Deep Learning Based Approach for Bearing Fault Diagnosis

短时傅里叶变换 断层(地质) 方位(导航) 计算机科学 深度学习 人工神经网络 人工智能 状态监测 数据挖掘 模式识别(心理学) 机器学习 工程类 傅里叶变换 地质学 地震学 数学分析 电气工程 傅里叶分析 数学
作者
Miao He,David He
出处
期刊:IEEE Transactions on Industry Applications [Institute of Electrical and Electronics Engineers]
卷期号:53 (3): 3057-3065 被引量:399
标识
DOI:10.1109/tia.2017.2661250
摘要

Bearing is one of the most critical components in most electrical and power drives. Effective bearing fault diagnosis is important for keeping the electrical and power drives safe and operating normally. In the age of Internet of Things and Industrial 4.0, massive real-time data are collected from bearing health monitoring systems. Mechanical big data have the characteristics of large volume, diversity, and high velocity. There are two major problems in using the existing methods for bearing fault diagnosis with big data. The features are manually extracted relying on much prior knowledge about signal processing techniques and diagnostic expertise, and the used models have shallow architectures, limiting their capability in fault diagnosis. Effectively mining features from big data and accurately identifying the bearing health conditions with new advanced methods have become new issues. This paper presents a deep learning-based approach for bearing fault diagnosis. The presented approach preprocesses sensor signals using short-time Fourier transform (STFT). Based on a simple spectrum matrix obtained by STFT, an optimized deep learning structure, large memory storage retrieval (LAMSTAR) neural network, is built to diagnose the bearing faults. Acoustic emission signals acquired from a bearing test rig are used to validate the presented method. The validation results show the accurate classification performance on various bearing faults under different working conditions. The performance of the presented method is also compared with other effective bearing fault diagnosis methods reported in the literature. The comparison results have shown that the presented method gives much better diagnostic performance, even at relatively low rotating speeds.
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