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Maintenance work orders are commonly used to document information about wind turbine operation and maintenance. This includes details about proactive and reactive wind turbine downtimes, such as preventative and corrective maintenance. However, the information contained in maintenance work orders is often unstructured and difficult to analyze, presenting challenges for decision-makers wishing to use it for optimizing operation and maintenance. To address this issue, this work compares three different approaches to calculating reliability key performance indicators from maintenance work orders. The first approach involves manual labeling of the maintenance work orders by domain experts, using the schema defined in an industrial guideline to assign the label accordingly. The second approach involves the development of a model that automatically labels the maintenance work orders using text classification methods. Through this method, we are able to achieve macro average and weighted average F1-scores of 0.75 and 0.85 respectively. The third technique uses an AI-assisted tagging tool to tag and structure the raw maintenance information, together with a novel rule-based approach for extracting relevant maintenance work orders for failure rate calculation. In our experiments, the AI-assisted tool leads to an 88% drop in tagging time in comparison to the other two approaches, while expert labeling and text classification are more accurate in KPI extraction. Overall, our findings make extracting maintenance information from maintenance work orders more efficient, enable the assessment of reliability key performance indicators, and therefore support the optimization of wind turbine operation and maintenance.

Maintenance data of wind turbines is an important information source for calculating key performance indicators. Also, it can be used for developing models for early fault detection. Both activities aim for supporting informed decisions in operation and maintenance. However, such data is rarely available in a structured and standardized format which hinders the interoperability of different enterprises. Consequently, maintenance information is often unused or only usable with considerable personnel effort. To digitalize wind farm maintenance, a digitalization workflow is developed and presented in this paper. The workflow consists of the steps optical character recognition, information extraction and text classification. The workflow is applied on real-world wind turbine service reports and invoices. First results for each step show good performance metrics and potential for further real-world application of the proposed method.

The usage of machine learning techniques is widely spread and has also been implemented in the wind industry in the last years. Many of these techniques have shown great success but need to constantly prove the expectation of functionality. This paper describes a new method to monitor the health of a wind turbine using an undercomplete autoencoder. To evaluate the health monitoring quality of the autoencoder, the number of anomalies before an event has happened are to be considered. The results show that around 35% of all historical events that have resulted into a failure show many anomalies. Furthermore, the wind turbine subsystems which are subject to good detectability are the rotor system and the control system. If only one third of the service duties can be planned in advance, and thereby the scheduling time can be reduced, huge cost saving potentials can be seen.

Located in France on the eastern edge of the Massif Central, the Parc naturel regional des Monts d’Ardèche, inscribed as a UNESCO Global Geopark in 2014, presents great geological diversity. This includes a sedimentary boundary between the Jurassic and Cretaceous, represented by limestone and marl. Fossils of crocodilians have been discovered in these layers, highlighting the diversity of past marine environments. Sites of interest and their fossils are today protected and valorized by different public and private actors working in synergy, notably through the UNESCO Global Geopark label.