Abstract
The development of electrical control system faults leads to increased mechanical component degradation, severe reduction of asset performance, and a direct increase in annual maintenance costs. This paper presents a highly accurate data driven classification system for the diagnosis of electrical control system faults, in particular, wind turbine pitch faults. Early diagnosis of these faults can enable operators to move from traditional corrective or time based maintenance towards a predictive maintenance strategy, whilst simultaneously mitigating risks and requiring no further capital expenditure. Our approach provides transparent, human-readable rules for maintenance operators which have been validated by an independent domain expert. Data from 8 wind turbines was collected every 10 min over a period of 28 months with 10 attributes utilised to diagnose pitch faults. Three fault classes are identified, each represented by 6,000 instances in each of the testing and training sets. Of the turbines, 4 are used to train the system with a further 4 for validation. Repeated random sampling of the majority fault class was used to reduce computational overheads whilst retaining information content and balancing the training and validation sets. A classification accuracy of 85.50 % was achieved with 14 human readable rules generated via the RIPPER inductive rule learner. Of these, 11 were described as “useful and intuitive” by an independent domain-expert. An expert system was developed utilising the model along with domain knowledge, resulting in a pitch fault diagnostic accuracy of 87.05 % along with a 42.12 % reduction in pitch fault alarms.
