scholarly journals Gear Fault Diagnosis through Vibration and Acoustic Signal Combination Based on Convolutional Neural Network

Information ◽  
2020 ◽  
Vol 11 (5) ◽  
pp. 266
Author(s):  
Liya Yu ◽  
Xuemei Yao ◽  
Jing Yang ◽  
Chuanjiang Li
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Working Conditions ◽  
Evidence Theory ◽  
Mechanical Equipment ◽  
Measuring Device ◽  
Gear Fault ◽  
Fusion Methods ◽  
Gear Fault Diagnosis

Equipment condition monitoring and diagnosis is an important means to detect and eliminate mechanical faults in real time, thereby ensuring safe and reliable operation of equipment. This traditional method uses contact measurement vibration signals to perform fault diagnosis. However, a special environment of high temperature and high corrosion in the industrial field exists. Industrial needs cannot be met through measurement. Mechanical equipment with complex working conditions has various types of faults and different fault characterizations. The sound signal of the microphone non-contact measuring device can effectively adapt to the complex environment and also reflect the operating state of the device. For the same workpiece, if it can simultaneously collect its vibration and sound signals, the two complement each other, which is beneficial for fault diagnosis. One of the limitations of the signal source and sensor is the difficulty in assessing the gear state under different working conditions. This study proposes a method based on improved evidence theory method (IDS theory), which uses convolutional neural network to combine vibration and sound signals to realize gear fault diagnosis. Experimental results show that our fusion method based on IDS theory obtains a more accurate and reliable diagnostic rate than the other fusion methods.

A novel adaptive and fast deep convolutional neural network for bearing fault diagnosis under different working conditions

2019 ◽  
Vol 234 (4) ◽  
pp. 1167-1182 ◽  
Author(s):  
Kun Xu ◽  
Shunming Li ◽  
Jinrui Wang ◽  
Zenghui An ◽  
Yu Xin
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Receptive Field ◽  
Convolutional Neural Network ◽  
Network Model ◽  
Working Conditions ◽  
Neural Network Model ◽  
Network Architecture ◽  
Deep Convolutional Neural Network ◽  
Mechanical Equipment

Deep learning method is gradually applied in the field of mechanical equipment fault diagnosis because it can learn complex and useful features automatically from the vibration signals. Among the many intelligent diagnostic models, convolutional neural network has been gradually applied to intelligent fault diagnosis of bearings due to its advantages of local connection and weight sharing. However, there are still some drawbacks. (1) The training process of convolutional neural network is slow and unstable. It has more training parameters. (2) It cannot perform well under different working conditions, such as noisy environment and different workloads. In this paper, a novel model named adaptive and fast convolutional neural network with wide receptive field is presented to overcome the aforementioned deficiencies. The prime innovations include the following. First, a deep convolutional neural network architecture is constructed using the scaled exponential linear unit activation function and global average pooling. The model has fewer training parameters and can converge rapidly and stably. Second, the model has a wide receptive field with two medium and three small length convolutional kernels. It also has high diagnostic accuracy and robustness when the environment is noisy and workloads are changed compared with other models. Furthermore, to demonstrate how the wide receptive field convolutional neural network model works, the reasons for high model performance are analyzed and the learned features are also visualized. Finally, the wide receptive field convolutional neural network model is verified by the vibration dataset collected in the background of high noise, and the results indicate that it has high diagnostic performance.

scholarly journals End-To-End Convolutional Neural Network Model for Gear Fault Diagnosis Based on Sound Signals

2018 ◽  
Vol 8 (9) ◽  
pp. 1584 ◽  
Author(s):  
Yong Yao ◽  
Honglei Wang ◽  
Shaobo Li ◽  
Zhonghao Liu ◽  
Gui Gui ◽  
...  
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Signal Analysis ◽  
Sound Signal ◽  
Feature Engineering ◽  
Fusion Algorithm ◽  
Gear Fault ◽  
Gear Fault Diagnosis ◽  
End To End

Currently gear fault diagnosis is mainly based on vibration signals with a few studies on acoustic signal analysis. However, vibration signal acquisition is limited by its contact measuring while traditional acoustic-based gear fault diagnosis relies heavily on prior knowledge of signal processing techniques and diagnostic expertise. In this paper, a novel deep learning-based gear fault diagnosis method is proposed based on sound signal analysis. By establishing an end-to-end convolutional neural network (CNN), the time and frequency domain signals can be fed into the model as raw signals without feature engineering. Moreover, multi-channel information from different microphones can also be fused by CNN channels without using an extra fusion algorithm. Our experiment results show that our method achieved much better performance on gear fault diagnosis compared with other traditional gear fault diagnosis methods involving feature engineering. A publicly available sound signal dataset for gear fault diagnosis is also released and can be downloaded as instructed in the conclusion section.

scholarly journals An Intelligent Gear Fault Diagnosis Methodology Using a Complex Wavelet Enhanced Convolutional Neural Network

Materials ◽  
2017 ◽  
Vol 10 (7) ◽  
pp. 790 ◽  
Author(s):  
Weifang Sun ◽  
Bin Yao ◽  
Nianyin Zeng ◽  
Binqiang Chen ◽  
Yuchao He ◽  
...  
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Gear Fault ◽  
Gear Fault Diagnosis ◽  
Complex Wavelet

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.

Sign in / Sign up

Export Citation Format

Share Document