Damping identification from non-linear random responses using a multi-triggering random decrement technique

The concept of multi-triggering random decrement technique is introduced. Like the single triggering technique, it reduces multi-mode multi-measurement stationary random responses to free decay responses but has the advantage of increasing the apparent number of the resulting free decay time response functions. The maximum number of these free decay responses is equal to the square of the number of random measurements. These free decay responses are then used in a linear time domain modal identification algorithm to extract frequencies, damping factors and mode shapes of a structure under test. The quasi-linear modal approach is used to deal with non-linearities by repeating the linear identification process at different levels of inputs/responses. The procedure is applied to rectangular panels subjected to acoustic random input ranging from 130 to 157 dB. The changes in frequencies and damping factors with input level are reported. This application is part of a sonic fatigue research program.     

Rolling element bearing fault detection using an improved combination of Hilbert and wavelet transforms

As a kind of complicated mechanical component, rolling element bearing plays a significant role in rotating machines, and bearing fault detection benefits decision-making of maintenance and avoids undesired downtime cost. However, extraction of fault signatures from a collected signal in a practical working environment is always a great challenge. This paper proposes an improved combination of the Hilbert and wavelet transforms to identify early bearing fault signatures. Real rail vehicle bearing and motor bearing data were used to validate the proposed method. A traditional combination of Hilbert and wavelet transforms was employed for comparison purpose. An indicator to evaluate fault detection capability of methods was developed in this research. Analysis results showed that the extraction capability of bearing fault signatures is greatly enhanced by the proposed method.     

Robust parameter identification using forced responses

In this paper, a new model updating scheme is introduced to adjust the system matrices of a finite-element model by using experimental operating deflection shapes (ODS). An ODS is defined here as the response vector when the system is driven at a given degree of freedom with a unit force of fixed frequency. The proposed algorithm adjusts the numerical model in an iterative way. The matrix equilibrium equation is solved by first taking into account the frequency shift that appears between the non-updated finite element model and the experimental structure. In this way, numerical instabilities observed in state-of-the-art methods are avoided. We present results on two well-known numerical and experimental benchmark cases. They show the good convergence properties of the proposed approach.     

A robust condition monitoring methodology for grinding wheel wear identification using Hilbert Huang transform

Grinding is a finishing operation performed to obtain the desired finish on the component. Wheel wear is one of the primary constraints in achieving the desired productivity in grinding. A new methodology is proposed for accurate and timely identification of wheel wear in cylindrical grinding using Hilbert Huang transform and support vector machine. During the grinding of EN31 carbon steel, the condition of the wheel and its wear was monitored with an accelerometer and power cell. Both vibration and power signals captured were used to identify the condition of the wheel and its wear. An exhaustive feature set is generated in the frequency and the time-frequency domain. Hilbert Huang transform, an adaptive time-frequency analysis technique, was used to extract the features of tool wear in the time-frequency domain. The first three IMF constituents were further chosen for feature extraction of statistical parameters based on their mean energy. Random forests algorithm was used to identify the relevant features. The methodology was validated with several grinding experiments and, is found to give an accuracy of 100% with both low and high cutting depths. The results indicated the robust and reliable wheel wear detection in cylindrical grinding with the use of relatively cheap sensors like accelerometers. The proposed method can be widely used in many applications in the industry where grinding is predominantly used as the finishing operation.     

Estimating the degree of nonlinearity in transient responses with zeroed early-time fast Fourier transforms

This work presents time-frequency signal processing methods for detecting and characterizing nonlinearity in transient response measurements. The methods are intended for systems whose response becomes increasingly linear as the response amplitude decays. The discrete Fourier transform of the response data is found with various sections of the initial response set to zero. These frequency responses, dubbed zeroed early-time fast Fourier transforms (ZEFFTs), acquire the usual shape of linear frequency response functions (FRFs) as more of the initial nonlinear response is nullified. Hence, nonlinearity is evidenced by a qualitative change in the shape of the ZEFFT as the length of the initial nullified section is varied. These spectra are shown to be sensitive to nonlinearity, revealing its presence even if it is active in only the first few cycles of a response, as may be the case with macro-slip in mechanical joints. They also give insight into the character of the nonlinearity, potentially revealing nonlinear energy transfer between modes or the modal amplitudes below which a system behaves linearly. In some cases one can identify a linear model from the late time, linear response, and use it to reconstruct the response that the system would have executed at previous times if it had been linear. This gives an indication of the severity of the nonlinearity and its effect on the measured response. The methods are demonstrated on both analytical and experimental data from systems with slip and impact nonlinearities.     

Localization algorithm for small objects in an image using wavelet transforms

This paper presents an algorithm for determining the coordinates of small objects appearing in a sequence of scene images based on sum and difference processing of the detailing wavelet coefficients of the current and reference images, followed by a morphological analysis of the generated pseudo-image. The effectiveness of the algorithm is compared with the difference algorithm for analyzing changes in the observation scene. It is shown that the proposed algorithm is more resistant to the effects of relative shifts of the current and reference images.     

Bearing fault diagnosis using FFT of intrinsic mode functions in Hilbert–Huang transform

A number of techniques for detection of faults in rolling element bearing using frequency domain approach exist today. For analysing non-stationary signals arising out of defective rolling element bearings, use of conventional discrete Fourier transform (DFT) has been known to be less efficient. One of the most suited time–frequency approach, wavelet transform (WT) has inherent problems of large computational time and fixed-scale frequency resolution. In view of such constraints, the Hilbert–Huang Transform (HHT) technique provides multi-resolution in various frequency scales and takes the signal's frequency content and their variation into consideration. HHT analyses the vibration signal using intrinsic mode functions (IMFs), which are extracted using the process of empirical mode decomposition (EMD). However, use of Hilbert transform (HT)-based time domain approach in HHT for analysis of bearing vibration signature leads to scope for subjective error in calculation of characteristic defect frequencies (CDF) of the rolling element bearings. The time resolution significantly affects the calculation of corresponding frequency content of the signal. In the present work, FFT of IMFs from HHT process has been incorporated to utilise efficiency of HT in frequency domain. The comparative analysis presented in this paper indicates the effectiveness of using frequency domain approach in HHT and its efficiency as one of the best-suited techniques for bearing fault diagnosis (BFD).     

Design of magnetically levitated rotors in a large flywheel energy storage system from a stability standpoint

The stability of flywheels in an energy storage system supported by active magnetic bearings (AMBs) is studied in this paper. We designed and built two flywheel energy storage systems (FESS) that can store up to 5 kWh of usable energy at a maximum speed of 18,000 rpm. One is optimized to store as much energy as possible, resulting in a flywheel with a strong gyroscopic coupling. The other has a much smaller gyroscopic coupling for ease of control. To analyze the stability of the system accurately, we derived the dynamic models of the rotor using finite-element method and integrated them with the models of the bearings, amplifiers, and sensors to obtain the simulation model of the system. We validated the model through experiments and compared the stability of these two systems.     

Simultaneous identification of residual unbalances and bearing dynamic parameters from impulse responses of rotor–bearing systems

An identification algorithm for simultaneous estimation of residual unbalances and bearing dynamic parameters by using impulse response measurements is presented for multi-degree-of-freedom (mdofs) flexible rotor–bearing systems. The algorithm identifies speed-dependent bearing dynamic parameters for each bearing and residual unbalances at predefined balancing planes. Bearing dynamic parameters consist of four stiffness and four damping coefficients and residual unbalances contain the magnitude and phase information. Timoshenko beam with gyroscopic effects are included in the system finite element modelling. To overcome the practical difficulty of number of responses that can be measured, the standard condensation is used to reduce the number of degrees of freedom (dofs) of the model. For illustration, responses in time domain are simulated due to impulse forces in the presence of residual unbalances from a rotor–bearing model and transformed to frequency domain. The identification algorithm uses these responses to estimate bearing dynamic parameters along with residual unbalances. The proposed algorithm has the flexibility to incorporate any type and any number of bearings including seals. The identification algorithm has been tested with the measurement noise in the simulated response. Identified parameters match quite well with assumed parameters used for the simulation of responses. The response reproduction capability of identified parameters has been found to be excellent.     

Flow regime identification in spouted beds using gamma-ray densitometry

The mixing characteristics, flow patterns and hydrodynamics of gas-solid spouted beds depend on the type of operating flow regime. In the present work, Gamma-Ray Densitometry (GRD) technique has been developed and implemented for the first time to identify different flow regimes and their transition velocities in gas-solid spouted beds. Two spouted beds (0.152 m and 0.076 m diameter) have been used to identify various flow regimes. Three statistical parameters (mean, variance and flow regime indicator (I)) have been applied to analyze the photon counts from GRD. It was found that the implementation of such non-invasive radioisotope based technique, GRD, was successfully able to identify different flow regimes and their transition velocities in gas-solid spouted beds. Flow regimes of packed bed, stable spouting and unstable spouting have been identified in the two studied spouted beds. Furthermore, the stable spouting regime achieved at larger magnitudes of the range of the superficial gas velocity in a larger diameter bed as compared to those in a smaller diameter bed.     

Damage identification in composite plates using two-dimensional B-spline wavelets

In this paper the construction of general order two-dimensional B-spline wavelets was presented and applied for damage identification in polymeric composite plates. At the very beginning the algorithm of one- and two-dimensional discrete wavelet transform and formulation of one- and two-dimensional B-spline wavelets with examples were presented. The fully clamped square layered composite plate was modeled using finite element-based software. Estimated natural modes of vibration with various damage configurations were analyzed using the two-dimensional sixth-order B-spline wavelet, and the method of damage identification was presented. The effective damage identification is based on the evaluation of the singularities in horizontal, vertical and diagonal details coefficients. Results obtained based on the numerical data were verified experimentally. Research results show the effectiveness of B-spline wavelets in application to the diagnostics and structural health monitoring.     

频散补偿在导波缺陷形状辨识上的应用

针对超声导波具有的频散特性,导致导波缺陷检测信号波包在结构中传播时发生的展宽及衰减现象,研究了如何利用频散补偿技术对检测信号进行处理,将被频散特性淹没的缺陷信息再现,以实现缺陷形状的辨识问题.在进行实验验证的过程中,利用导波检测管道中的双槽形人工缺陷,通过改变端面加载的压电传感器位置,接收一组时域回波信号,根据波数-频率关系对导波传播过程中的频散进行补偿后,再现缺陷的形状特征.     

Crack identification in a freely vibrating plate using Bayesian parameter estimation

In this paper a new approach is proposed for identifying the presence and location of a crack in a simply supported plate undergoing free vibration. Specifically, the approach uses a Markov-chain Monte-Carlo implementation of Bayes’ Rule to estimate the crack parameters (i.e., its location, orientation, and size) and their probability distributions. Special attention is paid to developing a fast and accurate forward model for the response of the cracked plate. To generate the required time series, a semi-analytical free response is calculated using an FEM based eigen-solution. To speed up the simulations, modified elements are used at the crack tips; this permits a more course mesh without sacrificing accuracy. The approach is demonstrated to be effective at identifying all of the crack parameters. Furthermore, a natural by-product of this method is that it also provides a confidence (credible) interval for each of these parameters. The results show the utility and accuracy of this method in identifying cracks of various sizes, orientations, and locations.     

Gear parameter identification in a wind turbine gearbox using vibration signals

When carrying out vibration-based diagnosis of gearboxes it is desirable to know the numbers of teeth on all gears, so as to be able to calculate toothmesh frequencies and rotational speeds of all shafts. If the speed varies, this information must be obtained in the form of “shaft orders” related to the input and/or output speed. This paper describes how it was possible to extract most of this information from the vibration signal itself in the case of a wind turbine gearbox with one planetary and two helical parallel stages. Using a spectrogram, a section of signal was first found with minimal speed variation (about 4%) after which the instantaneous speed information was extracted by frequency demodulation of dominant speed related components. After order tracking based on this it was found possible to determine the numbers of teeth in the two parallel stages, using very accurate harmonic cursors applied to each of the shafts of pairs of gears in mesh (with common mesh frequency). This was successful for the two parallel stages, but the proposed estimates of the tooth numbers in the planetary section are subject to some doubt. Allowable combinations are quite restricted using the normally applied rules, but there can be exceptions. Even so, the presented approach is confirmed as a viable method.     

Cluster identification in AA5754 aluminium sheets using mathematical morphology analysis

Quantitative image analysis of particle distribution in the microstructure of continuous cast (CC) and direct chill cast (DC) AA5754 aluminium alloy sheets have been conducted. This information can be used as an input for modelling mechanical deformation and instability in these materials. The quantitative analysis reveals that there are significant differences in the microstructure of the two materials even though the total content of second‐phase particles is statistically similar. Qualitative observation shows the second‐phase particles to be arranged in the form of streaks parallel to the rolling direction in the CC sheets and in a uniform random manner in the DC sheets. The main difference in the geometric microstructure of the CC and DC material is the spatial arrangement of the second‐phase particles. A new mathematical technique called proximity analysis is developed to identify clusters and group of particles belonging to a cluster. Quantification through proximity analysis reveals that the particle clusters in CC sheet are in the form of long clusters (streaks) parallel to the rolling direction and are significantly longer than those in DC sheets (with the largest cluster in CC being four times larger than DC), and also have anisotropic angular orientation parallel to the rolling direction. The lower value of fracture strain observed in the CC sheets compared to DC sheets is attributed to a combination of large sizes of clusters and their preferential alignment along the rolling direction in the CC microstructure.     

Damage identification in composite materials using ultrasonic based Lamb wave method

The paper presents an ultrasonic based Lamb waves propagation method for identifying and measuring the damage location in a material for SHM. The present work determines the experimental and analytical effects of various parameters on the sensitivity of damage detection and a methodology is proposed for estimating and measuring the location of damage in the test specimens. An experimental setup is used for generating A_o Lamb waves by calibrating ultrasonic pulse generation for optimal value of the parameters. The experiment is performed on two carbon fiber reinforced plastic bars in both undamaged and damaged state, where the two damaged states are (1) having a cut partway through the bar, perpendicular to the long axis of the bar and (2) having a circular hole. The Lamb wave propagation parameters are calibrated using the ultrasonic pulse generator test setup and the method was compared with direct measured values of ultrasonic instrument.     

Damage identification in skeletal structures using the virtual distortion method in frequency domain

The paper is a continuation of research on the application of a structural reanalysis method—the virtual distortion method—to structural health monitoring. The first approach, formulated previously in the time domain, suffered from a considerable numerical cost. In order to reduce it, this paper presents an alternative approach, formulated in the frequency domain thanks to the assumption of using harmonic excitation (quasi-static problem). The hardware devices supposed to collect structural responses are piezoelectric patch sensors. The software tool for damage identification solves a nonlinear least squares optimization problem by employing analytically derived sensitivities. Strains are the analyzed quantities, contributing to the objective function. Considerations are restricted to skeletal structures and a simplified dynamic problem with no damping. Effectiveness of the frequency-domain identification is demonstrated in numerical examples. Experimental verification is envisaged.     

The identification of pollen grains using cathodoluminescence in the scanning electron microscope

A scanning electron microscope fitted with a sensitive cathodoluminescence detector system has been used for the identification of pollen grains stained with acridine orange or unstained. This approach provides a means of identifying and studying the morphological details of pollens. Cathodoluminescence detection can, in principle, extend the limits of resolution beyond those obtainable with the fluorescence microscope.     

Placement sequence identification using artificial neural networks in surface mount PCB assembly

The widespread use of automation in the printed circuit board (PCB) assembly domain has been dictated by the increasing density of components on PCBs coupled with the continual decrease in component lead pitch, greater product mix, smaller volumes, quality considerations, and the increased cost of labour. However, these advances in technology have also resulted in automated systems that are complex, and solving problems related to these systems requires the efficient use of extensive specialised knowledge.Expert (or knowledge-based) systems have become a widely accepted problem solving methodology for the surface mount PCB assembly domain. Nevertheless, problems in the PCB assembly domains are frequently unstructured, ill-defined, and difficult to communicate. Artificial neural networks provide a novel approach and an advanced technology to deal with the weaknesses and problems associated with expert systems.The surface mount component (SMC) placement process plays a vital and influential part in determining the throughput time of a PCB assembly line. It is important to identify an efficient component placement sequence while considering constraints such as feeder location and tooling and nozzle optimisation. This research studied the use of artificial neural networks as a complement to expert systems in PCB assembly. A prototype decision support system that combined the use of artificial neural networks and expert system techniques to identify a near optimal solution for the surface mount placement sequence problem was designed, implemented, and validated. Artificial intelligence based technologies such as expert systems and artificial neural networks were used in a mutually supportive manner to solve a complex problem within the surface mount PCB assembly domain.