Structural damage identification with noise polluted Frequency Response Functions (FRFs)

Damage detection in structures has become increasingly important in recent years. While a number of damage detection and localization methods have been proposed, very few attempts have been made to explore the structure damage with noise polluted data which is unavoidable effect in real world. The measurement data are contaminated by noise because of test environment as well as electronic devices and this noise tend to give error results with structural damage identification methods. Therefore it is important to investigate a method which can perform better with noise polluted data. This paper introduces a new damage index using principal component analysis (PCA) for damage detection of building structures being able to accept noise polluted frequency response functions (FRFs) as input. The FRF data are obtained from the function datagen of MATLAB program which is available on the web site of the IASC-ASCE (International Association for Structural Control– American Society of Civil Engineers) Structural Health Monitoring (SHM) Task Group. The proposed method involves a five-stage process: calculation of FRFs, calculation of damage index values using proposed algorithm, development of the artificial neural networks and introducing damage indices as input parameters and damage detection of the structure. This paper briefly describes the methodology and the results obtained in detecting damage in all six cases of the benchmark study with different noise levels. The proposed method is applied to a benchmark problem sponsored by the IASC-ASCE Task Group on Structural Health Monitoring, which was developed in order to facilitate the comparison of various damage identification methods. The illustrated results show that the PCA-based algorithm is effective for structural health monitoring with noise polluted FRFs which is of common occurrence when dealing with industrial structures.

Frequency response function based structural damage detection using artificial neural networks

Damage detection in structures has become increasingly important in recent years. While a number of damage detection and localization methods have been proposed, few attempts have been made to explore the structure damage with frequency response functions (FRFs). This paper illustrates the damage identification and condition assessment of a beam structure using a new frequency response functions (FRFs) based damage index and Artificial Neural Networks (ANNs). In practice, usage of all available FRF data as an input to artificial neural networks makes the training and convergence impossible. Therefore one of the data reduction techniques Principal Component Analysis (PCA) is introduced in the algorithm. In the proposed procedure, a large set of FRFs are divided into sub-sets in order to find the damage indices for different frequency points of different damage scenarios. The basic idea of this method is to establish features of damaged structure using FRFs from different measurement points of different sub-sets of intact structure. Then using these features, damage indices of different damage cases of the structure are identified after reconstructing of available FRF data using PCA. The obtained damage indices corresponding to different damage locations and severities are introduced as input variable to developed artificial neural networks. Finally, the effectiveness of the proposed method is illustrated and validated by using the finite element modal of a beam structure. The illustrated results show that the PCA based damage index is suitable and effective for structural damage detection and condition assessment of building structures.

Structural damage detection using frequency response functions and neural networks

This paper illustrates the damage identification and condition assessment of a three story bookshelf structure using a new frequency response functions (FRFs) based damage index and Artificial Neural Networks (ANNs). A major obstacle of using measured frequency response function data is a large size input variables to ANNs. This problem is overcome by applying a data reduction technique called principal component analysis (PCA). In the proposed procedure, ANNs with their powerful pattern recognition and classification ability were used to extract damage information such as damage locations and severities from measured FRFs. Therefore, simple neural network models are developed, trained by Back Propagation (BP), to associate the FRFs with the damage or undamaged locations and severity of the damage of the structure. Finally, the effectiveness of the proposed method is illustrated and validated by using the real data provided by the Los Alamos National Laboratory, USA. The illustrated results show that the PCA based artificial Neural Network method is suitable and effective for damage identification and condition assessment of building structures. In addition, it is clearly demonstrated that the accuracy of proposed damage detection method can also be improved by increasing number of baseline datasets and number of principal components of the baseline dataset.

A Kalman filter based strategy for linear structural system identification based on multiple static and dynamic test data

The problem of identification of stiffness, mass and damping properties of linear structural systems, based on multiple sets of measurement data originating from static and dynamic tests is considered. A strategy, within the framework of Kalman filter based dynamic state estimation, is proposed to tackle this problem. The static tests consists of measurement of response of the structure to slowly moving loads, and to static loads whose magnitude are varied incrementally; the dynamic tests involve measurement of a few elements of the frequency response function (FRF) matrix. These measurements are taken to be contaminated by additive Gaussian noise. An artificial independent variable τ, that simultaneously parameterizes the point of application of the moving load, the magnitude of the incrementally varied static load and the driving frequency in the FRFs, is introduced. The state vector is taken to consist of system parameters to be identified. The fact that these parameters are independent of the variable τ is taken to constitute the set of ‘process’ equations. The measurement equations are derived based on the mechanics of the problem and, quantities, such as displacements and/or strains, are taken to be measured. A recursive algorithm that employs a linearization strategy based on Neumann’s expansion of structural static and dynamic stiffness matrices, and, which provides posterior estimates of the mean and covariance of the unknown system parameters, is developed. The satisfactory performance of the proposed approach is illustrated by considering the problem of the identification of the dynamic properties of an inhomogeneous beam and the axial rigidities of members of a truss structure.

Fuzzy-Rough Sets Assisted Attribute Selection

R. Jensen and Q. Shen. Fuzzy-Rough Sets Assisted Attribute Selection. IEEE Transactions on Fuzzy Systems, vol. 15, no. 1, pp. 73-89, 2007.

Dynamic stationary response of reinforced plates by the boundary element method

A direct version of the boundary element method (BEM) is developed to model the stationary dynamic response of reinforced plate structures, such as reinforced panels in buildings, automobiles, and airplanes. The dynamic stationary fundamental solutions of thin plates and plane stress state are used to transform the governing partial differential equations into boundary integral equations (BIEs). Two sets of uncoupled BIEs are formulated, respectively, for the in-plane state ( membrane) and for the out-of-plane state ( bending). These uncoupled systems are joined to formamacro-element, in which membrane and bending effects are present. The association of these macro-elements is able to simulate thin-walled structures, including reinforced plate structures. In the present formulation, the BIE is discretized by continuous and/or discontinuous linear elements. Four displacement integral equations are written for every boundary node. Modal data, that is, natural frequencies and the corresponding mode shapes of reinforced plates, are obtained from information contained in the frequency response functions (FRFs). A specific example is presented to illustrate the versatility of the proposed methodology. Different configurations of the reinforcements are used to simulate simply supported and clamped boundary conditions for the plate structures. The procedure is validated by comparison with results determined by the finite element method (FEM).

A numerical program for Railway vehicle-track-structure dynamic interaction using a modal substructuring approach

This work presents a program for simulations of vehicle-track and vehicle-trackstructure dynamic interaction . The method used is computationally efficient in the sense that a reduced number of coordinates is sufficient and doesn’t require high efficiency computers. The method proposes a modal substructuring approach of the system by modelling rails , sleepers and underlying structure with modal coordinates, the vehicle with physical lumped elements coordinates and by introducing interconnection elements between these structures (wheel-rail contact, railpads and ballast) by means of their interaction forces. The Frequency response function (FRF) is also calculated for both cases of track over a structure (a bridge, a viaduct ...) and for the simple vehicle-track program; for each case the vehicle effect on the FRF is then analyzed through the comparison of the FRFs obtained introducing or not a simplified vehicle on the system.

Sul comportamento dinamico di macchine fresatrici in alcune condizioni critiche di lavorazione

Questa tesi affronta lo studio di una tipologia di vibrazione autoeccitata, nota come chatter, che si manifesta nei processi di lavorazione ad asportazione di truciolo ed in particolare nelle lavorazioni di fresatura. La tesi discute inoltre lo sviluppo di una tecnica di monitoraggio e diagnostica del chatter basato sul rilievo di vibrazioni. Il fenomeno del chatter è caratterizzato da violente oscillazioni tra utensile e pezzo in lavorazione ed elevate emissioni acustiche. Il chatter, se non controllato, causa uno scadimento qualitativo della finitura superficiale e delle tolleranze dimensionali del lavorato, una riduzione della vita degli utensili e dei componenti della macchina. Questa vibrazione affligge negativamente la produttività e la qualità del processo di lavorazione e pregiudica l’interazione uomo-macchina-ambiente. Per una data combinazione di macchina, utensile e pezzo lavorato, i fattori che controllano la velocità di asportazione del materiale sono gli stessi che controllano l’insorgenza del chatter: la velocità di rotazione del mandrino, la profondità assiale di passata e la velocità di avanzamento dell’utensile. Per studiare il fenomeno di chatter, con l’obbiettivo di individuare possibili soluzioni per limitarne o controllarne l’insorgenza, vengono proposti in questa tesi alcuni modelli del processo di fresatura. Tali modelli comprendono il modello viscoelastico della macchina fresatrice e il modello delle azioni di taglio. Per le azioni di taglio è stato utilizzato un modello presente in letteratura, mentre per la macchina fresatrice sono stati utilizzato modelli a parametri concentrati e modelli modali analitico-sperimentali. Questi ultimi sono stati ottenuti accoppiando un modello modale sperimentale del telaio, completo di mandrino, della macchina fresatrice con un modello analitico, basato sulla teoria delle travi, dell’utensile. Le equazioni del moto, associate al processo di fresatura, risultano essere equazioni differenziali con ritardo a coefficienti periodici o PDDE (Periodic Delay Diefferential Equations). È stata implementata una procedura numerica per mappare, nello spazio dei parametri di taglio, la stabilità e le caratteristiche spettrali (frequenze caratteristiche della vibrazione di chatter) delle equazioni del moto associate ai modelli del processo di fresatura proposti. Per testare i modelli e le procedure numeriche proposte, una macchina fresatrice CNC 4 assi, di proprietà del Dipartimento di Ingegneria delle Costruzioni Meccaniche Nucleari e Metallurgiche (DIEM) dell’Università di Bologna, è stata strumentata con accelerometri, con una tavola dinamometrica per la misura delle forze di taglio e con un adeguato sistema di acquisizione. Eseguendo varie prove di lavorazione sono stati identificati i coefficienti di pressione di taglio contenuti nel modello delle forze di taglio. Sono stati condotti, a macchina ferma, rilievi di FRFs (Funzioni Risposta in Frequenza) per identificare, tramite tecniche di analisi modale sperimentale, i modelli del solo telaio e della macchina fresatrice completa di utensile. I segnali acquisiti durante le numerose prove di lavorazione eseguite, al variare dei parametri di taglio, sono stati analizzati per valutare la stabilità di ciascun punto di lavoro e le caratteristiche spettrali della vibrazione associata. Questi risultati sono stati confrontati con quelli ottenuti applicando la procedura numerica proposta ai diversi modelli di macchina fresatrice implementati. Sono state individuate le criticità della procedura di modellazione delle macchine fresatrici a parametri concentrati, proposta in letteratura, che portano a previsioni erronee sulla stabilità delle lavorazioni. È stato mostrato come tali criticità vengano solo in parte superate con l’utilizzo dei modelli modali analitico-sperimentali proposti. Sulla base dei risultati ottenuti, è stato proposto un sistema automatico, basato su misure accelerometriche, per diagnosticare, in tempo reale, l’insorgenza del chatter durante una lavorazione. È stato realizzato un prototipo di tale sistema di diagnostica il cui funzionamento è stato provato mediante prove di lavorazione eseguite su due diverse macchine fresatrici CNC.

Accelerometer based measurements of combustion in an automotive turbocharged diesel engine

The capability to detect combustion in a diesel engine has the potential of being an important control feature to meet increasingly stringent emission regulations, develop alternative combustion strategies, and use of biofuels. In this dissertation, block mounted accelerometers were investigated as potential feedback sensors for detecting combustion characteristics in a high-speed, high pressure common rail (HPCR), 1.9L diesel engine. Accelerometers were positioned in multiple placements and orientations on the engine, and engine testing was conducted under motored, single and pilot-main injection conditions. Engine tests were conducted at varying injection timings, engine loads, and engine speeds to observe the resulting time and frequency domain changes of the cylinder pressure and accelerometer signals. The frequency content of the cylinder pressure based signals and the accelerometer signals between 0.5 kHz and 6 kHz indicated a strong correlation with coherence values of nearly 1. The accelerometers were used to produce estimated combustion signals using the Frequency Response Functions (FRF) measured from the frequency domain characteristics of the cylinder pressure signals and the response of the accelerometers attached to the engine block. When compared to the actual combustion signals, the estimated combustion signals produced from the accelerometer response had Root Mean Square Errors (RMSE) between 7% and 25% of the actual signals peak value. Weighting the FRF’s from multiple test conditions along their frequency axis with the coherent output power reduced the median RMSE of the estimated combustion signals and the 95th percentile of RMSE produced from each test condition. The RMSE’s of the magnitude based combustion metrics including peak cylinder pressure, MPG, peak ROHR, and work estimated from the combustion signals produced by the accelerometer responses were between 15% and 50% of their actual value. The MPG measured from the estimated pressure gradient shared a direct relationship to the actual MPG. The location based combustion metrics such as the location of peak values and burn durations were capable of RMSE measurements as low as 0.9°. Overall, accelerometer based combustion sensing system was capable of detecting combustion and providing feedback regarding the in cylinder combustion process

A novel silicon membrane-based biosensing platform using distributive sensing strategy and artificial neural networks for feature analysis

A novel biosensing system based on a micromachined rectangular silicon membrane is proposed and investigated in this paper. A distributive sensing scheme is designed to monitor the dynamics of the sensing structure. An artificial neural network is used to process the measured data and to identify cell presence and density. Without specifying any particular bio-application, the investigation is mainly concentrated on the performance testing of this kind of biosensor as a general biosensing platform. The biosensing experiments on the microfabricated membranes involve seeding different cell densities onto the sensing surface of membrane, and measuring the corresponding dynamics information of each tested silicon membrane in the form of a series of frequency response functions (FRFs). All of those experiments are carried out in cell culture medium to simulate a practical working environment. The EA.hy 926 endothelial cell lines are chosen in this paper for the bio-experiments. The EA.hy 926 endothelial cell lines represent a particular class of biological particles that have irregular shapes, non-uniform density and uncertain growth behaviour, which are difficult to monitor using the traditional biosensors. The final predicted results reveal that the methodology of a neural-network based algorithm to perform the feature identification of cells from distributive sensory measurement has great potential in biosensing applications.