Showing papers in "Mechanical Systems and Signal Processing in 2006"
TL;DR: This paper attempts to summarise and review the recent research and developments in diagnostics and prognostics of mechanical systems implementing CBM with emphasis on models, algorithms and technologies for data processing and maintenance decision-making.
Abstract: Condition-based maintenance (CBM) is a maintenance program that recommends maintenance decisions based on the information collected through condition monitoring. It consists of three main steps: data acquisition, data processing and maintenance decision-making. Diagnostics and prognostics are two important aspects of a CBM program. Research in the CBM area grows rapidly. Hundreds of papers in this area, including theory and practical applications, appear every year in academic journals, conference proceedings and technical reports. This paper attempts to summarise and review the recent research and developments in diagnostics and prognostics of mechanical systems implementing CBM with emphasis on models, algorithms and technologies for data processing and maintenance decision-making. Realising the increasing trend of using multiple sensors in condition monitoring, the authors also discuss different techniques for multiple sensor data fusion. The paper concludes with a brief discussion on current practices and possible future trends of CBM.
3,419 citations
TL;DR: In this article, a review of the past and recent developments in system identification of nonlinear dynamical structures is presented, highlighting their assets and limitations and identifying future directions in this research area.
Abstract: This survey paper contains a review of the past and recent developments in system identification of nonlinear dynamical structures. The objective is to present some of the popular approaches that have been proposed in the technical literature, to illustrate them using numerical and experimental applications, to highlight their assets and limitations and to identify future directions in this research area. The fundamental differences between linear and nonlinear oscillations are also detailed in a tutorial.
900 citations
TL;DR: In this article, the spectral kurtosis (SK) was used to detect and characterize nonstationary signals in the presence of strong masking noise and to detect incipient faults in rotating machines.
Abstract: In the previous paper, the authors have demonstrated the high potential of the spectral kurtosis (SK) to detect and characterise non-stationary signals. The present paper brings together these ideas and shows how the SK can be efficiently used in the vibration-based condition monitoring of rotating machines. First, and in contrast to classical kurtosis analysis, the SK provides a robust way of detecting incipient faults even in the presence of strong masking noise. Second, the SK offers an almost unique way of designing optimal filters for filtering out the mechanical signature of faults. The first property is of practical importance for monitoring purposes, whereas the second one proves very useful in diagnostics. Another originality of the paper is the introduction of the concept of kurtogram, from which optimal band-pass filters can be deduced, for instance as a prelude to envelope analysis. All the original findings presented in the paper are illustrated using actual industrial cases.
893 citations
TL;DR: A formalisation of the spectral kurtosis by means of the Wold–Cramer decomposition of “conditionally non-stationary” processes is proposed, which engenders many useful properties enjoyed by the SK.
Abstract: The spectral kurtosis (SK) is a statistical tool which can indicate the presence of series of transients and their locations in the frequency domain. As such, it helpfully supplements the classical power spectral density, which as is well known, completely eradicates non-stationary information. In spite of being particularly suited to many detection problems, the SK had rarely been used before now, probably because it lacked a formal definition and a well-understood estimation procedure. The aim of this paper is to partly fill these gaps. We propose a formalisation of the SK by means of the Wold–Cramer decomposition of “conditionally non-stationary” processes. This definition then engenders many useful properties enjoyed by the SK. In particular, we establish to which extent the SK is capable of detecting transients in the presence of strong additive noise by finding a closed-form relationship in terms of the noise-to-signal ratio. We finally propose a short-time Fourier-transform-based estimator of the SK which helps to link theoretical concepts with practical applications. This paper is also a prelude to a second paper where the SK is shown to find successful applications in vibration-based condition monitoring.
833 citations
TL;DR: The new trends on Laser Doppler Vibrometry (LDV) development are outlined with particular attention to the innovative solutions answering to the most recent technological requirements which are not met by the current systems as discussed by the authors.
Abstract: The new trends on Laser Doppler Vibrometry (LDV) development are outlined with particular attention to the innovative solutions answering to the most recent technological requirements which are not met by the current systems. Several LDV application areas are described and the limitations of actual technologies highlighted. The possible solutions needed to overcome these limits are anticipated and emerging technologies which are not completely entered the market but could positively answer to the industrial requirement are described.
374 citations
TL;DR: In this paper, an experimental investigation reported in this paper was centred on the application of the acoustic emission (AE) technique for identifying the presence and size of a defect on a radially loaded bearing.
Abstract: Vibration monitoring of rolling element bearings is probably the most established diagnostic technique for rotating machinery. The application of acoustic emission (AE) for bearing diagnosis is gaining ground as a complementary diagnostic tool, however, limitations in the successful application of the AE technique have been partly due to the difficulty in processing, interpreting and classifying the acquired data. Furthermore, the extent of bearing damage has eluded the diagnostician. The experimental investigation reported in this paper was centred on the application of the AE technique for identifying the presence and size of a defect on a radially loaded bearing. An experimental test rig was designed such that defects of varying sizes could be seeded onto the outer race of a test bearing. Comparisons between AE and vibration analysis over a range of speed and load conditions are presented. In addition, the primary source of AE activity from seeded defects is investigated. It is concluded that AE offers earlier fault detection and improved identification capabilities than vibration analysis. Furthermore, the AE technique also provided an indication of the defect size, allowing the user to monitor the rate of degradation on the bearing; unachievable with vibration analysis.
365 citations
TL;DR: In this article, the authors applied the empirical mode decomposition (EMD) and Hilbert spectrum for adaptive analysis of non-linear and non-stationary signals for gearbox fault diagnosis.
Abstract: The empirical mode decomposition (EMD) and Hilbert spectrum are a new method for adaptive analysis of non-linear and non-stationary signals. This paper applies this method to vibration signal analysis for localised gearbox fault diagnosis. We first study the properties of the recently developed B-spline EMD as a filter bank, which is helpful in understanding the mechanisms behind EMD. Then we investigate the effectiveness of the original and the B-spline EMD as well as their corresponding Hilbert spectrum in the fault diagnosis. Vibration signals collected from an automobile gearbox with an incipient tooth crack are used in the investigation. The results show that the EMD algorithms and the Hilbert spectrum perform excellently. They are found to be more effective than the often used continuous wavelet transform in detection of the vibration signatures.
303 citations
TL;DR: In this article, the authors proposed a new fault detection method that combines Hilbert transform and wavelet packet transform for gearbox demodulation, which can extract modulating signal and help to detect the early gear fault.
Abstract: Demodulation is an important issue in gearbox fault detection. Non-stationary modulating signals increase difficulties of demodulation. Though wavelet packet transform has better time–frequency localisation, because of the existence of meshing frequencies, their harmonics, and coupling frequencies generated by modulation, fault detection results using wavelet packet transform alone are usually unsatisfactory, especially for a multi-stage gearbox which contains close or identical frequency components. This paper proposes a new fault detection method that combines Hilbert transform and wavelet packet transform. Both simulated signals and real vibration signals collected from a gearbox dynamics simulator are used to verify the proposed method. Analysed results show that the proposed method is effective to extract modulating signal and help to detect the early gear fault.
289 citations
TL;DR: In this article, an experimental investigation has been carried out in order to evaluate the detection of cavitation in actual hydraulic turbines, based on the analysis of structural vibrations, acoustic emissions and hydrodynamic pressures measured in the machine.
Abstract: An experimental investigation has been carried out in order to evaluate the detection of cavitation in actual hydraulic turbines. The methodology is based on the analysis of structural vibrations, acoustic emissions and hydrodynamic pressures measured in the machine. The proposed techniques have been checked in real prototypes suffering from different types of cavitation. In particular, one Kaplan, two Francis and one Pump-Turbine have been investigated in the field. Additionally, one Francis located in a laboratory has also been tested. First, a brief description of the general features of cavitation phenomenon is given as well as of the main types of cavitation occurring in hydraulic turbines. The work presented here is focused on the most important ones which are the leading edge cavitation due to its erosive power, the bubble cavitation because it affects the machine performance and the draft tube swirl that limits the operation stability. Cavitation detection is based on the previous understanding of the cavity dynamics and its location inside the machine. This knowledge has been gained from flow visualisations and measurements in laboratory devices such as a high-speed cavitation tunnel and a reduced scale turbine test rig. The main techniques are the study of the high frequency spectral content of the signals and of their amplitude demodulation for a given frequency band. Moreover, low frequency spectral content can also be used in certain cases. The results obtained for the various types of cavitation found in the selected machines are presented and discussed in detail in the paper. Conclusions are drawn about the best sensor, measuring location, signal processing and analysis for each type of cavitation, which serve to validate and to improve the detection techniques.
284 citations
TL;DR: In this article, a multi-stage transmission gearbox (with and without defects) has been studied in order to replace the conventional vibration monitoring by MCSA, and it has been observed through FFT analysis that low frequencies of the vibration signatures have sidebands across line frequency of the motor current whereas high frequencies of vibration signature are difficult to be detected.
Abstract: In gearboxes, load fluctuations on the gearbox and gear defects are two major sources of vibration. Further, at times, measurement of vibration in the gearbox is not easy because of the inaccessibility in mounting the vibration transducers. An efficient and new but non-intrusive method to detect the fluctuation in gear load may be the motor current signature analysis (MCSA). In this paper, a multi-stage transmission gearbox (with and without defects) has been studied in order to replace the conventional vibration monitoring by MCSA. It has been observed through FFT analysis that low frequencies of the vibration signatures have sidebands across line frequency of the motor current whereas high frequencies of vibration signature are difficult to be detected. Hence, discrete wavelet transform (DWT) is suggested to decompose the current signal, and FFT analysis is carried out with the decomposed current signal to trace the sidebands of the high frequencies of vibration. The advantage of DWT technique to study the transients in MCSA has also been cited. The inability of CWT in detecting either defects or load fluctuation has been shown. The results indicate that MCSA along with DWT can be a good replacement for conventional vibration monitoring.
244 citations
TL;DR: A critical survey and comparison ofparametric time-domain methods for non-stationary random vibration modelling and analysis based upon a single vibration signal realization confirms the advantages and high performance characteristics of parametric methods.
Abstract: A critical survey and comparison of parametric time-domain methods for non-stationary random vibration modelling and analysis based upon a single vibration signal realization is presented. The considered methods are based upon time-dependent autoregressive moving average (TARMA) representations, and may be classified as unstructured parameter evolution, stochastic parameter evolution, and deterministic parameter evolution. The main methods within each class are presented, and model “structure” selection is discussed. The methods are compared, via a Monte Carlo study, in terms of achievable model parsimony, prediction accuracy, power spectral density and modal parameter accuracy and tracking, computational simplicity, and ease of use. Comparisons with basic non-parametric methods are also made. The results of the study confirm the advantages and high performance characteristics of parametric methods. They also confirm the increased accuracy and performance characteristics of the deterministic, as well as stochastic, parameter evolution methods over those of their unstructured parameter evolution counterparts.
TL;DR: In this article, a general description of smart material systems is given, focusing on the following fields of application: semi-passive concepts, energy harvesting, semi-active concepts, active vibration control and active structural acoustic control.
Abstract: This paper gives an overview of research in the area of smart structure dynamics. A general description of smart material systems is given. Particular focus is given to the following fields of application: semi-passive concepts, energy harvesting, semi-active concepts, active vibration control, and active structural acoustic control. The use of smart structures in structural health monitoring applications is also considered.
TL;DR: The efficiency of the proposed system is demonstrated by detecting motor electrical and mechanical faults originated from the induction motors by using Dempster–Shafer theory and it has potential for real-time applications.
Abstract: This paper presents an approach for the fault diagnosis in induction motors by using Dempster–Shafer theory. Features are extracted from motor stator current and vibration signals and with reducing data transfers. The technique makes it possible for on-line application. Neural network is trained and tested by the selected features of the measured data. The fusion of classification results from vibration and current classifiers increases the diagnostic accuracy. The efficiency of the proposed system is demonstrated by detecting motor electrical and mechanical faults originated from the induction motors. The results of the test confirm that the proposed system has potential for real-time applications.
TL;DR: In this article, the authors proposed a new fault feature extraction approach based on empirical mode decomposition (EMD) method and autoregressive (AR) model for roller bearings.
Abstract: The main purpose of this paper is to propose a new fault feature extraction approach based on empirical mode decomposition (EMD) method and autoregressive (AR) model for roller bearings. AR model is an effective approach to extract the fault feature of the vibration signals and the fault pattern can be identified directly by the extracted fault features without establishing the mathematical model and studying the fault mechanism of the system. However, AR model can only be applied to stationary signals, while the fault vibration signals of a roller bearing are non-stationary. Aiming at this problem, in this paper, the EMD method is used as a pretreatment to decompose the non-stationary vibration signal of a roller bearing into a number of intrinsic mode function (IMF) components which are stationary, then the AR model of each IMF component can be established. The AR parameters and the remnant's variance of the AR models of each IMF components are regarded as the feature vectors. The Mahalanobis distance criterion function is used to identify the condition and fault pattern of a roller bearing. Experimental analysis results show that the roller bearing fault features can be extracted by the proposed approach effectively.
TL;DR: A new multi-class classification of SVM named ‘one to others’ algorithm is presented to solve the multi- class recognition problems and the effectiveness of the method is verified by the application to the fault diagnosis for turbo pump rotor.
Abstract: Most artificial intelligence methods used in fault diagnosis are based on empirical risk minimisation principle and have poor generalisation when fault samples are few. Support vector machines (SVM) is a new general machine-learning tool based on structural risk minimisation principle that exhibits good generalisation even when fault samples are few. Fault diagnosis based on SVM is discussed. Since basic SVM is originally designed for two-class classification, while most of fault diagnosis problems are multi-class cases, a new multi-class classification of SVM named ‘one to others’ algorithm is presented to solve the multi-class recognition problems. It is a binary tree classifier composed of several two-class classifiers organised by fault priority, which is simple, and has little repeated training amount, and the rate of training and recognition is expedited. The effectiveness of the method is verified by the application to the fault diagnosis for turbo pump rotor.
TL;DR: In this article, an energy difference tracking method is proposed to define the intrinsic mode function (IMF) in the EMD method, based on the integrity and orthogonality of the EMF.
Abstract: Empirical mode decomposition (EMD) is a self-adaptive signal-processing method, which has been applied in non-stationary signal-processing successfully. Aiming at the problem of intrinsic mode function (IMF) criterion in the EMD method, the energy difference tracking method is proposed in this paper according to the integrity and orthogonality of the IMFs and used to define the IMF in the sifting process. By analyzing the simulated and actual signals, it is confirmed that the IMFs defined by the energy difference tracking method meet the orthogonality condition and reflect the intrinsic and real information of the analysed signal. By comparing the energy difference tracking method with the Cauchy-type convergence criterion, it is demonstrated that the IMFs obtained by the energy difference tracking method can reflect the intrinsic information included in the signal more clearly and their index of orthogonal (IO) is smaller.
TL;DR: In this article, the theory of stochastic model updating using a Monte-Carlo inverse procedure with multiple sets of experimental results is explained and then applied to the case of a simulated three degree-of-freedom system, which is used to fix ideas and also to illustrate some of the practical limitations of the method.
Abstract: The usual model updating method may be considered to be deterministic since it uses measurements from a single test system to correct a nominal finite element model. There may however be variability in seemingly identical test structures and uncertainties in the finite element model. Variability in test structures may arise from many sources including geometric tolerances and the manufacturing process, and modelling uncertainties may result from the use of nominal material properties, ill-defined joint stiffnesses and rigid boundary conditions. In this paper, the theory of stochastic model updating using a Monte-Carlo inverse procedure with multiple sets of experimental results is explained and then applied to the case of a simulated three degree-of-freedom system, which is used to fix ideas and also to illustrate some of the practical limitations of the method. In the companion paper, stochastic model updating is applied to a benchmark structure using a contact finite element model that includes common uncertainties in the modelling of the spot welds.
TL;DR: In this article, a 26 degree of freedom gear dynamic model of three shafts and two pairs of spur gears in mesh for comparison of localised tooth spalling and damage is presented.
Abstract: This paper presents a 26 degree of freedom gear dynamic model of three shafts and two pairs of spur gears in mesh for comparison of localised tooth spalling and damage. This paper details how tooth spalling and cracks can be included in the model by using the combined torsional mesh stiffness of the gears. The FEA models developed for calculation of the torsional stiffness and tooth load sharing ratio of the gears in mesh with the spalling and crack damage are also described. The dynamic simulation results of vibration from the gearbox were obtained by using Matlab and Simulink models, which were developed from the equations of motion. The simulation results were found to be consistent with results from previously published mathematical analysis and experimental investigations. The difference and comparison between the vibration signals with the tooth crack and spalling damage are discussed by investigating some of the common diagnostic functions and changes to the frequency spectra results. The result of this paper indicates that the amplitude and phase modulation of the coherent time synchronous vibration signal average can be effective in indicating the difference between localised tooth spalling and crack damage.
TL;DR: In this paper, the authors present an on-line current monitoring system that uses both techniques for fault detection and diagnosis in the stator and in the rotor of three phase induction motors.
Abstract: Induction motors are critical components in industrial processes. A motor failure may yield an unexpected interruption at the industrial plant, with consequences in costs, product quality, and safety. Many of these faulty situations in three phase induction motors have an electrical reason. Among different detection approaches proposed in the literature, those based on stator current monitoring are advantageous due to its non-invasive properties. One of these techniques resorts to spectrum analysis of machine line current. Another non-invasive technique is the Extended Park's Vector Approach, which allows the detection of inter-turn short circuits in the stator winding. This article presents the development of an on-line current monitoring system that uses both techniques for fault detection and diagnosis in the stator and in the rotor. Based on experimental observations and on the knowledge of the electrical machine, a knowledge-based system was constructed in order to carry out the diagnosis task from these estimated data.
TL;DR: In this paper, a method based on the finite element vibration analysis is presented for defect detection in rolling element bearings with single or multiple defects on different components of the bearing structure using the time and frequency domain parameters.
Abstract: In this paper, a method based on the finite element vibration analysis is presented for defect detection in rolling element bearings with single or multiple defects on different components of the bearing structure using the time and frequency domain parameters. A dynamic loading model is proposed in order to create the nodal excitation functions used in the finite element vibration analysis as external loading. A computer code written in Visual Basic programming language with a graphical user interface is developed to create the nodal excitations for different cases including the outer ring, inner ring or rolling element defects. Forced vibration analysis of a bearing structure is performed using the commercial finite element package I-DEAS under the action of an unbalanced force transferred to the structure via a ball bearing. Time and frequency domain parameters such as rms, crest factor, kurtosis and band energy ratio for the frequency spectrum of the enveloped signals are used to analyse the effect of the defect location and the number of defects on the time and frequency domain parameters. The role of the receiving point for vibration measurements is also investigated. The vibration data for various defect cases including the housing structure effect can be obtained using the finite element vibration analysis in order to develop an optimum monitoring method in condition monitoring studies.
TL;DR: In this article, a model-based transverse crack identification method for industrial machines is presented, which is validated by experimental results obtained on a large test rig, which was expressly designed for investigating the dynamic behavior of cracked horizontal rotors.
Abstract: This paper presents a model-based transverse crack identification method suitable for industrial machines. The method is validated by experimental results obtained on a large test rig, which was expressly designed for investigating the dynamical behaviour of cracked horizontal rotors. The identification method and the relative theory is briefly presented, while three different types of cracks are considered: the first is a slot, therefore not actually a crack since it has not the typical breathing behaviour, the second a small crack (14% of the diameter) and the third a deep crack (47% of the diameter). The excellent accuracy obtained in identifying position and depth of different cracks proves the effectiveness and reliability of the proposed method.
TL;DR: In this article, the authors proposed a novel technique for state detection of gearbox, which fits a time-varying autoregressive model to the gear motion residual signals applying a noise-adaptive Kalman filter, in the healthy state of the target gear.
Abstract: Conventional vibration monitoring techniques are unable to provide accurate state analysis of a gearbox under varying load condition. This paper proposes a novel technique for state detection of gearbox, which fits a time-varying autoregressive model to the gear motion residual signals applying a noise-adaptive Kalman filter, in the healthy state of the target gear. The optimum autoregressive model order, which provides a compromised model fitting for the healthy gear motion residual signals collected under various load conditions, is determined with the aid of a specific model order selection method proposed in this study. Consequently, a robust statistical measure, which takes the percentage of outliers exceeding the three standard deviation limits is applied to evaluate the state of the target gear, where the standard deviation of autoregressive model residuals takes its maximum in all tested gear motion residual signals for model order selection. The proposed technique is validated using full lifetime vibration data of gearboxes operating from new to failure under four distinct load conditions. The investigated load conditions include: (1) constant load, (2) one jump from 100 to 200% nominal torque level, (3) one jump from 100 to 300% nominal torque level, and (4) constant changed to sinusoidal. In each application, the specific model order selection and comparison of the proposed gear state indicator with three counterparts proposed in recent studies are addressed in detail. The Kolmogorov–Smirnov test is also performed as a complementary statistical analysis. The results show that the proposed technique possesses a highly effective and robust property in the state detection of gearbox, which is independent of varying load condition as well as remarkable stability, early alarm for incipient fault and significant presence of fault effects. The proposed gear state indicator can be directly employed by an on-line maintenance program as a reliable quantitative covariate to schedule optimal maintenance decision for rotating machinery.
TL;DR: In this paper, the state of the art of the mathematical theory of vibration absorption is presented and illustrated for the benefit of the reader with numerous simple examples, including structural modification by passive elements and active control.
Abstract: The abiding problem of vibration absorption has occupied engineering scientists for over a century and there remain abundant examples of the need for vibration suppression in many industries. For example, in the automotive industry the resolution of noise, vibration and harshness (NVH) problems is of extreme importance to customer satisfaction. In rotorcraft it is vital to avoid resonance close to the blade passing speed and its harmonics. An objective of the greatest importance, and extremely difficult to achieve, is the isolation of the pilot's seat in a helicopter. It is presently impossible to achieve the objectives of vibration absorption in these industries at the design stage because of limitations inherent in finite element models. Therefore, it is necessary to develop techniques whereby the dynamic of the system (possibly a car or a helicopter) can be adjusted after it has been built. There are two main approaches: structural modification by passive elements and active control. The state of art of the mathematical theory of vibration absorption is presented and illustrated for the benefit of the reader with numerous simple examples.
TL;DR: In this paper, the instantaneous energy density is shown to obtain high values when defected teeth are engaged, and three methods are compared in terms of sensitivity, reliability and computation effectiveness.
Abstract: In this work, energy-based features for gear fault diagnosis and prediction are proposed. The instantaneous energy density is shown to obtain high values when defected teeth are engaged. Three methods are compared in terms of sensitivity, reliability and computation effectiveness. The Wigner–Ville distribution is contrasted to the wavelet transform and the newly proposed empirical mode decomposition scheme. It is shown that all three methods are capable of a reliable prediction. An empirical law, which relates the energy content to the crack magnitude is established.
TL;DR: In this article, a damage detection method of mechanical system based on subspace identification concepts and statistical process techniques is presented, where measured time-responses of structures subjected to artificial or environmental vibrations are assembled to form the Hankel matrix, which is further factorised by performing singular value decomposition to obtain characteristic subspaces.
Abstract: A damage detection method of mechanical system based on subspace identification concepts and statistical process techniques is presented. The aim is to propose a method that is sensitive to small-sized structural damages and suitable for on-line monitoring. Measured time-responses of structures subjected to artificial or environmental vibrations are assembled to form the Hankel matrix, which is further factorised by performing singular-value decomposition to obtain characteristic subspaces. It may be demonstrated that the structural responses are mainly located in the active subspace defined by the first principal components, which is orthonormal to the null subspace defined by the remaining principal components. If no structural damage occurs, the orthonormality relation between the subspaces remains valid with small residues when consecutive data sets are compared, and these residues may be evaluated by the proposed damage indicators. The method is validated using an experimental mock-up of an airplane subjected to different levels of damages simulated. It is also applied in environmental vibration testing of a street lighting device to monitor structural fatigue evolution.
TL;DR: In this paper, the electro-mechanical coupling of typical MEMS devices is defined and introduced, followed by an in-depth review of the various existing modeling and simulation techniques.
Abstract: Microsystems or micro-electro-mechanical systems (MEMS), as a newly emerged revolutionary enabling technology, has brought both opportunities and challenges to the field of structural dynamics in a different scale, owing primarily to its interdisciplinary nature of research and extremely small feature size. This paper seeks to present a comprehensive yet critical review on some of the major issues that need to be tackled in the successful realisation of microsystems, with an ultimate objective of further developing and improving the current design capabilities of these systems. The electro-mechanical coupling of typical MEMS devices is first defined and introduced, followed by an in-depth review of the various existing modeling and simulation techniques. Special requirements are discussed when typical MEMS devices need to be tested and existing vibration testing techniques are reviewed. Of particular interest to MEMS devices, structural damping has become a major issue affecting dynamic performance due to the various energy dissipation mechanisms involved. These damping mechanisms have been examined, together with methods developed to model them. Finally, conclusions are made on what have been achieved to date and future prospects of structural dynamics of Microsystems are identified with an intention to stimulate further concerted research in this important emerging area.
TL;DR: In this article, a non-linear model based on complex radial basis function (RBF) networks is proposed for the reconstruction of in-cylinder pressure pulse waveforms, where the Fourier transforms of both engine structure vibration and crankshaft speed fluctuation are used for reconstruction process.
Abstract: Methods to measure and monitor the cylinder pressure in internal combustion engines can contribute to reduced fuel consumption, noise and exhaust emissions. As direct measurements of the cylinder pressure are expensive and not suitable for measurements in vehicles on the road indirect methods which measure cylinder pressure have great potential value. In this paper, a non-linear model based on complex radial basis function (RBF) networks is proposed for the reconstruction of in-cylinder pressure pulse waveforms. Input to the network is the Fourier transforms of both engine structure vibration and crankshaft speed fluctuation. The primary reason for the use of Fourier transforms is that different frequency regions of the signals are used for the reconstruction process. This approach also makes it easier to reduce the amount of information that is used as input to the RBF network. The complex RBF network was applied to measurements from a 6-cylinder ethanol powered diesel engine over a wide range of running conditions. Prediction accuracy was validated by comparing a number of parameters between the measured and predicted cylinder pressure waveform such as maximum pressure, maximum rate of pressure rise and indicated mean effective pressure. The performance of the network was also evaluated for a number of untrained running conditions that differ both in speed and load from the trained ones. The results for the validation set were comparable to the trained conditions.
TL;DR: A new envelope algorithm, the segment power function method, is put forward that is superior to existing algorithms because in most situations it is more flexible than the cubic spline interpolation algorithm and smoother than the Akima interpolation algorithms, and it is less likely to introduce a false frequency when applied to HHT.
Abstract: The algorithm to compute the envelope-line in Hilbert–Huang Transform (HHT) has major drawbacks. This paper first introduces the problem of an envelope-line algorithm in HHT, analyses the shortcomings of two classic algorithms, cubic spline interpolation algorithm and the Akima interpolation algorithm, and then proposes an important theory called the Segment Slide Theory in light of the principle of parabola parameter spline interpolation method and proves it. Based on the theory we proposed and with intuitive geometric meaning, a new envelope algorithm, the segment power function method, is put forward. The new algorithm is superior to existing algorithms because in most situations it is more flexible than the cubic spline interpolation algorithm and smoother than the Akima interpolation algorithm as shown in the experimental examples, and it is less likely to introduce a false frequency when applied to HHT.
TL;DR: This novel MCM method is based on Kohonen's self-organising map and adopts a multidimensional dissimilarity measure for dual class classification and designed to be highly modular and scale well for a multi-sensor condition monitoring environment.
Abstract: This paper proposes a novelty detection-based method for machine condition monitoring (MCM) using vibration signals and a new feature extraction method based on higher-order statistics of the power spectral density. This novel MCM method is based on Kohonen's self-organising map and adopts a multidimensional dissimilarity measure for dual class classification. The approach is designed to be highly modular and scale well for a multi-sensor condition monitoring environment. Experiments using real-world vibration data sets with upto eight sensors have shown high accuracy in classification and robustness across different condition monitoring applications.
TL;DR: In this paper, a local compliance matrix of two degrees of freedom, bending in the horizontal and the vertical planes is used to model the rotating transverse crack in the shaft and is calculated based on the available expressions of the stress intensity factors and the associated expressions for the strain energy release rates.
Abstract: The identification of a transverse crack on a beam is the subject of many investigators. Identifying the crack means to find its position and depth. In many cases there are more than one cracks on a beam. Then the solutions, or the combinations of parameters characterising the cracks are more and the problem becomes more complicated particularly when the crack must be identified using one more parameter, the relative each other angular position. In the present paper the dynamic behaviour of a cracked beam with two transverse surface cracks is studied. Each crack is characterised by its depth, position and relative angle. Both cracks are considered to lie in arbitrary angular positions with respect to the longitudinal axis of the beam and at any distance from the left end. A local compliance matrix of two degrees of freedom, bending in the horizontal and the vertical planes is used to model the rotating transverse crack in the shaft and is calculated based on the available expressions of the stress intensity factors and the associated expressions for the strain energy release rates. The compliance matrix is calculated for the first time at any angle of rotation. Thus, the compliance is given as a function of both the crack depth and the angular location. These expressions are usable, due to the stress intensity function limitations, only for limited regions around the zero angular position of the crack and not for every crack angle. For these cases, B-spline curves are used to interpolate the known points and a function in analytical form is given for every crack depth and angle. It is well known that when a crack exists in a structure, such as a beam, then the natural frequency of vibration decreases. This reduction is studied here for six independent parameters namely the depth, the location, and the rotational angle of each crack. By keeping these six parameters constant, the first three flexural eigenmodes can be computed and plotted. Due to its sensitivity in slope or displacement changes the theory of wavelets is used here to identify the locations of the cracks reducing thus the number of independent parameters. As it is well known the existence of a crack on a beam in bending, creates in the elastic line of the beam a slope discontinuity analog generally to the crack depth and additionally here to the angular position. The wavelet transformation of a vibration mode or of the vibration response of the structure under some circumstances could be used to locate the cracks. If the positions are known, then the depths and the respective angles can be determined. Here the diagrams of the first three eigenvalues versus both the crack depth and the rotational angle, are used to identify the remaining unknown parameters for both cracks.