Adaptive filtering of reverberation for active sonar signal detection

The extremely high absorption of energy of electromagnetic waves in underwater environments restricts the range of signals to be used to acoustic signals. In addition the sea is a complex medium in which many kinds of environmental changes, mul­tipath propagation phenomenon, masking of the signals of interest by noise and/or reverberation signals, and attenuation, among others, will affect the propagation of sound through it. On one hand, environmental changes will cause different degrees of nonstationarity at the signals to be processed. On the other hand, the use of acoustic waves will imply that, for the active sonar case, different Doppler shifts of the signals to track will take place as the relative radial velocity of the sonar platform to the contact varies. This will cause that in some instances the contact signals share not only time, but also frequency bins with the noise and/or the reverberation signals. For the noise-limited case, an optimum solution for signal detection based on the correlation receiver or Matched-filter, exists. However, for reverberation-limited environments there is not any optimum solution which is feasible to be implemented in a practical system. Adaptive filters grew out of the demand of systems capable of operating in uncertain, time-varying environments. Due to the wide range of applications for which they have shown to be useful, considerable amount of work has been dedicated during the last few years to their development. The preliminary part of the thesis presents a basic model of the underwater environment for the active sonar case upon which the suitability of certain adaptive structures for active echo detection and rang­ing is initially based. A classification and the description of some existing adaptive systems and their main characteristics are presented too. Subsequent parts of the thesis include the theoretical development of a generic adaptive algorithm which will operate with complex data sequences. Several sets of experiments are carried out and the results presented in order to investigate the suitability for the application of interest of several adaptive systems and algorithms. Adaptive processing the received signals as presented here must be understood as a preprocessing stage of the overall active sound navigation and ranging (sonar) problem. The study is restricted to the narrowband case

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