Identification of acoustic emission wave modes for accurate source location in plate-like structures

Acoustic emission (AE) technique is a popular tool used for structural health monitoring of civil, mechanical and aerospace structures. It is a non-destructive method based on rapid release of energy within a material by crack initiation or growth in the form of stress waves. Recording of these waves by means of sensors and subsequent analysis of the recorded signals convey information about the nature of the source. Ability to locate the source of stress waves is an important advantage of AE technique; but as AE waves travel in various modes and may undergo mode conversions, understanding of the modes (‘modal analysis’) is often necessary in order to determine source location accurately. This paper presents results of experiments aimed at finding locations of artificial AE sources on a thin plate and identifying wave modes in the recorded signal waveforms. Different source locating techniques will be investigated and importance of wave mode identification will be explored.

Study of acoustic emission data analysis tools for structural health monitoring applications

Acoustic emission (AE) technique is one of the popular diagnostic techniques used for structural health monitoring of mechanical, aerospace and civil structures. But several challenges still exist in successful application of AE technique. This paper explores various tools for analysis of recorded AE data to address two primary challenges: discriminating spurious signals from genuine signals and devising ways to quantify damage levels.

Structural health monitoring of bridges : source localisation in acoustic emission technique

Managing the sustainability of urban infrastructure requires regular health monitoring of key infrastructure such as bridges. The process of structural health monitoring involves monitoring a structure over a period of time using appropriate sensors, extracting damage sensitive features from the measurements made by the sensors, and analysing these features to determine the current state of the structure. Various techniques are available for structural health monitoring of structures, and acoustic emission is one technique that is finding an increasing use in the monitoring of civil infrastructures such as bridges. Acoustic emission technique is based on the recording of stress waves generated by rapid release of energy inside a material, followed by analysis of recorded signals to locate and identify the source of emission and assess its severity. This chapter first provides a brief background of the acoustic emission technique and the process of source localization. Results from laboratory experiments conducted to explore several aspects of the source localization process are also presented. The findings from the study can be expected to enhance knowledge of the acoustic emission process, and to aid the development of effective bridge structure diagnostics systems.

Enhancing acoustic emission signals from multi-cylinder diesel engine

This paper presents techniques which can be viewed as pre-processing step towards diagnosis of faults in a small size multi-cylinder diesel engine. Preliminary analysis of the acoustic emission (AE) signals is outlined, including time-frequency analysis, selection of optimum frequency band. Some results of applying mean field independent component analysis (MFICA) to separate the AE root mean square (RMS) signals are also outlined. The results on separation of RMS signals show this technique has the potential of increasing the probability to successfully identify the AE events associated with the various mechanical events.

Analysis of acoustic emission data for structural health monitoring applications

Acoustic emission (AE) is the phenomenon where high frequency stress waves are generated by rapid release of energy within a material by sources such as crack initiation or growth. AE technique involves recording these stress waves by means of sensors placed on the surface and subsequent analysis of the recorded signals to gather information such as the nature and location of the source. It is one of the several diagnostic techniques currently used for structural health monitoring (SHM) of civil infrastructure such as bridges. Some of its advantages include ability to provide continuous in-situ monitoring and high sensitivity to crack activity. But several challenges still exist. Due to high sampling rate required for data capture, large amount of data is generated during AE testing. This is further complicated by the presence of a number of spurious sources that can produce AE signals which can then mask desired signals. Hence, an effective data analysis strategy is needed to achieve source discrimination. This also becomes important for long term monitoring applications in order to avoid massive date overload. Analysis of frequency contents of recorded AE signals together with the use of pattern recognition algorithms are some of the advanced and promising data analysis approaches for source discrimination. This paper explores the use of various signal processing tools for analysis of experimental data, with an overall aim of finding an improved method for source identification and discrimination, with particular focus on monitoring of steel bridges.

Application of acoustic emission technology in monitoring structure integrity of bridges

Bridges are an important part of a nation’s infrastructure and reliable monitoring methods are necessary to ensure their safety and efficiency. Most bridges in use today were built decades ago and are now subjected to changes in load patterns that can cause localized distress, which can result in bridge failure if not corrected. Early detection of damage helps in prolonging lives of bridges and preventing catastrophic failures. This paper briefly reviews the various technologies currently used in health monitoring of bridge structures and in particular discusses the application and challenges of acoustic emission (AE) technology. Some of the results from laboratory experiments on a bridge model are also presented. The main objectives of these experiments are source localisation and assessment. The findings of the study can be expected to enhance the knowledge of acoustic emission process and thereby aid in the development of an effective bridge structure diagnostics system.

Use of acoustic emission technique for structural health monitoring of bridges

Bridges are valuable assets of every nation. They deteriorate with age and often are subjected to additional loads or different load patterns than originally designed for. These changes in loads can cause localized distress and may result in bridge failure if not corrected in time. Early detection of damage and appropriate retrofitting will aid in preventing bridge failures. Large amounts of money are spent in bridge maintenance all around the world. A need exists for a reliable technology capable of monitoring the structural health of bridges, thereby ensuring they operate safely and efficiently during the whole intended lives. Monitoring of bridges has been traditionally done by means of visual inspection. Visual inspection alone is not capable of locating and identifying all signs of damage, hence a variety of structural health monitoring (SHM) techniques is used regularly nowadays to monitor performance and to assess condition of bridges for early damage detection. Acoustic emission (AE) is one technique that is finding an increasing use in SHM applications of bridges all around the world. The chapter starts with a brief introduction to structural health monitoring and techniques commonly used for monitoring purposes. Acoustic emission technique, wave nature of AE phenomenon, previous applications and limitations and challenges in the use as a SHM technique are also discussed. Scope of the project and work carried out will be explained, followed by some recommendations of work planned in future.

Human-body shadowing effects on indoor MIMO-OFDM channels at 5.2 GHz

Pedestrian movement is known to cause significant effects on indoor MIMO channels. In this paper, a statistical characterization of the indoor MIMO-OFDM channel subject ot pedestrian movement is reported. The experiment used 4 sending and 4 receiving antennas and 114 sub-carriers at 5.2 GHz. Measurement scenarios varied from zero to ten pedestrians walking randomly between transmitter (tx) and receiver (Rx) arrays. The empirical cumulative distribution function (CDF) of the received fading envelope fits the Ricean distribution with K factors ranging from 7dB to 15 dB, for the 10 pedestrians and vacant scenarios respectively. In general, as the number of pedestrians increase, the CDF slope tends to decrease proportionally. Furthermore, as the number of pedestrians increase, increasing multipath contribution, the dynamic range of channel capacity increases proportionally. These results are consistent with measurement results obtained in controlled scenarios for a fixed narrowband Single-Input Single-Output (SISO) link at 5.2 GHz in previous work. The described empirical characterization provides an insight into the prediction of human-body shadowing effects for indoor MIMO-OFDM channels at 5.2 GHz.

Review: acoustic emission technique - opportunities, challenges and current work at QUT

Acoustic emission (AE) is the phenomenon where high frequency stress waves are generated by rapid release of energy within a material by sources such as crack initiation or growth. AE technique involves recording these stress waves by means of sensors placed on the surface and subsequent analysis of the recorded signals to gather information such as the nature and location of the source. AE is one of the several non-destructive testing (NDT) techniques currently used for structural health monitoring (SHM) of civil, mechanical and aerospace structures. Some of its advantages include ability to provide continuous in-situ monitoring and high sensitivity to crack activity. Despite these advantages, several challenges still exist in successful application of AE monitoring. Accurate localization of AE sources, discrimination between genuine AE sources and spurious noise sources and damage quantification for severity assessment are some of the important issues in AE testing and will be discussed in this paper. Various data analysis and processing approaches will be applied to manage those issues.

Turbulent velocity and suspended sediment concentration measurements in an urban environment of the Brisbane River Flood Plain at Gardens Point on 12-13 January 2011

The flood flow in urbanised areas constitutes a major hazard to the population and infrastructure as seen during the summer 2010-2011 floods in Queensland (Australia). Flood flows in urban environments have been studied relatively recently, although no study considered the impact of turbulence in the flow. During the 12-13 January 2011 flood of the Brisbane River, some turbulence measurements were conducted in an inundated urban environment in Gardens Point Road next to Brisbane's central business district (CBD) at relatively high frequency (50 Hz). The properties of the sediment flood deposits were characterised and the acoustic Doppler velocimeter unit was calibrated to obtain both instantaneous velocity components and suspended sediment concentration in the same sampling volume with the same temporal resolution. While the flow motion in Gardens Point Road was subcritical, the water elevations and velocities fluctuated with a distinctive period between 50 and 80 s. The low frequency fluctuations were linked with some local topographic effects: i.e, some local choke induced by an upstream constriction between stairwells caused some slow oscillations with a period close to the natural sloshing period of the car park. The instantaneous velocity data were analysed using a triple decomposition, and the same triple decomposition was applied to the water depth, velocity flux, suspended sediment concentration and suspended sediment flux data. The velocity fluctuation data showed a large energy component in the slow fluctuation range. For the first two tests at z = 0.35 m, the turbulence data suggested some isotropy. At z = 0.083 m, on the other hand, the findings indicated some flow anisotropy. The suspended sediment concentration (SSC) data presented a general trend with increasing SSC for decreasing water depth. During a test (T4), some long -period oscillations were observed with a period about 18 minutes. The cause of these oscillations remains unknown to the authors. The last test (T5) took place in very shallow waters and high suspended sediment concentrations. It is suggested that the flow in the car park was disconnected from the main channel. Overall the flow conditions at the sampling sites corresponded to a specific momentum between 0.2 to 0.4 m2 which would be near the upper end of the scale for safe evacuation of individuals in flooded areas. But the authors do not believe the evacuation of individuals in Gardens Point Road would have been safe because of the intense water surges and flow turbulence. More generally any criterion for safe evacuation solely based upon the flow velocity, water depth or specific momentum cannot account for the hazards caused by the flow turbulence, water depth fluctuations and water surges.

Characterizing the signal pattern of a four-cylinder diesel engine using acoustic emission and vibration analysis

Condition monitoring of diesel engines can prevent unpredicted engine failures and the associated consequence. This paper presents an experimental study of the signal characteristics of a 4-cylinder diesel engine under various loading conditions. Acoustic emission, vibration and in-cylinder pressure signals were employed to study the effectiveness of these techniques for condition monitoring and identifying symptoms of incipient failures. An event driven synchronous averaging technique was employed to average the quasi-periodic diesel engine signal in the time domain to eliminate or minimize the effect of engine speed and amplitude variations on the analysis of condition monitoring signal. It was shown that acoustic emission (AE) is a better technique than vibration method for condition monitor of diesel engines due to its ability to produce high quality signals (i.e., excellent signal to noise ratio) in a noisy diesel engine environment. It was found that the peak amplitude of AE RMS signals correlating to the impact-like combustion related events decreases in general due to a more stable mechanical process of the engine as the loading increases. A small shift in the exhaust valve closing time was observed as the engine load increases which indicates a prolong combustion process in the cylinder (to produce more power). On the contrary, peak amplitudes of the AE RMS attributing to fuel injection increase as the loading increases. This can be explained by the increase fuel friction caused by the increase volume flow rate during the injection. Multiple AE pulses during the combustion process were identified in the study, which were generated by the piston rocking motion and the interaction between the piston and the cylinder wall. The piston rocking motion is caused by the non-uniform pressure distribution acting on the piston head as a result of the non-linear combustion process of the engine. The rocking motion ceased when the pressure in the cylinder chamber stabilized.

Can audio-visual speech recognition outperform acoustically enhanced speech recognition in automotive environment?

The use of visual features in the form of lip movements to improve the performance of acoustic speech recognition has been shown to work well, particularly in noisy acoustic conditions. However, whether this technique can outperform speech recognition incorporating well-known acoustic enhancement techniques, such as spectral subtraction, or multi-channel beamforming is not known. This is an important question to be answered especially in an automotive environment, for the design of an efficient human-vehicle computer interface. We perform a variety of speech recognition experiments on a challenging automotive speech dataset and results show that synchronous HMM-based audio-visual fusion can outperform traditional single as well as multi-channel acoustic speech enhancement techniques. We also show that further improvement in recognition performance can be obtained by fusing speech-enhanced audio with the visual modality, demonstrating the complementary nature of the two robust speech recognition approaches.

Experimentally induced diesel engine injector faults and some preliminary acoustic emission signal observations

Diesel engine fuel injector faults can lead to reduced power, increased fuel consumption and greater exhaust emission levels and if left unchecked, can eventually lead to premature engine failure. This paper provides an overview of the Diesel, or compression ignition combustion process, and of the two basic fuel injector nozzle designs used in Diesel engines, namely, the pintle-type and hole-type nozzles. Also described are some common faults associated with these two types of fuel injector nozzles and the techniques previously used to experimentally simulate these faults. This paper also presents a recent experimental campaign undertaken using two different diesel engines whereby various fuel injector nozzle faults were induced into the engines. The first series of tests was undertaken using a turbo-charged 5.9 litre; Cummins Diesel engine whist the second series of tests was undertaken using a naturally aspirated 4 cylinder, 2.216 litre, Perkins Diesel engine. Data corresponding to different injector fault conditions was captured using in-cylinder pressure, and acoustic emission transducers along with both crank-angle encoder and top-dead centre reference signals. Using averaged in-cylinder pressure signals, it was possible to qualify the severity of the faults whilst averaged acoustic emission signals were in turn, used as the basis for wavelets decomposition. Initial observations from this signal decomposition are also presented and discussed.

A study on acoustic emission signal propagation of a small size multi-cylinder diesel engine

Acoustic emission has been found effective in offering earlier fault detection and improving identification capabilities of faults. However, the sensors are inherently uncalibrated. This paper presents a source to sensor paths calibration technique which can lead to diagnosis of faults in a small size multi-cylinder diesel engine. Preliminary analysis of the acoustic emission (AE) signals is outlined, including time domain, time-frequency domain, and the root mean square (RMS) energy. The results reveal how the RMS energy of a source propagates to the adjacent sensors. The findings lead to allocate the source and estimate its inferences to the adjacent sensor, and finally help to diagnose the small size diesel engines by minimising the crosstalk from multiple cylinders.