A novel method for real time gear fault detection based on pulse shape analysis

Early identification of faults in gearboxes is a challenging task, especially when the time is a critical factor. In this paper, a novel method for real time fault detection in gearboxes is proposed using adaptive features extraction algorithm to deal with non-stationary faulty signals. Moreover, integration of different techniques is presented in order to detect faults in a real time environment. Evolutionary algorithms are commonly used in different applications and have strong ability for optimization. However, they are inherently slow and not suitable for real time applications. The proposed method is based on a combination of conventional one-dimensional and multi-dimensional search methods, which showed high performance and accurate fault detection results compared with evolutionary algorithms. The effectiveness, feasibility and robustness of the proposed method have been demonstrated on experimental data. An average speed up factor of 87% has been successfully achieved with approximately 5% quality degradation in the results as compared with evolutionary algorithms like genetic algorithms.