Fault detection using dynamic time warping (DTW) algorithm and discriminant analysis for swine wastewater treatment

Jun, Byong-Hee

doi:10.1016/j.jhazmat.2010.09.027

Cited by 20 publications

(6 citation statements)

References 13 publications

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“…DTW is a well-known technique that is based on the principle of dynamic programming to deform two temporal sequences in a non-linear way and find optimal alignments between them [101,102]. To measure the similarity between two time series S {1:ηs} and T {1:ηt} the matrix of distances D of dimensions (η s × η t ) is built.…”

Section: Dynamic Time Warpingmentioning

confidence: 99%

Feature extraction and health status prediction in PV systems

Oviedo

Travé-Massuyès²,

Subias³

et al. 2022

Advanced Engineering Informatics

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Section: Dynamic Time Warpingmentioning

confidence: 99%

Feature extraction and health status prediction in PV systems

Oviedo

Travé-Massuyès²,

Subias³

et al. 2022

Advanced Engineering Informatics

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“…Moreover, the multiple time series signal usually has some kinds of co-relations, [53] propose a method to learn the relation graph on multiple time series. Some anomaly detection based on multiple time series applications are available for wastewater treatment [54], for ICU [55], and for sensors [56].…”

Section: Composite Modelsmentioning

confidence: 99%

Unsupervised Deep Anomaly Detection for Multi-Sensor Time-Series Signals

Zhang¹,

Chen

Wang

et al. 2021

Preprint

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Nowadays, multi-sensor technologies are applied in many fields, e.g., Health Care (HC), Human Activity Recognition (HAR), and Industrial Control System (ICS). These sensors can generate a substantial amount of multivariate time-series data. Unsupervised anomaly detection on multi-sensor time-series data has been proven critical in machine learning researches. The key challenge is to discover generalized normal patterns by capturing spatial-temporal correlation in multi-sensor data. Beyond this challenge, the noisy data is often intertwined with the training data, which is likely to mislead the model by making it hard to distinguish between the normal, abnormal, and noisy data. Few of previous researches can jointly address these two challenges. In this paper, we propose a novel deep learning-based anomaly detection algorithm called Deep Convolutional Autoencoding Memory network (CAE-M). We first build a Deep Convolutional Autoencoder to characterize spatial dependence of multi-sensor data with a Maximum Mean Discrepancy (MMD) to better distinguish between the noisy, normal, and abnormal data. Then, we construct a Memory Network consisting of linear (Autoregressive Model) and non-linear predictions (Bidirectional LSTM with Attention) to capture temporal dependence from time-series data. Finally, CAE-M jointly optimizes these two subnetworks. We empirically compare the proposed approach with several state-of-the-art anomaly detection methods on HAR and HC datasets. Experimental results demonstrate that our proposed model outperforms these existing methods.

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“…This method was easy to use and independent of specific experimental data. With relatively strong universality, the approach has been widely used [16][17][18]. However, the essence of similarity measure in SAX is based on ED or DTW, so it is inevitable to inherit their disadvantages.…”

Section: Related Workmentioning

confidence: 99%

“…, 25 }, which is implemented in the experiment. In terms of the real outliers in ECG Data, the 16th subsequence (e.g., 16 ) and the 17th subsequence (e.g., 17 ) are the outliers. We compared all three methods under consideration.…”

Section: Experiments 1 (Keogh Data) Keogh Data [21]mentioning

confidence: 99%

Locality-Based Visual Outlier Detection Algorithm for Time Series

Li¹,

Li²,

Yu³

et al. 2017

Security and Communication Networks

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Physiological theories indicate that the deepest impression for time series data with respect to the human visual system is its extreme value. Based on this principle, by researching the strategies of extreme-point-based hierarchy segmentation, the hierarchysegmentation-based data extraction method for time series, and the ideas of locality outlier, a novel outlier detection model and method for time series are proposed. The presented algorithm intuitively labels an outlier factor to each subsequence in time series such that the visual outlier detection gets relatively direct. The experimental results demonstrate the average advantage of the developed method over the compared methods and the efficient data reduction capability for time series, which indicates the promising performance of the proposed method and its practical application value.

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Fault detection using dynamic time warping (DTW) algorithm and discriminant analysis for swine wastewater treatment

Cited by 20 publications

References 13 publications

Feature extraction and health status prediction in PV systems

Feature extraction and health status prediction in PV systems

Unsupervised Deep Anomaly Detection for Multi-Sensor Time-Series Signals

Locality-Based Visual Outlier Detection Algorithm for Time Series

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