References of "Nguyen, Viet Ha"
     in
Bookmark and Share    
Full Text
See detailFault Diagnosis in Industrial Systems Based on Blind Source Separation Techniques Using One Single Vibration Sensor
Nguyen, Viet Ha ULg; Rutten, Christophe ULg; Golinval, Jean-Claude ULg

in Maia NNM, Neves MM (Ed.) International Conference on Structural Engineering Dynamics (ICEDyn 2011) - Proceedings (2011)

In the field of structural health monitoring or machine condition monitoring, most vibration based methods reported in the literature require to measure responses at several locations on the structure. In ... [more ▼]

In the field of structural health monitoring or machine condition monitoring, most vibration based methods reported in the literature require to measure responses at several locations on the structure. In machine condition monitoring, the number of available vibration sensors is often small and it is not unusual that only one single sensor is used to monitor a machine. The aim of this paper is to propose an extension of fault detection techniques that may be used when a reduced set of sensors or even one single sensor is available. Fault detection techniques considered here are based on output-only methods coming from the Blind Source Separation (BSS) family, namely Principal Component Analysis (PCA) and Second Order Blind Identification (SOBI). The advantages of PCA or SOBI rely on their rapidity of use and their reliability. Based on these methods, subspace identification may be performed by using the concept of block Hankel matrices which make possible the use of only one single measurement signal. Thus, the problem of fault detection in mechanical systems can be solved by using subspaces built from active principal components or modal vectors. It consists in comparing subspace features between the reference (undamaged) state and a current state. The angular coherence between subspaces is a good indicator of a dynamic change in the system due to the occurrence of faults or damages. The robustness of the methods is illustrated on industrial examples. [less ▲]

Detailed reference viewed: 53 (4 ULg)
Full Text
See detailDamage Detection Using Blind Source Separation Techniques
Nguyen, Viet Ha ULg; Golinval, Jean-Claude ULg

in IMAC-XXIX: Conference & Exposition on Structural Dynamics - Advanced Aerospace Applications (2011)

Blind source separation (BSS) techniques are applied in many domains since they allow separating a set of signals from their observed mixture without the knowledge (or with very little knowledge) of the ... [more ▼]

Blind source separation (BSS) techniques are applied in many domains since they allow separating a set of signals from their observed mixture without the knowledge (or with very little knowledge) of the source signals or the mixing process. Two particular BSS techniques called Second-Order Blind Identification (SOBI) and Blind Modal Identification (BMID) are considered in this paper for the purpose of structural damage detection or fault diagnosis in mechanical systems. As shown on experimental examples, the BMID method reveals significant advantages. In addition, it is demonstrated that damage detection results may be improved significantly with the help of the block Hankel matrix. The main advantage in this case is that damage detection still remains possible when the number of available sensors is small or even reduced to one. Damage detection is achieved by comparing the subspaces between the reference (healthy) state and a current state through the concept of subspace angle. The efficiency of the methods is illustrated using experimental data. [less ▲]

Detailed reference viewed: 128 (4 ULg)
Full Text
Peer Reviewed
See detailFault detection based on Kernel Principal Component Analysis
Nguyen, Viet Ha ULg; Golinval, Jean-Claude ULg

in Engineering Structures (2010), 32

In the field of structural health monitoring or machine condition monitoring, the activation of nonlinear dynamic behavior may render the procedure of damage or fault detection more difficult. Principal ... [more ▼]

In the field of structural health monitoring or machine condition monitoring, the activation of nonlinear dynamic behavior may render the procedure of damage or fault detection more difficult. Principal Component Analysis (PCA) is known as a popular method for diagnosis but as it is basically a linear method, it may pass over some useful nonlinear features of the system behavior. One possible extension of PCA is Kernel PCA (KPCA), owing to the use of nonlinear kernel functions that allow to introduce nonlinear dependences between variables. The objective of this paper is to address the problem of fault detection (in terms of nonlinear activation) in mechanical systems using a KPCA-based method. The detection is achieved by comparing the subspaces between the reference and a current state of the system through the concept of subspace angle. It is shown in this work that the exploitation of the measurements in the form of block Hankel matrices can improve effectively the detection results. The method is illustrated on an experimental example consisting of a beam with a geometric nonlinearity. [less ▲]

Detailed reference viewed: 56 (5 ULg)
Full Text
See detailFault detection in mechanical systems based on subspace features
Nguyen, Viet Ha ULg; Rutten, Christophe ULg; Golinval, Jean-Claude ULg

in International Conference on Noise and Vibration Engineering (2010, September)

In the field of structural health monitoring or machine condition monitoring, the activation of nonlinear dynamic behavior complicates the procedure of damage or fault detection. Principal Component ... [more ▼]

In the field of structural health monitoring or machine condition monitoring, the activation of nonlinear dynamic behavior complicates the procedure of damage or fault detection. Principal Component Analysis (PCA) is known as an efficient method for damage diagnosis. However, two drawbacks of PCA are the assumption of the linearity of the system and the need of many sensors. This article presents industrial applications of two possible extensions of PCA: Null subspace analysis (NSA) and Kernel PCA (KPCA). The advantages of NSA rely on its rapidity of use and its reliability. The KPCA method, through the use of nonlinear kernel functions, allows to introduce nonlinear dependences between variables. The objective is to address the problem of fault detection in mechanical systems using subspace-based methods. The detection is achieved by comparing the subspace features between the reference and a current state through statistics. Industrial data are used as illustration of the methods. [less ▲]

Detailed reference viewed: 60 (3 ULg)
Full Text
Peer Reviewed
See detailLocalization and quantification of damage in beam-like structures using sensitivities of principal component analysis results
Nguyen, Viet Ha ULg; Golinval, Jean-Claude ULg

in Mechanical Systems & Signal Processing (2010), 24(6), 1831-1843

Principal component analysis (PCA) is known as an efficient method for dynamic system identification and diagnosis. This paper addresses a damage diagnosis method based on sensitivities of PCA in the ... [more ▼]

Principal component analysis (PCA) is known as an efficient method for dynamic system identification and diagnosis. This paper addresses a damage diagnosis method based on sensitivities of PCA in the frequency domain for linear-form structures. The aim is not only to detect the presence of damage, but also to localize and to evaluate it. The Frequency response functions measured at different locations on the beam are considered as data for the PCA process. Sensitivities of principal components obtained from PCA to beam parameters are computed and inspected according to the location of sensors; their variation from the healthy state to the damaged state indicates damage locations. The damage can be evaluated next providing that a structural model is available; this evaluation is based on a model updating procedure. It is worth noting that the diagnosis process does not require a modal identification achievement. Both numerical and experimental examples are used for better illustration. [less ▲]

Detailed reference viewed: 54 (18 ULg)
Full Text
Peer Reviewed
See detailDetection of nonlinearity in a dynamic system using deformation modes obtained from the wavelet transform of measured responses
Nguyen, Viet Ha ULg; Peeters, Maxime ULg; Golinval, Jean-Claude ULg

in Shock and Vibration (2010), 17(4-5), 491-506

An efficient approach to Structural Health Monitoring of dynamical systems based on the Wavelet Transform (WT) and the concept of subspace angle is presented. The objective is to propose a detection ... [more ▼]

An efficient approach to Structural Health Monitoring of dynamical systems based on the Wavelet Transform (WT) and the concept of subspace angle is presented. The objective is to propose a detection method that is sensitive to the onset of nonlinear behaviour in a dynamic system. For this purpose, instantaneous frequencies are identified first from output-only vibration signals using the Wavelet Transform. Time varying deformation shapes are then extracted by analyzing the whole measurement data set on the structure. From this information, different dynamic states of the structure may be detected by inspecting time variations of 'modal' features. The experimental structure considered here as application example is a clamped beam with a geometric nonlinearity. Detection of nonlinearity is carried out by means of the concept of subspace angles between instantaneous deformation modes extracted from measurement data using the continuous Wavelet Transform. The method consists in controlling the angular coherence between active subspaces of the current and reference states respectively. The proposed technique, which shows a good sensitivity to small changes in the dynamic behaviour of the structure, may also be used for damage detection. [less ▲]

Detailed reference viewed: 37 (11 ULg)
See detailSubspace-based Methods for Machinery Analysis and Monitoring
Nguyen, Viet Ha ULg; Rutten, Christophe ULg; Golinval, Jean-Claude ULg

Conference (2010)

The objective of this presentation is to address the problem of structural damage detection or fault diagnosis in mechanical systems using subspace-based methods. Different methods are reviewed starting ... [more ▼]

The objective of this presentation is to address the problem of structural damage detection or fault diagnosis in mechanical systems using subspace-based methods. Different methods are reviewed starting from Principal Component Analysis (PCA) also known as Proper Orthogonal Decomposition (POD) of time responses. PCA is known as an efficient method for extracting modal features of linear structures from output-only measurements. Those features define a subspace which characterizes the dynamical behavior of the structure. It becomes than possible to detect structural damage by comparing a reference subspace (obtained from the healthy structure) with current subspaces on the basis of the concept of angles between subspaces. Other damage indexes based on statistics may also be used. One of the drawbacks of PCA is the need of several sensors. If the number of sensors is too small, modal identification and/or damage detection may not be performed in good conditions using PCA. An alternative PCA-based method named Null Subspace Analysis (NSA) may then be used. The NSA method generates data by means of block Hankel matrices and is proven to be efficient when the number of available sensors is small or even reduced to one sensor only. However, when damage activates nonlinearity, the detection problem may necessitate methods which are more sensitive to nonlinear behaviors. To this purpose, Kernel Principal Component Analysis (KPCA) is a nonlinear extension of PCA built to authorize features with nonlinear dependence between variables. The method is “flexible” in the sense that different kernel functions may be used to better fit the testing data. Industrial applications are presented to illustrate the proposed methods. [less ▲]

Detailed reference viewed: 24 (0 ULg)
Full Text
See detailDamage Diagnosis of Beam-like Structures Based on Sensitivities of Principal Component Analysis Results
Nguyen, Viet Ha ULg; Golinval, Jean-Claude ULg

in IMAC-XXVIII A Conference on Structural Dynamics (2010)

This paper addresses the problem of damage detection and localization in linear-form structures. Principal Component Analysis (PCA) is a popular technique for dynamic system investigation. The aim of the ... [more ▼]

This paper addresses the problem of damage detection and localization in linear-form structures. Principal Component Analysis (PCA) is a popular technique for dynamic system investigation. The aim of the paper is to present a damage diagnosis method based on sensitivities of PCA results in the frequency domain. Starting from Frequency Response Functions (FRFs) measured at different locations on the beam, PCA is performed to determine the main features of the signals. Sensitivities of principal directions obtained from PCA to beam parameters are then computed and inspected according to the location of sensors; their variation from the healthy state to the damaged state indicates damage locations. It is worth noting that damage localization is performed without the need of modal identification. Once the damage has been localized, its evaluation may be quantified if a structural model is available. This evaluation is based on a model updating procedure using previously estimated sensitivities. The efficiency and limitations of the proposed method are illustrated using numerical and experimental examples. [less ▲]

Detailed reference viewed: 66 (26 ULg)
Full Text
Peer Reviewed
See detailDamage localization in Linear-Form Structures Based on Sensitivity Investigation for Principal Component Analysis
Nguyen, Viet Ha ULg; Golinval, Jean-Claude ULg

in Journal of Sound & Vibration (2010), 329

This paper addresses the problem of damage detection and localization in linear-form structures. Principal Component Analysis (PCA) is a popular technique for dynamic system investigation. The aim of the ... [more ▼]

This paper addresses the problem of damage detection and localization in linear-form structures. Principal Component Analysis (PCA) is a popular technique for dynamic system investigation. The aim of the paper is to present a damage diagnosis method based on sensitivities of PCA results in the frequency domain. Starting from Frequency Response Functions (FRFs) measured at different locations on the structure; PCA is performed to determine the main features of the signals. Sensitivities of principal directions obtained from PCA to structural parameters are then computed and inspected according to the location of sensors; their variation from the healthy state to the damaged state indicates damage locations. It is worth noting that damage localization is performed without the need of modal identification. Influences of some features as noise, choice of parameter and number of sensors are discussed. The efficiency and limitations of the proposed method are illustrated using numerical and real-world examples. [less ▲]

Detailed reference viewed: 34 (10 ULg)
Full Text
See detailDetection of nonlinearity in a dynamic system using deformation modes obtained from the Wavelet Transfrom of measured responses
Nguyen, Viet Ha ULg; Peeters, Maxime ULg; Golinval, Jean-Claude ULg

Conference given outside the academic context (2009)

An efficient approach to Structural Health Monitoring of dynamical systems based on the Wavelet Transform (WT) and the concept of subspace angle is presented. The objective is to propose a detection ... [more ▼]

An efficient approach to Structural Health Monitoring of dynamical systems based on the Wavelet Transform (WT) and the concept of subspace angle is presented. The objective is to propose a detection method that is sensitive to the onset of nonlinear behaviour in a dynamic system. For this purpose, instantaneous frequencies are identified first from output-only vibration signals using the Wavelet Transform. Time varying deformation shapes are then extracted by analyzing the whole measurement data set on the structure. From this information, different dynamic states of the structure may be detected by inspecting time variations of ‘modal’ features. The experimental structure considered here as application example is a clamped beam with a geometric nonlinearity. Detection of nonlinearity is carried out by means of the concept of subspace angles between instantaneous deformation modes extracted from measurement data using the continuous Wavelet Transform. The method consists in controlling the angular coherence between active subspaces of the current and reference states respectively. The proposed technique, which shows a good sensitivity to small changes in the dynamic behaviour of the structure, may also be used for damage detection. [less ▲]

Detailed reference viewed: 84 (28 ULg)