Artificial Neural Network Application for Current Sensors Fault Detection in the Vector Controlled Induction Motor Drive.

Mateusz Dybkowski,Kamil Klimkowski

doi:10.3390/s19030571

Abstract

This paper describes a Fault Tolerant Control structure for the Induction Motor (IM) drive. We analyzed the influence of current sensor faults on the properties of the vector-controlled IM drive system. As a control algorithm, the Direct Field Oriented Control structure was chosen. For the proper operation of this system and for other vector algorithms, information about the stator currents components is required. It is important to monitor and detect these sensor faults, especially in drives with an increased safety level. We discuss the possibility of the neural network application in detecting stator current sensor faults in the vector control algorithm. Simulation and experimental results for various drive conditions are presented.

Highlights

The efficiency and performance levels of electrical machines gradually deteriorate as a result of their wearing and aging processes
The main goal of this paper was to demonstrate a stator current transducers fault detection and the theory related to artificial intelligence [35,36,37,38]
We presented and described the novel neural network based detector

Summary

Introduction

The efficiency and performance levels of electrical machines gradually deteriorate as a result of their wearing and aging processes. The reliability of entire technological processes decreases, which simultaneously increases the risk of basic system components failure [1]. The most common examples of well-known abnormalities are actuator lock, partial or full loss of sensor signal, short circuits, and system component sudden disconnection [2]. These may cause intermittent operation of the controllers or significant measurement errors. This leads to reduced system performance and even to total system failure. In order to prevent damage, or to enable early detection, various diagnostic techniques are used and tested [1], including adequate protection of key components, and redundancy and technical diagnostics [3,4,5,6,7,8]

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Methods

Conclusion