Cheng, Tuoyuan; Harrou, Fouzi; Sun, Ying; Leiknes, TorOve(IEEE Sensors Journal, Institute of Electrical and Electronics Engineers (IEEE), 2018-10-16)[Article]
Monitoring inflow measurements of water resource recovery facilities (WRRFs) is essential to promptly detect abnormalities and helpful in the decision making of the operators to better optimize, take corrective actions, and maintain downstream processes. In this paper, we introduced a flexible and reliable monitoring soft sensor approach to detect and identify abnormal influent measurements of WRRFs to enhance their efficiency and safety. The proposed data-driven soft sensor approach merges the desirable characteristics of principal component analysis (PCA) with k-nearest neighbor (KNN) scheme. PCA performed effective dimension reduction and revealed interrelationships between inflow measurements, while KNN distances demonstrated superior detection capacity, robustness to underlying data distribution, and efficiency in handling high-dimensional dataset. Furthermore, nonparametric thresholds derived from kernel density estimation further enhanced detection results of PCA-KNN approach when compared with parametric counterparts. Moreover, the radial visualization plot is innovatively employed for fault analysis and diagnosis in combination with PCA and delineated interpretable visualization of anomalies and detector performances. The effectiveness of these soft sensor schemes is evaluated by using real data from a coastal municipal WRRF located in Saudi Arabia. Also, we compared the proposed soft sensor scheme with the conventional PCA-based approaches, including standard prediction error, Hotelling’s T2, and joint univariate methods. Results demonstrate that this soft sensor-based monitoring approach outperforms conventional PCA-based methods.
Harrou, Fouzi; Khaldi, Belkacem; Sun, Ying; Cherif, Foudil(IEEE Sensors Journal, Institute of Electrical and Electronics Engineers (IEEE), 2018-10-22)[Article]
Fault detection in robotic swarm systems is imperative to guarantee their reliability, safety, and to maximize operating efficiency and avoid expensive maintenance. However, data from these systems are generally contaminated with noise, which masks important features in the data and degrades the fault detection capability. This paper introduces an effective fault detection approach against noise and uncertainties in data, which integrates the multiresolution representation of data using wavelets with the sensitivity to small changes of an exponentially weighted moving average scheme. Specifically, to monitor swarm robotics systems performing a virtual viscoelastic control model for circle formation task, the proposed scheme has been applied to the uncorrelated residuals form principal component analysis model. A simulated data from ARGoS simulator is used to evaluate the effectiveness of the proposed method. Also, we compared the performance of the proposed approach to that of the conventional principal component-based approach and found improved sensitivity to faults and robustness to noises. For all the fault types tested–abrupt faults, random walks, and complete stop faults–our approach resulted in a significant enhancement in fault detection compared with the conventional approach.
Zeroual, Abdelhafid; Harrou, Fouzi; Sun, Ying; Messai, Nadhir(IEEE Sensors Journal, Institute of Electrical and Electronics Engineers (IEEE), 2018-08-22)[Article]
Efficient detection of traffic congestion plays an important role in the development of intelligent transportation systems by providing useful information for rapid decisionmaking. The aim of this study is to design an approach for road traffic congestion estimation and detection. Here, we design an innovative observer by integrating a hybrid piecewise switched linear traffic model (PWSL) with Luenberger observer estimator for enhanced road traffic density estimation. This observer termed PWSL-LO combines the flexibility of the PWSL model with the simplicity and efficiency of Luenberger observer to estimate the unmeasured traffic density. Moreover, this paper proposes an approach to monitor traffic congestion based on Kullback-Leibler distance (KLD) and exponential weighted moving average (EWMA) procedure. Residuals from the PWSLLO model are used as the input for KLD-EWMA scheme for congestion detection. This is motivated by the high capacity of KLD to quantitatively discriminate between two distributions. Here, the EWMA scheme is applied to the KLD measurements for congestion detection. Moreover, wavelet-based multiscale filter, a powerful feature/noise separation tool, is used to deal with the problem of measurement noise in the data. We evaluated the detection performance of this scheme by using traffic data from the four-lane SR-60 freeway in southern California. The proposed approach showed good abilities to estimate, monitor traffic congestions and to handle noisy traffic data.
Harrou, Fouzi; Sun, Ying; Madakyaru, Muddu; Bouyedou, Benamar(IEEE Sensors Journal, Institute of Electrical and Electronics Engineers (IEEE), 2018-06-26)[Article]
Reliable fault detection systems in industrial processes provide pertinent information for improving the safety and process reliability and reducing manpower costs. Here, we present a flexible and efficient fault detection approach based on the continuous ranked probability score (CRPS) metric to detect faults in multivariate data. This approach merges partial least squares (PLS) models and the CRPS metric to separate normal from abnormal features by simultaneously taking advantage of the feature representation ability of a PLS and the fault detection capacity of a CRPS-based scheme. The proposed approach uses PLS to generate residuals, and then apply the CRPS-based chart to reveal any abnormality. Specifically, two monitoring schemes based on CRPS measure have been introduced in this paper. The first approach uses the Shewhart scheme to evaluate the CRPS of the response variables residuals from the PLS model. The second approach merges the CRPS into the exponentially weighted moving average monitoring chart. We assess the effectiveness of these approaches by using real and simulated distillation column data. We also compare the detection quality of PLS-based CRPS charts to that of PLS-based T2, Q, multivariate cumulative sum, and multivariate exponentially weighted moving average methods. Results show that the capacity of the proposed scheme can reliably detect faults in multivariate processes.
Harrou, Fouzi; Dairi, Abdelkader; Sun, Ying; Kadri, Farid(IEEE Sensors Journal, Institute of Electrical and Electronics Engineers (IEEE), 2018-07-02)[Article]
Air quality management and monitoring are vital to maintaining clean air, which is necessary for the health of human, vegetation, and ecosystems. Ozone pollution is one of the main pollutants that negatively affect human health and ecosystems. This paper reports the development of an unsupervised and efficient scheme to detecting anomalies in unlabelled ozone measurements. This scheme combines a Deep Belief Networks (DBN) model and a one-class support vector machine (OCSVM). The DBN model accounts for nonlinear variations in the ground-level ozone concentrations, while OCSVM detects the abnormal ozone measurements. The performance of this approach is evaluated using real data from Is`ere in France. We also compare the detection quality of DBN-based detection schemes to that of deep stacked auto-encoders, Restricted Boltzmann Machinesbased OCSVM and DBN-based clustering procedures (i.e., Kmeans, Birch and Expectation Maximization). The results show that the developed strategy is able to identify anomalies in ozone measurements.
Dairi, Abdelkader; Harrou, Fouzi; Sun, Ying; Senouci, Mohamed(IEEE Sensors Journal, Institute of Electrical and Electronics Engineers (IEEE), 2018-04-30)[Article]
Obstacle detection is an essential element for the development of intelligent transportation systems so that accidents can be avoided. In this study, we propose a stereovisionbased method for detecting obstacles in urban environment. The proposed method uses a deep stacked auto-encoders (DSA) model that combines the greedy learning features with the dimensionality reduction capacity and employs an unsupervised k-nearest neighbors algorithm (KNN) to accurately and reliably detect the presence of obstacles. We consider obstacle detection as an anomaly detection problem. We evaluated the proposed method by using practical data from three publicly available datasets, the Malaga stereovision urban dataset (MSVUD), the Daimler urban segmentation dataset (DUSD), and Bahnhof dataset. Also, we compared the efficiency of DSA-KNN approach to the deep belief network (DBN)-based clustering schemes. Results show that the DSA-KNN is suitable to visually monitor urban scenes.
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