Diesel Engine Noise Source Visualization by Using Compressive Sensing Algorithms

To identify sound source locations by using Near-field Acoustical Holography (NAH), a large number of microphone measurements is generally required in order to cover the region and ensure sufficiently high spatial sampling rate: it may require hundreds microphones. As result, such are costly, fact which has limited industrial application NAH locations. However, recently, been shown possible concentrated sources with measurement based on Compressive Sensing theory. In present work, radiation from front face diesel engine was measured one set thirty-five-channel combo-array placed engine. The significant noise were then identified two algorithms: i.e., l1-norm minimization hybrid approach combined Wideband (WBH) minimization. It found that both algorithms can successfully localize visualize major over broad range frequencies, even though relatively small Finally, comments made field reconstruction differences between algorithms.