In this paper,the sound intensity and airborne source quantification methods were used to estimate the airborne noise radiating from ag asoline direction injection engine and the relative contributions of each of the engine's components to such airborne noise. In order to estimate noise contribution using airborne source quantification, the sound volume velocity for the source should be estimated. In previous methods, the sound volume velocity wasestimated using the inverse method, whereas in this paper,the sound volume velocity is estimated directly. Forthis work, the sources of noise radiating from thegasoline direction injection engine were first identified by measuring the sound intensity of the noise sources while the engine wasrunning idle. The sound volume velocities of these sources were estimated from the product of the area of that noise source and the directly measured particle velocity.Inorder to validate this method, twospeakers with different frequencies were installed in an anechoic chamber and the contribution of each source wase stimated. Finally,t his proposed method wasa pplied to the identification and contribution of noise sources in agasoline direction injection engine.
Abnormal operating sounds radiated from a moving transfer robot in LCD (liquid crystal display) product lines have been used for the fault detection line of a robot instead of other source signals such as vibrations, acoustic emissions, and electrical signals. Its advantage as a source signal makes it possible to monitor the status of multiple faults by using only a microphone, despite a relatively low sensitivity. The wavelet packet transform for feature extraction and the artificial neural network for fault classification are employed. It can be observed that the abnormal operating sound is sufficiently useful as a source signal for the fault diagnosis of mechanical components as well as other source signals. † Corresponding
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