An AI-based Prediction-as-a-Service Model for Estimating Machine Bearing Health Status in Industry 4.0 5G Applications

Abstract

Intelligent Machine Condition Monitoring (MCM) and Prediction for machine bearings is very important for efficient Industrial 5G applications. Common fault diagnosis and other classification methods usually extract time domain and frequency features or try to decrease noise from raw time sensory data. Afterwards, features are sought in time domain and statistical classifiers can be applied do the diagnosis. However, these methods suffer from expertise of feature selection and robustness in real time condition monitoring. In this paper, we present a prediction-as-a-service model for estimating machine bearing health status in industry 4.0 5G applications based on Deep Neural Networks (DNN). The proposed model constructs 3D grayscale images from raw time series data and performs prediction more efficiently. The paper also presents testing and evaluation of the model’s prediction and categorization capacity.