Evaluating the challenges associated with the long-term reliable operation of industrial wind turbine gearboxes

Wind turbine gearboxes are required to operate under adverse operational conditions over a long service lifetime. Unfortunately, gearbox designers are yet to achieve the reliability anticipated by wind turbine manufacturers and operators. The poor understanding of variable loading conditions has resulted in the majority of wind turbine gearboxes being unable to reach their expected service lifetime of 20-25 years. This has led to an increasing need to investigate the fundamental issues associated with the degradation of wind turbine gearbox materials during operation in order to improve existing designs and optimise future ones. This paper investigates the various challenges that need to be addressed in order to achieve a noteworthy increase in the operational service lifetime of large-scale industrial wind turbine gearboxes

An experimental study on the applicability of acoustic emission for wind turbine gearbox health diagnosis

Condition monitoring of wind turbine gearboxes has mainly relied upon vibration, oil analysis and temperature monitoring. However, these techniques are not well suited for detecting early stage damage. Acoustic emission is gaining ground as a complementary condition monitoring technique as it offers earlier fault detection capability compared with other more established techniques. The objective of early fault detection in wind turbine gearboxes is to avoid unexpected catastrophic breakdowns, thereby reducing maintenance costs and increase safety. The aim of this investigation is to present an experimental study the impact of operational conditions (load and torque) in the acoustic emission activity generated within the wind turbine gearbox. The acoustic emission signature for a healthy wind turbine gearbox was obtained as a function of torque and power output, for the full range of operational conditions. Envelope analysis was applied to the acoustic emission signals to investigate repetitive patterns and correlate them with specific gearbox components. The analysis methodology presented herewith can be used for the reliable assessment of wind turbine gearbox subcomponents using acoustic emission.The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: IntelWind Project is an FP7 project partly funded by the EC under the Research for the Benefit of SMEs programme, Grant Agreement Number 283277, coordinated and managed by Innovative Technology and Science Ltd

Advanced remote condition monitoring of railway infrastructure and rolling stock

The rail network is an integral part of the modern transport system. Railway transportation accommodates the mobility of both passengers and goods cost-effectively and in an environmentally friendly way at large scale. Thus, the contribution of railway transportation to the global economy, sustainable growth and mitigation of climate change effects is profound. Rail operations have become more intense, with traffic density continuously increasing. At the same time rolling stock speed and axle loads have also increased. This has led to strong interest in re-evaluating the fundamentals of the way rail infrastructure and rolling stock are currently inspected and maintained. Recent attention has focused on the development of advanced remote condition monitoring techniques for the assessment of the structural integrity of critical rail infrastructure and rolling stock components. The widespread implementation of effective and proven remote condition monitoring technologies can result in the meaningful reduction of the demand for conventional time-consuming and costly inspection methodologies, helping increase the availability and hence capacity factor of the rail network. This paper presents the most recent developments and results obtained from the experimental work carried out by the authors on remote condition monitoring of rail infrastructure and rolling stock components using acoustic emission and vibration analysis techniques under laboratory and field conditions

Multivariable Analysis for Advanced Analytics of Wind Turbine Management

Operation and maintenance tasks on the wind turbines have an essen- tial role to ensure the correct condition of the system and to minimize losses and increase the productivity. The condition monitoring systems installed on the main components of the wind turbines provide information about the tasks that should be carried out over the time. A novel statistical methodology for multivariable analysis of big data from wind turbines is presented in this paper. The objective is to analyse the necessary information from the condition monitoring systems installed in wind farms. The novel approach filters the main parameters from the collected signals and uses advanced computational techniques for evaluating the data and giving mean- ing to them. The main advantage of the approach is the possibility of the big data analysis based on the main information available

Detection and evaluation of rail surface defects using alternating current field measurement techniques

Reliable and cost‐effective inspection of rail tracks is of paramount importance to ensure the safety of rolling stock operations. In this paper alternating current field measurement (ACFM) sensors are used to carry out experiments on artificially induced rail surface defects at various speeds using testing configurations that simulate actual inspection conditions found in the field. From the obtained results it can be clearly seen that the ACFM sensors can detect the artificially induced rail surface defects even when relatively significant lift-off is involved, i.e. ∼5 mm and that the effect of increasing speed on the amplitude of the Bx signal, which is directly related to the depth of the crack, is negligible. However, clustered defects cannot be easily resolved and the overall amplitude is related to the spacing of the defects within a cluster. The order of clustered defects also significantly influences the maximum amplitude of the recorded Bx signal. The validity of the results obtained from the tests on artificially induced defects was verified by conducting further ACFM tests on a rail sample removed from service that contained mild rolling contact fatigue cracks. </jats:p

Wayside detection of faults in railway axle bearings using time spectral kurtosis analysis on high-frequency acoustic emission signals

Typical railway wheelsets consist of the wheels, axle and axle bearings. Faults can develop on any of the aforementioned components, but the most common are related to wheel and axle bearing defects. The continuous increase in train operating speeds means that failure of an axle bearing can lead to serious derailments, causing loss of life and severe disruption in the operation of the network, damage to the track and loss of confidence in rail transport by the general public. The rail industry has focused on the improvement of maintenance and online condition monitoring of rolling stock to reduce the probability of failure as much as possible. Current wayside systems such as hot axle box detectors and acoustic arrays can fail to detect defective bearings. This paper discusses the results of wayside high-frequency Acoustic Emission (AE) measurements carried on freight wagons with artificially induced damage in axle bearings in Long Marston, UK. Time spectral kurtosis (TSK) is applied for the analysis of the AE data. From the results obtained it is evident that TSK is capable of distinguishing the axle bearing defects from the random noises produced by different sources such as the wheel-rail interaction and changes in train speed

A damage mechanics approach for lifetime estimation of wind turbine gearbox materials

Wind turbine gearboxes (WTG) have been suffering from premature failures and rarely live up to their designed lifetime. This study focuses on a better understanding of WTG failures. A finite element model of a gear pair was coupled with a constitutive model to quantify and predict damage, while also testing for different service conditions. A damage mechanics formulation is presented based on a physics-based dislocation slip model. Additionally, an estimation of the remaining useful lifetime of the component was generated, further assisting wind turbine farm operators to move towards the implementation of a truly predictive maintenance approach