A Novel Approach for Collective Anomaly Detection in Internet of Things

ElMenshawy, Dina; Helmy, Waleed; El-Tazi, Neamat

doi:10.1145/3379247.3379285

Cited by 3 publications

(1 citation statement)

References 9 publications

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“…There are 108 time stamped data recorded as failures in the sample. Other metrics widely used to evaluate the performance of unsupervised learning models are confusion matrix, accuracy, precision, recall, and precision [26]. For the DBSCAN algorithm, the Silhouette value is analyzed to validate the results.…”

Section: Performance Metrics For ML Methodsmentioning

confidence: 99%

Anomaly-based fault detection in wind turbines using unsupervised learning: a comparative study.

Vásquez-Rodríguez,

Maldonado-Correa

2024

IOP Conf. Ser.: Earth Environ. Sci.

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Wind energy has experienced significant growth in recent years thanks to the technological development of wind turbines (WTs). However, one of the main challenges for the wind industry remains the early detection of WT failures. An effective strategy to address this challenge is implementing condition monitoring (CM) to detect changes in WT operation that could indicate the onset of a potential failure. This paper uses data from the SCADA (Supervisory Control and Data Acquisition) system of a wind farm located in Ecuador to test three unsupervised machine learning (ML) methods to detect anomalies in the data, allowing for predicting potential WT failures. Evaluation metrics showed that the Mahalanobis Distance (MD) algorithm performed better in anomaly detection over Isolation Forest (IF) and Density-Based Spatial Clustering of Applications with Noise (DBSCAN), achieving an accuracy of 0.94, 0.90 and 0.74 respectively; however, IF more effectively detected the points determined as anomalies.

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Section: Performance Metrics For ML Methodsmentioning

confidence: 99%