System integration for predictive process adjustment and cloud computing-based real-time condition monitoring of vibration sensor signals in automated storage and retrieval systems

Abstract As automation and digitalization are being increasingly implemented in industrial applications, manufacturing systems comprising several functions becoming more complex. Consequently, fault analysis (e.g., detection, diagnosis, prediction) has attracted increased research attention. Investigations involving usually performed using real-time, online, or automated techniques for detection alarming. Conversely, recovery of faulty states to their healthy forms is manually under offline conditions. However, the development intelligent requires that appropriate feedback be provided automatically, facilitate faulty-state without need manual operator intervention and/or decision-making. To this end, paper proposes a system integration technique predictive process adjustment determines actions performs them automatically by analyzing relevant sensor signals pertaining current situation unit via cloud computing machine learning. The proposed corresponds an module storage retrieval (ASRS). said integrated collects analyzes temperature vibration product transporter internet-of-things-based programmable logic controller identify ASRS system. Upon states, control program identifies corresponding variables controls recover its previous no-fault state. will automatic prognostics health management complex providing diagnosis feedback.