Improved anomaly detection using multi-scale PLS and generalized likelihood ratio test

Madakyaru, Muddu; Harrou, Fouzi; Sun, Ying

doi:10.1109/ssci.2016.7849880

Cited by 9 publications

(3 citation statements)

References 25 publications

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“…One possible way to alleviate this limitation is to incorporate a classification stage based on a support vector machine or other classifier applied to detected sequences. Another alternative is to apply univariate monitoring charts, such as the generalized likelihood ratio test [13], to the residuals from the KPCA. Furthermore, another direction of improvement consists of using data augmentation techniques to generate largesized data, which improves the construction of models and thus enhances the detection process.…”

Section: Discussionmentioning

confidence: 99%

Automatic Detection of Unbalanced Sitting Postures in Wheelchairs Using Unlabeled Sensor Data

et al. 2022

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This letter presents an effective data-driven anomaly detection scheme for automatically recognizing unbalanced sitting posture in a wheelchair using data from pressure sensors embedded in the wheelchair. Essentially, the designed approach merges the desirable features of the kernel principal components analysis (KPCA) as a feature extractor with the Kantorovich Distance (KD)-driven monitoring chart to detect abnormal sitting posture in a wheelchair. It is worth noting that this approach does not require labeled data and only employs normal events data to train the detection model, which makes it more appealing in practice. Specifically, we used the KPCA model to exploit its capacity to reduce the dimensionality of nonlinear data to obtain good detection. At the same time, the KD monitoring scheme is an efficient distribution-driven anomaly detection approach in multivariate data. Furthermore, a nonparametric decision threshold using kernel density estimation is adopted to extend the flexibility of the proposed approach. Publically available data has been used to verify the detection capacity of the proposed approach. The overall detection system proved promising, outperforming some commonly used monitoring methods.

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Section: Discussionmentioning

confidence: 99%

Automatic Detection of Unbalanced Sitting Postures in Wheelchairs Using Unlabeled Sensor Data

et al. 2022

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“…Combining wavelet analysis with partial least squares was proposed for process monitoring using the same approach we described before (Lee et al, 2009;Madakyaru et al, 2016;Roodbali and Shahbazian, 2011;Teppola and Minkkinen, 2000;Zhang and Hu, 2011) These methodologies are used with control chart statistics such as Hotelling-T 2 and Q.…”

Section: Ccpsmentioning

confidence: 99%

On wavelet-based statistical process monitoring

Cohen

Atoui

2020

Transactions of the Institute of Measurement and Control

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This paper presents an overview of wavelet-based techniques for statistical process monitoring. The use of wavelet has already had an effective contribution to many applications. The increase of data availability has led to the use of wavelet analysis as a tool to reduce, denoise, and process the data before using statistical models for monitoring. The most recent review paper on wavelet-based methods for process monitoring had the goal to review the findings up to 2004. In this paper, we provide a recent reference for researchers and engineers with a different focus. We focus on: (i) wavelet statistical properties, (ii) control charts based on wavelet coefficients, and (iii) wavelet-based process monitoring methods within a machine learning framework. It is clear from the literature that wavelets are widely used with multivariate methods compared to univariate methods. We also found some potential research areas regarding the use of wavelet in image process monitoring and designing control charts based on wavelet statistics, and listed them in the paper.

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“…We also plan to develop deep learning-driven monitoring charts by merging the extended capacity of deep learning models (e.g., long short-term memory (LSTM) and gated recurrent unit (GRU) [71,72]) in automatically extracting important features from multivariate data with statistical monitoring charts such as the generalized likelihood ratio test [73,74] to improve fault detection in PV systems.…”

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confidence: 99%

Ensemble Learning Techniques-Based Monitoring Charts for Fault Detection in Photovoltaic Systems

et al. 2022

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Over the past few years, there has been a significant increase in the interest in and adoption of solar energy all over the world. However, despite ongoing efforts to protect photovoltaic (PV) plants, they are continuously exposed to numerous anomalies. If not detected accurately and in a timely manner, anomalies in PV plants may degrade the desired performance and result in severe consequences. Hence, developing effective and flexible methods capable of early detection of anomalies in PV plants is essential for enhancing their management. This paper proposes flexible data-driven techniques to accurately detect anomalies in the DC side of the PV plants. Essentially, this approach amalgamates the desirable characteristics of ensemble learning approaches (i.e., the boosting (BS) and bagging (BG)) and the sensitivity of the Double Exponentially Weighted Moving Average (DEWMA) chart. Here, we employ ensemble learning techniques to exploit their capability to enhance the modeling accuracy and the sensitivity of the DEWMA monitoring chart to uncover potential anomalies. In the ensemble models, the values of parameters are selected with the assistance of the Bayesian optimization algorithm. Here, BS and BG are adopted to obtain residuals, which are then monitored by the DEWMA chart. Kernel density estimation is utilized to define the decision thresholds of the proposed ensemble learning-based charts. The proposed monitoring schemes are illustrated via actual measurements from a 9.54 kW PV plant. Results showed the superior detection performance of the BS and BG-based DEWMA charts with non-parametric threshold in uncovering different types of anomalies, including circuit breaker faults, inverter disconnections, and short-circuit faults. In addition, the performance of the proposed schemes is compared to that of BG and BS-based DEWMA and EWMA charts with parametric thresholds.

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Improved anomaly detection using multi-scale PLS and generalized likelihood ratio test

Cited by 9 publications

References 25 publications

Automatic Detection of Unbalanced Sitting Postures in Wheelchairs Using Unlabeled Sensor Data

Automatic Detection of Unbalanced Sitting Postures in Wheelchairs Using Unlabeled Sensor Data

On wavelet-based statistical process monitoring

Ensemble Learning Techniques-Based Monitoring Charts for Fault Detection in Photovoltaic Systems

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