Research on Time Series Fault Diagnosis Method Based on Unsupervised Learning

With the development of information technology and sensor technology, data driven fault diagnosis technology is one of the key technologies to ensure the efficient and safe operation of large industrial equipment. Due to its powerful feature representation ability and the advantages of feature extraction based on big data, machine learning has become one of the most commonly used feature extraction methods in the field of fault diagnosis. However, the data collected by the monitoring equipment includes a large amount of unlabeled data, and the traditional deep neural network model does not make full use of it, resulting in the waste of some useful information. For unlabeled data, we adopt the idea of unsupervised learning, train a feature extraction model by maximizing mutual information, and on this basis, we design a fault diagnosis method for time series data, and verify it on the public dataset Case Western Reserve University bearing dataset, achieving higher diagnostic accuracy than previous traditional methods. Further verification on satellite monitoring data, our feature extraction model can distinguish different stages of failure and capture the data characteristics of different stages. The results show that the fault diagnosis method based on unsupervised learning proposed in this paper can effectively and fully utilize a large amount of unlabeled data and improve the fault diagnosis accuracy of time series data.