Wind turbine intelligent gear fault identification

This paper aims to present the development of a framework for monitoring of wind turbine gearboxes and prognosis of gear fracture faults, using vibration data and machine learning techniques. The proposed methodology analyses gear vibration signals in the order domain, using a shaft tachometer pulse. Indicators that represent the health state of the gear are algorithmically extracted. Those indicators are used as features to train diagnostic models that predict the health status of the gear. The efficacy of the proposed methodology is demonstrated with a case study using real wind turbine vibration data. Data is collected for a wind turbine at various time steps prior to failure and according to the maintenance reports there is enough data to form a healthy baseline. The data is classified according to the time before failure that the signal was collected.The learning algorithms used are discussed and their results are compared. The case study results indicate that this data driven model can lay the groundwork for a robust framework for the early detection of emerging gear tooth fracture faults. This can lead to minimisation of wind turbine downtime and revenue increase

The Analysis of Dynamic Interaction in Legume Binary Mixture Under Controlled Conditions of Irrigation and Clipping

The objective of this study was to analyse the type of interference that occurred between the annual legume species, purple clover (Trifolium purpureum L.) and narrow leaved crimson clover (Trifolium angustifolium Loisel. ), growing in mixed conditions under two different watering regimes and two different clipping treatments. A replacement series experiment was conducted in pots placed in the field. The above ground biomass (gr/plant) were measured. The recently proposed Inverse Linear Model was implied in order to analyse the competitive interaction between the above species. The results suggest that Tr. purpureum was the superior competitor to Tr. angustifolium and a remarkable niche differentiation was occurred after the clipping treatment

Investigating parallel multi-step vibration processing pipelines for planetary stage fault detection in wind turbine drivetrains

This paper proposes a signal processing approach for wind turbine gearbox vibration signals based on employing multiple analysis pipelines. These so-called pipelines consist of combinations of various advanced signal processing methods that have been proven to be effective in literature when applied to wind turbine vibration signals. The performance of the pipelines is examined on vibration data containing different wind turbine gearbox faults in the planetary stages. Condition indicators are extracted from every pipeline to evaluate the fault detection capability for such incipient failures. The results indicate that the multipronged approach with the different pipelines increases the reliability of successfully detecting incipient planetary stage gearbox faults. The type, location, and severity of the fault influences the choice for the appropriate processing method combination. It is therefore often insufficient to only utilize a single processing pipeline for vibration analysis of wind turbine gearbox faults. Besides investigating the performance of the different processing techniques, the main outcome and recommendation of this paper is thus to employ a diversified analysis methodology which is not limited to a sole method combination, to improve the early detection rate of planetary stage gearbox faults

Combination of thermal modelling and machine learning approaches for fault detection in wind turbine gearboxes

This research aims to bring together thermal modelling and machine learning approaches to improve the understanding on the operation and fault detection of a wind turbine gearbox. Recent fault detection research has focused on machine learning, black box approaches. Although it can be successful, it provides no indication of the physical behaviour. In this paper, thermal network modelling was applied to two datasets using SCADA (Supervisory Control and Data Acquisition) temperature data, with the aim of detecting a fault one month before failure. A machine learning approach was used on the same data to compare the results to thermal modelling. The results found that thermal network modelling could successfully detect a fault in many of the turbines examined and was validated by the machine learning approach for one of the datasets. For that same dataset, it was found that combining the thermal model losses and the machine learning approach by using the modelled losses as a feature in the classifier resulted in the engineered feature becoming the most important feature in the classifier. It was also found that the results from thermal modelling had a significantly greater effect on successfully classifying the health of a turbine compared to temperature data. The other dataset gave less conclusive results, suggesting that the location of the fault and the temperature sensors could impact the fault-detection ability