LAUSR

Motor and gearbox are considered the main components in various machines related to its supplying power and transmitting motion role. Operating machines acquire vibration signal that are continuously monitoring by sensors placing close to vibration source. This for processing and identify the machine’ components status. Breakdown of the rotating machine causes significant losses and costs, so the analysis of its vibration signals proved literately avoiding these drawbacks with effective faults diagnosis. This paper proposing two models for gearbox and motor faults identification as an attempt towards finding the optimal performance: The first developed model is a fuzzy logic (FL) based model and the other is genetic algorithm (GA) based model. The intended output of both models reduce time and cost of maintenance. It also indirectly increases the machine component’s life. Additionally, the computational analysis proved that, concerning execution time and accuracy; and with the powerful straight forward representation for uncertainties offered by the Fuzzy Logic; it is indeed reliable, however it presented lower classification accuracy (96% for gear box faults and 93% for motor faults) and lower generalization schema. Yet, the proposed strategy which integrates GA and SVM recorded high performances in optimization and higher classification capabilities (97% for both gear box and motors faults). These factors illustrate the effectiveness and optimal performance of the genetic based model.