Nondestructive Evaluation of Radio Frequency Connector Continuity Using Stimulated Emissions

A wireless nondestructive fault detection test for loose or damaged connectors is demonstrated. An architecture known as the conditioned multiclassification of stimulated emissions (CMSE) is pretrained on simulated and empirical radar outputs, and transfer learning is applied to classify connected and disconnected coaxial interconnections. The two main data conditioning methods of this architecture, a statistical signal analysis tool and a convolutional filter bank, are evaluated in order to determine the cost-value proposition of each component. Novel contributions of this technique include the use of two simulation-aided convolutional filter banks to generate a multinetwork ensemble and transfer learning from artificial neural networks trained on two primitive datasets revolving around the electromagnetic phenomena of reflection and filtering. A total of 560 different neural network topologies across four different signal conditioning configurations are considered, with all results compared against the current standard for measurement of cable and connection faults, time-domain reflectometry. Metrics used for comparison are time (training and evaluation), detection (connector engagement at state change detection), and clustering (projection space performance, used as a measure of transfer learning potential). It is determined that the full CMSE architecture performs best, with nearly any neural network topology of this configuration displaying an early detection improvement of 113% and requiring 30% less time to execute an individual classification versus the current standard, all while meeting the most stringent definitions of nondestructive evaluation (NDE).

Измерение расстояния между излучателем и приемником в беспроводном канале связи при помощи сверхширокополосных хаотических радиоимпульсов

The possibility of wireless distance measurement using UWB chaotic radio pulses based on statistical signal analysis is considered. The results of experimental approbation of the proposed approach are given.

AN IMPULSIVE EXCITATION APPROACH FOR FATIGUE STRENGTH PROPERTY CHARACTERISATION ANALYSIS OF POLYMERS

This paper proposes a method by analysing the free vibration behavior of various polymers for fatigue strength property characterisation. Four disc-shaped specimens with different types of polymer were prepared, namely polyethylene, polycarbonate, polyoxymethylene and polyamide. Experimental dynamic tests were carried out based on ASTM E1876 to measure the transient vibration characteristics using an accelerometer positioned on the flat of the disc close to the outer circumference. The specimen has been lightly striked for pulse loading at the center point using an impact hammer. Using the method of Mesokurtosis Zonal Nonparametric (M-Z-N), the instantaneous data point of the selected decaying signals have been analysed and from the obtained results, the correlation between the M-Z-N coefficient and fatigue strength property has been investigated. It is revealed that the proposed statistical signal analysis method can be applied on the transient impulsive free vibration signal for effectively characterise fatigue strength property of polymers.

Comparison of Young's Modulus Property Determination of Metallic Materials under Two Statistical Analysis Methods

This paper presents an alternative statistical signal analysis method to characterise and determine Youngs modulus property of metallic materials. For this characterisation purpose, we propose an alternative method called Integrated Kurtosis-based Algorithm for Z-notch filter (I-kazTM) and Mesokurtosis Zonal Nonparametric (M-Z-N). Impulsive excitation test has been performed according to ASTM E1876 on three metallic materials of medium carbon steel S50C, stainless steel AISI 304 and brass to measure the piezoelectric and acoustic signals. The transient acoustic signal has been analysed using M-Z-N analysis while I-kazTM has been used to analyse the impulsive piezoelectric signal. Correlation expression between Youngs modulus property and the calculated statistical parameters has been discussed and the accuracy of these two methods has been identified using cast iron FCD 500 specimen.