微控制器
计算机科学
断层(地质)
振动
故障检测与隔离
感应电动机
噪音(视频)
卡尔曼滤波器
加速度计
实时计算
汽车工程
控制工程
嵌入式系统
工程类
电气工程
声学
人工智能
执行机构
地震学
电压
操作系统
地质学
物理
图像(数学)
作者
Massine GANA,H. Achour,Kamel Belaid,Zakia Chelli,Mourad Laghrouche,Mohamed Jouini
标识
DOI:10.1088/1361-6501/ac4b8f
摘要
Abstract This paper presents a design of a low-cost integrated system for the preventive detection of unbalance faults in an induction motor. In this regard, two non-invasive measurements were collected then monitored in real time and transmitted via an ESP32 board. A new, flexible, lead-free piezoelectric sensor, developed previously in our laboratory, was used for vibration analysis (VA). An infrared thermopile was used for non-contact temperature measurement. The data is transmitted via Wi-Fi to a monitoring station that intervenes to detect an anomaly. The diagnosis of the motor condition is realized using an artificial neural network (ANN) algorithm implemented on the microcontroller. Additionally, a Kalman filter is employed to predict the vibrations while eliminating the noise. The combination of VA, thermal signature analysis and ANN provides a better diagnosis and provides efficiency, accuracy, easy access to data and remote control, which significantly reduces human intervention.
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