Abstract
Autonomous fault detection plays a major role in the Critical Energy Infrastructure (CEI) domain, since sensor faults cause irreparable damage and lead to incorrect results on the condition monitoring of Cyber-Physical (CP) systems. This paper focuses on the challenging application of wind turbine (WT) monitoring. Specifically, we propose the two challenging architectures based on learning deep features, namely—Long Short Term Memory-Stacked Autoencoders (LSTM-SAE), and Convolutional Neural Network (CNN-SAE), for semi-supervised fault detection in wind CPs. The internal learnt features will facilitate the classification task by assigning each upcoming measurement into its corresponding faulty/normal operation status. To illustrate the quality of our schemes, their performance is evaluated against real-world’s wind turbine data. From the experimental section we are able to validate that both LSTM-SAE and CNN-SAE schemes provide high classification scores, indicating the high detection rate of the fault level of the wind turbines. Additionally, slight modification on our architectures are able to be applied on different fault/anomaly detection categories on variant Cyber-Physical systems.
Highlights
Nowadays, the demand for designing autonomous condition assessment and fault detection of cyber-physical systems and critical energy infrastructures has drawn tremendously
In order to tackle the aforementioned limitations we design our proposed Deep Learning (DL) schemes, namely: Long-Short Term Memory [15]-Stacked Autoencoders [16] (LSTM-SAE), and the Convolutional [17,18]-Stacked Autoencoders (CNN-SAE) in order to address the problem of semi-supervised wind turbine fault detection
The first architecture that we developed adheres to a Long Short Term Memory- Stacked Autoencoders (LSTM-SAE) scheme, while the second one follows a Convolutional Neural Network- Stacked Autoencoders (CNN-SAE) formulation
Summary
The demand for designing autonomous condition assessment and fault detection of cyber-physical systems and critical energy infrastructures has drawn tremendously. A major cause regards the current and widely-diverse structure of CEIs that makes extremely difficult the physical monitoring On this direction, wind turbine (WT) systems are considered among the most complex Cyber-Physical infrastructures causing huge (cascading) effects to other critical infrastructures, such as Electrical Power and Energy Systems (EPES), communications, transportation, industry and finance. In order to tackle the aforementioned limitations we design our proposed Deep Learning (DL) schemes, namely: Long-Short Term Memory [15]-Stacked Autoencoders [16] (LSTM-SAE), and the Convolutional [17,18]-Stacked Autoencoders (CNN-SAE) in order to address the problem of semi-supervised wind turbine fault detection. The proposed architectures can be extended to detect complex fault patterns or new anomaly types, that vary significantly from the current operating status, while they can be applied to detect abnormal patterns in other cyber-physical systems’ applications.
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