Increasing Wind Turbine Drivetrain Bearing Vibration Monitoring Detectability Using an Artificial Neural Network Implementation

Kim Berglund,Pär Marklund,Daniel Strömbergsson

doi:10.3390/app11083588

Abstract

The highest costs due to premature failures in wind turbine drivetrains are related to defects in the gearbox, with bearing failures being overrepresented. Vibration monitoring has been identified as the primary tool to detect and diagnose these types of failures. However, late or no signs of the failures are still being reported. Artificial neural networks (ANNs) has been shown to favourably be used as a classifier of bearing failures to increase the detection and diagnosis performance, which requires labelled data when training for all types of considered failures. However, less work has been done with an ANN used to create descriptive functions of the vibration and turbine operation data relationship and thereby negating inherent variance in the vibration data and increasing the detectability when a defect appears. Therefore, this study utilizes the relationship between the rotational speed recorded during a vibration measurement and the calculated condition indicator values of specific bearing failures in three wind turbine gearbox failures. An ANN establishes a function between the rotational speed and condition indicator values with healthy training data collected before the failure occurred. Thereafter, whole datasets leading up to the changing of the gearboxes is used to predict the condition indicator values without the failure influence. The difference between the predicted and true values show an increased sensitivity of the detection in two cases of gearbox output shaft bearing failures as well as indicating a planet bearing failure which with the previous data had gone undetected.

Highlights

Wind power is one of the main growing energy generating sectors, which in 2019 saw its second-largest increase in capacity with 60.4 GW, making the total 651 GW [1]
The Artificial neural networks (ANNs)’s predicted condition indicator (CI)-values are seen in red and closely followed the true values, in blue, until the defect appeared except for three distinct peaks. These three predicted values coincided with the three mentioned measurements in Section 2.3 which were taken during a high rotational speed and large variance
Failure Case 3 Results For failure case 3, CI-values directly extracted from the condition monitoring database were available which had been calculated from the fast Fourier transform (FFT) spectra of enveloped measurements with a 0–10 Hz frequency range

Summary

Introduction

Wind power is one of the main growing energy generating sectors, which in 2019 saw its second-largest increase in capacity with 60.4 GW, making the total 651 GW [1]. The gearbox is the highest contribution to these costs, partly due to the long downtime associated with these failures and e.g., the price of a new 2 MW gearbox being as high as 400 ke [2]. Out of these gearbox failures, the bearings are over-represented compared to the gears by a factor of 76% to 17% [3]. To efficiently combat the costs associated with the drivetrain failures, online condition monitoring has become the main method to ensure the health of the bearings and gears [4]. Thereby, the developing failure can be monitored live, maintenance planning start at an earlier date limiting the downtime and actions in operation of the turbine, such as limiting the power output for a period of time, can be taken to reduce the cost of the failure

Objectives

Methods

Results

Conclusion