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Early Fault Detection of Wind Turbines Based on Operational Condition Clustering and Optimized Deep Belief Network Modeling

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Outgoing claims: the papers this work cites, builds on, or argues with.

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Online wind turbine fault detection through automated SCADA data analysis
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Wind turbine condition monitoring based on SCADA data using normal behavior models. Part 1: System description
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Wind turbine condition monitoring based on SCADA data using normal behavior models. Part 2: Application examples
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Unsupervised local deep feature for image recognition
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Fault diagnosis of rolling bearings using a genetic algorithm optimized neural network
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Health monitoring of electronic products based on Mahalanobis distance and Weibull decision metrics
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A novel adaptive fault detection methodology for complex system using deep belief networks and multiple models: A case study on cryogenic propellant loading system
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Data clustering: 50 years beyond K-means
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The prediction and diagnosis of wind turbine faults
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Wind turbine performance assessment using multi-regime modeling approach
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Analyzing bearing faults in wind turbines: A data-mining approach
inferredcites →https://doi.org/10.1016/j.renene.2012.11.030openalex-snapshot
Wind turbine condition monitoring by the approach of SCADA data analysis
inferredcites →https://doi.org/10.1016/j.renene.2016.12.075openalex-snapshot
Detection and classification of faults in pitch-regulated wind turbine generators using normal behaviour models based on performance curves
inferredcites →https://doi.org/10.1016/j.renene.2017.09.003openalex-snapshot
An approach combining data mining and control charts-based model for fault detection in wind turbines
inferredcites →https://doi.org/10.1016/j.ymssp.2015.03.003openalex-snapshot
A time–frequency analysis approach for condition monitoring of a wind turbine gearbox under varying load conditions
inferredcites →https://doi.org/10.1088/0957-0233/26/11/115002openalex-snapshot
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A Survey on Wind Turbine Condition Monitoring and Fault Diagnosis—Part I: Components and Subsystems
inferredcites →https://doi.org/10.1109/tim.2017.2669947openalex-snapshot
Multisensor Feature Fusion for Bearing Fault Diagnosis Using Sparse Autoencoder and Deep Belief Network
inferredcites →https://doi.org/10.1109/tim.2017.2698738openalex-snapshot
Stacked Multilevel-Denoising Autoencoders: A New Representation Learning Approach for Wind Turbine Gearbox Fault Diagnosis
inferredcites →https://doi.org/10.1109/tmech.2017.2759301openalex-snapshot
Wind Turbine Fault Detection Using a Denoising Autoencoder With Temporal Information
inferredcites →https://doi.org/10.1109/tsg.2014.2386305openalex-snapshot
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inferredcites →https://doi.org/10.1109/tste.2011.2163430openalex-snapshot
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Training Products of Experts by Minimizing Contrastive Divergence
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A Fast Learning Algorithm for Deep Belief Nets
inferredcites →https://doi.org/10.1177/1748006x16686899openalex-snapshot
Wind turbine prognostics and maintenance management based on a hybrid approach of neural networks and a proportional hazards model
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inferredcites →https://doi.org/10.2307/2347111openalex-snapshot
Algorithm AS 89: The Upper Tail Probabilities of Spearman's Rho
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Kendall's and Spearman's Correlation Coefficients in the Presence of a Blocking Variable
inferredcites →https://doi.org/10.3390/en7042595openalex-snapshot
Wind Turbine Condition Monitoring: State-of-the-Art Review, New Trends, and Future Challenges
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Representational Learning for Fault Diagnosis of Wind Turbine Equipment: A Multi-Layered Extreme Learning Machines Approach

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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The Comprehensive Review of Wind Turbine Main Bearing Failures, Reinforcement Measures and Condition Monitoring
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