A Novel Markov Model and its Application to BIT System

2012 ◽  
Vol 239-240 ◽  
pp. 721-725
Author(s):  
Wen Rong Zheng ◽  
Shu Zong Wang
Keyword(s):  
Complex System ◽  
Fault Diagnosis ◽  
Fault Detection ◽  
Markov Model ◽  
False Alarm ◽  
False Alarm Rate ◽  
Detection Rate ◽  
Intermittent Fault ◽  
System A ◽  
Main Factor

Intermittent fault is the main factor for the raise of false alarm during the process of the detection in built-in test (BIT). Two-state Markov model and three-state Markov model for test is built for system fault diagnosis with BIT. According to the application of BIT in some complex system, a comparison of the false alarm rate between two-state Markov model and three-state Markov model is present, which shows we can reduce the false alarm rate (FAR) and improve fault detection rate by using three-state Markov model in BIT.

scholarly journals Integrated design of observer based fault detection for a class of uncertain nonlinear systems

2011 ◽  
Vol 21 (3) ◽  
pp. 423-430 ◽  
Author(s):  
Wei Chen ◽  
Abdul Khan ◽  
Muhammmad Abid ◽  
Steven Ding
Keyword(s):  
Nonlinear Systems ◽  
Fault Detection ◽  
False Alarm ◽  
False Alarm Rate ◽  
Detection Rate ◽  
Design Method ◽  
Integrated Design ◽  
Uncertain Nonlinear Systems ◽  
Trade Off ◽  
Residual Generator

Integrated design of observer based fault detection for a class of uncertain nonlinear systems Integrated design of observer based Fault Detection (FD) for a class of uncertain nonlinear systems with Lipschitz nonlinearities is studied. In the context of norm based residual evaluation, the residual generator and evaluator are designed together in an integrated form, and, based on it, a trade-off FD system is finally achieved in the sense that, for a given Fault Detection Rate (FDR), the False Alarm Rate (FAR) is minimized. A numerical example is given to illustrate the effectiveness of the proposed design method.

scholarly journals Cost-Sensitive LightGBM-Based Online Fault Detection Method for Wind Turbine Gearboxes

2021 ◽  
Vol 9 ◽  
Author(s):  
Mingzhu Tang ◽  
Qi Zhao ◽  
Huawei Wu ◽  
Zimin Wang
Keyword(s):  
Fault Detection ◽  
False Alarm ◽  
Wind Turbine ◽  
False Alarm Rate ◽  
Detection Rate ◽  
Rank Correlation ◽  
Spearman Rank Correlation ◽  
Misclassification Cost ◽  
Spearman Rank ◽  
The Cost

In practice, faulty samples of wind turbine (WT) gearboxes are far smaller than normal samples during operation, and most of the existing fault diagnosis methods for WT gearboxes only focus on the improvement of classification accuracy and ignore the decrease of missed alarms and the reduction of the average cost. To this end, a new framework is proposed through combining the Spearman rank correlation feature extraction and cost-sensitive LightGBM algorithm for WT gearbox’s fault detection. In this article, features from wind turbine supervisory control and data acquisition (SCADA) systems are firstly extracted. Then, the feature selection is employed by using the expert experience and Spearman rank correlation coefficient to analyze the correlation between the big data of WT gearboxes. Moreover, the cost-sensitive LightGBM fault detection framework is established by optimizing the misclassification cost. The false alarm rate and the missed detection rate of the WT gearbox under different working conditions are finally obtained. Experiments have verified that the proposed method can significantly improve the fault detection accuracy. Meanwhile, the proposed method can consistently outperform traditional classifiers such as AdaCost, cost-sensitive GBDT, and cost-sensitive XGBoost in terms of low false alarm rate and missed detection rate. Owing to its high Matthews correlation coefficient scores and low average misclassification cost, the cost-sensitive LightGBM (CS LightGBM) method is preferred for imbalanced WT gearbox fault detection in practice.

Multiple faults detection and isolation approach applied to a kraft recovery boiler

TAPPI Journal ◽  
2014 ◽  
Vol 13 (1) ◽  
pp. 33-41
Author(s):  
YVON THARRAULT ◽  
MOULOUD AMAZOUZ
Keyword(s):  
Fault Detection ◽  
Early Detection ◽  
False Alarm ◽  
False Alarm Rate ◽  
Principal Component ◽  
Fault Isolation ◽  
Fault Detection And Isolation ◽  
Detection Scheme ◽  
Multiple Faults ◽  
Recovery Boiler

Recovery boilers play a key role in chemical pulp mills. Early detection of defects, such as water leaks, in a recovery boiler is critical to the prevention of explosions, which can occur when water reaches the molten smelt bed of the boiler. Early detection is difficult to achieve because of the complexity and the multitude of recovery boiler operating parameters. Multiple faults can occur in multiple components of the boiler simultaneously, and an efficient and robust fault isolation method is needed. In this paper, we present a new fault detection and isolation scheme for multiple faults. The proposed approach is based on principal component analysis (PCA), a popular fault detection technique. For fault detection, the Mahalanobis distance with an exponentially weighted moving average filter to reduce the false alarm rate is used. This filter is used to adapt the sensitivity of the fault detection scheme versus false alarm rate. For fault isolation, the reconstruction-based contribution is used. To avoid a combinatorial excess of faulty scenarios related to multiple faults, an iterative approach is used. This new method was validated using real data from a pulp and paper mill in Canada. The results demonstrate that the proposed method can effectively detect sensor faults and water leakage.

scholarly journals Infrared small target detection based on region proposal and CNN classifier

2021 ◽  
Author(s):  
Mingming Fan ◽  
Shaoqing Tian ◽  
Kai Liu ◽  
Jiaxin Zhao ◽  
Yunsong Li
Keyword(s):  
Neural Network ◽  
False Alarm ◽  
False Alarm Rate ◽  
Target Detection ◽  
Detection Rate ◽  
Corner Detection ◽  
Small Target ◽  
Small Target Detection ◽  
Potential Target ◽  
Infrared Small Target

AbstractInfrared small target detection has been a challenging task due to the weak radiation intensity of targets and the complexity of the background. Traditional methods using hand-designed features are usually effective for specific background and have the problems of low detection rate and high false alarm rate in complex infrared scene. In order to fully exploit the features of infrared image, this paper proposes an infrared small target detection method based on region proposal and convolution neural network. Firstly, the small target intensity is enhanced according to the local intensity characteristics. Then, potential target regions are proposed by corner detection to ensure high detection rate of the method. Finally, the potential target regions are fed into the classifier based on convolutional neural network to eliminate the non-target regions, which can effectively suppress the complex background clutter. Extensive experiments demonstrate that the proposed method can effectively reduce the false alarm rate, and outperform other state-of-the-art methods in terms of subjective visual impression and quantitative evaluation metrics.

scholarly journals Security of Things Intrusion Detection System for Smart Healthcare

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1375
Author(s):  
Celestine Iwendi ◽  
Joseph Henry Anajemba ◽  
Cresantus Biamba ◽  
Desire Ngabo
Keyword(s):  
Genetic Algorithm ◽  
Intrusion Detection ◽  
False Alarm ◽  
False Alarm Rate ◽  
Detection Rate ◽  
Web Security ◽  
Intrusion Detection Systems ◽  
High Detection Rate ◽  
Detection Systems ◽  
Smart Healthcare

Web security plays a very crucial role in the Security of Things (SoT) paradigm for smart healthcare and will continue to be impactful in medical infrastructures in the near future. This paper addressed a key component of security-intrusion detection systems due to the number of web security attacks, which have increased dramatically in recent years in healthcare, as well as the privacy issues. Various intrusion-detection systems have been proposed in different works to detect cyber threats in smart healthcare and to identify network-based attacks and privacy violations. This study was carried out as a result of the limitations of the intrusion detection systems in responding to attacks and challenges and in implementing privacy control and attacks in the smart healthcare industry. The research proposed a machine learning support system that combined a Random Forest (RF) and a genetic algorithm: a feature optimization method that built new intrusion detection systems with a high detection rate and a more accurate false alarm rate. To optimize the functionality of our approach, a weighted genetic algorithm and RF were combined to generate the best subset of functionality that achieved a high detection rate and a low false alarm rate. This study used the NSL-KDD dataset to simultaneously classify RF, Naive Bayes (NB) and logistic regression classifiers for machine learning. The results confirmed the importance of optimizing functionality, which gave better results in terms of the false alarm rate, precision, detection rate, recall and F1 metrics. The combination of our genetic algorithm and RF models achieved a detection rate of 98.81% and a false alarm rate of 0.8%. This research raised awareness of privacy and authentication in the smart healthcare domain, wireless communications and privacy control and developed the necessary intelligent and efficient web system. Furthermore, the proposed algorithm was applied to examine the F1-score and precisionperformance as compared to the NSL-KDD and CSE-CIC-IDS2018 datasets using different scaling factors. The results showed that the proposed GA was greatly optimized, for which the average precision was optimized by 5.65% and the average F1-score by 8.2%.

scholarly journals A Multiple Target Positioning and Tracking System Behind Brick-Concrete Walls Using Multiple Monostatic IR-UWB Radars

Sensors ◽  
2019 ◽  
Vol 19 (18) ◽  
pp. 4033 ◽  
Author(s):  
Yoo ◽  
Wang ◽  
Seol ◽  
Lee ◽  
Chung ◽  
...  
Keyword(s):  
False Alarm ◽  
False Alarm Rate ◽  
Detection Rate ◽  
Tracking System ◽  
Radar System ◽  
Ultra Wideband ◽  
Radar Signal ◽  
High Detection Rate ◽  
Concrete Walls ◽  
Uwb Radar

Recognizing and tracking the targets located behind walls through impulse radio ultra-wideband (IR-UWB) radar provides a significant advantage, as the characteristics of the IR-UWB radar signal enable it to penetrate obstacles. In this study, we design a through-wall radar system to estimate and track multiple targets behind a wall. The radar signal received through the wall experiences distortion, such as attenuation and delay, and the characteristics of the wall are estimated to compensate the distance error. In addition, unlike general cases, it is difficult to maintain a high detection rate and low false alarm rate in this through-wall radar application due to the attenuation and distortion caused by the wall. In particular, the generally used delay-and-sum algorithm is significantly affected by the motion of targets and distortion caused by the wall, rendering it difficult to obtain a good performance. Thus, we propose a novel method, which calculates the likelihood that a target exists in a certain location through a detection process. Unlike the delay-and-sum algorithm, this method does not use the radar signal directly. Simulations and experiments are conducted in different cases to show the validity of our through-wall radar system. The results obtained by using the proposed algorithm as well as delay-and-sum and trilateration are compared in terms of the detection rate, false alarm rate, and positioning error.

Optimal Sensor Placement for Damage Detection in Complex Structures

2009 ◽  
Author(s):  
Sunilkumar Soni ◽  
Santanu Das ◽  
Aditi Chattopadhyay
Keyword(s):  
False Alarm ◽  
Damage Detection ◽  
False Alarm Rate ◽  
Structural Component ◽  
Detection Rate ◽  
Sensor Placement ◽  
False Alarms ◽  
Optimal Sensor Placement ◽  
Detection Scheme ◽  
Sensing Radius

An optimal sensor placement methodology is proposed based on detection theory framework to maximize the detection rate and minimize the false alarm rate. Minimizing the false alarm rate for a given detection rate plays an important role in improving the efficiency of a Structural Health Monitoring (SHM) system as it reduces the number of false alarms. The placement technique is such that the sensor features are as directly correlated and as sensitive to damage as possible. The technique accounts for a number of factors, like actuation frequency and strength, minimum damage size, damage detection scheme, material damping, signal to noise ratio (SNR) and sensing radius. These factors are not independent and affect each other. Optimal sensor placement is done in two steps. First, a sensing radius, which can capture any detectable change caused by a perturbation and above a certain threshold, is calculated. This threshold value is based on Neyman-Pearson detector that maximizes the detection rate for a fixed false alarm rate. To avoid sensor redundancy, a criterion to minimize sensing region overlaps of neighboring sensors is defined. Based on the sensing region and the minimum overlap concept, number of sensors needed on a structural component is calculated. In the second step, a damage distribution pattern, known as probability of failure distribute, is calculated for a structural component using finite element analysis. This failure distribution helps in selecting the most sensitive sensors, thereby removing those making remote contributions to the overall detection scheme.

An Intelligent Approach for Intrusion Detection using Convolutional Neural Network

2022 ◽  
Vol 8 (1) ◽  
Author(s):  
P. Manoj Kumar ◽  
M. Parvathy ◽  
C. Abinaya Devi
Keyword(s):  
Neural Network ◽  
Intrusion Detection ◽  
False Alarm ◽  
Convolutional Neural Network ◽  
False Alarm Rate ◽  
Real Time ◽  
Detection Rate ◽  
Performance Metrics ◽  
Classification Model ◽  
Real Time Traffic

Intrusion Detection Systems (IDS) is one of the important aspects of cyber security that can detect the anomalies in the network traffic. IDS are a part of Second defense line of a system that can be deployed along with other security measures such as access control, authentication mechanisms and encryption techniques to secure the systems against cyber-attacks. However, IDS suffers from the problem of handling large volume of data and in detecting zero-day attacks (new types of attacks) in a real-time traffic environment. To overcome this problem, an intelligent Deep Learning approach for Intrusion Detection is proposed based on Convolutional Neural Network (CNN-IDS). Initially, the model is trained and tested under a new real-time traffic dataset, CSE-CIC-IDS 2018 dataset. Then, the performance of CNN-IDS model is studied based on three important performance metrics namely, accuracy / training time, detection rate and false alarm rate. Finally, the experimental results are compared with those of various Deep Discriminative models including Recurrent Neural network (RNN), Deep Neural Network (DNN) etc., proposed for IDS under the same dataset. The Comparative results show that the proposed CNN-IDS model is very much suitable for modelling a classification model both in terms of binary and multi-class classification with higher detection rate, accuracy, and lower false alarm rate. The CNN-IDS model improves the accuracy of intrusion detection and provides a new research method for intrusion detection.

scholarly journals The Use of Ensemble Models for Multiple Class and Binary Class Classification for Improving Intrusion Detection Systems

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2559 ◽  
Author(s):  
Celestine Iwendi ◽  
Suleman Khan ◽  
Joseph Henry Anajemba ◽  
Mohit Mittal ◽  
Mamdouh Alenezi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
False Alarm ◽  
False Alarm Rate ◽  
Detection Rate ◽  
Detection System ◽  
Denial Of Service ◽  
Intrusion Detection Systems ◽  
Ensemble Models ◽  
Detection Systems

The pursuit to spot abnormal behaviors in and out of a network system is what led to a system known as intrusion detection systems for soft computing besides many researchers have applied machine learning around this area. Obviously, a single classifier alone in the classifications seems impossible to control network intruders. This limitation is what led us to perform dimensionality reduction by means of correlation-based feature selection approach (CFS approach) in addition to a refined ensemble model. The paper aims to improve the Intrusion Detection System (IDS) by proposing a CFS + Ensemble Classifiers (Bagging and Adaboost) which has high accuracy, high packet detection rate, and low false alarm rate. Machine Learning Ensemble Models with base classifiers (J48, Random Forest, and Reptree) were built. Binary classification, as well as Multiclass classification for KDD99 and NSLKDD datasets, was done while all the attacks were named as an anomaly and normal traffic. Class labels consisted of five major attacks, namely Denial of Service (DoS), Probe, User-to-Root (U2R), Root to Local attacks (R2L), and Normal class attacks. Results from the experiment showed that our proposed model produces 0 false alarm rate (FAR) and 99.90% detection rate (DR) for the KDD99 dataset, and 0.5% FAR and 98.60% DR for NSLKDD dataset when working with 6 and 13 selected features.

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