Precision Fault Diagnosis Procedure for a Structural System Having a Defect Employing Hidden Markov Models

Abstract

Condition-based maintenance (CBM) is one of the most effective maintenance methods since it can minimize operation shutdowns for maintenance. In CBM, a fault diagnosis is conducted using vibration signals caused by various fault types. To analyze signal variations automatically and efficiently, pattern recognition methods are usually employed. Typical pattern recognition methods employed for CBM include the Artificial Neural Network (ANN) and the Hidden Markov Model (HMM). Among them, HMMs were employed in this paper to identify the size and location of a crack in a structural system. In CBM employing HMMs, feature vector extraction is the most important step for reliable diagnosis. In this paper, Fast Fourier Transform (FFT) was employed for feature vector extraction. Even though it is relatively easy to identify the fault type, it is difficult to identify the size and location of a defect since signal variation due to defect size or location variation is extremely small. In this paper, a diagnosis procedure to identify the size and location of a defect is proposed. The effectiveness of the proposed method is validated using a numerical analysis model of a rotating blade having a crack.