A Text-Mining Approach to Assess the Failure Condition of Wind Turbines Using Maintenance Service History

Abstract

Detecting and determining which systems or subsystems of a wind turbine have more failures is essential to improve their design, which will reduce the costs of generating wind power. Two of the most critical failures, the generator and gearbox, are analyzed and characterized with four metrics. This failure analysis usually begins with the identification of the turbine’s condition, a process normally performed by an expert examining the wind turbine’s service history. This is a time-consuming task, as a human expert has to examine each service entry. To automate this process, a new methodology is presented here, which is based on a set of steps to preprocess and decompose the service history to find relevant words and sentences that discriminate an unhealthy wind turbine period from a healthy one. This is achieved by means of two classifiers fed with the matrix of terms from the decomposed document of the training wind turbines. The classifiers can extract essential words and determine the conditions of new turbines of unknown status using the text from the service history, emulating what a human expert manually does when labelling the training set. Experimental results are promising, with accuracy and F-score above 90% in some cases. Condition monitoring system can be improved and automated using this system, which helps the expert in the tedious task of identifying the relevant words from the turbine service history. In addition, the system can be retrained when new knowledge becomes available and may therefore always be as accurate as a human expert. With this new tool, the expert can focus on identifying which systems or subsystems can be redesigned to increase the efficiency of wind turbines.

Digitalization Workflow for Automated Structuring and Standardization of Maintenance Information of Wind Turbines into Domain Standard as a Basis for Reliability KPI Calculation

“…mean time to failure, and therefore implement a pipeline for machine reading of unstructured maintenance work orders [8]. Approaches that are specific to the wind energy domain can be seen in [9,10,11]. Blanco-M. et al show a text mining approach to asses the failure condition of WTs by using the maintenance history [9].…”

Analyzing a Decade of Wind Turbine Accident News with Topic Modeling

“…The work in [51] applied text mining to analyze wind turbine service history (operation and maintenance (O&M)) reports and identified failure-associated words and terms. The authors analyzed data from wind turbines with generator or gearbox issues and applied decision tree and random forest classifiers to identify contextual words in failure situations.…”

“…The ultimate objective of this research was to automatically detect possible failures. The work in [51] is related to our research because of the use of text mining; while [51] focuses on O&M reports for a subset of possible types of accidents for operational planning, our research considers news accidents for all possible types of accidents for strategic planning.…”

Using SCADA Data for Wind Turbine Condition Monitoring: A Systematic Literature Review

“…Moreover, another approach to predict the faults and remaining useful life of gearboxes was presented in [58], where the failure was predicted up to a month before it occurred, using ML techniques on large amounts of SCADA data. Further, a new approach was proposed in [53], where automatic text mining was used as an emerging and complementary tool to predict the possible failures in WTs from the automatic processing of O&M information. The generator and gearbox were analyzed using the relevant words from the WT service history.…”