计算机科学
断层(地质)
振动
故障检测与隔离
人工神经网络
卷积神经网络
卷积(计算机科学)
模式识别(心理学)
电子工程
加速度计
频道(广播)
工程类
实时计算
人工智能
特征提取
执行机构
电信
物理
量子力学
操作系统
地震学
地质学
作者
Ronny Francis Ribeiro,Isac Antônio dos Santos Areias,Mateus Mendes Campos,Carlos Eduardo Garcez Teixeira,Luiz Eduardo Borges da Silva,Guilherme Ferreira Gomes
出处
期刊:Measurement
[Elsevier BV]
日期:2022-01-19
卷期号:190: 110759-110759
被引量:114
标识
DOI:10.1016/j.measurement.2022.110759
摘要
Fault detection and diagnosis in time series data are becoming mainstream in most industrial applications since the increase of monitoring sensors in machinery. Traditional methods generally require pre-processing techniques before training; however, this task becomes very time-consuming with multiple sensors. Recently, deep learning methods have shown great results on time series data. This paper proposes a multi-head 1D Convolution Neural Network (1D CNN) to detect and diagnose six different types of faults in an electric motor using two accelerometers measuring in two different directions. This architecture was chosen due to each head can deal with each sensor individually, increasing feature extraction. The proposed method is verified through a series of experiments with seven different induced faults and operation conditions. The results show that the proposed architecture is very accurate for multi-sensor fault detection using vibration time series. Since the experiments are based on real electric motors and faults, these results are promising in real applications.
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