Anomaly detection in earth dam and levee passive seismic data using support vector machines and automatic feature selection

Fisher, Wendy; Camp, Tracy; Krzhizhanovskaya, Valeria V.

doi:10.1016/j.jocs.2016.11.016

Cited by 79 publications

(35 citation statements)

References 8 publications

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“…Hence, we must first pinpoint an anomaly and then consider its relationships with the adjacent points in the aim of filtering the anomaly while retaining the initial measurements. Presently, anomaly detection methods are classified into three kinds: prediction-based, statistical-based, and distance-based techniques [25]. For instance, the autoregressive integrated moving average (ARIMA) strategy, as a representative of the prediction-based method, was utilized to capture outliers in an intricate sensor dataset [26].…”

Section: A Noise Cleansingmentioning

confidence: 99%

“…The stimulus behind the posterior distribution approximation is to search for a cluster of distributions and estimate the intractable posterior p(f  , f  |y) as the closest member considering Kullback-Leibler divergence (KLD). In this regard, the variational distribution q(f  , f  ), as expressed in (24), is employed to approach p(f  , f  |y), and the KLD's calculation is formulated as (25).…”

Section: ) Sparse Variational Inferencementioning

confidence: 99%

See 1 more Smart Citation

State-of-Health Prognosis for Lithium-Ion Batteries Considering the Limitations in Measurements via Maximal Information Entropy and Collective Sparse Variational Gaussian Process

Xiang

Zhang

et al. 2020

IEEE Access

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Prognostics and health management (PHM) for electronic devices is intricate yet crucial in an era of electricity. The impending future of electric vehicles and clean energy requires more in-depth scrutiny and monitoring of their storage elements, lithium-ion batteries. State-of-health (SOH), as one of the most evident indicators for battery degradation quantifications, is a matter of vital importance that worths more attention. In this regard, this paper proposes a novel SOH prognostics framework for lithium-ion batteries considering the limitations in the recorded measurements through the use of linear statistical knearest neighbors (LSKNN) data interpolation, maximal information entropy search (MIES), and collective sparse variational Gaussian process regression (CSVGPR). First, the incomplete charging measurements are processed by LSKNN to infer the missing data points and suppress the unanticipated noises in the extracted temporal features, which indicate the trend of degradation. Then, the MIES scheme is proposed to filter the features that are extraneous to the SOHs of the corresponding batteries and that greatly correlate to the other features in the feature set. Finally, the CSVGPR model, considering the uncertainties within each of the sparse variational Gaussian processes, is utilized to implement SOH prognosis. The proposed framework is verified by a subset of the repository from NASA. In the test, multiple prognostics comparisons of innerbattery tests, cross-battery tests, and tests with other statistical learning methods are presented. The experiment results lend support to the superiority and effectiveness of the work. INDEX TERMS Lithium-ion batteries, prognostics and health management (PHM), maximal information entropy (MIE), sparse variational Gaussian process, k-nearest neighbors, state-of-health.

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Section: A Noise Cleansingmentioning

confidence: 99%

Section: ) Sparse Variational Inferencementioning

confidence: 99%

State-of-Health Prognosis for Lithium-Ion Batteries Considering the Limitations in Measurements via Maximal Information Entropy and Collective Sparse Variational Gaussian Process

Xiang

Zhang

et al. 2020

IEEE Access

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“…It has a great advantage in solving nonlinear problems with a small sample. SVM has been widely used in hydrological prediction [33,34] and anomaly detection [23,35]. Figure 12 shows the structure of support vector machine.…”

Section: Support Vector Machinementioning

confidence: 99%

“…Dong et al [19] established five different neural network models to predict the stability numbers and damage levels, and compared the structure and accuracy of the five neural network models. Besides the neural network, a fuzzy logic approach [20,21] and a support vector machine [22,23] can also be effective tools to predict the stability number and the damage level. Little research has been done on the methods for evaluating the damage level of river training structures on the Yangtze River.…”

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confidence: 99%

A Machine Learning Approach to Evaluating the Damage Level of Tooth-Shape Spur Dikes

Wei

Wang

et al. 2018

Water

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Little research has been done on the application of machine learning approaches to evaluating the damage level of river training structures on the Yangtze River. In this paper, two machine learning approaches to evaluating the damage level of spur dikes with tooth-shaped structures are proposed: a supervised support vector machine (SVM) model and an unsupervised model combining a Kohonen neural network with an SVM model (KNN-SVM). It was found that the supervised SVM model predicted the damage level of the validation samples with high accuracy, and the unsupervised data-mining KNN-SVM model agreed well with the empirical evaluation result. It is shown that both machine learning approaches could become effective tools to evaluate the damage level of spur dikes and other river training structures.

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“…To this point, Park et al [20] presented a dual-feature functional SVM approach that uses the first and second derivatives of the degradation profiles for the early detection of faulty batteries. Fisher et al [21] used an SVM and automatic feature selection to perform anomaly detection in passive seismic data of earth dams and levees. SVMs have been used in a variety of classification applications, for example, Ma et al [22], Ebrahimi et al [23], Jaramillo et al [24], and Li et al [25].…”

Section: Introductionmentioning

confidence: 99%

An Optimal Burn-In Policy for Cellular Phone Lithium-Ion Batteries Using a Feature Selection Strategy and Relevance Vector Machine

Yang

Tang

et al. 2018

Energies

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The early detection of defective lithium-ion batteries in cellular phones is critical due to the rapid increase in popularity and mass production of cellular phones. It is essential for manufacturers to design an optimal burn-in policy to differentiate between normal and weak batteries in short cycles prior to shipping them to the marketplace. A novel approach to determine the optimal burn-in policy using a feature selection strategy and relevance vector machine (RVM) is proposed. The sequential floating forward search (SFFS) is used as the feature selection method to find an optimal feature subset from the entire sequence of the batteries’ quality characteristics while preserving the original variables. Given the selected feature subset, the RVM is applied to classify batteries into two groups and simultaneously obtain the posterior probabilities. To achieve better discrimination performance with less risk, a new characteristic is extracted from the discharge profile. Subsequently, an optimization cost model is developed by introducing a classification instability penalty to ensure the stability of the optimal number of burn-in cycles. A case study utilizing cellular phone lithium-ion batteries randomly selected from manufactured lots is presented to illustrate the proposed methodology. Furthermore, we conduct a comparison with the cumulative degradation (CD) method and non-cumulative degradation (NCD) method based on the Wiener process. The results show that our proposed burn-in test method performs better than comparable methods.

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Anomaly detection in earth dam and levee passive seismic data using support vector machines and automatic feature selection

Cited by 79 publications

References 8 publications

State-of-Health Prognosis for Lithium-Ion Batteries Considering the Limitations in Measurements via Maximal Information Entropy and Collective Sparse Variational Gaussian Process

State-of-Health Prognosis for Lithium-Ion Batteries Considering the Limitations in Measurements via Maximal Information Entropy and Collective Sparse Variational Gaussian Process

A Machine Learning Approach to Evaluating the Damage Level of Tooth-Shape Spur Dikes

An Optimal Burn-In Policy for Cellular Phone Lithium-Ion Batteries Using a Feature Selection Strategy and Relevance Vector Machine

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