Integrated Vibro-Acoustic Analysis and Empirical Mode Decomposition for Fault Diagnosis of Gears in a Wind Turbine

Abstract

Over the last few years, wind turbine technology has experienced a rapid growth among the other renewable power developing technologies with respect to market share, size and technological design. Generally, wind turbines are subjected to harsh operating conditions which further yields to damage of the critical components. Hence, health monitoring of key components is a vital task which predicts the damage severity and gives the flexibility to plan the maintenance tasks. Wind turbine condition monitoring is a major area of interest in recent years aiming to improve the life of the machine components simultaneously reducing the operational and maintenance cost. Gearbox in wind turbines has the largest share of downtime among all other components affecting directly the cost of operation and maintenance. In this current investigation, an attempt has been made to diagnose the gear faults by using Empirical Mode Decomposition (EMD) methodology. Two condition monitoring techniques such as vibration analysis and acoustic signal analysis are integrated and the experiments are performed on a laboratory scaled three-stage gearbox having the speed ratio of 48:1. Local gear faults such as tooth chip and tooth root crack are seeded and the response is recorded in the form of vibration and acoustic signals. EMD analysis is implemented and the statistical features are extracted from the acquired data. The representative features are identified using a decision tree algorithm and these are classified using pattern recognition techniques – Support Vector Machine (SVM) to distinguish between the healthy and faulty classes. The challenges and the potential advantages are also discussed in this paper to establish the focus of integrated condition monitoring systems.