In-process noise inspection system for product fault detection in a loud shop-floor environment

Abstract

Abnormal noise originating from within faulty products often irritates customers, which may lead to expensive warranty claims. Therefore, it is important to identify these faulty products proactively in the manufacturing process. However, noise detection in a loud shop-floor is not straightforward because inspection in an anechoic chamber is very costly, and some prerequisites for conventional noise reduction and source separation methods, such as stationary and independent signals and prior knowledge about the signal of interest, are sometimes not feasible in practice. Therefore, we developed an in-process noise inspection system that supports dual-channel acoustic data collection during the inspection process. By using two different groups of acoustic signals, abnormal sound separation and noise detection are made possible through three main steps: in-process background noise training, abnormal noise separation, and significance evaluation. The efficiency of the proposed procedure is demonstrated with two case studies: car door trim panels and dual-channel sound generator and collector. © 1963-2012 IEEE.