Automatic Inspection of Transmission Devices Using Acoustic Data

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. This paper was motivated by the problem of developing an intelligent evaluation system in place of skilled workers to evaluate the quality of transmission devices automatically based on acoustic data. Most of the prior works in quality evaluation of transmission devices are based on processing vibrometer signals for system vibrations. Such vibrometers must be installed on the surface of vibrating part of transmission devices, which alter the physical integrity. In this paper, the acoustic data of operating transmission devices are recorded. We first compute the ASFTS and FVAVT, where ASFTS denotes the average - - of a serial of spectra calculated from time segments of an acoustic data and FVAVT denotes the feature vector of amplitude variation with time in a certain band. By using the different characteristics of the ASFTS and the FVAVT, we apply the LVQ and the FGA to the quality evaluation of transmission devices, respectively. Utilizing the advantages of the SOM, we classify the defective products successfully. In the industry production, the quality of each batch of products will change according to the situation of equipments. Similarly, the quality evaluation rule will be adjusted according to the yield and the request of customers. The proposed system can evaluate the quality of transmission devices correctly as demanded so long as we change the nondefective and defective samples.