Recorded Acoustic Emission Signals Research Articles

Experimental Characterization of Quenching Bath Contamination Using Acoustic Emission

This paper presents a quenching bath contamination characterization method using non-destructive acoustic emission (AE) testing. A 4-channel continuous recording AE setup with container side-mounted AE piezoelectric sensors is proposed. Cylindrical steel specimens are quenched in tap water, deionized water, 5% salt solution or 5% detergent solution. The AE peak amplitude over time characteristics are recorded and analysed. A referential signal polynomial fit with a specific confidence interval is established based on the model fit statistics. A comparison of the recorded AE signals and the referential fit is presented. The quenching bath property deviations are compared using outlying AE peak amplitude signal packets and a maximum out of bounds acceptance level is determined. Specimen microstructural analysis is performed in order to evaluate the correlation of microstructure and AE characteristics. The experimental results confirm the possibility of detecting quenching bath contamination or property deviations with regards to a predetermined referential AE signal, thus paving the way for possible industrial application.

Acoustic Emission Based on Cluster and Sentry Function to Monitor Tensile Progressive Damage of Carbon Fiber Woven Composites

Understanding the tensile failure mechanisms in carbon fiber woven composites based on the acoustic emission (AE) technique is a challenging task. In this study, the mechanical behaviors of composites were studied under uniaxial tensile loading. Meanwhile, the internal damage evolution process in composites was monitored by AE and the recorded AE signals were analyzed. To achieve the dominant damage mechanisms in composites, five AE parameters such as rise time, duration, energy, peak amplitude, and frequency were selected for cluster analysis by a k-means algorithm. The results show that AE signals can be divided into three clusters based on microscopic observations and frequency range. The three clusters correspond to three kinds of damage modes such as matrix cracking, fiber/matrix debonding, and fiber breakage. In addition, the sentry function (SF) was adopted to investigate AE signals originated from the internal damage evolution in composites. It was found that the drop in the SF curve corresponds to the serious damage of the composites.

Tribological behaviour diagnostic and fault detection of mechanical seals based on acoustic emission measurements

Acoustic emission (AE) has been studied for monitoring the condition of mechanical seals by many researchers, however to the best knowledge of the authors, typical fault cases and their effects on tribological behaviour of mechanical seals have not yet been successfully investigated. In this paper, AE signatures from common faults of mechanical seals are studied in association with tribological behaviour of sealing gap to develop more reliable condition monitoring approaches. A purpose-built test rig was employed for recording AE signals from the mechanical seals under healthy and faulty conditions. The collected data was then processed using time domain and frequency domain analysis methods. The study has shown that AE signal parameters: root mean squared (RMS) along with AE spectrum, allows fault conditions including dry running, spring out and defective seal faces to be diagnosed under a wide range of operating conditions. However, when mechanical seals operate around their transition point, conventional signal processing methods may not allow a clear separation of the fault conditions from the healthy baseline. Therefore an auto-regressive (AR) model has been developed on recorded AE signals to classify different fault conditions of mechanical seals and satisfactory results have been perceived.

Acoustic emission characterization of microcracking in laboratory-scale hydraulic fracturing tests

Understanding microcracking near coalesced fracture generation is critically important for hydrocarbon and geothermal reservoir characterization as well as damage evaluation in civil engineering structures. Dense and sometimes random microcracking near coalesced fracture formation alters the mechanical properties of the nearby virgin material. Individual microcrack characterization is also significant in quantifying the material changes near the fracture faces (i.e. damage). Acoustic emission (AE) monitoring and analysis provide unique information regarding the microcracking process temporally, and information concerning the source characterization of individual microcracks can be extracted. In this context, laboratory hydraulic fracture tests were carried out while monitoring the AEs from several piezoelectric transducers. In-depth post-processing of the AE event data was performed for the purpose of understanding the individual source mechanisms. Several source characterization techniques including moment tensor inversion, event parametric analysis, and volumetric deformation analysis were adopted. Post-test fracture characterization through coring, slicing and micro-computed tomographic imaging was performed to determine the coalesced fracture location and structure. Distinct differences in fracture characteristics were found spatially in relation to the openhole injection interval. Individual microcrack AE analysis showed substantial energy reduction emanating spatially from the injection interval. It was quantitatively observed that the recorded AE signals provided sufficient information to generalize the damage radiating spatially away from the injection wellbore.

A probabilistic framework for single-sensor acoustic emission source localization in thin metallic plates

Tracking edge-reflected acoustic emission (AE) waves can allow the localization of their sources. Specifically, in bounded isotropic plate structures, only one sensor may be used to perform these source localizations. The primary goal of this paper is to develop a three-step probabilistic framework to quantify the uncertainties associated with such single-sensor localizations. According to this framework, a probabilistic approach is first used to estimate the direct distances between AE sources and the sensor. Then, an analytical model is used to reconstruct the envelope of edge-reflected AE signals based on the source-to-sensor distance estimations and their first arrivals. Finally, the correlation between the probabilistically reconstructed envelopes and recorded AE signals are used to estimate confidence contours for the location of AE sources. To validate the proposed framework, Hsu-Nielsen pencil lead break (PLB) tests were performed on the surface as well as the edges of an aluminum plate. The localization results show that the estimated confidence contours surround the actual source locations. In addition, the performance of the framework was tested in a noisy environment simulated by two dummy transducers and an arbitrary wave generator. The results show that in low-noise environments, the shape and size of the confidence contours depend on the sources and their locations. However, at highly noisy environments, the size of the confidence contours monotonically increases with the noise floor. Such probabilistic results suggest that the proposed probabilistic framework could thus provide more comprehensive information regarding the location of AE sources.

A new iterative near-field coherent subspace method for rub-impact fault localization using AE technique

Acoustic emission (AE) localization is an important method to detect defects in bearing of rotatory machine for faults maintenance. However, only the faults near the sensor array can be detected due to severe attenuation in the recorded AE signals. Therefore, we propose a highly reliable new Iterative near-field Coherent subspace method (IN-CSM) for multiple rub-impact faults localization. The proposed approach contains four improved processes: Modal plate wave theory (MPWT) analysis for the multi-modes decomposition and group velocity revision; Discrete wavelet transform (DWT) for the useful narrow band extraction; Near field Multiple signal classification (N-MUSIC) method for the preliminary position estimations; the IN-CSM algorithm for the multiple coherent sources separation and the precise localizations. The simulations based on N-MUSIC and IN-CSM methods were compared by rubbing teston the test rig of rotation machinery. The results indicate that the proposed method can effectively locate multiple coherent rubbing faults at once. Thus, it is an effective analysis tool for rub-impact fault detection.

Acoustic emission detection of fatigue cracks in wind turbine blades based on blind deconvolution separation

AbstractThe occurrence and expansion of fatigue cracks in large wind turbine blades may lead to catastrophic blade failure. Each fatigue phase of a material has been associated with a typical set of acoustic emission (AE) signal frequency components, providing a logical base for establishing a clear connection between AE signals and the fatigue condition of a material. The relevance of efforts to relate recorded AE signals to a material's mechanical behaviour relies heavily on accurate AE signal processing. The main objective of the present study is to establish a direct correlation between the fatigue condition of a material and recorded AE signals. We introduce the blind deconvolution separation (BDS) approach because the result of AE monitoring is usually a convoluted mixture of signals from multiple sources. The method is implemented on data acquired from a fatigue test rig employing a wind turbine blade with an artificial transverse crack seeded in the surface at the base of the blade. Two different sets of fatigue loading were conducted. The convoluted signals are collected from the AE acquisition system, and the weak crack feature is extracted and analysed based on the BDS algorithm. The study reveals that the application of BDS‐based AE signal analysis is an appropriate approach for distinguishing and interpreting the different fatigue damage states of a wind turbine blade. The novel methodology proposed for fatigue crack identification will allow for improved predictive maintenance strategies for the glass‐epoxy blades of wind turbines. The experimental results clearly demonstrate that the AE signals generated by a fatigue crack on a wind turbine blade can be synchronously separated and identified. Characterizing and assessing fatigue conditions by AE monitoring based on BDS can prevent catastrophic failure and the development of secondary defects, as well as reduce unscheduled downtime and costs. The possibility of using AE monitoring to assess the fatigue condition of fibre composite blades is also considered.

Classification of acoustic emission signals generated from journal bearing at different lubrication conditions based on wavelet analysis in combination with artificial neural network and genetic algorithm

This paper presents the results of acoustic emission (AE) investigation for monitoring lubrication conditions of a journal bearing under various operating conditions. Hydrodynamic Lubrication (HL), Mixed Lubrication (ML), and Boundary Lubrication (BL), are the basic types of the fluid film lubrication. The aim of this investigation is to identify effective frequencies and most useful features of the AE signals for classification of the lubrication types. Continuous wavelet transform (CWT) and time domain signal analysis methods are used for feature extraction of the recorded AE signals. Then, Genetic Algorithms (GAs) in combination with Artificial Neural Networks (ANNs) are applied to select and classify the extracted features. The experimental results showed that the proposed system using AE signal is effective.

On-line Sizing of Pneumatically Conveyed Particles Through Acoustic Emission Detection and Signal Analysis

Accurate measurement of the particle size distribution of pneumatically conveyed pulverized fuel is critical to achieving optimal combustion efficiency and minimum pollutant emissions at coal and biomass-fired power plants. This paper presents a prototype instrumentation system for the on-line continuous measurement of particle size distribution through acoustic emission (AE) detection. The proposed method extracts particle size information from the impulsive AE signals arising from impacts of particles with a metallic waveguide protruding into the flow. The relationship between the particle size and the peak AE voltage is established through mathematical modeling of the particle impact process. Identification of the peak AE voltage is achieved with an energy-based peak detection algorithm. Experimental results obtained with glass beads demonstrate the capability of the system to discriminate particles of different sizes from the recorded AE signals. The system performs better in terms of accuracy for higher speed, lower concentration particles as the impact signals are stronger and better separated in the time domain.

Wear detection by means of wavelet-based acoustic emission analysis

Wear detection and monitoring during operation are complex and difficult tasks especially for materials under sliding conditions. Due to the permanent contact and repetitive motion, the material surface remains during tests non-accessible for optical inspection so that attrition of the contact partners cannot be easily detected. This paper introduces the relevant scientific components of reliable and efficient condition monitoring system for online detection and automated classification of wear phenomena by means of acoustic emission (AE) and advanced signal processing approaches. The related experiments were performed using a tribological system consisting of two martensitic plates, sliding against each other. High sensitive piezoelectric transducer was used to provide the continuous measurement of AE signals. The recorded AE signals were analyzed mainly by time-frequency analysis. A feature extraction module using a novel combination of Short-Time Fourier Transform (STFT) and Continuous Wavelet Transform (CWT) were used for the first time. A detailed correlation analysis between complex signal characteristics and the surface damage resulting from contact fatigue was investigated. Three wear process stages were detected and could be distinguished. To obtain quantitative and detailed information about different wear phases, the AE energy was calculated using STFT and decomposed into a suitable number of frequency levels. The individual energy distribution and the cumulative AE energy of each frequency components were analyzed using CWT. Results show that the behavior of individual frequency component changes when the wear state changes. Here, specific frequency ranges are attributed to the different wear states. The study reveals that the application of the STFT-/CWT-based AE analysis is an appropriate approach to distinguish and to interpret the different damage states occurred during sliding contact. Based on this results a new generation of condition monitoring systems can be build, able to evaluate automatically the surface condition of machine components with sliding surfaces.

Effective Discrimination of Acoustic Emission Source Signals for Structural Health Monitoring

Acoustic emission (AE) is the phenomenon where stress waves are generated due to rapid release of energy within a material caused by sources such as crack initiation or growth. AE technique involves recording the stress waves by means of sensors and subsequent analysis of the recorded signals to gather information about the nature of the source. Though AE technique is one of the popular non destructive evaluation (NDE) techniques for structural health monitoring of mechanical, aerospace and civil structures; several challenges still exist in successful application of this technique. Presence of spurious noise signals can mask genuine damage-related AE signals; hence a major challenge identified is finding ways to discriminate signals from different sources. Analysis of parameters of recorded AE signals, comparison of amplitudes of AE wave modes and investigation of uniqueness of recorded AE signals have been mentioned as possible criteria for source differentiation. This paper reviews common approaches currently in use for source discrimination, particularly focusing on structural health monitoring of civil engineering structural components such as beams; and further investigates the applications of some of these methods by analyzing AE data from laboratory tests.

Detecting hazardous sources of acoustic-emission signals using the estimated energy of clusters

A method for detecting hazardous sources of acoustic-emission (AE) signals by the level of their energy activity using clustering by digitized signal shape was considered. The influence of the distance between a source and an AE transducer on the energy of recorded AE signals was analyzed. The energy distribution median of signals in a cluster was proposed for evaluating the hazard of an AE signal source. The application of the developed method was tested based on the example of the analysis of AE inspection data on the welding of steel specimens with incomplete fusion and a crack at the root of a weld seam. This testing confirms the results of the fractographic inspection of a defective weld seam.

Diesel Engine Valve Clearance Detection Using Acoustic Emission

This paper investigated, using experimental method, the suitability of acoustic emission (AE) technique for the condition monitoring of diesel engine valve faults. The clearance fault was adjusted experimentally in an exhaust valve and successfully detected and diagnosed in a Ford FSD 425 four-cylinder, four-stroke, in-line OHV, direct injection diesel engine. The effect of faulty exhaust valve clearance on engine performance was monitored and the difference between the healthy and faulty engine was observed from the recorded AE signals. The measured results from this technique show that using only time domain and frequency domain analysis of acoustic emission signals can give a superior measure of engine condition. This concludes that acoustic emission is a powerful and reliable method of detection and diagnosis of the faults in diesel engines and this is considered to be a unique approach to condition monitoring of valve performance.

Characterisation of Al corrosion and its impact on the mechanical performance of composite cement wasteforms by the acoustic emission technique

In this study acoustic emission (AE) non-destructive method was used to evaluate the mechanical performance of cementitious wasteforms with encapsulated Al waste. AE waves generated as a result of Al corrosion in small-size blast furnace slag/ordinary Portland cement wasteforms were recorded and analysed. The basic principles of the conventional parameter-based AE approach and signal-based analysis were combined to establish a relationship between recorded AE signals and different interactions between the Al and the encapsulating cement matrix. The AE technique was shown as a potential and valuable tool for a new area of application related to monitoring and inspection of the mechanical stability of cementitious wasteforms with encapsulated metallic wastes such as Al.

Evaluation of Acoustic Emission Sources during Monitoring of Incipient Damage Detection in Rolling Contact Fatigue

The study highlighted the potentiality and effectiveness of Acoustic Emission (AE) signals monitoring along with data clustering analysis as a powerful tool applicable to the rolling elements under examination for incipient damage failure. The AE technique was applied to rollers in contact using rolling contact fatigue test-rig running under constant load and speed for detecting the incipient damage initiation and its damage location. The results demonstrated the successful use of the AE activity monitoring combination with AE source locator and AE data analyzer as a new technique for incipient damage detection. The recorded AE signals from run-to-incipient damage life testing were investigated by unsupervised clustering analysis to examine and to produce numerical validation of the results by separating AE sources data into several classes that reflected the internal structure of the data during contact. A methodology including descriptor selection, methods, procedures for numerical verification and cluster validity criteria were followed.

Damage evolution in center-holed glass/polyester composites under quasi-static loading using time/frequency analysis of acoustic emission monitored waveforms

This work addresses and compares the application of advanced signal processing methodologies developed in two different labs for the analysis of the large number of acoustic emission (AE) data as obtained from the quasi-static tension tests of center-holed glass/polyester (Gl/Pe) composite materials; the event primary frequency technique and the wavelet-based AE analysis.Primary goal of the present study is to investigate the effectiveness of frequency-based methodologies in AE data analysis for damage evolution monitoring. Further on, the monitoring of the damage state and the identification of the different damage mechanisms appearing within the material structure during loading is also intended. Both techniques were applied to analyze the plethora of the recorded AE signals during the tests. The results are thoroughly discussed and an evaluation of the two methods for handling AE transients is attempted.

Integration of the Kohonen's self-organising map and k-means algorithm for the segmentation of the AE data collected during tensile tests on cross-ply composites

The acoustic emission (AE) technique is a useful way for the investigation of local damage in materials. This study deals with the ability of a Kohonen's map to classify recorded AE signals collected during tensile tests on cross-ply glass/epoxy composites in order to monitor the chronology of the damaging process. An unsupervised clustering analysis shows that AE signals are distributed into three clusters. The proposed two-stage procedure is a combination of the Self-Organising Map (SOM) and the k-means methods. In the present work, Kohonen's map is applied as an unsupervised clustering method for the AE signals generated in cross-ply composite specimens during tensile tests. The input vectors of the signal descriptors used in the clustering procedure are calculated from the signal waveforms. The k-means method is then applied on the neurones of the map in order to delimit the clusters and to visualise the topology of the map.

Adhesion behaviour assessment on diamond coated silicon nitride by acoustic emission

Acoustic emission (AE) from cracking events in microwave plasma assisted chemical vapour deposition diamond coated Si 3N 4 ceramics during Brale indentation tests was investigated. The experiments were conducted in a modified macro-indentation technique, that avoids the discrete loading limitation of conventional indentation by using a constant indenter displacement rate. The recorded AE signals, in conjunction with scanning electron microscope post-observations of the cracking pattern, allowed the adhesion behaviour investigation of the diamond film/ceramic system. The AE graphs evidenced that the most intensive acoustic peaks arise when film spalling takes place. The Si 3N 4 surface roughness was used as a variable to confirm the feasibility of the method. A better adhesion performance was achieved for the substrate with a higher roughness. For the diamond coated as-ground substrate the critical load for film detachment is ∼1000 N, while the film adhesion on the coated polished material is weaker. The assessment of the interfacial cracking resistance of the films gives a maximum value of 1372 kN m −1.