Early Detection and Diagnosis of Wind Turbine Abnormal Conditions Using an Interpretable Supervised Variational Autoencoder Model

The operation and maintenance of wind turbines benefit from reliable information on the wind turbine condition. Data-driven models use data from the supervisory data acquisition system. In particular, great performance is reported for artificial intelligence models. However, the lack of interpretabi...

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Veröffentlicht in:	Energies (Basel) Jg. 16; H. 12; S. 4544
Hauptverfasser:	Oliveira-Filho, Adaiton, Zemouri, Ryad, Cambron, Philippe, Tahan, Antoine
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Basel MDPI AG 01.06.2023
Schlagworte:	Air-turbines Analysis Bearings Case studies condition monitoring diagnosis early detection False alarms Photovoltaic cells Preventive maintenance SCADA data Turbines variational autoencoder Visualization wind turbine Canada
ISSN:	1996-1073, 1996-1073
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Abstract	The operation and maintenance of wind turbines benefit from reliable information on the wind turbine condition. Data-driven models use data from the supervisory data acquisition system. In particular, great performance is reported for artificial intelligence models. However, the lack of interpretability limits their effective industrial implementation. The present work introduces a new condition-monitoring approach for wind turbines featuring a built-in visualization tool that confers interpretability upon the model outcomes. The proposed approach is based on a supervised implementation of the variational autoencoder model, which allows the projection of the wind turbine system onto a low-dimensional representation space. Three outcomes follow from such representation: a health indicator for the early detection of abnormal conditions, a classifier providing the diagnosis status, and a visualization tool depicting the wind turbine condition as a trajectory in a 2D plot. The approach is implemented with a vast database. Two case studies demonstrate the potential of the proposed approach. The proposed health indicator detects the main bearing overtemperature 11 days before the control system alarm, one week earlier than a competing approach. Study cases illustrate that the built-in visualization tool enhances the interpretability and trust in the model outcomes, thus supporting wind turbine operation and maintenance.
AbstractList	The operation and maintenance of wind turbines benefit from reliable information on the wind turbine condition. Data-driven models use data from the supervisory data acquisition system. In particular, great performance is reported for artificial intelligence models. However, the lack of interpretability limits their effective industrial implementation. The present work introduces a new condition-monitoring approach for wind turbines featuring a built-in visualization tool that confers interpretability upon the model outcomes. The proposed approach is based on a supervised implementation of the variational autoencoder model, which allows the projection of the wind turbine system onto a low-dimensional representation space. Three outcomes follow from such representation: a health indicator for the early detection of abnormal conditions, a classifier providing the diagnosis status, and a visualization tool depicting the wind turbine condition as a trajectory in a 2D plot. The approach is implemented with a vast database. Two case studies demonstrate the potential of the proposed approach. The proposed health indicator detects the main bearing overtemperature 11 days before the control system alarm, one week earlier than a competing approach. Study cases illustrate that the built-in visualization tool enhances the interpretability and trust in the model outcomes, thus supporting wind turbine operation and maintenance.
Audience	Academic
Author	Tahan, Antoine Oliveira-Filho, Adaiton Cambron, Philippe Zemouri, Ryad
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References	ref_50 Wang (ref_16) 2018; 9 Badihi (ref_7) 2022; 110 ref_57 ref_11 ref_54 ref_53 Dao (ref_1) 2019; 22 Hemmer (ref_18) 2020; 8 ref_17 Zhao (ref_15) 2018; 127 Cheng (ref_14) 2022; 120 Deng (ref_43) 2012; 29 Guo (ref_35) 2021; 179 Chadebec (ref_52) 2022; 45 Wu (ref_20) 2019; 7 Farnham (ref_56) 2021; 16 Roelofs (ref_23) 2021; 4 ref_29 ref_26 Watson (ref_4) 2017; 11 Kingma (ref_39) 2019; 12 Banko (ref_13) 2021; 7 Hochart (ref_32) 2008; 11 Kaewniam (ref_59) 2022; 167 ref_36 Lipton (ref_10) 2018; 16 ref_33 Proteau (ref_12) 2020; 111 ref_31 Jiang (ref_19) 2017; 23 ref_37 (ref_38) 2018; 110 Bangalore (ref_21) 2020; 157 Cambron (ref_55) 2017; 23 Peeters (ref_25) 2018; 116 Roberts (ref_30) 1959; 1 Liu (ref_28) 2021; 183 Zemouri (ref_8) 2019; 8 Lei (ref_22) 2019; 133 ref_47 ref_46 Tanner (ref_51) 1987; 82 ref_45 ref_44 Zhang (ref_27) 2022; 71 ref_42 Stetco (ref_24) 2019; 133 ref_41 ref_40 Helbing (ref_6) 2018; 98 ref_3 ref_2 Pandit (ref_34) 2022; 1 ref_49 ref_48 ref_9 (ref_58) 2020; 161 ref_5
References_xml	– volume: 23 start-page: 479 year: 2017 ident: ref_55 article-title: Bearing temperature monitoring of a wind turbine using physics-based model publication-title: J. Qual. Maint. Eng. doi: 10.1108/JQME-06-2016-0028 – volume: 120 start-page: 4803 year: 2022 ident: ref_14 article-title: Ball bearing multiple failure diagnosis using feature-selected autoencoder model publication-title: Int. J. Adv. Manuf. Technol. doi: 10.1007/s00170-022-09054-x – ident: ref_5 – ident: ref_11 doi: 10.21437/Interspeech.2019-1426 – volume: 82 start-page: 528 year: 1987 ident: ref_51 article-title: The calculation of posterior distributions by data augmentation publication-title: J. Am. Stat. Assoc. doi: 10.1080/01621459.1987.10478458 – ident: ref_3 doi: 10.1002/9781119436805 – ident: ref_26 doi: 10.1007/978-3-030-05971-2 – ident: ref_31 doi: 10.3390/s21041512 – ident: ref_29 doi: 10.1109/PHM2022-London52454.2022.00069 – volume: 11 start-page: 319 year: 2008 ident: ref_32 article-title: Wind turbine performance under icing conditions publication-title: Wind Energy Int. J. Prog. Appl. Wind Power Convers. Technol. – volume: 127 start-page: 825 year: 2018 ident: ref_15 article-title: Anomaly detection and fault analysis of wind turbine components based on deep learning network publication-title: Renew. Energy doi: 10.1016/j.renene.2018.05.024 – volume: 8 start-page: 35842 year: 2020 ident: ref_18 article-title: Health indicator for low-speed axial bearings using variational autoencoders publication-title: IEEE Access doi: 10.1109/ACCESS.2020.2974942 – volume: 45 start-page: 2879 year: 2022 ident: ref_52 article-title: Data augmentation in high dimensional low sample size setting using a geometry-based variational autoencoder publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/TPAMI.2022.3185773 – ident: ref_48 – volume: 116 start-page: 74 year: 2018 ident: ref_25 article-title: Vibration-based bearing fault detection for operations and maintenance cost reduction in wind energy publication-title: Renew. Energy doi: 10.1016/j.renene.2017.01.056 – ident: ref_49 doi: 10.1109/PHM2022-London52454.2022.00055 – volume: 22 start-page: 1848 year: 2019 ident: ref_1 article-title: Wind turbine reliability data review and impacts on levelised cost of energy publication-title: Wind Energy doi: 10.1002/we.2404 – ident: ref_41 – ident: ref_50 doi: 10.17148/IARJSET.2015.2305 – ident: ref_17 – ident: ref_45 – volume: 98 start-page: 189 year: 2018 ident: ref_6 article-title: Deep Learning for fault detection in wind turbines publication-title: Renew. Sustain. Energy Rev. doi: 10.1016/j.rser.2018.09.012 – volume: 179 start-page: 1098 year: 2021 ident: ref_35 article-title: Wind turbine blade icing detection with multi-model collaborative monitoring method publication-title: Renew. Energy doi: 10.1016/j.renene.2021.07.120 – ident: ref_36 doi: 10.3390/en15145280 – volume: 16 start-page: 31 year: 2018 ident: ref_10 article-title: The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery publication-title: Queue doi: 10.1145/3236386.3241340 – ident: ref_42 doi: 10.1109/PHM2022-London52454.2022.00042 – ident: ref_53 – volume: 8 start-page: 5438 year: 2019 ident: ref_8 article-title: Deep convolutional variational autoencoder as a 2D-visualization tool for partial discharge source classification in hydrogenerators publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2962775 – volume: 7 start-page: 1 year: 2021 ident: ref_13 article-title: Deep learning for visualization and novelty detection in large X-ray diffraction datasets publication-title: Npj Comput. Mater. doi: 10.1038/s41524-021-00575-9 – volume: 111 start-page: 3597 year: 2020 ident: ref_12 article-title: Dimension reduction and 2D-visualization for early change of state detection in a machining process with a variational autoencoder approach publication-title: Int. J. Adv. Manuf. Technol. doi: 10.1007/s00170-020-06338-y – volume: 133 start-page: 620 year: 2019 ident: ref_24 article-title: Machine learning methods for wind turbine condition monitoring: A review publication-title: Renew. Energy doi: 10.1016/j.renene.2018.10.047 – ident: ref_2 doi: 10.3390/app11041386 – ident: ref_47 – volume: 9 start-page: 2824 year: 2018 ident: ref_16 article-title: Wind Turbine Blade Breakage Monitoring with Deep Autoencoders publication-title: IEEE Trans. Smart Grid doi: 10.1109/TSG.2016.2621135 – volume: 16 start-page: 064056 year: 2021 ident: ref_56 article-title: How unprecedented was the February 2021 Texas cold snap? publication-title: Environ. Res. Lett. doi: 10.1088/1748-9326/ac0278 – ident: ref_37 doi: 10.3390/app11178065 – ident: ref_40 – volume: 12 start-page: 307 year: 2019 ident: ref_39 article-title: An introduction to variational autoencoders publication-title: Found. Trends® Mach. Learn. doi: 10.1561/2200000056 – volume: 183 start-page: 109810 year: 2021 ident: ref_28 article-title: Sparse dictionary learning based adversarial variational auto-encoders for fault identification of wind turbines publication-title: Measurement doi: 10.1016/j.measurement.2021.109810 – ident: ref_9 doi: 10.1109/DSAA.2018.00018 – volume: 71 start-page: 1 year: 2022 ident: ref_27 article-title: The multiclass fault diagnosis of wind turbine bearing based on multisource signal fusion and deep learning generative model publication-title: IEEE Trans. Instrum. Meas. – ident: ref_44 – volume: 23 start-page: 89 year: 2017 ident: ref_19 article-title: Wind Turbine Fault Detection Using a Denoising Autoencoder With Temporal Information publication-title: IEEE-Asme Trans. Mechatron. doi: 10.1109/TMECH.2017.2759301 – volume: 29 start-page: 141 year: 2012 ident: ref_43 article-title: The mnist database of handwritten digit images for machine learning research [best of the web] publication-title: IEEE Signal Process. Mag. doi: 10.1109/MSP.2012.2211477 – volume: 157 start-page: 647 year: 2020 ident: ref_21 article-title: System-wide anomaly detection in wind turbines using deep autoencoders publication-title: Renew. Energy doi: 10.1016/j.renene.2020.04.148 – volume: 110 start-page: 754 year: 2022 ident: ref_7 article-title: A Comprehensive Review on Signal-Based and Model-Based Condition Monitoring of Wind Turbines: Fault Diagnosis and Lifetime Prognosis publication-title: Proc. IEEE doi: 10.1109/JPROC.2022.3171691 – ident: ref_57 doi: 10.2172/1812486 – volume: 133 start-page: 422 year: 2019 ident: ref_22 article-title: Fault diagnosis of wind turbine based on Long Short-term memory networks publication-title: Renew. Energy doi: 10.1016/j.renene.2018.10.031 – volume: 1 start-page: 20 year: 2022 ident: ref_34 article-title: SCADA data for wind turbine data-driven condition/performance monitoring: A review on state-of-art, challenges and future trends publication-title: Wind Eng. – volume: 4 start-page: 100065 year: 2021 ident: ref_23 article-title: Autoencoder-based anomaly root cause analysis for wind turbines publication-title: Energy AI doi: 10.1016/j.egyai.2021.100065 – ident: ref_33 – ident: ref_54 – ident: ref_46 – volume: 110 start-page: 119 year: 2018 ident: ref_38 article-title: Effects of the pre-processing algorithms in fault diagnosis of wind turbines publication-title: Environ. Model. Softw. doi: 10.1016/j.envsoft.2018.05.002 – volume: 167 start-page: 112723 year: 2022 ident: ref_59 article-title: Recent advances in damage detection of wind turbine blades: A state-of-the-art review publication-title: Renew. Sustain. Energy Rev. doi: 10.1016/j.rser.2022.112723 – volume: 11 start-page: 382 year: 2017 ident: ref_4 article-title: Using SCADA data for wind turbine condition monitoring—A review publication-title: IET Renew. Power Gener. doi: 10.1049/iet-rpg.2016.0248 – volume: 7 start-page: 59376 year: 2019 ident: ref_20 article-title: A Multi-Level-Denoising Autoencoder Approach for Wind Turbine Fault Detection publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2914731 – volume: 161 start-page: 998 year: 2020 ident: ref_58 article-title: A review of non-destructive testing on wind turbines blades publication-title: Renew. Energy doi: 10.1016/j.renene.2020.07.145 – volume: 1 start-page: 239 year: 1959 ident: ref_30 article-title: Control Chart Tests Based on Geometric Moving Averages publication-title: Technometrics doi: 10.1080/00401706.1959.10489860
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SubjectTerms	Air-turbines Analysis Bearings Case studies condition monitoring diagnosis early detection False alarms Photovoltaic cells Preventive maintenance SCADA data Turbines variational autoencoder Visualization wind turbine
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Title	Early Detection and Diagnosis of Wind Turbine Abnormal Conditions Using an Interpretable Supervised Variational Autoencoder Model
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