Introduction

Chiang, Leo H.; Russell, Evan L.; Braatz, Richard D.

doi:10.1007/978-1-4471-0347-9_1

Cited by 132 publications

(252 citation statements)

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“…Principal Component Analysis (PCA). Principal component analysis (PCA) is the most widely used multivariate statistical technique for the data analysis in various applications such as pattern recognition, finance and economic trend analysis, fault detection and diagnosis etc [ 19 , 20 ]. In the present study, PCA has been used for the multivariate analysis of climatic variables as well as for identification of important climatic variables that are responsible for the prevalence of malaria.…”

Section: Methodsmentioning

confidence: 99%

Climate Drivers on Malaria Transmission in Arunachal Pradesh, India

et al. 2015

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The present study was conducted during the years 2006 to 2012 and provides information on prevalence of malaria and its regulation with effect to various climatic factors in East Siang district of Arunachal Pradesh, India. Correlation analysis, Principal Component Analysis and Hotelling’s T2 statistics models are adopted to understand the effect of weather variables on malaria transmission. The epidemiological study shows that the prevalence of malaria is mostly caused by the parasite Plasmodium vivax followed by Plasmodium falciparum. It is noted that, the intensity of malaria cases declined gradually from the year 2006 to 2012. The transmission of malaria observed was more during the rainy season, as compared to summer and winter seasons. Further, the data analysis study with Principal Component Analysis and Hotelling’s T2 statistic has revealed that the climatic variables such as temperature and rainfall are the most influencing factors for the high rate of malaria transmission in East Siang district of Arunachal Pradesh.

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Section: Methodsmentioning

confidence: 99%

Climate Drivers on Malaria Transmission in Arunachal Pradesh, India

et al. 2015

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“…We adopt the closed‐loop simulated process data developed by Braatz . The sampling time is selected as 3 min, and the training and testing datasets for each process fault consist of 4000 and 2000 observations, respectively.…”

Section: Simulationmentioning

confidence: 99%

Covariance eigenpairs neighbour distance for fault detection in chemical processes

2017

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This paper presents a new data-driven fault detection method called covariance eigenpairs neighbour distance (CEND) for monitoring chemical processes. It processes measured data in one-step sliding windows to estimate covariance, and the eigenpairs of each sample covariance matrix are recursively calculated using rank-one modification. The eigenvalues and selected elements of eigenvectors are stacked into reference vectors. For each window data containing the latest measurement vector, the neighbour distance of eigenpairs from the reference matrix can be effectively calculated by virtue of k-d tree. The neighbour distance serves as the detection index, whose control limit can be determined by validation dataset with assigning a significance level. A high neighbour distance exceeding the control limit implies the occurrence of fault. Simulations on the continuous stirred tank reactor (CSTR) and the Tennessee Eastman process (TEP) both indicate the superior fault detectability of the proposed method, compared with conventional multivariate statistical process monitoring (MSPM) methods such as principal component analysis (PCA) and independent component analysis (ICA).

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“…Recently, significant growth has been observed in the size and complexity of the technological installations in the automotive, power, chemical and food industries [9]. A side effect of this growth is an increase in the concentration of measuring, processing and control devices.…”

Section: Introductionmentioning

confidence: 99%

“…To tackle this problem, as well as to address the increasingly restrictive safety and environmental regulations, a significant rise in the demands on automatic fault detection, isolation and correction (FDIC) algorithms has been observed [18,9]. These systems are required to cope with large dimensionality of the measured process variables, high sampling rates, nonstationary patterns, false alarms etc.…”

Section: Introductionmentioning

confidence: 99%

“…There is a considerable body of literature concerning fault detection, isolation and correction (FDIC) in industrial measurement systems [8,9,12,14,18]. The approaches can be loosely divided into the following: The classification-based approaches use techniques such as principal component analysis (PCA), Fisher discriminant analysis (FDA), and work normally off-line (based on a batch set of data collected from the industrial system) [9]. The second group relies on statistical tests of hypotheses about the structure of the model.…”

Section: Introductionmentioning

confidence: 99%

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An approach to model-based fault detection in industrial measurement systems with application to engine test benches

Angelov

Giglio

Guardiola

et al. 2006

Meas. Sci. Technol.

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An approach to fault detection (FD) in industrial measurement systems is proposed in this paper which includes an identification strategy for early detection of the appearance of a fault. This approach is model based, i.e. nominal models are used which represent the fault-free state of the on-line measured process. This approach is also suitable for off-line FD. The framework that combines FD with isolation and correction (FDIC) is outlined in this paper. The proposed approach is characterized by automatic threshold determination, ability to analyse local properties of the models, and aggregation of different fault detection statements. The nominal models are built using data-driven and hybrid approaches, combining first principle models with on-line data-driven techniques. At the same time the models are transparent and interpretable. This novel approach is then verified on a number of real and simulated data sets of car engine test benches (both gasoline-Alfa Romeo JTS, and diesel-Caterpillar). It is demonstrated that the approach can work effectively in real industrial measurement systems with data of large dimensions in both on-line and off-line modes.

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Introduction

Cited by 132 publications

References 0 publications

Climate Drivers on Malaria Transmission in Arunachal Pradesh, India

Climate Drivers on Malaria Transmission in Arunachal Pradesh, India

Covariance eigenpairs neighbour distance for fault detection in chemical processes

An approach to model-based fault detection in industrial measurement systems with application to engine test benches

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