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Anomaly Detection Based on a Multi-class cusum algorithm for WSN

JOURNAL OF COMPUTERS 2010年第2期5卷 306-313页

作者： Xiao Zhenghong Chen Zhigang Deng Xiaoheng Cent S Univ Sch Informat Sci & Engn Changsha 410083 Hunan Peoples R China Guangdong Polytech Normal Univ Sch Comp Sci Guangzhou 510665 Guangdong Peoples R China

Security is one of the most important research issues in wireless sensor networks (WSN) applications. Given that the single detection threshold of the cumulative sum (cusum) algorithm causes longer detection delays and a lower detection rate, a multi-class cusum algorithm is hereby proposed. Firstly a maximum and minimum thresholds, which sensor nodes are able to reach during sending packet, are set to eliminate abnormal flow to enhance the detection efficiency. Secondly, cusum algorithms of different thresholds, all of which are selected according to the mean of traffic sequences, are applied to detect anomalous nodes. This study aims to optimize threshold parameters, the size of which increases with the number of traffic sequence. Using the NS2 tool, the different values of network traffic sequence are generated and simulated. Based on these values, the detection rates of the cusum algorithm and multi-class cusum algorithms, as well as their false positive rates, are then evaluated. Results show that the proposed algorithm achieves a higher and more accurate rate of detection and lower false positive rates than do the current important intrusion detection schemes of WSN.

关键词： WSN cusum algorithm multi-class cusum algorithm threshold anomaly detection

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Phase onset time estimation at regional distances using the cusum algorithm

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PHYSICS OF THE EARTH AND PLANETARY INTERIORS 1999年第1-4期113卷 227-246页

作者： Der, ZA Shumway, RH ENSCO APA Div Springfield VA USA Univ Calif Davis Dept Stat Davis CA 95616 USA

A simple approach for the estimation of onset times of regional arrivals was tested. The method consists of the computation of the cumulative sum (cusum) of a test statistic, in this case, the absolute value of the trace amplitude, addition of a linear trend to the result and finding minima of the resulting function by various methods. The minima are located at the times when sudden increases in the trace amplitudes occur, i.e., at the phase onsets. The method requires frequency prefiltering in order to enhance the amplitude changes at the phase onsets. It was found that this algorithm performed comparably to a human analyst in finding onset times for Pn arrivals with varying S/N ratios. Moreover, when applied to the rest of the seismogram, it was effective in finding the onset times of the rest of the regional arrivals as long as these were clearly visible. In the rest of the cases, where there are many poorly defined arrivals, an analyst would also have difficulties in uniquely defining onset times. The method also shares with the human analyst the tendency to pick the onset times late when an emergent arrival is buried in noise. (C) 1999 Elsevier Science B.V. All rights reserved.

关键词： phase onset time cusum algorithm regional arrivals

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An In-depth Analysis of cusum algorithm for the Detection of Mean and Variability Deviation in Time Series 1

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16th International Symposium on Web and Wireless Geographical Information Systems (W2GIS)

作者： El Sibai, Rayane Chabchoub, Yousra Chiky, Raja Demerjian, Jacques Barbar, Kablan ISEP LlSITE Lab F-92130 Issy Les Moulineaux France Lebanese Univ LARIFA EDST Lab Fanar Lebanon

ISBN: (数字)9783319900537

ISBN: (纸本)9783319900537;9783319900520

Assessing and enhancing data quality in sensors networks is an important challenge. In fact, data recorded and sent by the sensors are often dirty, they contain noisy, erroneous and missing values. This can be due to many reasons such as sensor malfunction, uncalibrated sensor and low battery of the sensor or caused by external factors such as climatic conditions or interference. We focus in this paper on change detection in the time series data issued from sensors. We particularly address slow and gradual changes as they illustrate sensor calibration drift. For this purpose, we provide in this paper an in-depth analysis and improvement of the well known Cumulative Sum (cusum) control chart algorithm, as it is well adapted to small shifts detection. First, we discuss the choice of the different parameters of cusum in order to optimize its results based on the trade-off between the false positives and the Average Run Length (ARL(delta)) needed by the algorithm to detect a process mean shift of delta. A study of the variability of the Run Length (RL delta) is provided using simulation. Then, we introduce two improvements to the cusum algorithm: we propose an efficient method to estimate the starting point and the end point of the mean shift. Moreover, we adapt cusum to detect not only process mean deviation but also process variability deviation. All these improvements are validated by simulation and against real data stream issued from water flow-meters.

关键词： cusum algorithm Change point detection Process variability Sensors networks Data quality

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Network Equipment Fault Prediction Based on cusum algorithm

Network Equipment Fault Prediction Based on CUSUM Algorithm

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2015 4th National Conference on Electrical,Electronics and Computer Engineering(NCEECE 2015)

作者： Liu Peishun Lin LiFang Zhao Changjian Jia Peiwei Ocean University of china Qingdao Huanghai University

ISBN: (纸本)9781510819085

In this paper, the cusum algorithm is used to detect and predict the state of network equipment. By collecting information on network equipment operating characteristics, features of the device obtained sample set, the application design is complete training sample set and obtained parameters of algorithm, build fault prediction model based on cusum algorithm to realize the equipment fault predictive analysis. The experimental results show that the detection algorithm can detect the abnormal state of the system in advance, and provide the decision-making basis for the equipment fault prediction.

关键词： Fault Prediction cusum algorithm Network Equipment Anomaly detection

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Structural Health Monitoring of Dams Based on Acoustic Monitoring, Deep Neural Networks, Fuzzy Logic and a cusum Control algorithm

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SENSORS 2022年第7期22卷 2482页

作者： Ozelim, Luan Carlos de Sena Monteiro Borges, Lucas Parreira de Faria Cavalcante, Andre Luis Brasil Albuquerque, Enzo Aldo Cunha Diniz, Mariana dos Santos Gois, Manuelle Santos da Costa, Katherin Rocio Cano Bezerra de Sousa, Patricia Figuereido Dantas, Ana Paola do Nascimento Jorge, Rafael Mendes Moreira, Gabriela Rodrigues de Barros, Matheus Lima de Aquino, Fernando Rodrigo Univ Brasilia Dept Civil & Environm Engn BR-70910900 Brasilia DF Brazil

Internal erosion is the most important failure mechanism of earth and rockfill dams. Since this type of erosion develops internally and silently, methodologies of data acquisition and processing for dam monitoring are crucial to guarantee a safe operation during the lifespan of these structures. In this context, artificial intelligence techniques show up as tools that can simplify the analysis and verification process not of the internal erosion itself, but of the effects that this pathology causes in the response of the dam to external stimuli. Therefore, within the scope of this paper, a methodological framework for monitoring internal erosion in the body of earth and rockfill dams will be proposed. For that, artificial intelligence methods, especially deep neural autoencoders, will be used to treat the acoustic data collected by geophones installed on a dam. The sensor data is processed to identify patterns and anomalies as well as to classify the dam's structural health status. In short, the acoustic dataset is preprocessed to reduce its dimensionality. In this process, for each second of acquired data, three parameters are calculated (Hjorth parameters). For each parameter, the data from all the available sensors are used to calibrate an autoencoder. Then, the reconstruction error of each autoencoder is used to monitor how far from the original (normal) state the acoustic signature of the dam is. The time series of reconstruction errors are combined with a cumulative sum (cusum) algorithm, which indicates changes in the sequential data collected. Additionally, the outputs of the cusum algorithms are treated by a fuzzy logic framework to predict the status of the structure. A scale model is built and monitored to check the effectiveness of the methodology hereby developed, showing that the existence of anomalies is promptly detected by the algorithm. The framework introduced in the present paper aims to detect internal erosion inside dams by combining different

关键词： structural monitoring dams geotechnical engineering deep learning autoencoder fuzzy logic cusum algorithm

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A cusum change-point detection algorithm for non-stationary sequences with application to data network surveillance

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JOURNAL OF SYSTEMS AND SOFTWARE 2010年第7期83卷 1288-1297页

作者： De Oca, Veronica Montes Jeske, Daniel R. Zhang, Qi Rendon, Carlos Marvasti, Mazda Univ Calif Riverside Dept Stat Riverside CA 92521 USA United Hlth Grp Minneapolis MN 55440 USA Experian Costa Mesa CA 92626 USA Symantec Corp Mountain View CA 94043 USA Integrien Corp Irvine CA USA

We adapt the classic cusum change-point detection algorithm to handle non-stationary sequences that are typical with network surveillance applications. The proposed algorithm uses a defined timeslot structure to take into account time varying distributions, and uses historical samples of observations within each timeslot to facilitate a nonparametric methodology. Our proposed solution includes an on-line screening feature that fully automates the implementation of the algorithm and eliminates the need for manual oversight up until the point where root cause analysis begins. (C) 2010 Elsevier Inc. All rights reserved.

关键词： Network surveillance On-line detection Screening cusum algorithm

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cusum algorithms for Parameter Estimation in Queueing Systems with Jump Intensity of the Arrival Process 14th

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14th International Scientific Conference on Information Technologies and Mathematical Modelling (ITMM)

作者： Burkatovskaya, Yulia Kabanova, Tatiana Vorobeychikov, Sergey Tomsk Polytech Univ Inst Cybernet 30 Lenin Prospekt Tomsk 634050 Russia Tomsk State Univ Dept Appl Math & Cybernet Tomsk 634050 Russia Tomsk State Univ Int Lab Stat Stochast Proc & Quantitat Finance Tomsk 634050 Russia

ISBN: (纸本)9783319258614;9783319258607

The problem of Markov-modulated Poisson process intensities estimating is studied. A new approach based on sequential change point detection method is proposed to determine switching points of the flow parameter. Both the intensities of the controlling Markovian chain and the intensities of the flow of events are estimated. The results of simulation are presented.

关键词： Markov-modulated poisson process Jump intensity cusum algorithm

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Application of new void detection algorithm for analysis of feed pressure and rotation pressure of roof bolters

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International Journal of Mining Science and Technology 2017年第1期27卷 77-81页

作者： Liu Wenpeng Jamal Rostami Eric Keller Department of Energy and Mineral Engineering The Pennsylvania State UniversityState CollegePA 16802USA Earth Mechanics Institute Department of Mining EngineeringColorado School of MineGoldenCO 80401USA Electro-Mechanical Systems Laboratory The Pennsylvania State UniversityState CollegePA 16802USA

Roof and rib instability is an important issue in underground mining. To optimize ground support design,enhance ground stability, and reduce the possibility of roof or rib failure with minimal use of artificial ground support, it is essential to have an accurate understanding of ground conditions. This includes the location of voids, cracks, and discontinuities, as well as information about the different strata in the immediate roof. This paper briefly introduces ongoing research on void detection by using the roof bolter feed and rotation pressure. The goal of this project is to improve the sensitivity of detection programs to locate smaller joints and reduce the number of false alarms. This paper presents a brief review of the testing procedures, data analysis, logic, and algorithms used for void detection. In addition, this paper discusses the results of preliminary laboratory tests and statistical analysis of the data from these two drilling parameters used for void detection.

关键词： Roof bolter Void detection Feed pressure Rotation pressure cusum algorithm Ground support optimization

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Improving the capability of detecting joints and fractures in rock mass from roof bolt drilling data by using wavelet analysis

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INTERNATIONAL JOURNAL OF OIL GAS AND COAL TECHNOLOGY 2019年第1期20卷 97-112页

作者： Liu, Wenpeng Saab, Samer S., Jr. Rostami, Jamal Ray, Asok Colorado Sch Mines Earth Mech Inst Dept Min Engn Golden CO 80401 USA Penn State Univ Dept Elect Engn State Coll PA 16802 USA Penn State Univ Dept Mech & Nucl Engn State Coll PA 16802 USA

To optimise ground supporting and mitigate ground instability, a proper understanding of the ground conditions is critical. The concept of monitoring drilling parameters of a bolter for ground characterisation, which refers to identifying geological features included locations of joints and strengths of rock layers, has been studied in the past few decades. Several intelligent drilling units have been developed for joint detection but have limited capabilities. For instance, the existing systems fail to discriminate joints with the aperture of less than 3.175 mm and tend to generate false alarms. The objective of this research was to develop more efficient and sensitive detection programs for joint detection. To achieve this objective, a series of full-scale drilling tests with various simulated joint conditions have been conducted, and new detection programs have been proposed based on pattern recognition algorithms. Moreover, wavelet analysis has been applied to pre-process data to further promote detection programs.

关键词： wavelet analysis cumulative sum algorithm cusum algorithm roof bolter drilling parameters joint detection ground support optimisation

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Self-tuning breakthrough detection for EDM drilling micro holes

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JOURNAL OF MANUFACTURING PROCESSES 2020年 57卷 630-640页

作者： Bellotti, Mattia Qian, Jun Reynaerts, Dominiek Katholieke Univ Leuven Dept Mech Engn Leuven Belgium Member Flanders Make Leuven Belgium

In electrical discharge machining (EDM), accurate and timely detection of the breakthrough of the tool electrode is rather intricate when drilling though-holes due to considerable longitudinal tool wear. This paper proposes a novel method for detecting the completion of the drilling process using the cumulative sum (cusum) control chart. In this method, variations in the amount of normal discharge pulses per unit time are monitored for detecting the completion of the drilling process. The actual process conditions are sensed at the start of the drilling process an the control limit for detecting the hole completion is accordingly self-tuned. The robustness of the detection method to different process conditions is supported by a design of experiments study. Experimental validation has been carried out by drilling through micro holes at relative angles between the tool and workpiece of 30 degrees, 45 degrees, and 60 degrees. The proposed method has been shown to guarantee the hole completion, while in the same time avoiding machining a surface situated 1 mm below the outlet of the drilled hole. This is critical for EDM drilling applications in the aerospace and automotive sectors.

关键词： Electrical discharge machining (EDM) Process monitoring Breakthrough detection Film cooling holes cusum algorithm

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