1. Godwin, J. L. & Matthews, P. C. (2014a) "Robust Statistical Methods for Rapid Data Labelling" in V. Bhatnagar (Ed.) "Data Mining and Analysis in

the Engineering Field", Hershey, Pennsylvania, USA : IGI Global

2. Godwin, J. L. & Matthews, P. C. (2014b) "Accurate empirical encapsulation of asset operational behaviour utilizing elitist memetic algorithms for

reliability analysis" In proceedings of the annual conference of Condition Monitoring and Diagnostic Engineering Management (COMADEM). 16 - 18

September, Brisbane, Australia.

3. Godwin, J. L. & Matthews, P. C. (2014c) "Rapid labelling of SCADA data to extract transparent rules using RIPPER". In proceedings of the IEEE

reliability, availability and maintainability symposium (RAMS). 27 - 30 January, Colorado Springs, Colorado, USA.

4. Godwin, J. L., Matthews, P. C., Chen, B. (2014) "Prediction of Wind turbine Gearbox Condition Based on Hybrid Prognostic Techniques with Robust

Multivariate Statistics and Artificial Neural Networks". In proceedings of the annual conference of the European Wind Energy Association (EWEA).

10 - 13 March, Barcelona, Spain.

5. Godwin, J. L., Matthews, P. C. & Watson, C. (2014) "Robust Multivariate Statistical Ensembles for Bearing Fault Detection and Identification". In

proceedings of the IEEE Prognostics and health management (PHM) conference. 22 - 24 June, Spokane, Washington State, USA.

Given the prevalence of cheap sensors,

the aims of this work are:

•To utilise existing data sources more

effectively

•To model the interactions between

many competing sensors

•To provide an extendable model which

can utilised regardless of gearbox

design, size and operational usage

•To enable the early detection, diagnosis

and prognosis of wind turbine gearbox

components in a holistic manner

Accurate encapsulation of real-time wind turbine operational behavior is essential

for identifying the precursors to failure. This work presents a holistic methodology

based upon robust multivariate techniques for the early detection, diagnosis and

RUL prediction for bearings within a three stage wind turbine planetary gearbox.

A validated physics of failure model is utilised to develop an extendable

multivariate model for real-time condition assessment based upon SCADA data.

Statistically sound thresholds identify potential degradation which can be used as

evidence to perform diagnosis and RUL prediction. RUL prediction based upon

current behaviour utilises a traditional FFBP ANN whereas bearing diagnosis is

based upon high frequency (>5 KHz) using a 4-dimensional multivariate model.

This enables the operator to have access to the required information, when it’s

needed, in order to correctly determine the best course of action. As such, these

techniques collectively enable a reduction in maintenance through increased

proactive and opportunistic maintenance, and provides empirical evidence for the

optimisation of current maintenance resources

Utilising robust multivariate statistical techniques, the early detection,

diagnosis and prognosis of faults is possible.

•Gearbox degradation can be identified as early as 6 months prior to

failure (Pictured right).

•Bearing diagnosis can accurately

determine between normal behaviour,

inner race, outer race and ball faults

(Pictured bottom right).

•RUL prediction accuracy is increased

over utilising raw data

(Pictured bottom left)

This enables proactive and opportunistic

maintenance within an organisation.

This work has presented a robust, extendable and practical methodology which

enables holistic analysis for the purposes of early detection, diagnosis and

prognosis.

Early detection of wind turbine gearbox faults can be seen 6 months before

failure, with accurate prediction of each novel event identified through the use of

artificial neural networks. These precursors can be then be utilised to determine

the remaining useful life (RUL) of bearings within the gearbox through time-

domain features. Due to the robust nature of the techniques demonstrated, both

high and low frequency data can be exploited.

Abstract

Wind Turbine HealthMetrics for Operational Analysis

Jamie L. Godwin & Peter Matthews

University of Durham

PO. ID

A005

Results

Objectives

Conclusions

Methods

References

Analysis of Operating Wind Farms 2014 - EWEA Technology Workshop, Malmö, 9-10 December 2014

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By exploiting multivariate techniques, interactions between data sources can be

modelled effectively, and in real time. The robust Mahalanobis distance (Godwin

& Matthews, 2014a, 2014b) provides a means to do this:

Having derived a health index, this can be exploited for condition prediction

(Godwin, Matthews & Chen, 2014), tacit knowledge extraction (Godwin &

Matthews, 2014c) and diagnosis (Godwin, Matthews & Watson 2014). This

enables vast quantities of historical data (high and low frequency) to be explored

and exploited in unprecedented ways. Thus, these techniques holistically enable

organizations to unlock the true value and potential of their data assets.

By empirical deriving the typical gear temperatures for known operating

conditions, the efficiency of the gear can be inferred, and thus related to

degradation (below). Deviations of efficiencies from known behaviours can then

be modelled in the multivariate domain through the robust Mahalanobis distance.