A Study on Gear Defect Detection via Frequency Analysis Based on DNN

Abstract

In this paper, we introduce a gear defect detection system using frequency analysis based on deep learning. The existing defect diagnosis systems using acoustic analysis use spectrogram, scalogram, and MFCC (Mel-Frequency Cepstral Coefficient) images as inputs to the convolutional neural network (CNN) model to diagnose defects. However, using visualized acoustic data as input to the CNN models requires a lot of computation time. Although computing power has improved, there is a situation in which a processor with low performance is used for reasons such as cost-effectiveness. In this paper, only the sums of frequency bands are used as input to the deep neural network (DNN) model to diagnose the gear fault. This system diagnoses the defects using only a few specific frequency bands, so it ignores unnecessary data and does not require high performance when diagnosing defects because it uses a relatively simple deep learning model for classification. We evaluate the performance of the proposed system through experiments and verify that real-time diagnosis of gears is possible compared to the CNN model. The result showed 95.5% accuracy for 1000 test data, and it took 18.48 ms, so that verified the capability of real-time diagnosis in a low-spec environment. The proposed system is expected to be effectively used to diagnose defects in various sound-based facilities at a low cost.