Machinery Value Estimation Method Based on IIoT System Utilizing 1D-CNN Model for Low Sampling Rate Vibration Signals From MEMS

Accurately estimating the value of an equipment is a significant challenge in the industrial environment. Conventional methods mainly considered discounting the value over time, but they are limited in that they do not consider the status of individual equipment. Recent developments in Industrial Internet of Things (IIoT) and AI technologies have opened up the possibility of real-time remote monitoring on the status of machinery, thus providing an opportunity to more accurately estimate the value of machine equipments. In this study, we designed a sensor that can acquire the vibration and magnetic field data of an equipment, with which we proposed a 1-D convolutional neural network that can classify the status of machinery based on the data obtained by the designed sensor. In addition, based on the results of the classification model, the cumulative fatigue of equipment was predicted using Pålmgren–Miner’s linear damage rule, with which we proposed a model for estimating the value of the movable property based on the cumulative fatigue.