A Dendritic Cell Immune System Inspired Scheme for Sensor Fault Detection and Isolation of Wind Turbines

Alizadeh, Esmaeil; Meskin, Nader; Khorasani, K.

doi:10.1109/tii.2017.2746761

Cited by 27 publications

(12 citation statements)

References 20 publications

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“…Previous research studies [15] [16] [17] demonstrate that a DCA inspired approach can effectively detect anomalies and model time series data. In this paper, we adopt the notion of integrating AIS with clustering analysis to better detect anomalies in financial stock market datasets.…”

Section: Close R Kashef Journal Of Intelligent Learning Systems mentioning

confidence: 99%

“…Although [2] is the first application of DCA in stock price manipulation, this approach has proven to be useful for similar anomaly detection problems in other domains. The authors in [15] applied DCA to the field of fault detection in Wind Turbines and showed an improvement in performance compared to different existing approaches with a FAR of less than 0.2 and an F-Score of over 0.93 for most datasets. In [16], a DCA approach is adopted for online error de-L. Close, R. Kashef Journal of Intelligent Learning Systems and Applications tection of robotic systems, where it showed the ability to detect errors in online data immediately.…”

Section: Artificial Immune Systems (Ais)mentioning

confidence: 99%

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Combining Artificial Immune System and Clustering Analysis: A Stock Market Anomaly Detection Model

Close¹,

Kashef²

2020

JILSA

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Artificial intelligence research in the stock market sector has been heavily geared towards stock price prediction rather than stock price manipulation. As online trading systems have increased the amount of high volume and real-time data transactions, the stock market has increased vulnerability to attacks. This paper aims to detect these attacks based on normal trade behavior using an Artificial Immune System (AIS) approach combined with one of four clustering algorithms. The AIS approach is inspired by its proven ability to handle time-series data and its ability to detect abnormal behavior while only being trained on regular trade behavior. These two main points are essential as the models need to adapt over time to adjust to normal trade behavior as it evolves, and due to confidentiality and data restrictions, real-world manipulations are not available for training. This paper discovers a competitive alternative to the leading approach and investigates the effects of combining AIS with clustering algorithms; Kernel Density Estimation, Self-Organized Maps, Density-Based Spatial Clustering of Applications with Noise and Spectral clustering. The best performing solution achieves leading performance using common clustering metrics, including Area Under the Curve, False Alarm Rate, False Negative Rate, and Computation Time.

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Section: Close R Kashef Journal Of Intelligent Learning Systems mentioning

confidence: 99%

Section: Artificial Immune Systems (Ais)mentioning

confidence: 99%

Combining Artificial Immune System and Clustering Analysis: A Stock Market Anomaly Detection Model

Close¹,

Kashef²

2020

JILSA

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“…It should be pointed out that the wind turbine implemented here is a simple model tailored for this study, which focuses more on the performance assessment of the gas turbine in a hybrid gas/wind power plant. However, if one pursues a high fidelity analysis of a wind turbine for its integration into a hybrid wind/gas turbine plant then the reader is prompted to studies [52,53,54,55], which explore in a more detailed fashion the dynamic behavior and control of wind turbines.…”

Section: Wind Turbine Modelmentioning

confidence: 99%

Dynamic performance simulation and control of gas turbines used for hybrid gas/wind energy applications

Tsoutsanis

Meskin

2019

Applied Thermal Engineering

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Where a licence is displayed above, please note the terms and conditions of the licence govern your use of this document. When citing, please reference the published version. Take down policy While the University of Birmingham exercises care and attention in making items available there are rare occasions when an item has been uploaded in error or has been deemed to be commercially or otherwise sensitive.

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“…Statistically, sensor failures account for approximately 15% of the total wind turbine failures [ 2 ]. Furthermore, sensor failures can cause signal corruption for condition monitoring and fault diagnosis, which in turn affect the health status of other key subassemblies, thereby reducing the reliability of the turbine and increasing economic losses [ 3 , 4 ]. As a result, it is particularly important and challenging to research effective and valuable fault detection approaches for wind turbine sensors.…”

Section: Introductionmentioning

confidence: 99%

A Multiscale Spatio-Temporal Convolutional Deep Belief Network for Sensor Fault Detection of Wind Turbine

Wang

Jiang

et al. 2020

Sensors

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Sensor fault detection of wind turbines plays an important role in improving the reliability and stable operation of turbines. The supervisory control and data acquisition (SCADA) system of a wind turbine provides promising insights into sensor fault detection due to the accessibility of the data and the abundance of sensor information. However, SCADA data are essentially multivariate time series with inherent spatio-temporal correlation characteristics, which has not been well considered in the existing wind turbine fault detection research. This paper proposes a novel classification-based fault detection method for wind turbine sensors. To better capture the spatio-temporal characteristics hidden in SCADA data, a multiscale spatio-temporal convolutional deep belief network (MSTCDBN) was developed to perform feature learning and classification to fulfill the sensor fault detection. A major superiority of the proposed method is that it can not only learn the spatial correlation information between several different variables but also capture the temporal characteristics of each variable. Furthermore, this method with multiscale learning capability can excavate interactive characteristics between variables at different scales of filters. A generic wind turbine benchmark model was used to evaluate the proposed approach. The comparative results demonstrate that the proposed method can significantly enhance the fault detection performance.

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A Dendritic Cell Immune System Inspired Scheme for Sensor Fault Detection and Isolation of Wind Turbines

Cited by 27 publications

References 20 publications

Combining Artificial Immune System and Clustering Analysis: A Stock Market Anomaly Detection Model

Combining Artificial Immune System and Clustering Analysis: A Stock Market Anomaly Detection Model

Dynamic performance simulation and control of gas turbines used for hybrid gas/wind energy applications

A Multiscale Spatio-Temporal Convolutional Deep Belief Network for Sensor Fault Detection of Wind Turbine

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