Design and Implementation of Machine Tool Life Inspection System Based on Sound Sensing

Abstract

The main causes of damage to industrial machinery are aging, corrosion, and the wear of parts, which affect the accuracy of machinery and product precision. Identifying problems early and predicting the life cycle of a machine for early maintenance can avoid costly plant failures. Compared with other sensing and monitoring instruments, sound sensors are inexpensive, portable, and have less computational data. This paper proposed a machine tool life cycle model with noise reduction. The life cycle model uses Mel-Frequency Cepstral Coefficients (MFCC) to extract audio features. A Deep Neural Network (DNN) is used to understand the relationship between audio features and life cycle, and then determine the audio signal corresponding to the aging degree. The noise reduction model simulates the actual environment by adding noise and extracts features by Power Normalized Cepstral Coefficients (PNCC), and designs Mask as the DNN’s learning target to eliminate the effect of noise. The effect of the denoising model is improved by 6.8% under Short-Time Objective Intelligibility (STOI). There is a 3.9% improvement under Perceptual Evaluation of Speech Quality (PESQ). The life cycle model accuracy before denoising is 76%. After adding the noise reduction system, the accuracy of the life cycle model is increased to 80%.

Development of Noncontact Microdistance-measurement Devices Based on Giant Magnetoresistance Sensors

“…Intelligent machine tools consist of measuring devices that monitor the real-time processing status through the Internet of Things. (1,2) The measuring devices are sensors for converting the detected physical quantity or phenomenon into an interpretable voltage or current signal. Since the magnetic field strength or direction changes due to strain, motion, rotation, or electric current can be measured without physical contact with the objects, magnetic sensors have been used in many industrial and navigation control systems.…”