Design and development of a wind turbine test rig for condition monitoring studies

Abstract

Wind energy is an emerging, clean and renewable source of energy. It is estimated that by year 2035, wind energy will be generating more than 25% of the world's electricity according to International Energy Agency (IEA). With the increase in demand for wind energy, its maintenance issues are becoming more prominent .The scheduled maintenance is more economical than unscheduled repair resulting from failure. So a continuous condition monitoring of various critical components like bearings, gearbox, and shafts of wind turbine is essential in order to enable predictive maintenance. 10% of the total failure is contributed by the bearings, shaft and gear box failures, but the downtime is more than 50% of the total downtime. This paper discusses the development of a bench-top test rig which is designed to mimic the operating condition of an actual wind turbine and use it for monitoring its condition so as to diagnose the incipient faults in its critical components using latest machine learning algorithms such as Artificial Neural Network (ANN).

Machine Learning in Predictive Maintenance towards Sustainable Smart Manufacturing in Industry 4.0

“…[41]. ANN is an intelligent computational technique that has been inspired by biological neurons [10]. It is a massively parallel computing system consisting of an extremely large number of simple processors with many interconnections.…”

“…Biswal, S. and Sabareesh, G.R. [10] designed and developed a bench top test-rig for investigating the time domain vibration signatures of several critical components in wind turbine by imitating the operating condition of an actual wind turbine and use it for monitoring its condition. In their work, they acquired the healthy and faulty condition vibration signature of the critical components, then developed and applied ANN model to carry out the classification of the faulty and healthy state features.…”

“…They have been successfully applied in several field of PdM applications. Some authors [10,30,[46][47][48][75][76][77]107,114,[118][119][120][121] focused on ANN ML algorithms. Some other authors [64][65][66][67]81,82,[84][85][86][87][88]90,116,117] studied RF technique.…”

“…Based on the literature, it is observed that RF algorithm is the most extensively applied ML technique for PdM, as it has been applied on various industrial equipment, components, or systems, including rotating machineries, turbofan engines, rotor bar-LS-PMSM, aircrafts, production lines, semiconductors, industrial pumps, cutting machines, refrigeration systems of supermarket, hard drive disk (HDD), wind turbine, vending machines, and many others. However, authors usually focused on CNC Machines [46,78,79], wind turbine [10,89,99,124], aircraft [67,94,97,122,123], and semiconductor [13,[81][82][83].…”

Predictive System of Semiconductor Failures based on Machine Learning Approach

“…In addition, analyzing and processing these data can show up valuable information and knowledge from system dynamics and manufacturing process [4]. Using various approaches based on data, it is possible to find illustrative results for strategic decision-making, providing advantages such as, increased production, machine fault reduction, maintenance cost reduction, among others [5] [6] [7]. The advantages above have strong relation with maintenance procedures.…”