Symbolic time-series analysis for anomaly detection in mechanical systems

Khatkhate, Amol M; Ray, Asok; Keller, Eric; Gupta, Savita; Chin, Shin

doi:10.1109/tmech.2006.878544

Cited by 37 publications

(12 citation statements)

References 21 publications

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“…Symbolic dynamic filtering has been experimentally validated for anomaly detection in large order and distributed parameter systems [3,29,30]. However, in these systems, it is difficult to obtain a clearly decisive evaluation of SDF performance by comparison with other pattern recognition tools (e.g., particle filtering) because of potential errors and ambiguities in modeling of both process dynamics and noise statistics.…”

Section: Experimental Validation Results and Discussionmentioning

confidence: 99%

Review and comparative evaluation of symbolic dynamic filtering for detection of anomaly patterns

Rao

Ray

Sarkar

et al. 2008

SIViP

107

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Symbolic dynamic filtering (SDF) has been recently reported in literature as a pattern recognition tool for early detection of anomalies (i.e., deviations from the nominal behavior) in complex dynamical systems. This paper presents a review of SDF and its performance evaluation relative to other classes of pattern recognition tools, such as Bayesian Filters and Artificial Neural Networks, from the perspectives of: (i) anomaly detection capability, (ii) decision making for failure mitigation and (iii) computational efficiency. The evaluation is based on analysis of time series data generated from a nonlinear active electronic system.

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Section: Experimental Validation Results and Discussionmentioning

confidence: 99%

Review and comparative evaluation of symbolic dynamic filtering for detection of anomaly patterns

Rao

Ray

Sarkar

et al. 2008

SIViP

107

View full text Add to dashboard Cite

show abstract

“…22,37 Takeuchi and Yamanishi detected change points in time series data based on an AR model, 38 whereas Khatkhate et al modeled time series data through a hidden Markov model from a symbolic representation. 39 However, model-based methods are difficult to tune dynamically.…”

Section: Modeling-based Methodsmentioning

confidence: 99%

ADARC: An anomaly detection algorithm based on relative outlier distance and biseries correlation

Zou

Liu

et al. 2019

Softw Pract Exp

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The application of anomaly detection to data monitoring is a fundamental requirement of the public service systems of a smart city. Many detection methods have been proposed for identifying anomalous situations, including methods based on periodicity or biseries correlations. However, the detection results of these methods are not ideal. Thus, we present a new anomaly detection algorithm for time series based on the relative outlier distance (ROD) and biseries correlations. The proposed algorithm detects outliers based on the ROD and identifies abnormal points and change points based on biseries correlations. Experimental results show that our method achieves better recall and F1-measure scores than various time series-based techniques while maintaining a high level of precision.

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“…The transition matrix was normalized and the connectivity value of each node was calculated and nodes with a low value of connectivity were con- sidered as anomalies. Khatkhate et al [11] proposed modeling time series data through a hidden Markov model (called the D-Markov machine model) from a symbolic representation. For each time epoch t k , an anomaly measure was defined aŝ…”

Section: Literature Reviewmentioning

confidence: 99%

Anomaly Detection and Characterization in Spatial Time Series Data: A Cluster-Centric Approach

Izakian

Pedrycz

2014

IEEE Trans. Fuzzy Syst.

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Anomaly detection in spatial time series (spatiotemporal data) is a challenging problem with numerous potential applications. A comprehensive anomaly detection approach not only should be able to detect and identify the emerging anomalies but has to characterize the essence of these anomalies by visualizing the structures revealed within data in a way that is understandable to the end-user as well. In this paper, we consider fuzzy c-means (FCM) as a conceptual and algorithmic setting to deal with the problem of anomaly detection. Using a sliding window, the time series are divided into a number of subsequences, and the available spatiotemporal structure within each time window is discovered using the FCM method. In the sequel, an anomaly score is assigned to each cluster, and using a fuzzy relation formed between revealed structures, a propagation of anomalies occurring in consecutive time intervals is visualized. To illustrate the proposed method, several datasets (synthetic data, a simulated disease outbreak scenario, and Alberta temperature data) have been investigated.

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Symbolic time-series analysis for anomaly detection in mechanical systems

Cited by 37 publications

References 21 publications

Review and comparative evaluation of symbolic dynamic filtering for detection of anomaly patterns

Review and comparative evaluation of symbolic dynamic filtering for detection of anomaly patterns

ADARC: An anomaly detection algorithm based on relative outlier distance and biseries correlation

Anomaly Detection and Characterization in Spatial Time Series Data: A Cluster-Centric Approach

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