Fault Detection in Rotating Machinery Based on Sound Signal Using Edge Machine Learning

被引：17

作者：

Shubita, Rashad R. ^{[1
]}

Alsadeh, Ahmad S. ^{[1
]}

Khater, Ismail M. ^{[1
]}

机构：

[1] Birzeit Univ, Fac Engn & Technol, Dept Elect & Comp Engn, P627, Birzeit, Palestine

来源：

IEEE ACCESS | 2023年 / 11卷

关键词：

Feature extraction; Fault diagnosis; Vibrations; Mathematical models; Fault detection; Image edge detection; Engines; condition monitoring; acoustic emission; time-domain analysis; frequency domain analysis; ACOUSTIC-EMISSION; DIAGNOSIS;

D O I：

10.1109/ACCESS.2023.3237074

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Fault detection at the early stage is very important in modern industrial processes to avoid failure with life-threatening results and to reduce the cost of maintenance and machine downtime. In this paper, we present a workflow for building a fault diagnosis system based on acoustic emission (AE) using machine learning (ML) techniques. Our fault diagnosis approach is implemented on an embedded device with the internet of things (IoT) connectivity for real-time faults detection and classification in rotating machines. The achieved accuracy for our approach with a fine decision tree ML model is 96.1%.

引用

页码：6665 / 6672

页数：8

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