CPS-based Automation Model Prediction for Inspection

Traditional maintenance is incapable for mechanical solving and equipment failures in order-to meet production needs. The complexity of machine operation, isolating the problem, the nature of the damage and variables in the production process are affect to the maintenance operation. Even though factory used preventive maintenance, the same problems still occurred. The objective of this research aims to develop a machine inspection system as an automated-cyber-physical system. The results of the study indicated that machinery and equipment in the manufacturing industry is headed towards industry 4.0. Maintenance of machinery and equipment in the production process in this era must be adjusted accordingly. The cyber-physical nature of modern maintenance is one of the key to the success of Smart Maintenance based on machine learning methods. It used for collecting, integrating and analyzing data and photos related to the symptoms or environment of machines and equipment in the production process to automate inspections that can be decided, display the condition of the machine with precision and reliability. The research also shows the synergies between physical and cyber areas to guide their application to other machines.