Pattern recognition of acoustic emission signal during the mode I fracture mechanisms in carbon- epoxy composite

The aim of the paper is to use Acoustic Emission technique to distinguish the micro/macro failure mechanisms of carbon-epoxy composite laminates during Double Cantilever Beam (DCB) tests. In order to recognize and detect different damage mechanisms, Self-Organizing Map (SOM) method has been used to cluster the AE signals according with the fracture mode that originated them. In addition, most significate Learning vector quantization (LVQ) program has been applied to verify the signals. Five AE features were selected as main parameters: Rise-time, Counts, Energy, Duration and Amplitude. The results highlighted that different signals can be recognized and classified related to their origin. The failure mechanisms detected are Matrix cracking, delamination, and fiber breakage. Scanning Electron Microscopy (SEM) images validate the results. Mathematics data and experimental results confirmed a good converging of AE dat

Damage identification in structural health monitoring: a brief review from its implementation to the Use of data-driven applications

The damage identification process provides relevant information about the current state of a structure under inspection, and it can be approached from two different points of view. The first approach uses data-driven algorithms, which are usually associated with the collection of data using sensors. Data are subsequently processed and analyzed. The second approach uses models to analyze information about the structure. In the latter case, the overall performance of the approach is associated with the accuracy of the model and the information that is used to define it. Although both approaches are widely used, data-driven algorithms are preferred in most cases because they afford the ability to analyze data acquired from sensors and to provide a real-time solution for decision making; however, these approaches involve high-performance processors due to the high computational cost. As a contribution to the researchers working with data-driven algorithms and applications, this work presents a brief review of data-driven algorithms for damage identification in structural health-monitoring applications. This review covers damage detection, localization, classification, extension, and prognosis, as well as the development of smart structures. The literature is systematically reviewed according to the natural steps of a structural health-monitoring system. This review also includes information on the types of sensors used as well as on the development of data-driven algorithms for damage identification.Peer ReviewedPostprint (published version

Damage detection and identification in fiber reinforced plastic structural members and field bridges using acoustic emission technique

With the increased use of fiber reinforced polymer (FRP) based structural systems for rehabilitation of existing and construction of new bridges there is a requirement for identification of critical components of these structural systems and the determination of critical damage thresholds in them. Of the many available non-destructive techniques (NDT), acoustic emission (AE) monitoring had been identified as one of the most popular techniques applicable for damage discrimination in composites. The current study aimed at using patterns in AE data for the identification of damage modes exhibited by composite structural systems. The extensive experimental program involved testing of two structural systems: (i) Reinforced concrete specimens with CFRP retrofit to study debonding failure mechanism and (ii) GFRP laminates coupon specimens tested under varied load conditions to study critical failure modes such as fiber breakage, matrix cracking, delamination and debonding. Real-time AE monitoring was also conducted for a newly installed FRP deck field bridge subjected to live load tests. The AE data collected from the bridge revealed the overall structural performance of the new bridge and helped establish baseline AE activity for future condition evaluation. The AE data acquired from all the experimental tests conducted in this research were subjected two methods of analysis. The first analysis technique involved subjecting the data to the traditional signal processing techniques and identifying various AE sources by visual observations of trends in correlation plots. Meanwhile the same dataset was analyzed using neural networks to perform pattern recognition. In this work, a methodology based on the use of an unsupervised k-means clustering to generate the learning dataset for the training of the multi-layer perceptron (MLP) classifier was developed. The method adopted here showed good results for the clustering and classification of AE signals from different sources for the specimens studied in this research. But, clustering does not always lead to a unique solution and some failure mode characteristics were more easily identifiable than others. Thus further study for enriching of the training dataset is warranted. The high performance efficiency achieved by the developed neural network model for damage identification in full scale specimens further confirms the potential of the developed methodology in being feasible for damage identification in full-scale structures

Machine Learning And A Workflow Engine For An Agent-Based Structural Health Monitoring System

This thesis reports on work in machine learning and high-performance computing for structural heath monitoring. The data used are acoustic emission signals, and we classify these signals according to source mechanisms, those associated with crack growth being particularly significant. The work reported here is part of a larger project to develop an agent-based structural health monitoring system. The agents are proxies for communication- and computation-intensive techniques and respond to the situation at hand by determining an appropriate constellation of techniques. The techniques thus structured are executed by a workflow engine, which is part of the contribution reported here. It is critical that the system have a repertoire of classifiers with different characteristics so that a combination appropriate for the situation at hand can generally be found. The classifiers are trained using machine-learning techniques, and we report on investigations we conducted on three supervised and two unsupervised learning techniques to determine which techniques are the best to use in a particular situation

Optimization of indentification of particle impacts using acoustic emission.

Air-borne or liquid-laden solid particle transport is a common phenomenon in various industrial applications. Solid particles transported at severe operating conditions such as high-flow velocity can cause concerns for structural integrity, through wear originating from particle impacts with the structure. To apply Acoustic Emission (AE) in particle-impact monitoring, previous researchers focused primarily on dry particle impacts on dry target plate and/or wet particle impacts on wet or dry target plate. For dry particle impacts on dry target plate, AE events energy - calculated from the recorded free-falling or air-borne particle impact AE signals - was correlated with particle size, concentration, height, target material and thickness. For a given system, once calibrated for a specific particle type and operating condition, this technique might be sufficient to serve the purpose. However, if more than one particle type is present in the system (particularly with similar size, density and impact velocity), calculated AE event energy is not unique for a specific particle type. For wet particle impacts on dry or wet target plate (either submerged or in a flow loop), AE event energy was related to the particle size, concentration, target material, impact velocity and angle between the nozzle and the target plate. In these studies, the experimental arrangements and operating conditions either did not account for any bubble formation in the system or, if they did, did so at least an order of magnitude lower in amplitude than the sand particle impact. In reality, bubble formation can be comparable with particle impacts in terms of AE amplitude in process industries such as sand production during oil and gas transportation away from a reservoir. Current practice is to calibrate an installed AE monitoring system against a range of sand-free flow conditions. In real-time monitoring for a specific calibrated flow, the flow-generated AE amplitude/energy is deducted from the recorded AE amplitude/energy and the difference is attributed to the sand particle impacts. However, if the flow condition changes, which it often does in the process industry, the calibration is no longer valid and AE events from bubbles can be misinterpreted as sand particle impacts, and vice versa. In this thesis, sand particles and glass beads (with similar size, density and impact velocity) have been studied, dropping from 200 mm using a small cylindrical stepped mild steel coupon as a target plate. For signal recording purposes, two identical broadband AE sensors are installed, one at the centre and one 30 mm off-centred, on the opposite side of the impacting surface. Signal analysis has been carried out by evaluating seven standard AE parameters (amplitude, energy, rise time, duration, power spectral density (PSD), peak frequency at PSD, and spectral centroid) in the time and frequency domain, and time-frequency domain analysis has been performed by applying Gabor Wavelet Transformation. The signal interpretation becomes difficult due to reflections, dispersions and mode conversions caused by close proximity of the boundaries. So, a new signal analysis parameter - frequency-band energy ratio - has been proposed. This technique is able to distinguish between the populations of two very similar (in terms of size, mass and energy) groups of sand particles and glass beads impacting on mild steel, based on the coefficient of variation (Cv) of the frequency-band AE energy ratios. To facilitate individual particle impact identification, further analysis has been performed using a Support Vector Machine (SVM)-based classification algorithm with seven standard AE parameters, evaluated in both the time and frequency domain. The available dataset has been segmented into two parts: training set (80%) and test set (20%). The developed model has been applied on the test data for the purpose of model performance evaluation. The overall success rate in individually identifying each category (PLB, Glass bead and Sand particle impacts) at S1 has been found as 86%, and at S2 as 92%. To study wet particle impacts on a wet target surface in the presence of bubbles, the target plate was sealed to a cylindrical perspex tube. Single and multiple sand particles were introduced in the system using a constant-speed blower, to impact the target surface under water-loading. Two sensor locations, the same as those used in the previous sets of experiments, were monitored. From frequency domain analysis, it has been observed that the characteristic frequencies for particle impacts are centred at 300-350 kHz, and the frequencies for bubble formations are centred at 135-150 kHz. Based upon this, two frequency bands - 100-200 kHz (E1) and 300-400 kHz (E3) - and the frequency-band energy ratio (E3/E1) have been identified as optimal for identifying particle impacts for the given system. E3/E1 > 1 has been associated with particle impacts and E3/E1 < 1 has been associated with bubble formations. By applying these frequency-band energy ratios and setting an amplitude threshold, an automatic event identification technique has been developed for identification of sand particle impacts in presence of bubbles. The method developed can be used to optimize the identification of sand particle impacts. The optimal setting of an amplitude threshold is sensitive to the number of particles and the noise levels. For example, a high threshold of 10% will clearly identify sand particle impacts, but for multiparticle tests the same threshold is unlikely to detect about 20% of lower energy particles. On the other hand, a threshold lower than 3% is likely to result in the detection of AE events with poor frequency content and incorrect classification of the weakest events. The optimal setting of the parameters used in the framework - such as thresholds, frequency bands and ratios of AE energy - is therefore likely to make identification of sand particle impacts in a laboratory environment possible within 10%. An additional advantage of this technique is that calibration of the signal levels is not required, once the optimal frequency bands and ratios have been identified

Fault Detection and Diagnosis of Electric Drives Using Intelligent Machine Learning Approaches

Electric motor condition monitoring can detect anomalies in the motor performance which have the potential to result in unexpected failure and financial loss. This study examines different fault detection and diagnosis approaches in induction motors and is presented in six chapters. First, an anomaly technique or outlier detection is applied to increase the accuracy of detecting broken rotor bars. It is shown how the proposed method can significantly improve network reliability by using one-class classification technique. Then, ensemble-based anomaly detection is utilized to compare different methods in ensemble learning in detection of broken rotor bars. Finally, a deep neural network is developed to extract significant features to be used as input parameters of the network. Deep autoencoder is then employed to build an advanced model to make predictions of broken rotor bars and bearing faults occurring in induction motors with a high accuracy