Fault diagnosis of wind turbine structures using decision tree learning algorithms with big data

Abstract

In the context of optimized Operation & Maintenance of wind energy infrastructure, it is important to develop decision support tools, able to guide operators and engineers in the management of these assets. This task is particularly challenging given the multiplicity of uncertainties involved, from the point of view of the aggregated data, the available knowledge with respect to the wind turbine structures, and sub-components, as well as the constantly varying operational and environmental loads. We here propose to propagate wind turbine telemetry through a decision tree learning algorithm to detect faults, errors, damage, patterns, anomalies and abnormal operation. The use of decision trees is motivated by the fact that they tend to be easier to implement and interpret than other quantitative data-driven methods. Furthermore, the telemetry consists of data from condition and structural health monitoring systems, which lends itself nicely in the field of Big Data as large amounts are continuously sampled at high rate from thousands of wind turbines. In this paper, we review several decision tree algorithms formerly proposed by the machine learning community (i.e. ID3, C4.5, C5.0, J48, SPRINT, FACT, FIRM, SLIQ, CHAID, QUEST, CRUISE, PUBLIC,BOAT, RAINFOREST, MARS, RIPPER and CART), we then train an ensemble Bagged decision tree classifier on a large condition monitoring dataset from an offshore wind farm comprising 48 wind turbines, and use it to automatically extract paths linking excessive vibrations faults to their possible root causes. We finally give an outlook of an architecture to implement decision tree learning in the context of cloud computing for big data, namely involving a cloud based Apache Hadoop software for very large data storage and handling, and Apache Spark for efficiently running machine-learning algorithms.

A Framework for Big Data Analytical Process and Mapping—BAProM: Description of an Application in an Industrial Environment

“…The literature review presented in Section 2 shows an intense use of algorithms such as Artificial Neural Network, Support Vector Machine and others considered to be black box [41]. This term, black box, is used because the algorithm's decision criterion is not visible to humans.…”

“…This term, black box, is used because the algorithm's decision criterion is not visible to humans. In this research, it was preferred for its first model, to use an AI algorithm more similar to a white box [41], where decision-making based on the algorithm, can be more easily interpreted by experts, and even by laypeople. In this sense, one of the algorithms employed in the predictive modeling, part 1, of BAProM was the Decision Tree (DT) and for the time series forecasting model was employed an ANN model.…”

“…There are different types of algorithms for prediction. One of the classical ones is the Decision Tree (DT), which is a type of AI algorithm whose model, generated after the training process, can be interpreted by humans and machines [41].…”

Prognostics and Health Management of Industrial Assets: Current Progress and Road Ahead

“…DTs have been widely employed in the context of fault diagnosis over the last two decades. For example, they have been applied to process data gathered from rolling element bearing systems (Sugumaran and Ramachandran, 2007;Sugumaran, 2012), gearboxes (Saravanan and Ramachandran, 2009;Praveenkumar et al, 2018), wind turbines (Abdallah et al, 2018), centrifugal pumps (Sakthivel et al, 2010), and photovoltaic systems (Benkercha and Moulahoum, 2018).…”

Deep Unsupervised Learning for Condition Monitoring and Prediction of High Dimensional Data with Application on Windfarm SCADA Data

“…In our examples we didn't have any simulated faults and therefore no results are presented for that application. This may be a complementary approach to fault detection as proposed in [7].…”