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Induction motors are widely used in transportation, mining, petrochemical, manufacturing and in almost every other field dealing with electrical power. These motors are simple, efficient, highly robust and rugged thus offering a very high degree of reliability. But like any other machine, they are vulnerable to faults, which if left unmonitored and detected, might lead to catastrophic failure of the machine in the long run. Early detection and diagnosis of faults that may occur are desirable to ensure that, operational effectiveness of an induction motor is improved. In this work, faults detection and classification using fuzzy logic controller is presented. A series of simulations using the models of 3-phase squirrel cage induction motor was performed using MATLAB/Simulink under different faults conditions, such as, break in stator windings, turn-to-turn short-circuit, 3-phase to ground and unbalance in input voltage. Models were designed on the basis of characteristics and parameters of a real motor. The results that were obtained showed that the fuzzy logic controller was able to detect the various faults of the 3-phase induction motor with high accuracy.

Most electrical cable manufacturing companies in Ghana and other parts of the world use solid-state based relay for controlling temperatures of heaters during cable extrusion process. Due to the on-off nature of such relays, the flow of heat is not continuous and mostly unreliable. As a result, most of the extruded cables have surface defects such as cracks, pimples and voids which affects the quality of the manufactured cable. The consequence of this is that the cable does not pass the quality assurance test hence, it is usually scraped off. This causes the companies to loose large amount of money due to material wastage. To overcome this challenge, an Adaptive Neuro Fuzzy Inference System (ANFIS) controller tuned with Particle Swarm Optimisation (PSO) algorithm was designed to provide continuous temperature control during the extrusion process. To ascertain the robustness of the designed controller, it was compared with other conventional and intelligent controllers such as the Proportional Integral (PI), Fuzzy, Fuzzy-PI, and ANFIS controller. The controllers were designed and simulated using MATLAB/Simulink software. The results obtained indicated that the PSO-ANFIS controller was robust in achieving stability in terms of temperature variations.