Indirect Input Identification in Multi-Source Environments by Principal Component Analysis

Cho, M.-S.; Kim, K.-J.

doi:10.1006/mssp.2001.1430

Cited by 6 publications

(5 citation statements)

References 3 publications

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“…PCA, introduced in 1979 is a standard technique used in the context of multivariate analysis to extract constrained information from data by reducing its dimensionality (23) . However it retains most of the variation present in the data set.…”

Section: Principal Component Analysis (Pca)mentioning

confidence: 99%

“…PCA, introduced in 1979 is a standard technique used in the context of multivariate analysis to extract constrained information from data by reducing its dimensionality. 23 However, it retains most of the variation present in the data set. This is achieved by transforming to a new set of variables, the principle components (PCs), which are uncorrelated, and which are ordered so that the first few retain most of the variation present in all of the original variables.…”

Section: Pcamentioning

confidence: 99%

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Technique for optimal placement of transducers for fault detection in rotating machines

Fatima

Dastidar

Mohanty

et al. 2013

Proceedings of the Institution of Mechanical Engineers, Part O:

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Online fault detection and diagnosis of rotating machinery requires a number of transducers which can be significantly expensive for industrial processes. The sensitivity of various transducers and their appropriate positioning are dependent on different types of fault conditions. It is critical to formulate a method to systematically determine the effectiveness of transducer locations for monitoring the condition of a machine. In this paper, Number of Independent Sources analysis is used as an effective tool for reducing the number of vibration sources within the system which is then followed by Principal Component Analysis to identify the incoherent transducers to be employed for fault detection. This experiment is conducted on a machine fault simulator for unbalanced rotor, 1 misaligned shaft and cracked shaft. The validation of the proposed selection process is illustrated using spectral analysis for each defect.

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Section: Principal Component Analysis (Pca)mentioning

confidence: 99%

Section: Pcamentioning

confidence: 99%

Technique for optimal placement of transducers for fault detection in rotating machines

Fatima

Dastidar

Mohanty

et al. 2013

Proceedings of the Institution of Mechanical Engineers, Part O:

View full text Add to dashboard Cite

show abstract

“…In order to alleviate the difficulty of data handling and to improve the computational speed, in the proposed work, PCA [30,31] is used. PCA is a useful statistical technique that has found application in fields such as face recognition and image compression, and is a common technique for finding patterns in data of high dimension.…”

Section: Pca For Data Compressionmentioning

confidence: 99%

A robust damage-detection technique with environmental variability combining time-series models with principal components

Lakshmi

Rao

2014

Nondestructive Testing and Evaluation

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In this paper, a novel output-only damage-detection technique based on time-series models for structural health monitoring in the presence of environmental variability and measurement noise is presented. The large amount of data obtained in the form of time-history response is transformed using principal component analysis, in order to reduce the data size and thereby improve the computational efficiency of the proposed algorithm. The time instant of damage is obtained by fitting the acceleration timehistory data from the structure using autoregressive (AR) and AR with exogenous inputs time-series prediction models. The probability density functions (PDFs) of damage features obtained from the variances of prediction errors corresponding to references and healthy current data are found to be shifting from each other due to the presence of various uncertainties such as environmental variability and measurement noise. Control limits using novelty index are obtained using the distances of the peaks of the PDF curves in healthy condition and used later for determining the current condition of the structure. Numerical simulation studies have been carried out using a simply supported beam and also validated using an experimental benchmark data corresponding to a three-storey-framed bookshelf structure proposed by Los Alamos National Laboratory. Studies carried out in this paper clearly indicate the efficiency of the proposed algorithm for damage detection in the presence of measurement noise and environmental variability.

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“…Through SVD of Equation (8), the singular values are the diagonal of matrix S in Equation (8) that decrease from top left to bottom right. From the point of principal component analysis, the number of principal components can be determined by ignoring singular values smaller than a given value related to the noise level (Cho and Kim, 2002). For the model calibration in this research, the singular values are adopted to determine the number of neurons in the hidden layer.…”

Section: Model Calibrationmentioning

confidence: 99%

Using recurrent neural networks to reconstruct rainfall‐runoff processes

Pan

Wang

2005

Hydrological Processes

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Abstract:Many novel techniques for reconstructing rainfall-runoff processes require hydrometeorologic and geomorphologic information for modelling. However, certain information is not always measurable. In this paper, we employ a special recurrent neural network to reconstruct the rainfall-runoff process by using collected rainfall data. In addition, we propose an indirect system identification to overcome the drawback of a traditional, time-consuming trial-and-error search. The indirect system identification is an efficient method to recognize the structure of a recurrent neural network. The unit hydrograph can be derived directly from the weights of the network due to a state-space form embedded in the recurrent neural network. This improves the link between the weights of the network and the physical concepts that most neural networks fail to connect.The case studies of 41 events from 1966 to 1997 have been implemented in Taiwan's Wu-Tu watershed, where the runoff path-lines are short and steep. Two recurrent neural networks and one state-space model are utilized to simulate the rainfall-runoff processes for comparison. The results are validated by four criteria: coefficient of efficiency; peak discharge error; time to peak arrival error; total discharge volume error. The resulting data from the recurrent neural network reveal that the neural network proposed herein is appropriate for hydrological systems.

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Indirect Input Identification in Multi-Source Environments by Principal Component Analysis

Cited by 6 publications

References 3 publications

Technique for optimal placement of transducers for fault detection in rotating machines

Technique for optimal placement of transducers for fault detection in rotating machines

A robust damage-detection technique with environmental variability combining time-series models with principal components

Using recurrent neural networks to reconstruct rainfall‐runoff processes

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