Mel倒谱
计算机科学
光谱图
卷积神经网络
人工智能
人工神经网络
模式识别(心理学)
医学诊断
断层(地质)
深度学习
计算
语音识别
特征提取
算法
地质学
病理
医学
地震学
作者
Jeonghyeon Kim,Jonghoek Kim,Hyuntai Kim
出处
期刊:Machines
[MDPI AG]
日期:2022-08-05
卷期号:10 (8): 659-659
被引量:4
标识
DOI:10.3390/machines10080659
摘要
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.
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