Wind Turbine Maintenance Log Labelling Framework: LLM-Driven Data Correction and Enrichment via Semantic Extraction of Reliability Intelligence

Technical Summary: LLM-Driven Data Correction and Enrichment for Wind Turbine Maintenance Logs

Problem Statement
As wind turbine fleets age, optimizing operation and maintenance (O&M) is critical for extending service life and reducing the levelized cost of energy. However, a significant bottleneck exists in the reliability engineering sector: valuable failure intelligence is trapped within unstructured natural language entries in historical maintenance logs. These records, stored in Computerized Maintenance Management Systems (CMMS), suffer from systemic data quality issues, including missing or incorrect hierarchical system codes, semantic ambiguity, and generic or inaccurate maintenance type labels.

Traditional reliability methodologies, such as Failure Modes and Effects Analysis (FMEA), rely heavily on subjective expert judgment to curate failure modes and assign risk parameters because the empirical truth remains locked in unstructured text. Furthermore, existing automated approaches using traditional Natural Language Processing (NLP) and Machine Learning (ML) require massive, manually labeled training datasets and often introduce algorithmic misclassifications that skew downstream reliability calculations. The industry lacks a scalable, automated pipeline capable of transforming these qualitative field observations into quantitative, machine-readable reliability metrics.

Methodology
The paper presents a novel, model-agnostic framework that leverages Large Language Models (LLMs) to autonomously correct, structure, and enrich maintenance logs. The methodology operates on a dataset of 16,316 maintenance logs from 280 wind turbines across 32 onshore farms, monitored over nine years (2017–2026).

The automated pipeline executes a sequential, multi-phase process:

1. Database Integration and Pre-processing: The system merges data from the WORKORDER, WORKLOG, and FAILURECODE tables to create a unified chronological description for each intervention, removing syntactic noise and sensitive identifiers.
2. Taxonomy Alignment and System Code Corrections: Using the Reference Designation System for Power Plants (RDS-PP) as a baseline, the LLM performs a three-step audit: correcting pitch system misclassifications (often incorrectly nested under rotor blades), recovering missing system codes, and auditing semantic discrepancies between assigned codes and text descriptions.
3. Semantic Extraction of Empirical Taxonomies: Logs are batched by corrected system codes and specific component topologies (e.g., electrical vs. hydraulic pitch systems). The LLM analyzes these batches to construct evidence-based dictionaries of maintenance actions and failure modes, defining failure modes by a name, dominant mechanism, observed symptom, and candidate cause.
4. Granular Labelling: The LLM processes individual logs to apply the derived taxonomies. It reclassifies maintenance types (e.g., distinguishing between corrective repairs, inspections, and resets) and assigns specific action labels. Crucially, the system employs a Chain-of-Thought (CoT) reasoning framework, requiring the model to generate a rationale and a self-assigned confidence score before finalizing a label.
5. Safeguards and Validation: The pipeline incorporates deterministic rules to handle non-failure events (e.g., routine inspections) and "escape categories" for uninformative data. A randomised sample of 300 logs was manually reviewed, showing an 80–90% agreement rate between LLM outputs and expert judgment.

Key Contributions

• Autonomous Data Structuring: The framework successfully structured over 70% of the dataset, autonomously correcting hierarchical system codes and extracting granular failure mode profiles without the need for pre-existing, manually labeled training data.
• Correction of Systemic Misclassifications: The study identified and resolved pervasive errors, such as the misclassification of pitch system faults under broader rotor blade categories and the mislabeling of inspections as corrective repairs.
• Evidence-Based Taxonomy Generation: Unlike traditional FMEA which relies on subjective expert panels, this method derives failure mode dictionaries directly from aggregated historical evidence, capturing dominant degradation mechanisms and candidate causes specific to different hardware topologies.
• Scalable and Cost-Effective Pipeline: The approach demonstrates that LLMs can process complex industrial data at a fraction of the time and cost of manual expert review, with a total processing cost of approximately $370 for the entire dataset (approx. $0.0226 per log).

Results
The application of the pipeline yielded significant quantitative and qualitative shifts in the data:

• System Code Recovery: 73.0% of logs with hierarchical defects were successfully relabelled. Notably, the pitch system extraction achieved a 99.1% success rate, isolating faults previously hidden under generic codes.
• Maintenance Type Correction: 24.50% of the global dataset had its maintenance type corrected. A major finding was the reclassification of many "corrective" logs into "preventive" or "inspection" categories, which is vital for accurate failure rate calculations.
• Action Granularity: The pipeline recovered specific action labels for 8.27% of previously empty logs and inverted the legacy ratio of "repairs" to "replacements," revealing that replacements were far more common than generic "repair" labels suggested.
• Failure Mode Extraction: 71.5% of the dataset was assigned a comprehensive failure mode profile. The resulting empirical dictionary highlighted specific vulnerabilities, such as blade coating damage and vibration sensor detachment, and revealed distinct degradation profiles between electrical and hydraulic pitch systems.
• Analytical Impact: The structured data altered the perceived failure rate rankings of major systems. For instance, the pitch system emerged as a distinct category (9.67% of data), while the perceived failure rates of rotor blades and hydraulic systems decreased significantly after removing non-failure events and correcting misclassifications.

Significance
The paper claims that this methodology provides a highly scalable, cost-effective blueprint for translating qualitative field observations into quantitative reliability metrics. By moving beyond simple text classification to deep semantic reasoning, the framework addresses the inherent subjectivity of traditional FMEA and the data bottlenecks of legacy ML approaches.

The significance lies in its ability to:

1. Unlock Historical Data: Make decades of unstructured maintenance logs accessible for advanced reliability analytics and root-cause analysis.
2. Standardize Industry Practices: Offer a pathway to transition from static, theoretical taxonomies to dynamic, evidence-based maintenance taxonomies derived from actual operational data.
3. Enable Advanced Analytics: Lay the foundation for integrated root-cause analysis, improved FMEA with rigorously quantified risk parameters (occurrence, severity, detection), and advanced predictive maintenance strategies across the renewable energy sector.

The authors emphasize that while the study focuses on wind turbines, the modular architecture is applicable to other heavy industries relying on CMMS, provided a domain-specific taxonomy is supplied. The work concludes that this approach represents a systemic shift toward evidence-based asset management, reducing reliance on subjective expert estimation.