A Novel Fault Diagnosis Method Based on Ensemble Feature Selection in The Industrial IoT Scenario

Fault diagnosis as a research hotspot in the field of prognostics and health management (PHM) has attracted the attention of academia and industry. Deep learning is widely used in academia due to its strong self-learning ability. However, as a "black-box" model, the features extracted by deep learning have poor interpretability which can't be understood by IoT devices. In this paper, we propose a novel fault diagnosis method based on ensemble feature selection. We separately evaluate our method on the four-fault hydraulic components, which are derived from the real-world hydraulic time series data sets. The results show that compared with the deep learning method the proposed method can greatly reduce the complexity of the model without much reduction accuracy. Meanwhile, the balanced ensemble feature selection method we proposed is better than traditional ensemble methods such as union, intersection, and weighted linear aggregation. After testing, the method we proposed can be applied to the industrial IoT fault diagnosis.