Diagnostic and Prediction of Machines Health Status as Exemplary Best Practice for Vehicle Production System

Diagnosis and prediction of the health status of vehicle components production line machine is the core requirement for global manufacturing system. With the development of Internet of things (IoT), there are enormous big data of production line could be collected quickly and stored in large quantities. The development of artificial intelligence makes it possible to deal with big data efficiently. Due to the industrial requirement of health self-diagnosis for vehicle production line, this paper presents a method based on Artificial Neural Network (ANN) to diagnose the health status of production line machines using the data produced by the machines. The PID control parameters of motors are segmented to simulate the health status of the machines in a long duration. We use three kinds of artificial neural network (ANN) methods to train the model of the relationship between the large data trend and the diagnostic score of the machine, it is demonstrated that it becomes more efficient than traditional empirical analysis to improve the speed and accuracy for diagnostic and prediction of machines health status.

I. Introduction

If we would like to communicate with the world, the Internet of Things (IoT) is the key and gets information of all “Things” together to the customers, such as industrial robot for Industrial Cyber-Physical Systems (iCPS) applications[1]. With the research and development of vehicle technology and Internet of Things(IoT), this combination of two technology creates many applications and solutions, such as semiautomatic dual drive machine[2], 3D printing process for manufacturing systems[3], Vehicular Adhoc Networks (VANETs) combination with Wireless Sensor Networks (WSNs) and itself[4], vehicular area networks(VANETs) have now transformed to Internet of Vehicles(IoV) paradigm[5], and ceramics production line needs to build an IoT-based energy efficiency management system[6]. At the same time, vehicle technology innovations also promote the development of the Internet of Things and solve many other technology issues. Electric vehicles (EVs) represent the potential innovation that can contribute to mitigating these global challenges such as Climate change and Environment problem[7], the 3-D Space Using Adaptive Control Law With Point-Cloud-Based Limb Regression Approach[8], a new tracker design and its application for 3D environmental mapping in vehicular technology applications[9], it has been designed that medium independent handover based emergency communication scheme using vehicular technology apply to a large-scale disaster in an area[10].