Numerical prediction of temperature effect on propagation of rubbing acoustic emission waves in a thin-walled cylinder structure

Temperature field has serious effects on the accuracy of rubbing acoustic emission (AE) source localization in a thin-walled cylinder structure, but it is difficult to explore the functioning mechanism through experiments. This paper aims to propose a thermos-elastic coupling simulation procedure to reveal the effect of the uniform temperature and non-uniform temperature field on the propagation characteristics of AE waves. To obtain the behaviors of guiding wave in the thin-walled cylinder, an efficient numerical simulation tool for AE wave propagation modeling is explored. The numerical results of AE propagation in a plate are compared with the experimental data. Then the semi-analytical finite element method is introduced to calculate the characteristics of multi-modal and dispersion. To remove the unwanted reflections from boundaries generated by the numerical simulation, a methodology combined with the infinite element and Rayleigh damping is presented. Consequently, several AE wave propagation simulations are carried out respectively, including the model with the uniform temperature in a range of 20-700 °C, and the non-uniform temperature field with the temperature of the central region, 649 °C. On the basis of the modeling and evaluation results, both the peak-to-peak amplitude and arrival time versus temperatures are summarized and analyzed. The validation results demonstrate that the proposed approach could be used efficiently to research rubbing AE source localization applications with a high degree of accuracy

Application of Short Time Fourier Transform and Wavelet Transform for Sound Source Localization Using Single Moving Microphone in Machine Condition Monitoring

The paper discusses means to predict sound source position emitted by fault machine components based on a single microphone moving in a linear track with constant speed. The position of sound source that consists of some frequency spectrum is detected by time-frequency distribution of the sound signal through Short Time Fourier Transform (STFT) and Continues Wavelet Transform (CWT). As the amplitude of sound pressure increases when the microphone moves closer, the source position and frequency are predicted from the peaks of time-frequency contour map. Firstly, numerical simulation is conducted using two sound sources that generate four different frequencies of sound. The second case is experimental analysis using rotating machine being monitored with unbalanced, misalignment and bearing defect. The result shows that application of both STFT and CWT are able to detect multiple sound sources position with multiple frequency peaks caused by machine fault. The STFT can indicate the frequency very clearly, but not for the peak position. On the other hand, the CWT is able to predict the position of sound at low frequency very clearly. However, it is failed to detect the exact frequency because of overlapping

Ultrafast Ultrasound Imaging

Among medical imaging modalities, such as computed tomography (CT) and magnetic resonance imaging (MRI), ultrasound imaging stands out due to its temporal resolution. Owing to the nature of medical ultrasound imaging, it has been used for not only observation of the morphology of living organs but also functional imaging, such as blood flow imaging and evaluation of the cardiac function. Ultrafast ultrasound imaging, which has recently become widely available, significantly increases the opportunities for medical functional imaging. Ultrafast ultrasound imaging typically enables imaging frame-rates of up to ten thousand frames per second (fps). Due to the extremely high temporal resolution, this enables visualization of rapid dynamic responses of biological tissues, which cannot be observed and analyzed by conventional ultrasound imaging. This Special Issue includes various studies of improvements to the performance of ultrafast ultrasoun

Identification of Turbomachinery Noise Sources via Processing Beamforming Data Using Principal Component Analysis

Complex turbomachinery systems produce a wide range of noise components. The goal is to identify noise source categories, determine their characteristic noise patterns and locations. Researchers can then use this information to quantify the impact of these noise sources, based on which new design guidelines can be proposed. Phased array microphone measurements processed with acoustic beamforming technology provide noise source maps for pre-determined frequency bands (i.e., bins) of the investigated spectrum. However, multiple noise generation mechanisms can be active in any given frequency bin. Therefore, the identification of individual noise sources is difficult and time consuming when using conventional methods, such as manual sorting. This study presents a method for combining beamforming with Principal Component Analysis (PCA) methods in order to identify and separate apart turbomachinery noise sources with strong harmonics. The method is presented through the investigation of Counter-Rotating Open Rotor (CROR) noise sources. It has been found that the proposed semi-automatic method was able to extract even weak noise source patterns that repeat throughout the data set of the beamforming maps. The analysis yields results that are easy to comprehend without special prior knowledge and is an effective tool for identifying and localizing noise sources for the acoustic investigation of various turbomachinery applications

A Machine Learning approach for damage detection and localisation in Wind Turbine Gearbox Bearings

Increasing demand for renewable sources requires more cost-effective solutions to mitigate the cost of maintenance and produce more energy. Preventive maintenance is the most normally adopted scheme in industry for maintenance but despite being well accepted has severe limitations. Its inability to intelligently schedule maintenance at the right time and prevent unexpected breakdowns are the main downsides of this approach and consequently leads to several problems such as unnecessary maintenances. This strategy does not justify the additional costs and thereby represents a negative aspect for renewable energy resource companies that try to generate cost-competitive energy. These challenges are progressively leading towards the predictive maintenance approach to overcome these aforementioned issues. Wind Turbine Gearbox Bearings have received a lot of attention due to the high incidence failure rates provoked by the harsh operational and environmental conditions. Current techniques only reach a level one of diagnostics commonly known as the Novelty Detection stage and normally requires the expertise of a skilled operator to interpret data and infer damage from it. A data-driven approach by using Machine Learning methods has been used to tackle the damage detection and location stage in bearing components. The damage location was performed by using non-destructive methods such as the Acoustic Emission technique — these measurements were used as features to locate damage around the bearing component once the damage was detected. The implementation of this stages also led to the exploration of damage generation due to overload defects and proposed a methodology to simulate these defects in bearings — the study of this concept was implemented in a scaled-down experiment where damage detection and localisation was performed. Due to the importance of the implementation of a damage location stage, damage in AE sensors was also explored in this work. Features extracted from impedance curves allowed to train Machine Learning methods to trigger a novelty when a bonding scenario occurred. This ultimately allowed the identification of unhealthy sensors in the network that could potentially generate spurious results in the damage predictions stage

Improved railway vehicle inspection and monitoring through the integration of multiple monitoring technologies

The effectiveness and efficiency of railway vehicle condition monitoring is increasingly critical to railway operations as it directly affects safety, reliability, maintenance efficiency, and overall system performance. Although there are a vast number of railway vehicle condition monitoring technologies, wayside systems are becoming increasingly popular because of the reduced cost of a single monitoring point, and because they do not interfere with the existing railway line. Acoustic sensing and visual imaging are two wayside monitoring technologies that can be applied to monitor the condition of vehicle components such as roller bearing, gearboxes, couplers, and pantographs, etc. The central hypothesis of this thesis is that it is possible to integrate acoustic sensing and visual imaging technologies to achieve enhancement in condition monitoring of railway vehicles. So this thesis presents improvements in railway vehicle condition monitoring through the integration of acoustic sensing and visual imaging technologies

Quantification of aeroacoustic noise sources from wind turbines

One of the major environmental concerns regarding wind energy is an increase in noise levels, both in the atmosphere and the ocean. In this study, the generation and propagation of aeroacoustic noise from wind turbines were studied using available measurements together with several signal processing tools, as well as some well-known models within the field. The thesis starts by introducing some general and more advanced concepts regarding wind energy and aeroacoustics. Observational measurements conducted in a gust wind tunnel on a scaled wind turbine model were used to quantify noise characteristics from wind turbines. The experiment was conducted using 48 microphones in a ring array, with a known distance from the source. Then, a simple beamforming algorithm based on delay-and-sum in both 1D and 2D has been applied to identify the distribution of acoustic source strength on the turbine blade and subregions. An open-source program for the Amiet model was adjusted for studying the turbulence interaction and noise generation on the same NACA4412-airfoil, with some simplifications applied. Next, the low-frequency Parabolic Equation model (PE-model) for an inhomogeneous atmosphere was used to study the effect of different atmospheric stability conditions on noise propagation from a wind turbine. Tests for three stability conditions (stable, neutral, and unstable) for variations of source frequencies, as well as for different topography were presented and discussed. The studies conducted in this thesis illustrate the complexity of aeroacoustic noise and the different parts that need to be accounted for in the development of new wind farms, both onshore and offshore.Masteroppgåve i energiENERGI399

An automated method for the identification of interaction tone noise sources on the beamforming maps of counter-rotating rotors

Counter-rotating rotor configurations are considered more efficient than their single rotor counterparts. Consequently, the coaxially aligned rotors have appeared in the fixed-wing aircraft sector and are appearing in the quickly developing unmanned aerial vehicle sector, where they are expected to play a significant role, especially for long haul and heavy load configurations. As their noise levels have proven to be rather significant, the localization and reduction of the noise of such counter-rotating blade sets is a relevant topic of interest. One of the dominant contributors to counter-rotating rotor noise is interaction tones. Interaction tones appear at combinations of the harmonics of the blade passing frequencies of the two rotors and are significant throughout the spectra. In this paper, an automated method is presented that analyzes an entire series of beamforming noise source maps using principal component analysis-based methods in order to identify the dominant noise generation mechanisms in frequency bins that are associated with interaction tones. The processing technique is presented herein through the investigation of counter-rotating open rotor datasets developed for a fixed-wing aircraft configuration. With the proposed method, an objective mean has been provided for separating apart contributions from various noise sources, which can be automated, making the processing and investigation of large sets of measurement data rather quick and easy. The method has been developed such that the results of the analysis are easy to comprehend even without specialized prior knowledge in the area of counter-rotating rotor noise