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Historical cars are an important part of the cultural heritage of the last 150 years. Their preservation in technical museums raises the question of how to preserve their primary functionality, namely their mobility. This implies being able to reactivate and to maintain their thermal engines, which are the source of their motion. However, the diversity and complexity of these engines generally require the presence of highly qualified personnel as well as detailed condition reports to assist the conservators. This study proposes to use acoustic emission techniques to facilitate these conservation procedures by objectifying the evaluation of the state of the engines and by providing systematic quantitative indicators for their health monitoring. To illustrate the implementation and the potentialities of this approach, different tests have been carried out at the National Automobile Museum of Mulhouse, on a Renault Type AG1. A dedicated experimental setup and the associated measurement protocol are presented in this paper. The derived results show the ability of this method to detect specific types of engine malfunctions, both during bench test and in situ measurement conditions. A critical discussion is finally proposed to highlight the feasibility and the possibilities of such laboratory techniques in the context of conservation assistance. [ABSTRACT FROM AUTHOR]

This paper investigates the failure behavior of reinforced concrete beam - column joints (BCJs) strengthened with different schemes of carbon fiber reinforced polymer (CFRP) and the factors causing fracture. In the study, five reinforced concrete BCJ specimens having 1/1 geometric scale were tested under reversed-cyclic lateral loading. While one of them was used as a reference, the others were strengthened with CFRP. Damage development of the specimens was simultaneously monitored by the acoustic emission (AE) technique. Both the structural and AE data were combined to reveal the effects of CFRP strengthening schemes on the fracture pattern ana damage progress. Quantities, scales, levels, and locations of the damage show that the scale of the damage decreased with all strengthening procedures and that the strength and other mechanical properties improved. In addition, extending the strengthening material to the columns and beams contributed more to the structural behavior in terms of strength, energy dissipation capacity and rigidity as well as crack development. Although strengthening by wrapping only the beam and column extensions of the joint can give the desired performance in terms of load bearing capacity, it is not recommended owing to an increased risk of major shear cracks. [ABSTRACT FROM AUTHOR]

The wood industry demands advanced methods for material classification and workpiece characteristic modelling to enhance process monitoring and adaptive process control. This paper presents a sensor fusion approach that integrates data from acoustic emissions, airborne sound, and power consumption during the milling of solid wood and wood-based composites. The aims are to achieve accurate material classification and to model workpiece characteristics such as surface roughness or density. A design matrix was generated by extracting relevant features from the multimodal signals to serve as an input for the classification and regression algorithms. The tested classification approaches to differentiate between workpiece type demonstrated high precision with an average validation accuracy of 92.16 %. Regression models for predicting the surface roughness showed R 2 values between 0.79 and 0.97. The density could be predicted with R2 values between 0.84 and 0.98. As a conclusion, workpiece types could be classified and important workpiece properties during machining, such as surface roughness and density, could be well described by using information from multiple sensors during machining. [ABSTRACT FROM AUTHOR]

Acoustic emission (AE) is a nondestructive real-time monitoring technology, which has been proven to be a valid way of monitoring dynamic damage to materials. The classification and recognition methods of the AE signals of the rotor are mostly focused on machine learning. Considering that the huge success of deep learning technologies, where the Recurrent Neural Network (RNN) has been widely applied to sequential classification tasks and Convolutional Neural Network (CNN) has been widely applied to image recognition tasks. A novel three-streams neural network (TSANN) model is proposed in this paper to deal with fault detection tasks. Based on residual connection and attention mechanism, each stream of the model is able to learn the most informative representation from Mel Frequency Cepstrum Coefficient (MFCC), Tempogram, and short-time Fourier transform (STFT) spectral respectively. Experimental results show that, in comparison with traditional classification methods and single-stream CNN networks, TSANN achieves the best overall performance and the classification error rate is reduced by up to 50%, which demonstrates the availability of the model proposed. [ABSTRACT FROM AUTHOR]

In this paper, triaxial loading and unloading tests under different conditions were carried out to explore the deformation and failure characteristics of hard rock marble, and the acoustic emission information of the test process was monitored synchronously to obtain the acoustic emission distribution characteristics. The test results show that: (1) when the confining pressure unloading rate is low, the rock yielding is more obvious, the damage peak is higher, and the Poisson effect is more obvious under loading and unloading, while when the unloading rate is high, it shows multi-stage damage; (2) there is a linear relationship between strain deviatoric stress flexibility and unloading rate, as the unloading rate accelerates, the circumferential and volumetric flexibility increases and the axial flexibility decreases; (3) the lower the unloading rate, the higher the confining pressure, and the main shear surface of the rock gathers a large number of tensile microcracks to form a shear zone, while the higher the unloading rate, the lower the confining pressure, and the main shear surface is thoroughly penetrated and ruptured; (4) acoustic emission can characterize the damage degree of marble in the process of loading and unloading, when the confining pressure is low, acoustic emission at the peak damage point appears sharp phenomenon, the overall damage increases with the accelerated rate of unloading, when the confining pressure is high, acoustic emission grows progressively, the overall damage decreases with the accelerated rate of unloading, the overall damage of the rock will appear " pre-peak to post-peak " hysteresis effect. [ABSTRACT FROM AUTHOR]

Copyright of China Textile Leader is the property of China Textile Information Center and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Rotating machinery, is one of the most basic and important equipment, that plays a very important role in any kind of industry. The majority of these machines are operated by means of gears, bearings and other parts, which can become faulty during their use and can affect the performance of the machine and can even result in the breakdown of the machine. Machine downtimes results in loss of production, upon which the whole industry relies on. Techniques such as vibration, thermography, shock pulse monitoring and noise or acoustic emission for gear fault diagnosis are well established, however, the application of wear particle analysis for diagnosis and Prognostic Health Management (PHM) is still in its infancy stage. This paper describes an experimental investigation on a pair of worm gear in which induced wear mechanisms were allowed to occur, namely, moisture corrosion wear, acidattacked corrosion wear, hard contaminant-related three body abrasive wear and adhesive wear. The tests samples of wear debris were collected and assessed through the utilization of an optical microscope in order to correlate and compare the debris morphology to wear degradation of the worn worm gears. Preliminary results presented in this particular work that wear debris characteristics exhibited a direct relationship with different wear modes, thus, it should be possible to detect, diagnose and/or prognoses oil lubricated worm gear wear utilization of wear debris morphological analysis when condition monitoring oil lubricated worm gear sets. [ABSTRACT FROM AUTHOR]

Milling of fibre-reinforced plastics is a challenging task. The highly abrasive fibres lead to high tool wear and coating failures, which cause increasing process forces and temperatures. Machining with a worn tool, in turn, can result in unwanted workpiece damages such as delamination or fibre protrusion. Reliable monitoring of the process must therefore be able to detect damages to the milling tool and the workpiece alike. The presented process monitoring approach measures the acoustic emission generated by the milling tool cutting edge entering the workpiece with a sensor attached to the tool holder. Specific acoustic emission frequency spectra and waveforms are emitted in the cutting zone for different tool wear states. Coating failures as well as other acoustic emission events due to workpiece damages can be robustly detected and distinguished by feature extraction and signal processing as well. The developed setup, the monitoring parameterisation techniques and signal processing algorithms as well as experimental and monitoring results are presented and discussed in this paper. [ABSTRACT FROM AUTHOR]

Cast parts tend to show quality relevant pores and cracks on the inside. During machining operations those defects are exposed, but often not detected. This paper presents an in-process pore detection method for machining operations using a structure-borne acoustic sensor. By detecting the defects in-process, the machining operation can be stopped immediately if those defects are detected. A test case using additive manufactured workpieces with repeatable cavities was implemented, demonstrating the in-process pore detection and localization. The acoustic signals are analyzed both in the time domain and in the frequency domain, using deep learning methods. On experimental AlSi10Mg parts, pores could be detected with a quantified uncertainty using the applied methodology during a milling process. [ABSTRACT FROM AUTHOR]

The paper analyzes the fracture of rock from its time-variant structural parameters reproduced by quantitative interpretation of six amplitude-frequency spectra of acoustic emission in uniaxially loaded granite. The interpretation provides detailed data on the crack size and acoustic energy distributions at six time (load) points corresponding to the spectra, on the surface energy rate and acoustic energy per unit granite volume, and on the correlation between these parameters and fracture modes. The analysis identifies what fracture mode is realized at each of the six points of acoustic measurements. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • The paper presents the applied method for indirect surface finishing measurement in aerospace manufacturing industry. • This paper introduces a method to quantify and correlate the deburring stage with the features extracted from sensors data. • This study covers signal processing, feature extraction and analytical method for surface finish quality estimation in deburring processes. • Wavelet decomposition and Welch spectrum estimate are used as a signal processing and feature extraction method. • The features are used as the basis for classification analysis by adaptive neuro-fuzzy inference system (ANFIS). Abstract Manufacturing of aerospace components consists of combination of different types of machining, finishing, and measuring processes. One of the finishing processes is deburring, i.e. a finishing process to remove burrs from work coupons after a boring hole process. Deburring is conducted to achieve required surface finish quality prior to further processes in assembly line. This paper introduces sensor data analysis as a tool to quantify and correlate the deburring stage with the features extracted from sensors data. This study covers signal processing, feature extraction and analytical method to determine its relevancy to the surface finish quality from deburring process. Wavelet decomposition and Welch's spectrum estimate is used as a signal processing and feature extraction method. Consequently, the features are used as the basis for analysis by adaptive neuro-fuzzy inference system (ANFIS). The ANFIS yields the output corresponding to the predicted surface finish quality in terms of boss hole chamfer length and the stage classification of deburring process. The results show a decreasing trend in measured vibration signal, which is qualitatively well correlated to the deburring stage and the development of chamfer length during deburring process. [ABSTRACT FROM AUTHOR]

Prediction of tool wear plays a key role in machining world due to its considerable impact on total costs in terms of fabricated part quality and disposal of cutting insert before reaching the tool life limit. In addition, comprehensive evaluation of machining characteristics provides a perspective for improved quality. In this study, during turning of AISI 5140 steel, on-line measurements of cutting tool tip temperature and acoustic emission (AE) and off-line measurement of flank wear (V B) of the cutting tool were performed. Very limited study has been published on the machinability characteristics of AISI 5140, while no research has been published on flank wear characteristics in addition to AE and temperature sensors before. Therefore, in this context, AE and tool tip temperature were measured with adapted sensor systems while V B measurement was performed with microscope when the machining was stopped. Three levels of cutting speed, feed rate, depth of cut and cutting-edge angle, experimental design was composed based on Taguchi's L 27 orthogonal array. The main aim of the paper is to predict the flank wear i.e., main criteria for tool life, cutting temperature and AE with the help of fuzzy inference model. In addition, for providing a holistic approach and comprehensive point of view to the machinability, statistical analysis and optimization of the input parameters were given in detail for temperature, AE and V B. According to results, the combined effect of depth of cut and cutting-edge angle having effect (40%) on V B while cutting edge angle and cutting speed have dominance on temperature (45.4%) and AE (46.23%) respectively. Considering the complexity of turning operations, obtained findings reveal the superiority of the fuzzy inference model which was found the estimations highly close to the test results (90–97%) for each machining characteristic. The applicability of fuzzy rule system for different types of variables in turning is promising for the new generation of materials utilized in manufacturing sector. [ABSTRACT FROM AUTHOR]

Impact is a substantial threat to the structural integrity of commercial and military aircraft components, and reliable evaluation of impact damage is therefore important for maintenance. Traditional impact detection for aircraft is based on scheduled visual inspection, intermittently followed by more comprehensive and detailed inspections. This approach is time consuming and detection may be subject to human error. This paper discusses a step in the development of a health monitoring system to automatically recognize and localize impact damage. One challenge to accomplish this goal is to minimize the number of sensors involved while still effectively localizing the zones of impact with an acceptable level of certainty. An artificial neural network and AdaBoost algorithm approach were employed to localize impact on an aircraft elevator having a thermoplastic composite surface, and results are compared and discussed. [ABSTRACT FROM AUTHOR]

Laser welding of copper to aluminum is challenging due to the formation of complex intermetallic phases. More Al (~18.5 at. %) can be dissolved in Cu, in contrast to Cu (~2.5 at. %) in Al. Therefore, welding from copper side, large melting of Al can be achieved. However optimum Cu and Al must be melted for a strong joint. Finding the right amount is difficult and time consuming by tradition analysis technique like inspection by weld cross-sections. Considering the speed of the welding process and complexity of analysis involving with metallography cross-sections, alternative rapid method to qualify the welds are necessary. The acoustic emission during laser welding can give proportional information of the Al, Cu melted. With such an approach the weld status can be obtained in real time. In this paper the acoustic welding signal using an airborne sensor in the audible range of 20 Hz to 20 kHz, is correlated to the weld strength and material mixing (Al, Cu melt). Finally, the weld status is predicted by an artificial neural network based on the acquired signal. [ABSTRACT FROM AUTHOR]

The paper investigates the mechanical and physical properties of low-carbon pipe steel (analog of steel 20 corresponding to TU-28-FR-73) after 39 years of service life in a crude gas pipeline at the Orenburg oil and gas condensate field. It is shown that the standard mechanical properties of the gas pipeline steel after long-term operation correspond to the TU-28-FR-73 standard. The effect of preliminary cyclic loading on the residual tensile strength of steel, the acoustic emission characteristics, and the residual magnetic field intensity estimated by the metal magnetic memory method is studied. After preliminary cycling, the steel was hardened and the acoustic regime changed due to damage accumulation. Four stages of damage evolution in the tensile steel are identified both before and after preliminary cyclic loading, and informative diagnostic criteria for the stages are proposed. Relationships are derived to relate the relative number of preloading cycles with the cumulative acoustic count, acoustic activity, slope of cumulative count-strain curves, acoustic gap duration, and residual magnetic field intensity. The listed characteristics are shown to be promising as diagnostic criteria for the state of the material. [ABSTRACT FROM AUTHOR]

Whilst vibration, noise and acoustic emission analysis for gear fault diagnosis/prognostic are well established, however, the application of wear debris morphological analysis to diagnosis and prognostic health management field is still in its infancy stage. This paper describes an experimental investigation on a pair of spur gear in which "induced" wear modes were allowed to occur, namely, moisture corrosion, acid-attacked corrosion, hard contaminant-related wear. It can be concluded from preliminary results presented in this particular paper that wear debris characteristics exhibited a direct relationship with different wear modes, thus, it should be possible to detect, diagnose and/or prognoses gear wear utilization of wear debris morphological analysis. [ABSTRACT FROM AUTHOR]

Non-destructive testing methods have gained popularity as they become more widely available. Although there are several techniques that could be used for this purpose, this paper focuses on acoustic emission sensors for detecting surface fractures and the use of the Bees Algorithm, a swarm-based technique, for optimizing the number of sensors required to reliably detect surface fractures. The paper describes the approach that has been used in this study where the dimension of the surface is specified by the user. The results show that, in theory and through simulation, that the Bees Algorithm is capable of determining the minimum number of sensors needed to locate the surface fracture with an acceptable level of accuracy. The method described could be used for the purpose of optimization in other engineering as well as non-engineering applications. [ABSTRACT FROM AUTHOR]

The aim of this paper is to present a reliable methodology for condition monitoring of components of high performance centerless grinding machines. This enables the detection and localization of the defects on the ball screw. The fault detection is realized using a self-implemented classification algorithm and other pattern recognition algorithms. The diagnosis is based on acceleration and AE measurement data performed on an axis test rig using various damaged ball screws at different operating parameters. Moreover, the structure of the pattern recognition process will be introduced, this includes the signal pre-processing, the selection of the most suitable features for this specific application using MATLAB®. Finally, the evaluation of the developed solution will be showed. The actions allowing improving the accuracy of measurement data and the effectiveness of the processing algorithms, based on MATLAB® applications, are described throughout the paper. Satisfactory classification results will be obtained and discussed. [ABSTRACT FROM AUTHOR]

Centrifugal compressors have been widely applied and acted as a crucial component in gas and oil industries, due to their excellent performance in providing high pressure ratios, large operating ranges with relatively high efficiencies etc. Breakdowns or deteriorated performance can bring significant economic loss to the companies. Given the hnportance and the crucial role played by these machines, monitoring their status and performing fault diagnosis have become increasingly important. This paper provides a systematical review of condition monitoring and fault diagnosis techniques for centrifugal compressor. Some unique fault types of centrifugal compressors are also investigated, like stall, surge, fouling and impeller fault, which are not usually seen on other rotating machinery. In terms of fault diagnosis, condition indicators which are calculated from multiple signals, can effectively reflect the health status of the machinery. This paper reviewed the common faults and the related state-of-the-art fault diagnostic techniques of a centrifugal compressor. Some condition indicators that are extracted from performance, vibration, acoustic emission and other measurements for centrifugal compressor incipient diagnosis are recommended. Furthermore, several currently used machinery fault diagnostic systems for the centrifugal compressor are investigated. [ABSTRACT FROM AUTHOR]

Nowadays, the product quality of turned parts is measured downstream of the actual manufacturing process. This leads to a time-consuming quality control and the risk of a high number of waste and reworking. Even incidents like the fracture of the lathe tool remain undetected until quality issues of the turned parts are measured. Furthermore, certain material defects can’t be detected by post-production quality control, which leads to customer complaints because of damages during the use of the parts. This paper presents an in-process approach for evaluating the product quality and tool defects in real-time by using an acoustic emission sensor applied to the tool holder. This paper outlines the identification of feasible quality indicators and explains how the data is recorded and which data sources have to be correlated. This includes for example the recording and correlation of high-frequent acoustic emission signal with further acquired data like machine and computer aided quality (CAQ) data. In dissociation to previous work, this correlation is used directly to develop characteristic factors to predict product quality and to detect tool defects. An overview of several characteristic factors is given. In addition, the test setup is shown and first results are presented, followed by an outlook on further research. The test setup is implemented at a series production without disrupting the daily manufacturing processes. It is shown that solutions in context of Industry 4.0 can be implemented in small and medium-sized companies without a loss of production capacity. The venture is realized within a funded project regarding Industry 4.0 and intelligent quality control systems. Its target is to design smart technologies for manufacturing systems. [ABSTRACT FROM AUTHOR]

The paper deals with detection of fault conditions based on measurements of vibration made on rotary machines in various industries. Detection of sources of vibration will be made based on the amplitude spectra and phase relationships of vibrations of individual machine parts, using the envelope of technology in detecting recurring events with low levels of the measured signal and processing of high-frequency signal in the range of acoustic emission. Because the display of a set of symptoms to the space of fault conditions for these methods is not simple, it is advisable to use a multi-parameter approach which means that important decisions are made on the basis of different types of measurements, for example, with using techniques of modal analysis and operational modal analysis. The paper presented the results of various measurements on the machinery. [ABSTRACT FROM AUTHOR]

Copyright of Rudarsko-Geolosko-Naftni Zbornik is the property of Faculty of Mining, Geology & Petroleum Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of DYNA - Ingeniería e Industria is the property of Publicaciones Dyna SL and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper is a contribution to the discussion on the possibilities of identification of the time behaviour of various stages of fatigue lifetime directly during experiments using non-destructive testing methods (mainly acoustic emission method). We focus especially on detection and characterization of the transition from the stage of cycling damage accumulation to micro-cracks creation. In the past years, the laboratory of fatigue properties of FME received and published plenty of information about propagation of damage mainly in Al and Ti alloys. In this paper are presented and compared the results of new experiments with steel used for nuclear reactors and Inconel alloy. This work is focused on comparison of selected parameters of acoustic emission signal in pre-initiation and initiation stage of fatigue crack creation. Acoustic emission hits with higher stress were detected in the pre-initiation stage whereas the initiation stage hits exhibited low stress. [ABSTRACT FROM AUTHOR]

One of the most important products of the majority of tools, processes and, phenomena in mining engineering and earth sciences are high-frequency elastic waves, often referred to as acoustic waves. These acoustic waves can be a useful tool for prediction and measurement, monitoring and diagnosis. Due to the importance of engineering activities that should be specific, measurable, available, reliable and time-based (SMART), Acoustic Emission (AE) techniques can be integrated into engineering operations such as excavation to give exact estimations or simplify some aspects of investigations. Hence, these techniques are expected to play an increasing role in the detection, monitoring, and measurement in engineering applications. With the development of acoustic signal processing methods in earth sciences and the growing need for accurate, fast, and cost-effective prediction, monitoring and measurement methods, AE tools and techniques are expected to turn into powerful alternatives to current methods. The purpose of this paper is to review the application of the acoustic methods as a tool in various projects. For this purpose, the application of AE methods has been reviewed in different fields related to earth sciences including mining engineering, drilling engineering, geology, petroleum engineering, and other related fields. According to the purpose of using these methods, the application of AE is divided into three groups: prediction and estimation, monitoring and performance assessment, and detection. The use of AE methods, among other laboratory or in-situ methods, is very fast and accurate, and by only installing specific sensors with data logging equipment, it has been able to process and analyse data online and remotely. Finally, the possibility of using AE techniques in design and calculating in various projects and as a tool for decision making was evaluated. [ABSTRACT FROM AUTHOR]

The rolling bearing is an important part between the wheel axle and the bogie frame of Electric Multiple Units (EMU), and the running state directly affects the vehicle stability. Therefore, early fault diagnosis is the technical guarantee of the safe operation of the EMU. At present, as temperature and vibration detection technology cannot identify early faults, this paper proposes a signal processing method based on acoustic emissions. LMS adaptive filter technology is used to filter the acoustic emission original waveform stream to improve the signal-to-noise ratio of the fault signal. The envelope demodulation method is used to extract the low frequency fault signal by means of the cubic spline interpolation Method; then, the power spectrum of the low frequency fault signal is analysed. Taking a natural small defect in an EMU bearing as an example, the acoustic emission signal is analysed. The results show that the proposed method could identify early faults in rolling bearings under different operating EUM speeds. [ABSTRACT FROM AUTHOR]

Acoustic Emission (AE) technology is probably one of the most recent entries in the field of machinery condition monitoring. This paper investigates the application of AE parameters for valve faults detection in reciprocating compressor. The defective valves were designed by emulating the actual valve corrosion with varying sizes such that defects could be applied onto the reciprocating compressor. A set of experiments was performed to acquire the AE signal. The primary source of AE signal was verified using waveform analysis. The AE parameters versus different operational and valve condition were illustrated individually. In addition, the significance of the change and sensitivity of AE parameters versus different experimental conditions was verified using MANOVA and coefficient of variance analysis. It is concluded that the AE signal parameters can be used to detect the valve faults in the reciprocating compressor with the consideration of the variation in the AE parameters sensitivity. [ABSTRACT FROM AUTHOR]

Abstract The purpose of this paper is to optimize and apply probabilistic methods and compare the obtained results to geometric method for locating acoustic emission (AE) source in a plate-like structure. The time of arrival (TOA) of the AE waves in each sensor taking into account the uncertainties, was determined by the continuous wavelet transform (CWT). Then the group wave's velocity was calculated by Monte Carlo simulation. As a probabilistic method, the extended Kalman filter (EKF) is used to iteratively estimate the location of AE sources. Experimental results have shown that the probabilistic method estimates the location of the pencil lead break better than the geometric method. Experimental tests were performed on a copper plate to validate the comparison of the two approaches performances. [ABSTRACT FROM AUTHOR]

Abstract The knowledge of the tool wear condition is essential for the dimensional accuracy of the workpiece. Commonly used systems for wear monitoring are usually based on the piezoelectric force measurement. However, in industry these systems are difficult to integrate. A suitable to integrate sensor type is the acoustic emission (AE) sensor. In this paper the main focus will be on the flank wear of drilling tools. For the investigation of the emitted frequency of the flank wear different analogy experiments needs to be realized. With the help of machine learning algorithms the recorded data will be classified. [ABSTRACT FROM AUTHOR]

As a dynamic non-destructive testing technology, acoustic emission (AE) detection technology can locate the early damage of equipment and provide data support for health management. In order to improve the accuracy of wireless AE-based location, this paper proposes a Taylor expansion least squares algorithm to concern network delay. Firstly, according to the planar four-point positioning structure, a wireless AE detection system based on star topology is constructed, and the Taylor expanded least squares localization algorithm is then proposed. Secondly, the network queuing delay is analyzed, and the algorithm is modified with weighting factors of delay. Finally, simulation results show that the accuracy of the network delay-weighted algorithm has been improved. [ABSTRACT FROM AUTHOR]

This paper presents a statistical learning method capable of classifying the incidence level of partial discharges in power transformers. By using the results from acoustic emission measurements, it is possible to detect the presence of partial discharges inside the equipment, allowing the qualitative health monitoring of the transformer’s insulation. Therefore, the use of a Bayesian Network is proposed, combined with a Compact Genetic Algorithm tailored for solving mixed integer programming problems, for discretization of the continuous metrics extracted from acoustic emission measurement. Comparing the results with Multilayer Perceptron Neural Network and Decision Tree and after a suitable amount of runs of the algorithm, it was verified that the Bayesian Networks presented superior results. [ABSTRACT FROM AUTHOR]

The detection and quantification of an underwater gas release are becoming increasingly important for oceanographic and industrial applications. Whilst the detection of each individual bubble injection events, with commensurate sizing from the natural frequency of the acoustic emission, has been common for decades in laboratory applications, it is impractical to do this when hundreds of bubbles are released simultaneously, as can occur with large methane seeps, or leaks from gas pipelines or undersea facilities for carbon capture and storage. This paper draws on data from two experimental studies and demonstrates the usefulness of passive acoustics to monitor gas leaks of this level. It firstly shows experimental validation tests of a recent model aimed at inverting the acoustic emissions of gas releases in a water tank. Different gas flow rates for two different nozzle types are estimated using this acoustic inversion and compared to measurements from a mass flow meter. The estimates are found to predict accurately volumes of released gas. Secondly, this paper demonstrates the use of this method at sea in the framework of the QICS project (controlled release of CO 2 gas). The results in the form of gas flow rate estimates from bubbles are presented. These track, with good agreement, the injected gas and correlate within an order of magnitude with diver measurements. Data also suggest correlation with tidal effects with a decrease of 15.1 kg d −1 gas flow for every 1 m increase in tidal height (equivalent to 5.9 L/min when converted to standard ambient temperature [25 °C] and absolute pressure [100 kPa] conditions, SATP). [ABSTRACT FROM AUTHOR]

For years, vibration analysis has been the industry standard for bearing fault diagnosis. However, due to the various advantages over vibration based techniques, the quantifica-tion of acoustic emission (AE) for bearing health diagnosis has been an area of interest for recent years. Additionally, most AE based methodologies to date utilize data mining technologies. Presented in this paper is a new approach, combining a heterodyne based frequency reduction technique, time synchronous resampling, and spectral averaging to process AE signals and compute condition indicators (CIs) for bearing fault diagnostics. First, the heterodyne based frequency reduction technique allows the AE signal fre-quency to be down shifted from several MHz to less than 50 kHz, which approaches that of vibration based methodologies. Next, the sampled AE signals are band pass filtered to retain the useful information related to the bearing defects. Last, a trigger signal is uti-lized to time synchronously resample the AE signals to allow the calculation of a spectral average and the extraction and evaluation of CI s for bearing fault diagnosis. The tech-nique presented in this paper is validated using the AE signals of seeded fault steel bear-ings on a bearing test rig. Presented is an effective AE based approach validated to diagnose all four fault types: inner race, outer race, ball, and cage. Moreover, the effec-tiveness of the presented approach is established through the comparison of both AE and vibration data. [ABSTRACT FROM AUTHOR]

Reciprocating compressors are one of the most used types of compressors with wide applications in industry. The most common failure in reciprocating compressors is always related to the valves. Therefore, a reliable condition monitoring method is required to avoid the unplanned shutdown in this category of machines. Acoustic emission (AE) technique is one of the effective recent methods in the field of valve condition monitoring. However, a major challenge is related to the analysis of AE signal which perhaps only depends on the experience and knowledge of technicians. This paper proposes automated fault detection method using support vector machine (SVM) and AE parameters in an attempt to reduce human intervention in the process. Experiments were conducted on a single stage reciprocating air compressor by combining healthy and faulty valve conditions to acquire the AE signals. Valve functioning was identified through AE waveform analysis. SVM faults detection model was subsequently devised and validated based on training and testing samples respectively. The results demonstrated automatic valve fault detection model with accuracy exceeding 98%. It is believed that valve faults can be detected efficiently without human intervention by employing the proposed model for a single stage reciprocating compressor. [ABSTRACT FROM AUTHOR]

Acoustic Induced Vibration (AIV) refers to the high acoustic energy generated by pressure-reducing devices that excite pipe shell vibration modes, producing excessive dynamic stress. Analysis of this risk is an important part of Asset Integrity Management systems as AIV can cause catastrophic piping failure. Existing guidelines address this risk through an analytical assessment. However, these methodologies are not fully known and input parameters are limited. Some limits to the guidelines are pointed out with recommendations to improve them. The approach presented for identifying AIV damage is based on a dynamic stress evaluation at pipe discontinuities (welded connections and supports). This evaluation is performed through a fluid-structure coupling Finite Element Analysis. Pressure fluctuations inside the pipe are predicted and coupled with a pipe structural analysis. This methodology is provided with its validation through measurement on an actual AIV field case, corresponding to a crack initiation due to AIV on an FPSO flare network tail pipe. To conclude the paper, the method is then applied to quantitatively assess the mitigation actions’ efficiency on an actual case. Different solutions have been individually tested to end up with a final solution that reduces the damage to acceptable levels in the most cost-effective manner. [ABSTRACT FROM AUTHOR]

Piezoceramic transducers have been widely used in the health monitoring of civil structures. However, in most cases, they are used as sensors either to measure strain or receive stress waves. This paper proposes a method of using piezoelectric transducers as strain gauges and acoustic emission (AE) sensors simultaneously. The signals received by piezoceramic transducers are decomposed into different frequency components for various analysis purposes. The lowfrequency signals are used to measure strain, whereas the high-frequency signals are used as acoustic emission signal associated with local damage. The b-value theory is used to process the AE signal in piezoceramic transducers. The proposed method was applied in the bending failure experiments of two reinforced concrete beams to verify its feasibility. The results showed that the extracted low-frequency signals from the piezoceramic transducers had good agreement with that from the strain gauge, and the processed high-frequency signal from piezoceramic transducers as AE could indicate the local damage to concrete. The experimental results verified the feasibly of structural health monitoring using piezoceramic transducers as strain gauges and AE sensors simultaneously, which can advance their application in civil engineering.C [ABSTRACT FROM AUTHOR]

This paper presents numerical and experimental results on the use of guided waves for structural health monitoring (SHM) of crack growth during a fatigue test in a thick steel plate used for civil engineering application. Numerical simulation, analytical modeling, and experimental tests are used to prove that piezoelectric wafer active sensor (PWAS) can perform active SHM using guided wave pitch-catch method and passive SHM using acoustic emission (AE). AE simulation was performed with the multi-physic FEM (MP-FEM) approach. The MP-FEM approach permits that the output variables to be expressed directly in electric terms while the two-ways electromechanical conversion is done internally in the MP-FEM formulation. The AE event was simulated as a pulse of defined duration and amplitude. The electrical signal measured at a PWAS receiver was simulated. Experimental tests were performed with PWAS transducers acting as passive receivers of AE signals. An AE source was simulated using 0.5-mm pencil lead breaks. The PWAS transducers were able to pick up AE signal with good strength. Subsequently, PWAS transducers and traditional AE transducer were applied to a 12.7-mm CT specimen subjected to accelerated fatigue testing. Active sensing in pitch catch mode on the CT specimen was applied between the PWAS transducers pairs. Damage indexes were calculated and correlated with actual crack growth. The paper finishes with conclusions and suggestions for further work. [ABSTRACT FROM AUTHOR]

Abstract: In-situ stress is the key parameter for underground and slope rock engineering design optimization. Hydraulic fracturing method and trepanning stress relieving method are the two main in-situ stress measurement methods for the present, but both of the two methods have some disadvantages such as strict operating conditions, complicated operations, high cost, etc. which have not been overcome till now. With a case of high-steep open pit slope, the paper begins with hydraulic fracturing method to measure the in-situ stress and then, on this basis, based on plane stress condition, the paper adopts acoustic emission method to measure the horizontal maximum principle stress and its direction. It indicates that the test results of acoustic emission are consistent with that of hydraulic fracturing method. [Copyright &y& Elsevier]

Abstract: Microseismic monitoring technique has been developed for more than 50 years, and has successfully found it application in underground construction, tunnel, reservoir dam, oil production and so on. Because of its successful application in mine disaster prediction, tunnel safety, slope stability, people are getting more and more interested about it. This paper introduced the key technical points of microseismic signal acquisition, analyzed the physical meaning of several microseismic parameters, and introduced the application of microseismic monitoring technique in gas outburst monitoring, water disaster monitoring, rock burst monitoring, coalmine combustion zone monitoring, hydraulic fracturing zone monitoring, analyzed the technical breakthrough points of microseismic technique in each engineering field. Besides, this paper suggested that further studying the characteristic of rock deformation under stretching, compressing and bending stress, and establishing the relationship between rock characteristic and AE signal, investigating the AE signal characteristic of different rock broken pattern, are the developing direction of microseismic technique, and introduced some research progresses in this direction. [Copyright &y& Elsevier]

Acoustic emission (AE) can be used to monitor damage such as fracture and corrosion in situ. for AE from individual signal bursts produced by the corrosion process, the intensity of the AE energy and HIT (hit count) may be used potentially to grade the severity of the corrosion level. However. different conditions, such as the size of the tank, the type of AE sensor. or the liquid medium, may cause different levels of AE signals even when obtained from the identical corrosion source. This problem makes the quantitative understanding of corrosion damage using AE difficult. In this paper, the effects of the AE wave propagation paths, including the liquid medium, the AE sensor, the connection cable, and the amplified signals, on the AE signal received from a uniform corrosion process on carbon steel, are investigated and modeled with theoretical and empirical equations. In addition, to verify our proposed model for various types of AE sensors, the air jet implemented is demonstrated to convert AE energy from different AE sensors. Finally. an equation is proposed to estimate the AE energy for different wave propagation paths. Experimental tests were performed to validate the proposed equation. The error of prediction of the uniform corrosion energy is less than 5%. The novelty of this paper was to forecast the grade of the severity of uniform corrosion quantitatively when using various AE monitoring conditions. [ABSTRACT FROM AUTHOR]

This paper discusses the applicability of Acoustic Emission (AE) to assess the damage in reinforced concrete (RC) structures subjected to complex dynamic loadings such as those induced by earthquakes. The AE signals recorded during this type of event can be complicated due to the arbitrary and random nature of seismicity and the fact that the signals are highly contaminated by many spurious sources of noise. This paper demonstrates that by properly filtering the AE signals, a very good correlation can be found between AE and damage on the RC structure. The basic experimental data used for this research are the results of fourteen seismic simulations conducted with a shake table on an RC slab supported on four steel columns. The AE signals were recorded by several low-frequency piezoelectric sensors located on the bottom surface of the slab. The evolution of damage under increasing values of peak acceleration applied to the shake table was monitored in terms of AE and dissipated plastic strain energy. A strong correlation was found between the energy dissipated by the concrete through plastic deformations and the AE energy calculated after properly filtering the signals. For this reason, a procedure is proposed to analyze the AE measured in a RC structure during a seismic event so that it can be used for damage assessment. [ABSTRACT FROM AUTHOR]

This paper presents a feasibility study of flexible piezoelectric paint for use in wide-band low-profile surface-mount or embeddable ultrasonic sensor for in situ structural health monitoring. Piezoelectric paint is a piezoelectric composite with 0-3 connectivity. Because of its ease of application, piezoelectric paint can be readily fabricated into sensing element with complex pattern. This study examines the characteristics of piezoelectric paint in acoustic emission signal and ultrasonic guided wave sensing. A series of ultrasonic tests including pitch catch and pencil break tests were performed to validate the ultrasonic wave sensing capability of piezoelectric paint. The results of finite element simulation of ultrasonic wave propagation, and acoustic emission generated by a pencil lead break on an aluminum plate are also presented in this paper along with corresponding experimental data. Based on the preliminary experimental results, the piezoelectric paint appears to offer a promising sensing material for use in real-time monitoring of crack initiation and propagation in both metallic and composite structures. [ABSTRACT FROM AUTHOR]

The monitoring and diagnosis of rolling element bearings with the high frequency acoustic emission (AE) technology has been ongoing since the late 1960s. This paper demonstrates the use of AE measurements to detect, locate, and monitor natural defect initiation and propagation in a conventional rolling element bearing. To facilitate the investigation a special purpose test rig was built to allow for accelerated natural degradation of a bearing race. it is concluded that subsurface initiation and subsequent crack propagation can be detected with the AE technology. The paper also presents comparative results between AE and vibration diagnosis. [ABSTRACT FROM AUTHOR]

The paper deals with the experimental determination of the shrinkage progress of the polymer cement (PCC) mortars. The main motivation of performed measurements was to compare the results obtained from two different measurement procedures based on the measurement of the relative length changes and relative mass losses of the test specimens during whole time of ageing. Performed experimental analysis, focused on the determination of the progress of the relative length changes and relative mass losses, showed advantages and disadvantages of investigated measurement procedures. All performed measurements exhibited almost the same total progress of relative mass losses regardless of the used test procedure and size of specimens. The differences in the relative length changes originated from the capabilities, limitations and regulations of particular measurement procedures. [ABSTRACT FROM AUTHOR]

Deformation of soil bodies and soil-structure systems generates acoustic emission (AE), which are high-frequency stress waves. Listening to this AE by coupling sensors to structural elements can provide information on asset condition and early warning of accelerating deformation behaviour. There is a need for experimentation to model the propagation of AE in buried pipe systems to enhance understanding of real behaviour. Analytical solutions are often based on many assumptions (e.g. homogeneity, isotropy, boundary conditions and material properties) and cannot exactly represent the behaviour of thein situsystem. This paper details a series of experiments conducted on buried pipes to investigate AE attenuation in pipes due to couplings and soil surround. The attenuation coefficients reported provide guidance to engineers for designing sensor spacing along buried pipes for monitoring ground deformations, and active waveguide installation depths for slope deformation monitoring. Attenuation coefficients have been quantified for both air–pipe–air and air–pipe–soil trilayer systems for the frequency range of 20–30 kHz. [ABSTRACT FROM PUBLISHER]

Fire response of concrete structural members depends on thermal, mechanical, and deformation properties of concrete. These properties vary significantly with temperature and also depend on the composition and characteristics of the concrete batch mix as well as heating rate and other environmental conditions. Concrete structures could be exposed to extreme temperature conditions. Examples of such conditions are concrete foundations for launching rockets carrying spaceships, concrete structures in nuclear power stations or those accidentally exposed to fire, for instance in the case of tunnel fires. This paper analyses acoustic emission signals captured during three-point bending test on thermal-stressed concrete specimens. The method of acoustic emission is an experimental tool suitable for monitoring the failure processes in materials. The typical parameters of acoustic emission signal were identified during the acoustic emission records for different concrete specimens to further describe the under-the-stress behaviour and failure development. The amount of crack growth was continuously monitored using four acoustic emission sensors mounted on the specimens. [ABSTRACT FROM AUTHOR]

Galling is an important issue in metal forming of tribologically severe materials such as high strength steel, stainless steel, Al- or Ti-alloys, since it leads to poor surface quality of the formed components, production stops and possibly deterioration of tools. The onset of galling is difficult to detect, since it is either based on the operator's personal judgement or indirect measuring techniques. The application of acoustic emission measuring technique for characterization of onset of galling in sheet metal forming is discussed in the presented paper. The strip reduction test, which emulates the ironing process, has been examined in order to evaluate onset of galling and how this is related to the generated acoustic emission parameters. Preliminary investigations have shown that differences can be found in the acoustic emission signal parameters depending on the frictional conditions between the tool and the workpiece surfaces. A correlation to the severity of galling is found. This is inspected through observations of tested workpiece surfaces in SEM and measurements of the surface roughness. The acoustic emission measuring technique is found to possess promising aspects for online monitoring of galling in metal forming processes. [ABSTRACT FROM AUTHOR]

In this paper, a tool wear diagnostic system based on the information contained in the signal of acoustic emission (AE) is considered. In the process, experiments on milling of steel billets 1035 were carried out, with the reference values of cutting force being dynamically monitored with the help of a Kistler 9257B multi-component dynamometer. Registration of the AE signal is performed by an LTR22 modular data acquisition system equipped with an OKTAFON 110 sensor. The method of useful signal filtering from the entire spectrum of AE is carried out using wavelet decomposition. Detection of necessary time periods from the decomposed milling AE signal is suggested for further analysis based on Fourier analysis. [ABSTRACT FROM AUTHOR]

With the increasing consumption of conventional oil and gas reservoirs for gas recovery/production, unconventional reservoirs, such as coalbed methane, shale gas, and gas hydrate, have become very popular in recent times. In this regard, hydraulic fracturing is an effective technique commonly used for enhanced coalbed methane recovery. In previous studies, the fracture morphology was described by comparing the fracture morphology before and after hydraulic fracturing from a macroscopic perspective. Because fracture initiation and subsequent networks of fractures are formed instantly when coal mass is subjected to hydraulic fracturing, it is almost impossible to acquire complete information about fracture initiation by only analyzing the change in hydraulic pressure and fracture morphology. In this paper, a triaxial experimental system was developed to simulate hydraulic fracturing using raw coal and briquette coal samples, respectively. The borehole wall strain observed during hydraulic fracturing was plotted (borehole wall strain curves) and the acoustic emission response was also obtained. In addition, the fracture behaviors during hydraulic fracturing were analyzed. Our results show that the response of coal subjected to hydraulic fracturing can be divided into the following four stages: microcrack formation, fracture initiation, unstable crack propagation, and fracture closure. The borehole wall strain curves effectively reflected the deformation and failure of borehole wall. Acoustic emission response can thus be utilized to identify the orientation of fractures during hydraulic fracturing. The combination of the two methods offers an effective option for clarifying the fracture initiation and instability mechanism near the borehole subjected to hydraulic fracturing. [ABSTRACT FROM AUTHOR]

Emotionally congruent sound effect generation can be particularly useful in interactive audio applications with non-linear narrative requirements (such as video gaming). The study of affect (broadly construed here as recognition of emotional suggestion rather than individual emotional response) increasingly concludes that acoustic features can have an influence on the perceived emotional characteristics of an audio signal, from environmental sounds to musical stimuli. This paper presents an investigative approach to measuring the relative impact of musical voicing, in particular musical timbre, on perceived emotion as reported by a panel of listeners, as part of work towards an automated system designed to manipulate sound effects in real-time according to a particular affective intent. In the future this type of system could combine selective spectro-temporal morphing between source sounds and affective acoustic correlates with musical feature quantification and interpolation in order to manipulate a palette of sound effects or music through an emotional space based on quantified affective correlates. [ABSTRACT FROM AUTHOR]