An improved wrapper-based feature selection method for machinery fault diagnosis.
k24a4 block A major issue of machinery fault diagnosis using vibration signals is that it is over-reliant on personnel knowledge and experience in interpreting the signal.Thus, machine learning has been adapted for machinery fault diagnosis.The quantity and quality of the input features, however, influence the fault classification performance.Featu