Diagnostyka pittingu kół zębatych na podstawie surowego sygnału emisji akustycznej w oparciu o głębokie uczenie maszynowe

Xueyi Li,Yongzhi Qu,David He,Jialin Li

doi:10.17531/ein.2019.3.6

Abstract

Gear pitting fault is one of the most common faults in mechanical transmission. Acoustic emission (AE) signals have been effective for gear fault detection because they are less affected by ambient noise than traditional vibration signals. To overcome the problem of low gear pitting fault recognition rate using AE signals and convolutional neural networks, this paper proposes a new method named augmented convolution sparse autoencoder (ACSAE) for gear pitting fault diagnosis using raw AE signals. First, the proposed method combines sparse autoencoder and one-dimensional convolutional neural networks for unsupervised learning and then uses the reinforcement theory to enhance the adaptability and robustness of the network. The ACSAE method can automatically extract fault features directly from the original AE signals without time and frequency domain conversion of the AE signals. AE signals collected from gear test experiments are used to validate the ACSAE method. The analysis result of the gear pitting fault test shows that the proposed method can effectively performing recognition of the gear pitting faults, and the recognition rate reaches above 98%. The comparative analysis shows that in comparison with fully-connected neural networks, convolutional neural networks, and recurrent neural networks, the ACSAE method has achieved a better diagnostic accuracy for gear fitting faults.

Highlights

With the rapid development of the modern industries, the detection and identification of gear pitting faults in mechanical transmission systems have become one of the critical issues
The experimental results show that the proposed method improves the accuracy of gear fault identification, and improves the correct rate of gear faults from 90.5% of the one-dimensional convolutional neural network (CNN) alone to 97.9% of the augmented convolution sparse autoencoder (ACSAE)
The ADCAE algorithm is superior to other algorithms for gear pitting fault diagnosis

Summary

Introduction

With the rapid development of the modern industries, the detection and identification of gear pitting faults in mechanical transmission systems have become one of the critical issues. The traditional intelligent diagnosis algorithm based on signal processing for feature extraction and classifier has high requirements for expert experience and cannot guarantee universality It cannot meet the big data requirements of gear fault detection. Cao et al [5] proposed a transfer learning method using the vibration signal to use the convolutional neural network (CNN) for gear fault detection These methods of deep learning do not require manual extraction of fault features and achieve better fault detection results. Through the fusion of one-dimensional CNN automatic learning features on the softmax classifier, a number of gearbox AE data is selected for fine-tuning the network, training classification model, to achieve accurate identification of gear pitting fault.

The methodology

Sparse autoencoder

One-dimensional CNN

Gear test experimental setup and data processing

Results and discussions

Conclusions