Conference Papers are technical papers that have been presented at sound and vibration conferences around the world.
Over the past five years, MTS Systems Corp. has witnessed, through customer feedback from around the world, a slow but definite shift within the noise and vibration practices of the automotive industry. The automotive industry represents more than half of the $300 million market for noise and vibration data acquisition and analysis hardware and software.
The noise of a residential refrigerator exhibited some undesirable high frequency tones. The tones did not affect the overall A-weighted sound power level of the unit, but they represented a sound quality concern for the refrigerator manufacturer. This paper describes the analytical and experimental techniques that were applied to troubleshoot and solve the problem. An analytical model of the compressor discharge muffler was built, validated with test data, and used to identify the best countermeasures.
For the development of a self-propelled crop spraying machine, a hybrid experimental and analytical Source-Path-Contribution (SPC) approach is utilized by a leading agricultural equipment manufacturer. The objective is to predict noise and sound quality in the cab before prototypes are assembled, so that dB(A) and SQ targets can be assessed early on and better specifications sent to suppliers to achieve these vehicle-level targets. The experimental SPC task is conducted on the current crop sprayer model, which has the same cab but different engine, transmission and hydraulics than the new model. A hybrid FE-SEA model of the current cab is developed and run at load cases derived from test data. The SEA approach is needed to evaluate the effect of cab acoustic treatments, which are not accounted for in the SPC experimental model. Contributions to in-cab noise for the current sprayer are estimated from both experimental and analytical SPC. Acoustic and structural loads to the cab in the new model are estimated from measurements on hardware components and from supplier provided data. The hybrid FE-SEA model is then updated with the new loads and re-run to predict total in-cab noise and contributions from each source in the new model, which is then used to identify the best countermeasures for noise reduction and SQ improvement.
Ubiquitous availability of media content through portable devices like media players and smartphones has resulted in an immensely increased popularity of headphones in recent years. However, while conventional stereo recordings usually create a good sense of space when listened to through loudspeakers, the sounds tend to be perceived inside the head (internalized) when headphones are used for listening. A more natural perception in headphone listening with sounds being perceived outside the head (externalized) can be achieved when recordings are made with dummy head microphones or with microphones placed inside the ear canals of a person. In this study, binaural room impulse responses (BRIRs) were measured with several commercially available binaural microphones, both placed inside the listeners' ears (individual BRIR) and on a head and torso simulator (generic BRIR). The degree of externalization of speech and noise stimuli was tested in a listening experiment with a multi-stimulus test. No inuence was found for the stimulus signal, but the externalization scores were found to be lower for 0 incidence. With all microphones, relatively high externalization scores were achieved, and for all but one microphone, individual BRIRs resulted in slightly better externalization than generic ones.
Subspace based algorithms for estimating modal parameter have now become common within modal analysis domain. This is especially true for Operational Modal Analysis, where Stochastic Subspace Identification (SSI) algorithm is a well-known and commonly used algorithm. Despite their increasing use and popularity, one often encounters basic questions such as (and not limited to)
In fact, even before addressing the questions listed above, there is a fundamental need to look at these algorithms from the perspective of modal parameter estimation, whose requirements and demands differ from those of system identification within Control Systems Engineering, where these algorithms originated.
This paper aims at addressing these issues and examine subspace algorithms from a purely modal parameter estimation perspective. The author expects that this paper will provide readers with a simple and clear understanding of these algorithms towards their utilization for modal parameter estimation.
Indoor vehicle pass-by noise applications deal with measuring the exterior noise from a vehicle fixed on a chassis dynamometer in a large hemi-anechoic room. During a standardised acceleration test, the noise is measured with an array of microphones placed in the far-field, and the overall noise level versus vehicle position can be simulated. The indoor facility allows controlled and repeatable measurements independent of weather. For engineering purposes, pass-by contribution analysis can be included in the test leading to information about the pass-by noise contribution from major noise sources. This work presents a novel application of blind source separation to vehicle measurements from an indoor pass-by measurement campaign. In contrast to the classical transfer path approach using point sources for modelling vehicle noise sources and combining an operational measurement with transfer functions, the blind approach does not consider a specific noise source model. It only assumes that the noise is produces by a set of independent noise sources using only a single operational measurement for a given vehicle condition as input. Near-field microphone measurements are blindly separated into independent components and further correlated with the signals measured at the far-field indoor pass-by microphones to get the time-domain contributions. Finally, we apply the indoor simulated pass-by algorithm to produce noise contribution levels as a function of vehicle position. We discuss the specified application of blind source separation to vehicle measurements for different operating conditions from a real indoor pass-by test. Separation of tyre and engine related noise at tyre near-field microphones is verified. Furthermore, the tyre pass-by noise contribution is extracted from the overall vehicle measurement.
Though Blind Source Separation (BSS) techniques have typically been applied within Operational Modal Analysis (OMA) framework, there joint diagonalization property can also be utilized within traditional input-output Experimental Modal Analysis (EMA) for the purpose of modal parameter estimation. This paper explores this possibility and suggests an approach to achieve the same. The paper presents the mathematical concepts that make it possible for Second Order Blind Identification (SOBI), a blind source separation algorithm to be applied to impulse response functions for the purpose of modal parameter estimation. It is also shown in the paper that, in its current form, the approach provides real mode shapes, irrespective of the nature of the system under investigation. In addition to demonstrating the approach by means of studies conducted on an analytical 5 degrees-of-freedom system, the real mode shapes extracted using the suggested approach are also used for model updating purposes.
In last decade, Operational modal analysis has emerged as a technique of choice for identifying dynamic characteristics of large complex structures whose identification, using traditional experimental modal analysis techniques, is otherwise quite challenging. In this regard, several parameter estimation techniques have been suggested, which can broadly be categorized in three categories: 1) those based on traditional EMA algorithms, 2) those based on subspace identification and 3) those based on single degree-of-freedom system techniques. This paper reviews various algorithms used for modal parameter estimation in OMA framework. In the process of reviewing these algorithms, associated data preparation and signal processing aspects are also covered. Overall, emphasis is on understanding common aspects that these seemingly different algorithms share.
In this paper, a modal appropriation methodology is suggested for Operational Modal Analysis. The method is based on performing a numerical convolution of a sine wave force with correlation functions of observed output responses of the structure. Classical modal appropriation based techniques are then used to estimate modal parameters characterizing the dynamics of the structure. The paper explains the theory and steps associated with this method and its performance is illustrated by means of studies conducted on an analytical 15 DOF system. Further, the results are compared with those obtained using standard OMA algorithms such as Stochastic Subspace Identification (SSI) algorithm.
The localization of sound sources with delay-and-sum (DAS) beamforming is limited by a poor spatial resolution—particularly at low frequencies. Various methods based on deconvolution are examined to improve the resolution of the beamforming map, which can be modeled by a convolution of the unknown acoustic source distribution and the beamformer’s response to a point source, i.e., point-spread function. A significant limitation of deconvolution is, however, an additional computational effort compared to beamforming. In this paper, computationally efficient deconvolution algorithms are examined with computer simulations and experimental data. Specifically, the deconvolution problem is solved with a fast gradient projection method called Fast Iterative Shrikage-Thresholding Algorithm (FISTA), and compared with a Fourier-based non-negative least squares algorithm. The results indicate that FISTA tends to provide an improved spatial resolution and is up to 30% faster and more robust to noise. In the spirit of reproducible research, the source code is available online.
This study presents an vibration-based system designed for structural health monitoring of wind turbine blades. Mechanical energy is introduced by means of an electromechanical actuator mounted inside the blade. The actuator's plunger periodically hits the blade structure; the induced vibrations propagate along the blade and are measured by an array of accelerometers. Unsupervised learning is applied to the data: the vibration patterns corresponding to the undamaged blade are used to create a statistical model of the reference state. During the detection stage, the current vibration pattern is compared with the reference state, and the novelties can be associated with damage. The vibration pattern is described by the covariance matrix between the accelerometer signals. The mid-range frequencies are used: this range is above the frequencies excited by blade-wind interaction, thus ensuring a good signal-to-noise ratio. Simultaneously, the frequencies are low enough to be able to propagate the entire blade length, so good results can be obtained even using only one actuator. The system is demonstrated on a real 34m blade mounted on a test rig. Using the suggested approach, the system enables detection of, e.g., a 20cm long trailing edge opening under realistic noise conditions. It is also demonstrated that the system provides rough information about damage location. Progression of damage, if any, can also be detected.
The stochastic dynamic damage location vector (SDDLV) method has previously proved to facilitate effective damage localization in truss- and plate-like structures. The method is based on interrogating damage-induced changes in transfer function matrices in cases where these matrices cannot be derived explicitly due to unknown input. Instead, vectors from the kernel of the transfer function matrix change are utilized; vectors which are derived on the basis of the system and state-to-output mapping matrices from output-only state-space realizations. The idea is then to convert the kernel vectors associated with the lowest singular values into static pseudo-loads and apply these alternately to an undamaged reference model with known stiffness matrix. By doing so, the stresses in the potentially damaged elements will, theoretically, approach zero. The present paper demonstrates an application of the SDDLV method for localization of structural damages in a cantilevered residential-sized wind turbine blade. The blade was excited by an unmeasured multi-impulse load and the resulting dynamic response was captured through accelerometers mounted along the blade. The static pseudo-loads were applied to a finite element (FE) blade model, which was tuned against the modal parameters of the actual blade. In the experiments, an undamaged blade configuration was analysed along with different damage scenarios, hereby testing the applicability of the SDDLV method.
The stochastic dynamic damage location vector (SDDLV) method utilizes the vectors from the kernel of a damaged-induced transfer function matrix change to localize damages in a structure. The kernel vectors associated with the lowest singular values are converted into static pseudo-loads and applied alternately to an undamaged reference model with known stiffness matrix, hereby, theoretically, yielding characteristic stress resultants approaching zero in the damaged elements. At present, the discrimination between potentially damaged elements and undamaged ones is typically conducted on the basis of modified characteristic stress resultants, which are compared to a pre-defined tolerance value, without any thorough statistical evaluation. In the present paper, it is tested whether three widely-used statistical pattern-recognition-based damage-detection methods can provide an effective statistical evaluation of the characteristic stress resultants, hence facilitating general discrimination between damaged and undamaged elements. The three detection methods in question enable outlier analysis on the basis of, respectively, Euclidian distance, Hotelling’s statistics, and Mahalanobis distance. The study of the applicability of these methods is based on experimentally obtained accelerations of a cantilevered residential-sized wind turbine blade subjected to an unmeasured multi-impulse load. The characteristic stress resultants are derived by applying the static pseudo-loads to a representative finite element (FE) model of the actual blade.
This paper describes some of the challenges experienced by many experimentalists and presents new ways of overcoming these. The Smart Setup can dramatically reduce the time required for the test setup and the presented Accelerometer Mounting Check procedure can check the transducer’s health and the integrity of the whole measurement channel. The goal is to get the data right first time and in the shortest possible time.
The presented study concerns experimental dynamic identification of (slightly) anisotropic bladed rotors under operating conditions. Since systems with a rotating rotor do not fall into a category of time invariant system, a straightforward application of modal analysis is not valid. Under assumptions of linearity and constant angular speed, a system with rotating rotor can be considered as a linear periodically time variant (LPTV) system; dynamic identification of such systems require dedicated methods. The Harmonic OMA Time Domain (H-OMA-TD) method is one of very few techniques able to deal with anisotropic rotors. This study demonstrates the method on a simple six degrees-of-freedom mechanical system with a three-bladed rotor. It shows that the method is capable of identifying the phenomena specific for anisotropic rotors. The technique is compared with another technique, multiblade coordinate (MBC) transformation, and the advantages of H-OMA-TD become apparent when the rotor is anisotropic. Finally, the method is demonstrated on data measured on a real Vestas V27 wind turbine and data obtain via HAWC2 simulations of the same wind turbine.
Three methods for reproduction of sound using a maximum of eight loudspeakers were investigated in the context of testing telecommunication devices. They are the four-loudspeaker-based method as described in ETSI EG 202 396-1, Higher-Order ambisonics (HOA), and a matrix inversion method. HOA optimizes the reproduced sound at a sweet spot in the center of the array with radius determined by a spherical microphone array, which is used to derive the spherical harmonics decomposition of the reference sound. The four-loudspeaker-based method equalizes the magnitude response at the ears of a head and torso simulator (HATS) for sound reproduction, while the matrix inversion method optimizes the local sound field around a few target positions. The matrix inversion method had two conditions, i.e. with or without the extra processing steps described in ETSI TS 103 224; and three sets of optimization positions were defined, i.e. the ears of the HATS, positions close to a device under test, and standardized positions as described in ETSI TS 103 224. A listening experiment was performed to evaluate the perceived quality of the reproduced sounds at the microphones close to a device under test and at the ears of the HATS. The matrix inversion method performed best when listening to the reproduced sounds at target positions used for sound-field optimization and when listening to the microphones close to the device. HOA resulted in similar perceived quality as the matrix inversion method while a large degree of perceptual degradation was observed using the four-loudspeaker-based method.
Realistically experiencing the sound and vibration data through actually listening to and feeling the data in a full-vehicle NVH simulator remarkably aids the understanding of the NVH phenomena and speeds up the decision-making process. In the case of idle vibration, the sound and vibration of the idle condition are perceived simultaneously, and both need to be accurately reproduced simultaneously in a simulated environment in order to be properly evaluated and understood.
In this work, a case is examined in which a perceived idle quality of a vehicle is addressed. In this case, two very similar vehicles, with the same powertrain but somewhat different body structures, are compared. One has a lower subjective idle quality rating than the other, despite the vehicles being so similar. An NVH vehicle simulator was used to compare the sound and vibration characteristics of the two vehicles back-to-back in a realistic vehicle environment in order to understand the difference in the subjective rating. With the ability to control the various specific sound and vibration stimuli, the reason for the difference of subjective rating between the vehiclesbecame apparent rapidly.
Further, the interaction of sound and vibration and the resulting effect on human subjective perception is explored, which emphasizes the importance of having both sound and vibration accurately reproduced and controlled for such simulations.
City noise management involves a variety of disciplines such as planning, mapping, action plans, policing, complaint management, abatement and public awareness. With the wide availability of mobile broadband internet access coupled with low cost noise sensors, many authorities and researchers are eager to use sound sensor networks for these tasks. A sensor network can be defined as a group of specialized transducers and processing with a communications infrastructure and is intended to monitor and record conditions at diverse locations, connected to a central software. This definition covers a wide range of different possibilities, designs and components such as MEMS microphones, processing software, type approved instrumentation, smart phones, etc. However, are all networks suitable for all tasks? Many sensors trade off measurement precision to reduce cost and enable an increase in number of measurement points within a budget. This paper describes different sensor classes and implementation strategies. It discusses the relative merits of different sensors and describes what is important to take account of when implementing these networks for application to one or multiple noise management tasks, outlining what each can be used for and what they shouldn’t be used for. Aspects covered include architecture, and practical applicability. The paper concludes with recommendations for using different smart networks and for further research.
Near-field Acoustical Holography (NAH) is based on performing 2D spatial Discrete Fourier Transforms (DFT), and therefore the method requires a regular mesh of measure-ment positions. To avoid spatial aliasing problems, the mesh spacing must be somewhat less than half of the acoustic wavelength. In practice, this requirement sets a serious lim-itation on the upper frequency limit.
This paper provides practical information on using a multi-shaker MIMO vibration testing approach for MIL-STD-810 single axis transport testing of large, resonant, land based payloads (up to 6 ton in mass and 20ft in size).Such payloads can often exhibit significant dynamic behaviour within the test frequency range including transients > 50 times control level. The purpose of the paper is to provide guidance to organisations expanding from single shaker system testing to multi-shaker testing. It shares lessons learnt through hundreds of hours of real world sine and random testing using a 420kN (94,420 lbf) force quad electrodynamic vibration system and MIMO vibration controller in both horizontal and vertical configuration designed for MESA (multi exciter single axis) testing of both military and non-military type payloads. It targets the real world practical considerations of the test engineer/manager when defining and developing a MIMO vibration test facility for large payload testing.
A method for reproduction of sound, based on crosstalk cancellation using inverse filters,
was implemented in the context of testing telecommunications devices.
The effect of the regularization parameter, number of loudspeakers,
type of background noise, and a technique to attenuate audible artefacts,
The quality of the reproduced sound was evaluated both objectively and subjectively
with respect to the reference sounds, at points where telecommunications devices
would be potentially placed around the head.
The highest regularization value gave the best results,
the performance was equally good when using eight or four loudspeakers,
and the reproduction method was shown to be robust for different program materials.
The proposed technique to reduce audible artefacts increased the perceived similarity.
Today, design of wind turbines is extensively done by the implementation of numerical models. These models simulate the dynamic behaviour of full-scale wind turbines which helps to ensure the structural integrity of prototypes. However, these numerical models need validation from experimental results, and in turn, numerical and analytical modelling help improve and validate new experimental techniques. Wind turbines are complex dynamic systems that consist of mutually moving substructures under high dynamic loads. At a standstill, the system can be modelled as linear time-invariant (LTI), and modal analysis requirements are thus fulfilled for the dynamic characterization. Under operation, the system cannot be considered as LTI and must be modelled as a linear periodic time-variant (LPTV) system, which allows for the application of the related theory for such systems. One of these methods is the Coleman transformation, which transforms the vibrations expressed in the blade rotating coordinates to the fixed-ground frame of reference. The application of this transformation, originally from helicopter theory, allows for the conversion of a LPTV system to a LTI system under certain assumptions, among which is the assumption of isotropic rotors. Since rotors are never completely isotropic in real life, this paper presents the application of operational modal analysis together with the Coleman transformation on both experimental data from a full-scale Vestas wind turbine with instrumented blades and nacelle, and its representative numerical model with a fully isotropic rotor. The results show that the first tower and rotor edgewise modes are well identified, and that the rotor edgewise modes can be identified from the nacelle signals. The results also uncover the challenge the excitation forces imply for the identification of flapwise modes.
The study addresses experimental identification of linear periodic time variant systems. The recently introduced Harmonic-OMA-Time Domain (H-OMA-TD) method is in focus. It is shown how this method can aid engineers and what additional information it can bring, compared to other methods. The method is demonstrated in application to experimental data obtained on operating wind turbine.
BHP Billiton Worsley Alumina Pty Ltd (BWAPL) consists of mining operations located near the town of Boddington, a 51km conveyor linking to an alumina refinery located in Worsley and a port load out facility located in Bunbury. BWAPL mining operations expanded in 2012, resulting in mining operations taking place much closer to a number of residential properties in the community and closer to the township of Boddington. Given the proximity of the mining operation to these sensitive receptors, noise was identified early on as a high risk to the operations that needed to be proactively managed to ensure that BWAPL’s environmental and social licences to operate were maintained. BWAPL adapted Brüel & Kjær’s Noise Sentinel monitoring system to monitor noise generated by mining operations and ensure that the impact on near neighbours was minimised. This was achieved by incorporating alert systems that allowed for proactive management. This paper will cover the compliance parameters required to be measured; the adaptations applied to the software and the key project challenges that were overcome. On the basis of the experiences gained and the positive outcomes achieved through implementing the Noise Sentinel system, BWAPL received a Highly Commended award at the 2013 BHP Billiton Health, Safety, Environment and Community Awards in the Environment category.
Laser scanning and accelerometer measurements are often used for analysing vibrations of for instance motors under operation. However, the methods have certain disadvantages: Laser techniques typically scan one point at a time, which limits the methods to be applied only for stationary conditions. Accelerometers can be used for non-stationary conditions, but their physical mounting will to some extend influence the vibration of the source surface and due to cabling they are not practical for rotating objects. Near-field Acoustical Holography (NAH) is a non-contact experimental technique for mapping sound and vibration, and since the sound field is measured at multiple points around the source simultaneously, it can also be applied for non-stationary conditions. In particular, the Equivalent Source Method (ESM) is an attractive holography method, because it does not require neither the source nor the array geometry to be given in separable coordinates (planar, spherical, cylindrical etc.). This paper describes a non-stationary ESM approach for reconstruction of quantities like surface velocity and displacement. A set of measurements has been made with a 60 channel microphone array on a small electrical motor. Vibrations were also measured with a laser vibrometer in order to validate the accuracy of the array calculations.
Continuous, unattended noise monitoring systems can immediately alert you should noise levels exceed defined criteria. Once alerted to an exceedance, operators can act to return levels to compliance. This approach has two significant limitations. Firstly the operator can only take action after the breach has occurred and therefore systems are only able to inform owners about problems that have occurred in the past, rather than allowing them to maintain compliance. Secondly the noise limit exceedances might not be due to specific noise from the operator but from unrelated, residual noise in the often complex noise climates around the site. Compliance breaches are frequently triggered by aircraft overflights, road traffic or community sources. Modern monitoring systems enable users to view noise characteristics and listen to the noise breach to determine the source and take action if it is relevant. However, this approach can create significant false positives each taking up operator time to address. This paper describes how airport noise management systems have addressed this problem by combining data from other systems. It also shows how different techniques are required in urban & industrial noise management, giving examples of techniques that allow operators to take action before a compliance breach occurs.
There is an increasing interest for automatic classification of sounds in various applications. The processing consists in extracting some selected features from the raw data and applying a classifier algorithm to automatically estimate the class of source the input data belongs to. In order to perform this task, the system is first trained on a set of input data (in case of supervised learning), then applied on new data. The performances of classification depend on many factors: the selected features, the classifier, but also the data. For some practical applications, this approach is of interest to estimate the sound pressure level or others acoustic quantities attached to a specific class of sources in a complex environment. For example, there is a benefit to quantify the only contributions of airplanes, in the sound acquired by a noise monitoring system located nearby an airport. One practical difficulty is the presence of other sources. In the present study, we are looking at this type of scenario, when multiple sources may act at the same time or in presence of background noise. We consider here different environmental sound sources: aircrafts, car traffic among others. In a first step, different features in connection with a classifier are evaluated on ‘clean’ data, meaning with no mixture. In a second step, we mix artificially data from different sound sources at different rates of mixtures and calculate the sound pressure level contribution from each source based on the proposed classification algorithm. We consider as well mixtures with sounds sources that are not among the predefined classes. Finally the most robust classification configuration is evaluated in the case of real outdoor measurements.
This paper deals with the source separation task related to indoor pass-by noise tests. Several different approaches are discussed. The classical transfer path modelling concept making use of operational data together with acoustic transfer functions is explained and a time-domain implementation is verified for a real operating vehicle. Pass-by noise contribution from components such as engine, intake and tyres are estimated and ranked as a result of such processing. Another and completely different source separation methodology is introduced in terms of a blind source separation method for clear separation of tyre noise from engine related noise. Features of the two methods are discussed and results for the same indoor pass-by dataset presented.
The indoor vehicle pass-by test is a simulation of a field pass-by noise measurement in a controlled environment allowing repeatable measurements independent of weather conditions. During vehicle development modifications can be tested out in a fast manner to see the immediate influence on the overall vehicle noise levels produced during a pass-by acceleration test. Besides performing the standard pass-by noise test, vehicle improvement work requires knowledge about the noise contribution from the different vehicle sources during the pass-by test. A source path contribution (SPC) concept involving modelling the main vehicle noise sources contributing to the pass-by noise will be presented. A special feature of this approach is that it processes entirely in the time-domain to produce source strength estimates for theconsidered noise sources followed by a synthesis to produce pass-by noise estimates for the vehicle and for individual noise sources based on indicator microphone near-field data and acoustic transfer functions. As a result major pass-by noise contributors are identified at a given vehicle position during the pass-by test. Results produced by this method will be compared to a blind source separation (BSS) methodology applied to the operational dataset only for extracting source signals related to the different noise processes during vehicle operation. Extracted source signals are correlated with far-field measurements to estimate pass-by noise contributions. The BSS methodology is verified using a speaker setup showing excellent separation of tyre and engine noise contributions. Data from indoor pass-by noise measurements with a vehicle on a chassis-dynamometer is finally used to test and evaluate the two methods.
Shale gas has transformed the energy market in the United States where drilling is underway in 17 states, with more than 80,000 wells drilled or permitted since 2005. Deposits elsewhere in the world are under varying degrees of exploration.
The main environmental impacts of the process are water usage, waste water management, methane emissions, groundwater and soil contamination. These are generally addressed as part of national environmental regulations and strong stakeholder engagement is critical in addressing concerns.
close to residential areas there is a significant risk of disturbance triggering complaints.
This study reports structural dynamic characteristics obtained experimentally from an extensive testing campaign on a 34m long wind turbine blade mounted on a test-rig under laboratory conditions. Further, these experimental results have been compared with analog numerical results obtained from a very detailed FE model of the same blade using 3D solid elements. Both an undamaged and a damaged blade are investigated, and it is observed that the natural frequencies of the first few modes of the blade change very little due to a significant artificial damage imposed in trailing edge, whereas the mode shapes - especially if decomposed into the flapwise, edgewise and torsional components - contain information which might be helpful for detecting and localizing wind turbine blade damages.
Creating the perfect vehicle sound is a critical challenge that needs the buy-in of diverse decision-makers to establish targets, and greater inter-departmental collaboration to realize them. Free-driving sound simulation captures subjective sound preferences with real-time modification. It helps to cascade that target sound down to subsystem or component level, and provides a focal point where all relevant and available data (CAE or test-based) can be assessed for its impact on sound style. In this way, NVH simulators can reduce uncertainty throughout the vehicle development programme: from target setting to delivery. This reduces the need for prototypes by more completely marrying the virtual-world design process with the real-world results, and enabling assessment of NVH predictions from CAE models.
The unified matrix polynomial approach (UMPA) was developed in order to understand and derive various experimental modal analysis algorithms (which have been developed in isolation) using a common mathematical formulation. Various commercially available algorithms – such as the polyreference time domain, least squares complex exponential, and eigensystem realization algorithm etc. – can be explained using UMPA methodology, which makes it easier to understand both the advantages and limitations of such algorithms. In view of this fundamental characteristic of the UMPA, this paper aims at using the approach to understand, explain and develop the stochastic subspace identification (SSI) algorithm - a popular time domain operational modal analysis (OMA) algorithm. The roots of SSI algorithm lie in the identification of linear dynamic systems, traditionally a communications and controls engineering area. By means of the UMPA, the SSI algorithm’s similarity to a high order time domain OMA algorithm can be shown. It can also be shown that state transition matrices identified using the SSI algorithm and UMPA formulation are related to each other through a similarity transformation, thus characterizing the same system.