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The conventional ultrasonic method for compressed air leak detection utilizes a directivity-based ultrasonic leak detector (DULD) to locate the leak. The location accuracy of this method is low due to the limit of the nominal frequency and the size of the ultrasonic sensor. In order to overcome this deficiency, a method based on time delay estimation (TDE) is proposed. The method utilizes three ultrasonic sensors arranged in an equilateral triangle to simultaneously receive the ultrasound generated by the leak. The leak can be located according to time delays between every two sensor signals. The theoretical accuracy of the method is analyzed, and it is found that the location error increases linearly with delay estimation error and the distance from the leak to the sensor plane, and the location error decreases with the distance between sensors. The average square difference function delay estimator with parabolic fitting is used and two practical techniques are devised to remove the anomalous delay estimates. Experimental results indicate that the location accuracy using the TDE-based ultrasonic leak detector is 6.5-8.3 times as high as that using the DULD. By adopting the proposed method, the leak can be located more accurately and easily, and then the detection efficiency is improved. [ABSTRACT FROM AUTHOR]

An improved denoising algorithm based on wavelet transform modulus maxima (WTMM) is proposed for processing non-intrusive measurement signals in pneumatic systems. Combined with the adaptive BayesShrink threshold and Witkin's scale space tracking theory, the process of tracing the evolution of WTMM over scales has been optimized. Thus, the signal and the noise can be identified and isolated effectively. Additionally, to improve the computational efficiency, a fast method based on a piecewise cubic Hermite interpolating polynomial is applied to reconstruct the signal. Numerical experiments confirm the advantages of the improved denoising algorithm: compared with the classical algorithm of WTMM, the improved denoising algorithm not only increases the signal to noise ratio by over 10% but also reduces the processing time by Over 90%.More generally, the algorithm has better performance than that of several typical algorithms in its denoising quality, adaptability and practicality. [ABSTRACT FROM AUTHOR]