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See detailComparison of output-only methods for condition monitoring of industrials systems
Rutten, Christophe ULg; Nguyen, Viet Ha; Golinval, Jean-Claude ULg

in Journal of Physics: Conference Series (2011), 305

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. Blind source separation ... [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. Blind source separation (BSS) techniques are known as efficient methods for damage diagnosis. However, most of BSS techniques repose on the assumption of the linearity of the system and the need of many sensors. This article presents some possible extensions of those techniques that may improve the damage detection, e.g. Enhanced-Principal Component Analysis (EPCA), Kernel PCA (KPCA) and Blind Modal Identification (BMID). The advantages of EPCA 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. BMID is adequate to identify and to detect damage for generally damped systems. In this paper, damage is firstly examined by Stochastic Subspace Identification (SSI); then the detection is achieved by comparing subspace features between the reference and a current state through statistics and the concept of subspace angle. Industrial data are used as illustration of the methods. [less ▲]

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See detailIndustrial applications of extended output-only Blind Source Separation techniques
Rutten, Christophe ULg; Nguyen, Viet Ha; Golinval, Jean-Claude ULg

in Vibration Problems Icovp 2011: The 10th International Conference on Vibration Problems (2011)

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

In the field of structural health monitoring or machine condition moni-toring, 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. This paper presents industrial applications of two possible extensions of output-only Blind Source Separation (BSS) techniques, namely Principal Component Analysis (PCA) and Second Order Blind Identification (SOBI). Through the use of block Hankel matrices, these methods may be used when a reduced set of sensors or even one single sensor is available. 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 using the concept of angular coherence between subspaces. [less ▲]

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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 ▲]

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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 ▲]

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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 ▲]

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See detailDamage Detection of Mechanical Components Using Null Subspace Analysis
Rutten, Christophe ULg; Loffet, Christophe; Golinval, Jean-Claude ULg

Conference given outside the academic context (2009)

This paper presents two original applications of the Null Subspace Analysis (NSA) method for fault diagnosis in mechanical components. The method is first applied to the case-study of electro-mechanical ... [more ▼]

This paper presents two original applications of the Null Subspace Analysis (NSA) method for fault diagnosis in mechanical components. The method is first applied to the case-study of electro-mechanical devices at the end of the assembly line with the aim of assessing their overall quality. The advantages of the proposed method rely in its rapidity of use and its reliability. At first, a set of five good (i.e. healthy) devices and four damaged devices was considered. The components were instrumented with one triaxial accelerometer on the flank and one monoaxial accelerometer on the top. Based on the NSA method, a mapping of the space [ active components, system order ] up to a system order of 100, was realized in order to select the appropriate order and number of active components. Eventually, thanks to this mapping, the method was able to successfully detect all the faulty components using the signal from only one accelerometer in one direction. The second application is related to the quality assessment of welded joints between stripes in a steel processing plan. Six welded joints with nominal welding parameters and twenty-seven welded joints with out-of-range parameters were realized. Again, the NSA method was able to diagnose successfully the welded joints using a single signal from one accelerometer. [less ▲]

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