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.

Application of infrared emission spectroscopy to the thermal transition of indium hydroxide to indium oxide nanocubes

Cubic indium hydroxide nanomaterials were obtained by a low temperature soft-chemical method without any surfactants. The transition of nano-cubic indium hydroxide to cubic indium oxide during dehydroxylation has been studied by infrared emission spectroscopy. The spectra are related to the structure of the materials and the changes in the structure upon thermal treatment. The infrared absorption spectrum of In(OH)3 is characterised by an intense OH deformation band at 1150 cm-1 and two O-H stretching bands at 3107 and 3221 cm-1. In the infrared emission spectra, the hydroxyl-stretching and hydroxyl-bending bands diminish dramatically upon heating, and no intensity remains after 200 °C. However, new low intensity bands are found in the OH deformation region at 915 cm-1 and in OH stretching region at 3437 cm-1. These bands are attributed to the vibrations of newly formed InOH bonds because of the release and transfer of protons during calcination of the nanomaterial. The use of infrared emission spectroscopy enables the low-temperature phase transition brought about through dehydration of In(OH)3 nanocubes to be studied.

Infrared and infrared emission spectroscopy of nesquehonite Mg(OH)(HCO3)•2H2O : implications for the formula of nesquehonite

The mineral nesquehonite Mg(OH)(HCO3)•2H2O has been analysed by a combination of infrared (IR) and infrared emission spectroscopy (IES). Both techniques show OH vibrations, both stretching and deformation modes. IES proves the OH units are stable up to 450°C. The strong IR band at 934 cm-1 is evidence for MgOH deformation modes supporting the concept of HCO3- units in the molecular structure. Infrared bands at 1027, 1052 and 1098 cm-1 are attributed to the symmetric stretching modes of HCO3- and CO32- units. Infrared bands at 1419, 1439, 1511, and 1528 cm-1 are assigned to the antisymmetric stretching modes of CO32- and HCO3- units. IES supported by thermoanalytical results defines the thermal stability of nesquehonite IES defines the changes in the molecular structure of nesquehonite with temperature. The results of IR and IES supports the concept that the formula of nesquehonite is better defined as Mg(OH)(HCO3)•2H2O.

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.

Thermal analysis and infrared emission spectroscopic study of kaolinite-potassium acetate intercalate complex

The thermal behavior and decomposition of kaolinite-potassium acetate intercalation complex was investigated through a combination of thermogravimetric analysis and infrared emission spectroscopy. Three main changes were observed at 48, 280, 323 and 460 °C which were attributed to (a) the loss of adsorbed water (b) loss of the water coordinated to acetate ion in the layer of kaolinite (c) loss of potassium acetate in the complex and (d) water through dehydroxylation. It is proposed that the KAc intercalation complex is stability except heating at above 300 °C. The infrared emission spectra clearly show the decomposition and dehydroxylation of the kaolinite intercalation complex when the temperature is raised. The dehydration of the intercalation complex is followed by the loss of intensity of the stretching vibration bands at region 3600-3200 cm-1. Dehydroxylation is followed by the decrease in intensity in the bands between 3695 and 3620 cm-1. Dehydration is completed by 400 °C and partial dehydroxylation by 650 °C. The inner hydroxyl group remained until around 700 °C.

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.

Krylov subspace approximations for the exponential Euler method: Error estimates and the harmonic Ritz approximant

We study Krylov subspace methods for approximating the matrix-function vector product φ(tA)b where φ(z) = [exp(z) - 1]/z. This product arises in the numerical integration of large stiff systems of differential equations by the Exponential Euler Method, where A is the Jacobian matrix of the system. Recently, this method has found application in the simulation of transport phenomena in porous media within mathematical models of wood drying and groundwater flow. We develop an a posteriori upper bound on the Krylov subspace approximation error and provide a new interpretation of a previously published error estimate. This leads to an alternative Krylov approximation to φ(tA)b, the so-called Harmonic Ritz approximant, which we find does not exhibit oscillatory behaviour of the residual error.

Mean and variance of estimates of the bispectrum of a harmonic random process-an analysis including leakage effects

Analytical expressions are derived for the mean and variance, of estimates of the bispectrum of a real-time series assuming a cosinusoidal model. The effects of spectral leakage, inherent in discrete Fourier transform operation when the modes present in the signal have a nonintegral number of wavelengths in the record, are included in the analysis. A single phase-coupled triad of modes can cause the bispectrum to have a nonzero mean value over the entire region of computation owing to leakage. The variance of bispectral estimates in the presence of leakage has contributions from individual modes and from triads of phase-coupled modes. Time-domain windowing reduces the leakage. The theoretical expressions for the mean and variance of bispectral estimates are derived in terms of a function dependent on an arbitrary symmetric time-domain window applied to the record. the number of data, and the statistics of the phase coupling among triads of modes. The theoretical results are verified by numerical simulations for simple test cases and applied to laboratory data to examine phase coupling in a hypothesis testing framework

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.

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.

Aircraft emission reduction through multi-disciplinary flight path optimisation

There is worldwide interest in reducing aircraft emissions. The difficulty of reducing emissions including water vapour, carbon dioxide (CO2) and oxides of nitrogen (NOx) is mainly due from the fact that a commercial aircraft is usually designed for a particular optimal cruise altitude but may be requested or required to operate and deviate at different altitude and speeds to archive a desired or commanded flight plan, resulting in increased emissions. This is a multi- disciplinary problem with multiple trade-offs such as optimising engine efficiency, minimising fuel burnt, minimise emissions while maintaining aircraft separation and air safety. This project presents the coupling of an advanced optimisation technique with mathematical models and algorithms for aircraft emission reduction through flight optimisation. Numerical results show that the method is able to capture a set of useful trade-offs between aircraft range and NOx, and mission fuel consumption and NOx. In addition, alternative cruise operating conditions including Mach and altitude that produce minimum NOx and CO2 (minimum mission fuel weight) are suggested.

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.