A modified kohonen self-organizing map (KSOM) clustering for four categorical data

The Kohonen Self-Organizing Map (KSOM) is one of the Neural Network unsupervised learning algorithms. This algorithm is used in solving problems in various areas, especially in clustering complex data sets. Despite its advantages, the KSOM algorithm has a few drawbacks; such as overlapped cluster and non-linear separable problems. Therefore, this paper proposes a modified KSOM that inspired from pheromone approach in Ant Colony Optimization. The modification is focusing on the distance calculation amongst objects. The proposed algorithm has been tested on four real categorical data that are obtained from UCI machine learning repository; Iris, Seeds, Glass and Wisconsin Breast Cancer Database. From the results, it shows that the modified KSOM has produced accurate clustering result and all clusters can clearly be identified

Classification of damage in structural systems using time series analysis and supervised and unsupervised pattern recognition techniques

Peer reviewedPostprin

Neural Network Fatigue Life Prediction in Notched Aluminum Specimens from Acoustic Emission Data

This purpose of this research was to identify fatigue crack growth and predict failure for 7075-T6 aluminum notched bars under uniaxial tensile loading using acoustic emission (AE) data. The experiments performed in this study extend the results obtained by previous researchers who used maximum cyclic loads of 4,000, 3,000, and 2,000 pounds at a stress ratio of R = 0.0 and a frequency of 1 Hz to perform the fatigue tests. For this research the cyclic load remained at 2,000 pounds, but an additional ten specimens were tested in order to increase the amount of AE data available to the backpropagation neural network (BPNN) for prediction of cyclic life to failure. In addition, the AE data obtained from cyclic testing were filtered and successfully classified using a Kohonen self-organizing map (SOM) to identify the plane stress and plane strain failure mode data. Furthermore, the early cycle (\u3c 25% of fatigue life) AE amplitude distribution data from the test samples were used to predict fatigue lives using the BPNN. The increased AE data from the ten new specimens allowed the neural network to predict fatigue lives on ten total samples with a worst case error of -9.39%. The prediction results are presented along with comparisons to the previous research. Thus, neural network analysis of acoustic emission data provided both accurate fatigue life prediction and classification of the failure mechanisms involved

Filtering of Acoustic Emission Data Through Principal Frequency Component Extraction

Rapid editing of acoustic emission (AE) data is required in order to make real-time acoustic emission flaw growth systems a viable testing method for materials and setups that contain noisy signals. It was hypothesized that extracting major frequency components from the acoustic emission signal would therefore provide a representative acoustic signature of the major waveforms occurring due to defect growth This research has verified that the aforementioned filtering technique does, in fact, extract a representative signal from the composite and metal specimens utilized herein These findings were verified both through visual analysis of the data as well as the low error occurrence in backpropagation neural network predictions and good classification in self-organizing map type neural networks applied to the testing data

The application of artificial neural networks to interpret acoustic emissions from submerged arc welding

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Automated fusion welding processes play a fundamental role in modern manufacturing industries. The proliferation of joint geometries together with the large permutation of associated process variable configurations has given rise to research into complex system modelling and control strategies. Many of these techniques have involved monitoring of not only the electrical characteristics of the process but visual and acoustic information. Acoustic information derived from certain welding processes is well documented as it is an established fact that skilled manual welders utilise such information as an aid to creating an optimum weld. The experimental investigation presented in this thesis is dedicated to the feasibility of monitoring airborne acoustic emissions of Submerged Arc Welding (SAW) for diagnostic and real time control purposes. The experimental method adopted for this research takes a cybernetic approach to data processing and interpretation in an attempt to replicate the robustness of human biological functions. A custom designed audio hardware system was used to analyse signals obtained from bead on mild steel plate fusion welds. Time and frequency domains were used in an attempt to establish salient characteristics or identify the signatures associated with changes of the process variables. The featured parameters were voltage / current and weld travel speed, due to their ease of validation. However, consideration has also been given to weld defect prediction due to process instabilities. As the data proved to be highly correlated and erratic when subjected to off line statistical analysis, extensive investigation was given to the application of artificial neural networks to signal processing and real time control scenarios. As a consequence, a dedicated neural based software system was developed, utilising supervised and unsupervised neural techniques to monitor the process. The research was aimed at proving the feasibility of monitoring the electrical process parameters and stability of the welding process in real time. It was shown to be possible, by the exploitation of artificial neural networks, to generate a number of monitoring parameters indicative of the welding process state. The limitations of the present neural method and proposed developments are discussed, together with an overview of applied neural network technology and its impact on artificial intelligence and robotic control. Further developments are considered together with recommendations for future areas of research

Mechanics and Mechanisms of Fracture for an Eastern Spruce Subject to Transverse Loading Using Acoustic Emission

Due to its excellent structural qualities and accessibility, wood is among the most often utilized structural materials. Despite its ubiquity, wood poses numerous challenges. It is heterogeneous and anisotropic. It has a complex hierarchical ultrastructure, and the properties can have wide variation within a species, and indeed within an individual tree. This work aims to improve our understanding of the strength and fracture behavior of spruce-pine-fir (south) (SPFs), particularly in cross-grain direction. This study’s primary goal is to examine the relationship between crack propagation and cross grain morphology under the following loading configurations: compact tension, compression, and rolling shear. The broader goal is to be able to use this information to improve our ability to predict the performance of mass timber structures. In order to better characterize micromechanical processes and damage progression, acoustic emission (AE) techniques were applied. In this investigation, fracture in compact tension specimens was characterized by both R-curve and bulk fracture energy approaches. Our results show that the fracture follows a distinct route that deviates from the initial crack direction depending on the end-grain angles. This deviation is driven by a competition between maximum strain energy release rate and minimum crack resistance. For crack propagation in the tangential direction, cracks are confined to an earlywood region, which corresponds to the direction of least resistance. This pattern continues even as the end-grain shifts until an angle of about 40°, when the crack begins to jump across earlywood/latewood rings. At roughly 45°, the crack path shifts to a strictly radial direction, corresponding to a path of least resistance. In order to further quantify different micromechanical mechanisms, acoustic emission monitoring was used to track the propagation of damage. To identify different damage sources, an artificialneural network (ANN) technique was used to detect, classify, and quantify the AE energy sources. Results showed that earlywood cell wall tearing, dominant at 0°, produced higher energy release than cell wall separation, which dominates 90°crack propagation. Fiber bridging was also identified as another damage mechanism that occurs in the later stages of the crack growth, but in cross-grain fracture, it produces minimal AE energy. The same ANN approach was used to identify the damage mechanisms in specimens under rolling shear. Cross-laminated timber’s (CLT) mechanical performance is greatly influenced byrolling shear characteristics. In this work, the impact of end-grain orientation on rolling shear strength and modulus was evaluated. AE signal classification was applied to separate the associated damage modes and to determine the AE energy sources. Macroscopically, damage typically initiates along the glue line, but further crack growth is highly dependent on end grain morphology. Specimens with end-grain parallel to the axis of shear showed tangential propagation along an earlywood line, but as the dominant grain angle changes, cracks jump across growth rings, or if the angle is high enough, shift to a radial direction. AE results showed cell wall tearing to be the dominant energy dissipation mechanism, but cell wall peeling and bridging have significant contributions at higher end-grain angles. Through this research, we are better able to link damage sources to particular micro mechanical energy dissipation. This information is in a suitable form for inclusion in computational models that can be used to simulate structural performance as a function of material morphology

Acoustic Emission Signal Denoising of Bridge Structures using SOM Neural Network Machine Learning

Identification Noise signal is one of the challenging problems in the health monitoring of bridge structure using acoustic emission monitoring and identification technology. Hardware filtering technology and spatial identification technologies are the most common method in identifying of the signals from the defect of the bridge, which have great limitations due to the presence of environmental noise. Therefore, this paper focus on the AE noise signal from a bridge in operation state and other specific loading state, which is diagnosed in the hardware filtering technology, spatial identification and SOM neural network, to obtain the new noise recognition methods. It is found that the first two methods can indeed filter the noise signal, but the filtering rate can only reach about 50 %, and can barely filter strong noise signal. The SOM neural network had strong self-recognition ability. The classification accuracy of simulated AE signals is 90 % and 100 % respectively. The trained network is used to test183 sample signals, the defect signal detection accuracy reaches 76 % and 78.8 %, therefore, the noise signal filtering effect is significantly improved

Data mining using intelligent systems : an optimized weighted fuzzy decision tree approach

Data mining can be said to have the aim to analyze the observational datasets to find relationships and to present the data in ways that are both understandable and useful. In this thesis, some existing intelligent systems techniques such as Self-Organizing Map, Fuzzy C-means and decision tree are used to analyze several datasets. The techniques are used to provide flexible information processing capability for handling real-life situations. This thesis is concerned with the design, implementation, testing and application of these techniques to those datasets. The thesis also introduces a hybrid intelligent systems technique: Optimized Weighted Fuzzy Decision Tree (OWFDT) with the aim of improving Fuzzy Decision Trees (FDT) and solving practical problems. This thesis first proposes an optimized weighted fuzzy decision tree, incorporating the introduction of Fuzzy C-Means to fuzzify the input instances but keeping the expected labels crisp. This leads to a different output layer activation function and weight connection in the neural network (NN) structure obtained by mapping the FDT to the NN. A momentum term was also introduced into the learning process to train the weight connections to avoid oscillation or divergence. A new reasoning mechanism has been also proposed to combine the constructed tree with those weights which had been optimized in the learning process. This thesis also makes a comparison between the OWFDT and two benchmark algorithms, Fuzzy ID3 and weighted FDT. SIx datasets ranging from material science to medical and civil engineering were introduced as case study applications. These datasets involve classification of composite material failure mechanism, classification of electrocorticography (ECoG)/Electroencephalogram (EEG) signals, eye bacteria prediction and wave overtopping prediction. Different intelligent systems techniques were used to cluster the patterns and predict the classes although OWFDT was used to design classifiers for all the datasets. In the material dataset, Self-Organizing Map and Fuzzy C-Means were used to cluster the acoustic event signals and classify those events to different failure mechanism, after the classification, OWFDT was introduced to design a classifier in an attempt to classify acoustic event signals. For the eye bacteria dataset, we use the bagging technique to improve the classification accuracy of Multilayer Perceptrons and Decision Trees. Bootstrap aggregating (bagging) to Decision Tree also helped to select those most important sensors (features) so that the dimension of the data could be reduced. Those features which were most important were used to grow the OWFDT and the curse of dimensionality problem could be solved using this approach. The last dataset, which is concerned with wave overtopping, was used to benchmark OWFDT with some other Intelligent Systems techniques, such as Adaptive Neuro-Fuzzy Inference System (ANFIS), Evolving Fuzzy Neural Network (EFuNN), Genetic Neural Mathematical Method (GNMM) and Fuzzy ARTMAP. Through analyzing these datasets using these Intelligent Systems Techniques, it has been shown that patterns and classes can be found or can be classified through combining those techniques together. OWFDT has also demonstrated its efficiency and effectiveness as compared with a conventional fuzzy Decision Tree and weighted fuzzy Decision Tree