Optimized Relative Transformation Matrix Using Bacterial Foraging Algorithm for Process Fault Detection

Jiang, Yi; Huang, Di; Fu, Siyao; He, Haibo; Li, Taifu

doi:10.1109/tie.2016.2515057

Cited by 39 publications

(9 citation statements)

References 31 publications

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“…To be able to deal accurately with benign outliers, we would initially need to identify them. To do so, we employ Hotelling's T -squared distribution technique Yi et al (2016).…”

Section: Benign Outliersmentioning

confidence: 99%

An anomaly-based intrusion detection system in presence of benign outliers with visualization capabilities

Karami

2018

Expert Systems with Applications

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Abnormal network traffic analysis through Intrusion Detection Systems (IDSs) and visualization techniques has considerably become an important research topic to protect computer networks from intruders. It has been still challenging to design an accurate and a robust IDS with visualization capabilities to discover security threats due to the high volume of network traffic. This research work introduces and describes a novel anomaly-based intrusion detection system in presence of long-range independence data called benign outliers, using a neural projection architecture by a modified Self-Organizing Map (SOM) to not only detect attacks and anomalies accurately, but also provide visualized information and insights to end users. The proposed approach enables better analysis by merging the large amount of network traffic into an easy-to-understand 2D format and a simple user interaction. To show the performance and validate the proposed visualization-based IDS, it has been trained and tested over synthetic and real benchmarking datasets (NSL-KDD, UNSW-NB15, AAGM and VPN-nonVPN) that are widely applied in this domain. The results of the conducted experimental study confirm the advantages and effectiveness of the proposed approach.

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“…To be able to deal accurately with benign outliers, we would initially need to identify them. To do so, we employ Hotelling's T -squared distribution technique Yi et al (2016).…”

Section: Benign Outliersmentioning

confidence: 99%

An anomaly-based intrusion detection system in presence of benign outliers with visualization capabilities

Karami

2018

Expert Systems with Applications

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“…Although these fault diagnosis approaches are able to detect certain industrial system faults, it remains a challenge to design a fault diagnosis model that meets practical production requirements [35][36][37]. In addition, how to accurately detect faults with a low complexity is still a challenge.…”

Section: Introductionmentioning

confidence: 99%

A Novel Low-Complexity Fault Diagnosis Algorithm for Energy Internet in Smart Cities

et al. 2020

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The smart energy system, viewed as an “Energy Internet”, consists of the intelligent integration of decentralized sustainable energy sources, efficient distribution, and optimized power consumption. That implies the fault diagnosis for a smart energy system should be of low complexity. In this paper, we propose a Strong Tracking Unscented Kalman Filter ( S T U K F ) and modified Bayes’ classification-based Modified Three Sigma test ( M T S ), abbreviated as S F B T , for smart energy networks. The theoretical analysis and simulations indicate that S F B T detects faults with a high accuracy and a low complexity of O ( n ) .

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“…What is more, after the standardization, it is inevitable to lose the diversity among different variables and present the property of distribution uniformity in the perspective of geometry which makes it hard to extract the principle component for compression and diagnosis. As to overcome these problem, some methods have been proposed recently [3][4][5][6][7][8]. Shi et al use the Mahalanobis distance for relative transformation to reduce the effect of the dimension standardization [4].…”

Section: Introductionmentioning

confidence: 99%

“…Tang et al propose a relative transformation principal component analysis to reduce the data noise for the transformation oil breakdown voltage prediction [5]. Yi et al introduce a relative transformation operator to change the original variables in the spatial distribution and eigenvalues of the covariance matrix in the feature space [6]. Wen et al propose a method called Relative Principle Component Analysis (RPCA); it introduces weighting for each variable based on the prior information of the system to eliminate the false information due to standardizing the variable units [7,8], but the shortage of this method is that it needs a large amount of prior information from the system which is hard to gain in real engineering application.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis Method Based on Information Entropy and Relative Principal Component Analysis

Wen

2017

Journal of Control Science and Engineering

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In traditional principle component analysis (PCA), because of the neglect of the dimensions influence between different variables in the system, the selected principal components (PCs) often fail to be representative. While the relative transformation PCA is able to solve the above problem, it is not easy to calculate the weight for each characteristic variable. In order to solve it, this paper proposes a kind of fault diagnosis method based on information entropy and Relative Principle Component Analysis. Firstly, the algorithm calculates the information entropy for each characteristic variable in the original dataset based on the information gain algorithm. Secondly, it standardizes every variable’s dimension in the dataset. And, then, according to the information entropy, it allocates the weight for each standardized characteristic variable. Finally, it utilizes the relative-principal-components model established for fault diagnosis. Furthermore, the simulation experiments based on Tennessee Eastman process and Wine datasets demonstrate the feasibility and effectiveness of the new method.

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Optimized Relative Transformation Matrix Using Bacterial Foraging Algorithm for Process Fault Detection

Cited by 39 publications

References 31 publications

An anomaly-based intrusion detection system in presence of benign outliers with visualization capabilities

An anomaly-based intrusion detection system in presence of benign outliers with visualization capabilities

A Novel Low-Complexity Fault Diagnosis Algorithm for Energy Internet in Smart Cities

Fault Diagnosis Method Based on Information Entropy and Relative Principal Component Analysis

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