A novel architecture and system for the provision of Reliability Centred Maintenance (RCM) for offshore wind power generation is presented. The architecture was developed by conducting a bottom-up analysis of the data required to support RCM within this specific industry, combined with a top-down analysis of the required maintenance functionality. The architecture and system consists of three integrated modules for intelligent condition monitoring, reliability and maintenance modelling, and maintenance scheduling that provide a scalable solution for performing dynamic, efficient and cost-effective preventative maintenance management within this extremely demanding renewable energy generation sector. The system demonstrates for the first time the integration of state-of-the-art advanced mathematical techniques: Random Forests, dynamic Bayesian networks and memetic algorithms in the development of an intelligent autonomous solution. The results from the application of the intelligent integrated system illustrated the automated detection of faults within a wind farm consisting of over 100 turbines, the modelling and updating of the turbines' survivability and creation of a hierarchy of maintenance actions, and the optimizing of the maintenance schedule with a view to maximizing the availability and revenue generation of the turbines.

The imbalance data refers to at least one of its classes which is usually outnumbered by the other classes. The imbalanced data sets exist widely in the real world, and the classification for them has become one of the hottest issues in the field of data mining. At present, the classification solutions for imbalanced data sets are mainly based on the algorithm-level and the data-level. On the data-level, both oversampling strategies and undersampling strategies are used to realize the data balance via data reconstruction. SMOTE and Random-SMOTE are two classic oversampling algorithms, but they still possess the drawbacks such as blind interpolation and fuzzy class boundaries. In this paper, an improved oversampling algorithm based on the samples’ selection strategy for the imbalanced data classification is proposed. On the basis of the Random-SMOTE algorithm, the support vectors (SV) are extracted and are treated as the parent samples to synthesize the new examples for the minority class in order to realize the balance of the data. Lastly, the imbalanced data sets are classified with the SVM classification algorithm. F-measure value, G-mean value, ROC curve, and AUC value are selected as the performance evaluation indexes. Experimental results show that this improved algorithm demonstrates a good classification performance for the imbalanced data sets.

Support vector machines (SVMs) are designed to solve the binary classification problems at the beginning, but in the real world, there are a lot of multiclassification cases. The multiclassification methods based on SVM are mainly divided into the direct methods and the indirect methods, in which the indirect methods, which consist of multiple binary classifiers integrated in accordance with certain rules to form the multiclassification model, are the most commonly used multiclassification methods at present. In this paper, an improved multiclassification algorithm based on the balanced binary decision tree is proposed, which is called the IBDT-SVM algorithm. In this algorithm, it considers not only the influence of “between-classes distance” and “class variance” in traditional measures of between-classes separability but also takes “between-classes variance” into consideration and proposes a new improved “between-classes separability measure.” Based on the new “between-classes separability measure,” it finds out the two classes with the largest between-classes separability measure and uses them as the positive and negative samples to train and learn the classifier. After that, according to the principle of the class-grouping-by-majority, the remaining classes are close to these two classes and merged into the positive samples and the negative samples to train SVM classifier again. For the samples with uneven distribution or sparse distribution, this method can avoid the error caused by the shortest canter distance classification method and overcome the “error accumulation” problem existing in traditional binary decision tree to the greatest extent so as to obtain a better classifier. According to the above algorithm, each layer node of the decision tree is traversed until the output classification result is a single-class label. The experimental results show that the IBDT-SVM algorithm proposed in this paper can achieve better classification accuracy and effectiveness for multiple classification problems.

In order to adapt to the classification of the large-scale data and the dynamic data, this paper proposes an incremental learning strategy of SVM called GGKKT–ISVM algorithm based on the generalized KKT condition. The algorithm sets the generalized extension factors by the samples distribution density in order to make the useful samples become new support vectors, and it trains a new classifier. Then this algorithm modifies the classifier secondly, and it can not only keep the historical classification information, also can make full use of the new samples’ information, and structure the classifier that has stronger generalization ability. The experimental results show that the algorithm has a good classification effect.