| 摘要: |
| 构建了一个基于深度玻尔兹曼机的故障诊断系统。首先，基于一个滚动轴承故障实验平台，对深度玻尔曼兹机的在滚动轴承故障诊断领域应用进行了深度分析；然后将方案应用于20 kg级的航空涡喷发动机的故障诊断中；通过与BP神经网络和支持向量机故障诊断模型进行对比，实验结果表明：采用深度玻尔兹曼机对机械设备故障进行故障识别，具有更高的准确性和可靠性。 |
| 关键词: 深度学习 深度波尔曼兹机 故障诊断 |
| DOI： |
| 分类号: |
| 基金项目: |
|
| Application of Deep Boltzmann Machine in Fault Diagnosis |
|
CHEN Zhi qiang1，2* ， DENG Sheng cai 1， CHEN Xu dong 1，2 ， LI Chuan 1
|
| Abstract: |
| Deep learning technology has been increasingly becoming a popular and intelligent tool in the field of mechanical fault identification. This paper presents an application of Deep Boltzmann Machines in fault diagnosis. Firstly， based on a rolling bearing fault test platform， the depth analysis of Deep Boltzmann machine in the field of rolling bearing fault diagnosis was carried out. Then， the scheme was applied to the fault diagnosis of 20 kg turbojet engine. Finally， the accuracy of BPNN and SVM-based models is compared with the proposed method. The experimental results show that the accuracy presented by DBM model is highly reliable and applicable in fault diagnosis. |
| Key words: deep learning Deep Boltzmann machine fault diagnosis |
