An Intelligent Fault Diagnosis Method Using Unsupervised Feature Learning Towards Mechanical Big Data
An Intelligent Fault Diagnosis Method Using Unsupervised Feature Learning Towards Mechanical Big Data
复制标题
一种基于机械大数据无监督特征学习的智能故障诊断方法
DOI：
10.1109/tie.2016.2519325
复制
发表时间：
2016-05-01
影响因子：
7.7
通讯作者：
Ding, Steven X.
中科院分区：
文献类型：
--
作者：
Lei, Yaguo;Jia, Feng;Ding, Steven X.
智能故障诊断能够快速、高效地处理采集到的信号并提供准确的诊断结果，是处理机械大数据的一种很有前途的工具。然而，在传统的智能诊断方法中，依赖于先验知识和诊断专业知识来手动提取特征。这样的过程利用了人类的聪明才智，但既耗时又费力。受无监督特征学习思想的启发，利用人工智能技术从原始数据中学习特征，提出了一种机器智能诊断的两阶段学习方法。在该方法的第一个学习阶段，使用稀疏滤波这一无监督的两层神经网络直接从机械振动信号中学习特征。在第二阶段，基于学习到的特征，采用Softmax回归对健康状况进行分类。分别用电机轴承数据集和机车轴承数据集对该方法进行了验证。结果表明，该方法具有较高的诊断精度，优于现有的电机轴承数据诊断方法。由于该方法具有自适应学习特性，减少了人工工作量，使智能故障诊断更容易处理大数据。
Intelligent fault diagnosis is a promising tool to deal with mechanical big data due to its ability in rapidly and efficiently processing collected signals and providing accurate diagnosis results. In traditional intelligent diagnosis methods, however, the features are manually extracted depending on prior knowledge and diagnostic expertise. Such processes take advantage of human ingenuity but are time-consuming and labor-intensive. Inspired by the idea of unsupervised feature learning that uses artificial intelligence techniques to learn features from raw data, a two-stage learning method is proposed for intelligent diagnosis of machines. In the first learning stage of the method, sparse filtering, an unsupervised two-layer neural network, is used to directly learn features from mechanical vibration signals. In the second stage, softmax regression is employed to classify the health conditions based on the learned features. The proposed method is validated by a motor bearing dataset and a locomotive bearing dataset, respectively. The results show that the proposed method obtains fairly high diagnosis accuracies and is superior to the existing methods for the motor bearing dataset. Because of learning features adaptively, the proposed method reduces the need of human labor and makes intelligent fault diagnosis handle big data more easily.