Anomaly detection in monitoring sensor data for preventive maintenance

Rabatel, Julien; Bringay, Sandra; Poncelet, Pascal

doi:10.1016/j.eswa.2010.12.014

Cited by 60 publications

(33 citation statements)

References 25 publications

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“…There are numerous studies on anomaly detection and fault diagnosis in industrial electronics [10][11][12][13][14][15][16], but these studies are mostly less focused on WSNs [17]. Due to the self-organizing and ad-hoc working mechanism of WSNs, it is often difficult to observe the structures, interactions, and operations of WSNs from an external monitoring system.…”

Section: Introductionmentioning

confidence: 98%

Kuiper test and autoregressive model-based approach for wireless sensor network fault diagnosis

et al. 2014

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Wireless sensor networks (WSNs) have recently received increasing attention in the areas of defense and civil applications of sensor networks. Automatic WSN fault detection and diagnosis is essential to assure system's reliability. Proactive WSNs fault diagnosis approaches use embedded functions scanning sensor node periodically for monitoring the health condition of WSNs. But this approach may speed up the depletion of limited energy in each sensor node. Thus, there is an increasing interest in using passive diagnosis approach. In this paper, WSN anomaly detection model based on autoregressive (AR) model and Kuiper test-based passive diagnosis is proposed. First, AR model with optimal order is developed based on the normal working condition of WSNs using Akaike information criterion. The AR model then acts as a filter to process the future incoming signal from different unknown conditions. A health indicator based on Kuiper test, which is used to test the similarity between the training error of normal condition and residual of test conditions, is derived for indicating the health conditions of WSN. In this study, synthetic WSNs data under different cases/conditions were generated and used for validating the approach. Experimental results show that the proposed approach could differentiate WSNs normal conditions from faulty conditions. At last, the overall results presented in this paper demonstrate that our approach is effective for performing WSNs anomalies detection.

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Section: Introductionmentioning

confidence: 98%

Kuiper test and autoregressive model-based approach for wireless sensor network fault diagnosis

et al. 2014

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“…It is important to note that there are many types of sensors that have been used in railway industry for monitoring and the measurements such as temperature, pressure, and humidity where the data can be integrated with other measures regimes [36]. It is possible to also use the sensor networks for monitoring in challenging environments such as in Sweden's railway system, with temperatures between +25 • C and −40 • C with a massive amount of snow [37].…”

Section: Sensing Technology and Railwaymentioning

confidence: 99%

Wireless Sensor Networks: Toward Smarter Railway Stations

Alawad

Kaewunruen

2018

Infrastructures

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Railway industry plays a critical role in transportation and transit systems attributed to the ever-growing demand for catering to both freight and passengers. However, owing to many challenges faced by railway stations such as harsh environments, traffic flow, safety and security risks, new and adaptive systems employing new technology are recommended. In this review, several wireless sensor networks (WSNs) applications are proposed for use in railway station systems, including advanced WSNs, which will enhance security, safety, and decision-making processes to achieve more cost-effective management in railway stations, as well as the development of integrated systems. The size, efficiency, and cost of WSNs are influential factors that attract the railway industry to adopt these devices. This paper presents a review of WSNs that have been designed for uses in monitoring and securing railway stations. This article will first briefly focus on the presence of different WSN applications in diverse applications. In addition, it is important to note that exploitation of the state-of-the-art tools and techniques such as WSNs to gain an enormous amount of data from a railway station is a new and novel concept requiring the development of artificial intelligence methods, such machine learning, which will be vital for the future of the railway industry.

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“…Rabatel et al [10] focused on the field of train maintenance. Monitoring of trains is provided using sensors positioned on the main train components, e.g., motors wheels, to transmit information regarding, e.g., the temperature, acceleration, and velocity.…”

Section: Introductionmentioning

confidence: 99%

Network anomaly detection for railway critical infrastructure based on autoregressive fractional integrated moving average

Andrysiak

Saganowski

Mazurczyk

2016

J Wireless Com Network

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The article proposes a novel two-stage network traffic anomaly detection method for the railway transportation critical infrastructure monitored using wireless sensor networks (WSN). The first step of the proposed solution is to find and eliminate any outlying observations in the analyzed parameters of the WSN traffic using a simple and fast one-dimensional quartile criterion. In the second step, the remaining data is used to estimate autoregressive fractional integrated moving average (ARFIMA) statistical models describing variability of the tested WSN parameters. The paper also introduces an effective method for the ARFIMA model parameters estimation and identification using Haslett and Raftery estimator and Hyndman and Khandakar technique. The choice of the "economically" parameterized form of the model was based on the compromise between the conciseness of representation and the estimation of the error size. To detect anomalous behavior, i.e., a potential network attack, the proposed detection method uses statistical relations between the estimated traffic model and its actual variability. The obtained experimental results prove the effectiveness of the presented approach and aptness of selection of the statistical models.

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Anomaly detection in monitoring sensor data for preventive maintenance

Cited by 60 publications

References 25 publications

Kuiper test and autoregressive model-based approach for wireless sensor network fault diagnosis

Kuiper test and autoregressive model-based approach for wireless sensor network fault diagnosis

Wireless Sensor Networks: Toward Smarter Railway Stations

Network anomaly detection for railway critical infrastructure based on autoregressive fractional integrated moving average

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