A multivariate encoder information based convolutional neural network for intelligent fault diagnosis of planetary gearboxes

Rotary encoder signal, as the built-in position information, possesses a wide variety of advantages over vibration signal and has aroused great interest in the field of health monitoring for rotating machinery. However, there are two major issues when attempting to detect and diagnose failures with encoder information. First of all, a series of proper signal processing methods need to be designed for fault feature extraction, which largely relies on the expert experience and domain knowledge. Furthermore, existing studies primarily concentrate on a single transform, such as instantaneous angular speed, which neglects the diversity of encoder information. In view of above deficiencies, a multivariate encoder information based convolutional neural network (MEI-CNN) is proposed for intelligent diagnosis in this paper. In this framework, three different types of dynamic encoder information are firstly acquired by analyzing and processing the raw position sequence, after that multivariate encoder information (MEI) data are constructed by data fusion. Finally, a concise and effective convolutional neural network is designed to extract discriminating features and provide diagnosis results. The proposed method not only overcomes drawbacks of traditional techniques based on vibration analysis, but also provides an intelligent way to achieve satisfactory diagnosis results. The effectiveness and superiority of MEI-CNN are validated by experimental data from a planetary gearbox test rig. The results also indicate that the proposed method may offer a promising tool for intelligent diagnosis of rotating machinery.