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Publication Type : Journal Article

Url :

Campus : Coimbatore

School : School of Business

Year : 2018

Abstract : Electronically Commutated Brushless DC (EC-BLDC) motors are being increasinglyused in many applications as they have many advantages over other motors, advancements inpower electronics and efficient control algorithm for electronic commutation. BLDC motorhas many attractive characteristics such as high instantaneous torque, low rotor inertia,noiseless high dynamic response, high power density, high efficiency, long operating lifeperiod and wider range of speed vs. torque characteristics. The control scheme of an EC-BLDC fan employs many sensors like hall effect current sensor, voltage transducer, resistancetemperature dependents (RTD) and hall sensors for measuring current, voltage, temperatureand orientation of magnetic poles for electronic commutation. These sensor data areaccessible for us over communication protocol by which application based predictivealgorithms can be devised.In this project, the real time data of BLDC fans such as actual speed, DC link voltage,current and temperatures of motor, electronics and the entire module are acquired and theperformance of EC-BLDC drive’s power electronics, BLDC motor and other associatedapplication parameters. For acquiring real time data, a communication processor withMODBUS protocol is used. By using LabVIEW application, the acquired data arecontinuously monitored and logged. The analysis of pre-fault data helps in identification ofroot cause of the fault occurrence. In future, the patterns of the data can be used for trainingthe machine learning algorithms which can be embedded into an edge device for anomaly detection.

Cite this Research Publication : Somasundaram Balasubramaniam "ENHANCING INTELLIGENCE OF EC-BLDC DRIVES USING DATA ANALYTICS SYNOPSIS", 2018

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