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Automatic Condition Monitoring of Industrial Machines Using FSA-Based Hall-Effect Transducer

机译:基于FSA的Hall效应传感器的工业机器自动化监测

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摘要

A reliable and swift real-time condition monitoring for an industrial machine is a need of the hour when dealing with critical uncontrollable machine failures such as stator winding insulation failure, which is more prominent while working in robust conditions. Early detection of stator winding faults, especially turn-turn fault, occurred mainly as a short-circuit between the winding of the same phase due to the insulation failure, which can prevent fault propagation and protect winding from further insulation damages. This paper presents steady and transient state analysis of three-phase induction motor using fault signature analyzer based hall-effect transducer under various fault conditions. A numerical model with stator winding faults has been implemented, taking into account two major faults such as stator turn-turn (STTF) and stator phase-neutral fault (SPhN), using finite element method (FEM). FEM obtains the symmetrical current components of the motor under different fault conditions using finite element analysis (FEA). The machine torque has also been modeled and compared in a healthy and faulty condition. The numerical finite element method (FEM) model could identify a healthy and faulty induction machine with 95% accuracy using finite element analysis (FEA) and the same was validated using experimental setup results under the same operating conditions.
机译:工业机器的可靠和快速的实时条件监测是处理批判性无法控制的机器故障(如定子绕组绝缘故障)时,这是一个小时,这在鲁棒条件下工作更加突出。定子绕组故障的早期检测,尤其是转弯断层,主要发生在相同相位之间的短路由于绝缘故障,这可以防止故障传播和保护绕组从进一步的绝缘损坏。本文在各种故障条件下,使用故障签名分析仪基于霍尔效应传感器的三相感应电动机稳态和瞬态分析。已经实施了具有定子绕组故障的数值模型,考虑了使用有限元方法(FEM)的两个主要断层,例如定子转弯(STTF)和定子相中错(SPHN)。使用有限元分析(FEA),FEM在不同的故障条件下获得电动机的对称电流分量。机器扭矩也被建模并以健康和故障的情况进行比较。数值有限元方法(FEM)模型可以使用有限元分析(FEA)识别具有95%精度的健康和故障的感应机,并且在相同的操作条件下使用实验设置验证了相同的验证。

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