep learning method is used to determine the defect characteristics in metallic structures by magnetic field C-scan images obtained by an anisotropic magneto-resistive sensor. Having designed and adjusting the deep convolution neural network and applied it to C-scan images obtained from the measurement system, the performance of deep learning method proposed is compared with conventional artificial neural network methods such as multilayer perceptron and radial basis function on a number of metallic specimens with different defects. The results confirm the superiority of the proposed method for characterizing defects compared to other classical training-oriented methods.

scholarly journals Comparisons of different deep learning-based methods on fault diagnosis for geared system

2019 ◽  
Vol 15 (11) ◽  
pp. 155014771988816 ◽  
Author(s):  
Bing Han ◽  
Xiaohui Yang ◽  
Yafeng Ren ◽  
Wanggui Lan
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Mechanical System ◽  
Network Model ◽  
Learning Theory ◽  
Neural Network Model ◽  
Deep Neural Network ◽  
Gear Fault

The running state of a geared transmission system affects the stability and reliability of the whole mechanical system. It will greatly reduce the maintenance cost of a mechanical system to identify the faulty state of the geared transmission system. Based on the measured gear fault vibration signals and the deep learning theory, four fault diagnosis neural network models including fast Fourier transform–deep belief network model, wavelet transform–convolutional neural network model, Hilbert-Huang transform–convolutional neural network model, and comprehensive deep neural network model are developed and trained respectively. The results show that the gear fault diagnosis method based on deep learning theory can effectively identify various gear faults under real test conditions. The comprehensive deep neural network model is the most effective one in gear fault recognition.

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