Wind Turbine Failure Forecasting Using Cepstral Sensor Features
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Solution Overview
Problem
Existing systems struggle to accurately and proactively predict component failures in wind turbines due to overwhelming data volumes and computational inefficiencies, leading to reactive maintenance and increased downtime and costs.
Innovation Solution
A scalable system that utilizes cepstral analysis and machine learning algorithms to process sensor data from wind turbines, transforming it into cepstrum domains to extract critical features, train models for fault detection, and provide proactive failure predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional sensor data processing methods are used, then data collection is simple, but predictive accuracy is insufficient due to overwhelming data volumes and computational inefficiencies
Solution Approach 1:
The patent extracts only the most relevant features from the overwhelming sensor data through cepstral analysis. By transforming raw vibration and acoustic data into cepstrum domain representations, the system isolates critical periodicities and patterns that indicate component failures, discarding redundant information and achieving high predictive accuracy with manageable computational load
Solution Approach 2:
The patent changes the parameter domain from raw time-domain sensor data to cepstrum domain parameters. This transformation reveals periodic patterns and fault characteristics that are invisible in raw data, enabling accurate failure prediction while reducing computational complexity through dimensionality reduction
2Reliability
If comprehensive sensor data is collected from all wind turbines, then detection coverage is improved, but processing time increases due to data volume
Solution Approach 1:
The system extracts only critical periodicity information from comprehensive sensor data using cepstral analysis. This selective extraction maintains full detection coverage across all wind turbines while processing only the essential failure-indicating patterns, dramatically reducing processing time despite comprehensive data collection
Solution Approach 2:
The patent applies cepstral transformation as a preliminary processing step that pre-extracts and organizes critical patterns from raw data. This preliminary action structures the data in advance, enabling faster subsequent analysis and failure prediction across multiple turbines without sacrificing detection coverage
3Productivity
If reactive maintenance is used, then maintenance costs are lower, but downtime increases due to unexpected failures
Solution Approach 1:
The system performs preliminary failure detection by continuously analyzing cepstrum features and identifying patterns that precede component failures. By detecting anomalies before actual failures occur, the system enables scheduled maintenance activities, eliminating unexpected downtime while maintaining cost-effectiveness through targeted interventions
Solution Approach 2:
The patent implements a feedback mechanism where cepstrum analysis results continuously inform maintenance decisions. The system monitors extracted features, compares them against failure patterns, and provides early warnings that trigger proactive maintenance actions, transforming reactive maintenance into a predictive framework that reduces downtime
Data Source
AI summary
Example systems and methods comprise receiving sensor measurements including time data from one or more wind turbines over time, aligning time domain data of the sensor measurements of a particular wind turbine with a rotation speed of the particular wind turbine, the particular wind turbine being at least one of the one or more wind turbines, transforming the aligned time domain data to obtain a cepstrum data, identifying one or more quefrency components of the cepstrum data that correspond to periodicities of interest, classifying at least one of the one or more quefrency components with future failure of at least one component of the particular wind turbine, and providing an alert to a user based on the classification to alert the user of a predicted failure of the particular wind turbine.