Wind Turbine Anomaly Detection Based on SCADA Data Mining

In this paper, a wind turbine anomaly detection method based on a generalized feature extraction is proposed. Firstly, wind turbine (WT) attributes collected from the Supervisory Control And Data Acquisition (SCADA) system are clustered with k-means, and the Silhouette Coefficient (SC) is adopted to judge the effectiveness of clustering. Correlation between attributes within a class becomes larger, correlation between classes becomes smaller by clustering. Then, dimensions of attributes within classes are reduced based on t-Distributed-Stochastic Neighbor Embedding (t-SNE) so that the low-dimensional attributes can be more full and more concise in reflecting the WT attributes. Finally, the detection model is trained and the normal or abnormal state is detected by the classification result 0 or 1 respectively. Experiments consists of three cases with SCADA data demonstrate the effectiveness of the proposed method.

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Behavior Based Anomaly Detection Model in SCADA System

MATEC Web of Conferences ◽

10.1051/matecconf/201817301011 ◽

2018 ◽

Vol 173 ◽

pp. 01011 ◽

Cited By ~ 1

Author(s):

Xiaojun Zhou ◽

Zhen Xu ◽

Liming Wang ◽

Kai Chen ◽

Cong Chen ◽

...

Keyword(s):

Anomaly Detection ◽

Control Systems ◽

Supervisory Control ◽

Industry 4.0 ◽

Industrial Control ◽

Industrial Control Systems ◽

Detection Model ◽

Scada System ◽

Behavior Based ◽

And Control

With the arrival of Industry 4.0, more and more industrial control systems are connected with the outside world, which brings tremendous convenience to industrial production and control, and also introduces many potential security hazards. After a large number of attack cases analysis, we found that attacks in SCADA systems can be divided into internal attacks and external attacks. Both types of attacks are inevitable. Traditional firewalls, IDSs and IPSs are no longer suitable for industrial control systems. Therefore, we propose behavior-based anomaly detection and build three baselines of normal behaviors. Experiments show that using our proposed detection model, we can quickly detect a variety of attacks on SCADA (Supervisory Control And Data Acquisition) systems.

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An anomaly detection method based on double encoder–decoder generative adversarial networks

Industrial Robot the international journal of robotics research and application ◽

10.1108/ir-09-2020-0200 ◽

2020 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Hui Liu ◽

Tinglong Tang ◽

Jake Luo ◽

Meng Zhao ◽

Baole Zheng ◽

...

Keyword(s):

Anomaly Detection ◽

Latent Variables ◽

Dimensional Space ◽

Superior Performance ◽

Generative Adversarial Networks ◽

Training Process ◽

Content Type ◽

Detection Model ◽

Adversarial Networks ◽

Low Dimensional

Purpose This study aims to address the challenge of training a detection model for the robot to detect the abnormal samples in the industrial environment, while abnormal patterns are very rare under this condition. Design/methodology/approach The authors propose a new model with double encoder–decoder (DED) generative adversarial networks to detect anomalies when the model is trained without any abnormal patterns. The DED approach is used to map high-dimensional input images to a low-dimensional space, through which the latent variables are obtained. Minimizing the change in the latent variables during the training process helps the model learn the data distribution. Anomaly detection is achieved by calculating the distance between two low-dimensional vectors obtained from two encoders. Findings The proposed method has better accuracy and F1 score when compared with traditional anomaly detection models. Originality/value A new architecture with a DED pipeline is designed to capture the distribution of images in the training process so that anomalous samples are accurately identified. A new weight function is introduced to control the proportion of losses in the encoding reconstruction and adversarial phases to achieve better results. An anomaly detection model is proposed to achieve superior performance against prior state-of-the-art approaches.

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