Iterative Self Organized Data Algorithm for Fault Classification of Mechanical System
The challenging issue for mechanical industry is to develop fast & reliable fault diagnosis systems before total breakdown of machine. Fault diagnosis & classification of faults of mechanical systems is a difficult task. It improves productivity & reduces cost of production. This paper presents an approach for classification of commonly observed faults in gears of mechanical system. These faults include weared gear, gear with one tooth broken & gear with crack on one tooth. The Power Spectral Density (PSD) of the vibration signals of faulty gears is used to construct feature vectors. Principle Component Analysis (PCA) is used to reduce the dimensions of feature vector.