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In this paper, a novel approach for condition and performance monitoring of induction motor through estimated motor losses is proposed and explained. The novelty in the proposed approach is minimal non-intrusive measurements and does not require disconnection of motors from the load. In many systems, the losses measured directly or indirectly in a motor are of the lump sum in nature. The main advantage of the system prescribed in this paper gives losses in the segregated form, which is directly used to assess the internal condition of the motor. This approach will assess the condition of induction motors for internal faults such as stator faults, rotor faults, core faults, bearings faults,  etc . The equivalent circuit parameters and motor losses are estimated using the developed Genetic Algorithm (GA). The estimated motor loss data are compared with the NEMA &amp; IE Standards and if any deviation found will be reported as faults. The reported information is linked to the developed fuzzy algorithm to identify the faults exactly along with its severity level. The proposed approach was tested and evaluated with the help of LT motors ranging from 5 to 200 HP in real-time from a selected industry.

Genetic Algorithm & Fuzzy Logic-based Condition Monitoring of Induction Motor Through Estimated Motor Losses