The detection of generator bearing failures on wind turbines using machine learning based anomaly detection

Fault Detection of Anti-friction Bearing using Ensemble Machine Learning Methods

Anti-Friction Bearing (AFB) is a very important machine component and its unscheduled failure leads to cause of malfunction in wide range of rotating machinery which results in unexpected downtime and economic loss. In this paper, ensemble machine learning techniques are demonstrated for the detection of different AFB faults. Initially, statistical features were extracted from temporal vibratio...

Machine Learning for Host-based Anomaly Detection

Machine Learning for Host-based Anomaly Detection by Gaurav Tandon Dissertation Advisor: Philip K. Chan, Ph.D. Anomaly detection techniques complement signature based methods for intrusion detection. Machine learning approaches are applied to anomaly detection for automated learning and detection. Traditional host-based anomaly detectors model system call sequences to detect novel attacks. This...

Behaviour Based Anomaly Detection for Smartphones Using Machine Learning Algorithm

Since the first handheld cellular phone was introduced in 1973, the mobile phones have evolved into immensely popular smartphones. These devices provide all-in-one expediency by integrating traditional mobile phones with handheld computing devices making them more open and general purpose. Smartphones have become hosts for sensitive or personal data and applications. However many smartphones ar...

Condition Parameter Modeling for Anomaly Detection in Wind Turbines

Data collected from the supervisory control and data acquisition (SCADA) system, used widely in wind farms to obtain operational and condition information about wind turbines (WTs), is of important significance for anomaly detection in wind turbines. The paper presents a novel model for wind turbine anomaly detection mainly based on SCADA data and a back-propagation neural network (BPNN) for au...

Anomaly detection with Machine learning