Anomaly Detection on Wind Turbines Based on a Deep Learning Analysis of Vibration Signals

ABSTRACT In this paper, we present a Semi-Supervised Deep Learning approach for anomaly detection of Wind Turbine generators based on vibration signals. The proposed solution is integrated into an IoT Platform as a real-time Workflow. The Workflow is responsible for the whole detection process when a new sample is inserted in the IoT Platform, performing data gathering, preprocessing, feature extraction, and classification. The chosen Semi-Supervised Deep Learning model is a DAE, which builds a normality model using healthy data. The classification consists of comparing the reconstruction error for the computed entry with a normality threshold. The normality threshold is selected through an F1-Score analysis of the reconstruction errors over labeled data. Finally, the Workflow can produce notifications to the users whenever unhealthy behavior is noticed. The ability of the proposed mechanism to detect abnormal behavior in wind turbines on an IoT Platform is evaluated using a case study of real-world healthy and unhealthy data from a Wind Turbine. The solution was able to correctly classify every unhealthy sample and presented a low false-positive rate. Moreover, Workflow results can be improved by conditioning alarm triggering with a windowed-based anomaly accumulation.

[1]  Zepeng Liu,et al.  A review of failure modes, condition monitoring and fault diagnosis methods for large-scale wind turbine bearings , 2020 .

[2]  Haidong Shao,et al.  A novel deep autoencoder feature learning method for rotating machinery fault diagnosis , 2017 .

[3]  Krishna Mohan Mishra,et al.  Fault Detection of Elevator Systems Using Deep Autoencoder Feature Extraction , 2019, 2019 13th International Conference on Research Challenges in Information Science (RCIS).

[4]  Shaoguang Liu,et al.  A Novel Autoencoder with Dynamic Feature Enhanced Factor for Fault Diagnosis of Wind Turbine , 2020, Electronics.

[5]  John P. Weyant,et al.  Costs of Reducing Global Carbon Emissions , 1993 .

[6]  Sunil Tyagi,et al.  An SVM—ANN Hybrid Classifier for Diagnosis of Gear Fault , 2017, Appl. Artif. Intell..

[7]  Wahyu Caesarendra,et al.  A Review of Feature Extraction Methods in Vibration-Based Condition Monitoring and Its Application for Degradation Trend Estimation of Low-Speed Slew Bearing , 2017 .

[8]  Sung-Ki Lyu,et al.  A Review of Recent Advances in Design Optimization of Gearbox , 2018, International Journal of Precision Engineering and Manufacturing.

[9]  Alex Maurício Araújo,et al.  A review on wind turbine control and its associated methods , 2018 .

[10]  Li Yang,et al.  Spectral Entropy Can Predict Changes of Working Memory Performance Reduced by Short-Time Training in the Delayed-Match-to-Sample Task , 2017, Front. Hum. Neurosci..

[11]  Goran Nenadic,et al.  Machine learning methods for wind turbine condition monitoring: A review , 2019, Renewable Energy.

[12]  Liu Qihe.  Integration of Vibration Acceleration Signal Based on LabVIEW , 2019, Journal of Physics: Conference Series.

[13]  Ali Gökhan Yavuz,et al.  Network Anomaly Detection with Stochastically Improved Autoencoder Based Models , 2017, 2017 IEEE 4th International Conference on Cyber Security and Cloud Computing (CSCloud).

[14]  Yoshua Bengio,et al.  Greedy Layer-Wise Training of Deep Networks , 2006, NIPS.

[15]  Fausto Pedro García Márquez,et al.  Machine Learning for Wind Turbine Blades Maintenance Management , 2017 .

[16]  Alexander Dekhtyar,et al.  Information Retrieval , 2018, Lecture Notes in Computer Science.

[17]  Pierre Baldi,et al.  Understanding Dropout , 2013, NIPS.

[18]  Victoria M. Catterson,et al.  Diagnosis of tidal turbine vibration data through deep neural networks , 2016 .

[19]  Konstantinos C. Gryllias,et al.  Rolling element bearing fault detection in industrial environments based on a K-means clustering approach , 2011, Expert Syst. Appl..

[20]  Zyad Shaaban,et al.  Data Mining: A Preprocessing Engine , 2006 .

[21]  Allaoui Tayeb,et al.  Intelligent Open Switch Fault Detection for Power Converter in Wind Energy System , 2016, Appl. Artif. Intell..

[22]  F. Porté-Agel,et al.  Wind-Turbine and Wind-Farm Flows: A Review , 2019, Boundary-Layer Meteorology.

[23]  Tim Rubert,et al.  Lifetime extension of onshore wind turbines: A review covering Germany, Spain, Denmark, and the UK , 2018 .

[24]  Antônio Augusto Fröhlich,et al.  SmartData: an IoT-ready API for sensor networks , 2018, Int. J. Sens. Networks.

[25]  Sunil Tyagi,et al.  A Hybrid Genetic Algorithm and Back-Propagation Classifier for Gearbox Fault Diagnosis , 2017, Appl. Artif. Intell..