A two-stage data quality improvement strategy for deep neural networks in fault severity estimation

As fault diagnosis based on deep neural network (DNN) has been developing from laboratory to industrial application, creating the appropriate data pipeline to train and evaluate the models has become a noticeable challenge. For mechanical fault diagnosis, high-quality data should be consistent with comprehensive fault information. In this paper, a two-stage data quality improvement strategy is proposed to sculpt the data for deep learning models in fault severity estimation. In the first stage, the spatial information reconstruction (SIR) approach is developed to transform the sensing data from time domain into spatial domain to establish the relationship between fault symptom and spatial position. In the second stage, spatial domain signals are organized based on fault characteristics and types of DNN models, which aims to convert data into useful information. The proposed strategy has been verified by experimental cases, and a comprehensive evaluation on the performance of DNNs trained by different input data has been applied. The experimental results proved that with thoughtfully engineered data, baseline DNN models can achieve high accuracy and robustness under different speed conditions. This paper provides a way for engineering practitioners to design industrial DNN applications with their domain knowledge.