1. Robust Fault analysis Technique for Permanent Magnet DC Motor In safety Critical Applications Wathiq Abed Wathiq Abed Supervisor - Sanjay Sharma University of Plymouth University of Plymouth UKACC PhD Presentation Showcase

2. Slide 2 Introduction   Condition monitoring and fault diagnosis of the electrical motor motor are thus necessary to optimise maintenance and improve reliability levels in electrical power system especially for critical applications. The isolation of the fault in time ensures that integrity of the power system and performance of the overall system are un affected.   Artificial Intelligence has been introduced into the fault diagnosis process for condition monitoring  Background and motivation for research .  Bearing faults are common faults in electric motors and represent about 40% to 50% of all motor faults.   Condition monitoring and fault diagnosis includes data acquisition, signal processing and fault identification

3. UKACC PhD Presentation Showcase Slide 3  Research methodology  Experimental set up to collect data under normal and abnormal operating conditions  Discrete wavelet transform for feature extraction  To avoid feature redundancy that effect on fault classification accuracy OFNDA have been applied as features reduction tool  Wavelet activated neural network nonlinear model  Online real time fault classification using Dynamic neural network (DNN)  Contribution to knowledge   A novel approach for identifying rolling element bearing defects in brushless DC motors under stationary and non-stationary operating conditions with different severities of fault

4. UKACC PhD Presentation Showcase Slide 4 Fault diagnosis Results DNN for fault classification Inner race re Outer race re Ball defect re DNN performance re

5. UKACC PhD Presentation Showcase Slide 5 Conclusion  Dynamic neural networks are more versatile and provide the capability to learn the dynamics of complicated nonlinear systems which conventional static neural networks cannot model  Orthogonal fuzzy neighbourhood discriminative analysis were applied to obtain the best features for fault classification