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Our research is for the development of a low-cost system providing real-time monitoring of critical welds using ultrasonic inspection. Also, ultrasonic inspection techniques are portable and effective in detecting mechanical defects, unlike the standard weld quality inspector’s wand. The detection time for thin (fraction of a millimeter thickness) weld defects is much shorter than that of the standard method. Prior to this work, several attempts to apply the ultrasonic principles to detection of speckled welds by attaching transducers on the tool, as shown in U.S. Pat. No. 5,408,209, failed to describe a dependable system. In this work, we present a novel, compact ultrasonic sensing system for hand-held use which encompasses the three key elements of sensor design, augmented by innovative signal conditioning and analysis algorithms. Key aspects of the ultrasonic sensing system design are the three prismatic transducers with individual sensors, which are mounted on the tool. The use of electronically controlled time-delay circuits and custom microcontroller algorithms enables the sensor/tool to detect defects efficiently. The transducer sensors must withstand the rigours of manufacturing and use and provide a stable, high-frequency signal while being close to the surface of the material being inspected. The digital signal processing (DSP) algorithms which are designed for real-time defect detection, are based on FFT. Key signal conditioning and analysis algorithms are then employed to enhance the performance, particularly in the case of speckled welds, which have an extended, due to the auto-correlation, baseline and inhibition of dynamic response. Further improvements in the DSP signal hardening are achieved by the notion of ‘time domain’ and ‘frequency domain’ since speckled welds have a strong effect on the time domain as demonstrated by experimental results. Further enhancements of the usability of the system is achieved through the development of a model-based adaptive detection algorithm, which is based on the notion of one-dimensional representations rather than the set of matrices used in the standard detection algorithm. To employ the model-adaptive algorithm, we have developed and validated on-line a statistical pattern recognition algorithm based on the notion of prevalence. The detection performance of the system is compared against the two standard detection algorithms: the standard FFT and peak detection. A specific set of laboratory test data is used for the analysis. d2c66b5586