Automatic Inspection of Transmission Devices Using Acoustic Data

摘要

Most factories depend on skilled workers to test the quality of transmission devices by listening to the sound. In this paper, an intelligent inspection system is proposed to evaluate the quality of transmission devices in place of experts. Since the causes of faults of transmission devices are complex and a defective product might simultaneously have many types of faults, the discrimination process between defective and nondefective products and the classification process of defective products are treated separately in the proposed system. From the acoustic data of operating transmission devices, we extract the feature vectors based on time-frequency analysis and train a neuroclassifier by using the learning vector quantization (LVQ). Furthermore, the genetic algorithm (GA) with floating point (FP) is utilized to select some significant frequencies from the spectra of acoustic data of defective and nondefective products and to make a quality evaluation rule automatically. The defective products are picked up from the automatic production line according to the evaluation rule and the trained neuroclassifier. At last, the self-organizing feature map (SOM) algorithm is used to identify the kinds of defective products. The experimental results show that the proposed intelligent system is able to perform the quality evaluation of transmission devices successfully.