IoT-based data-driven predictive maintenance relying on fuzzy system and artificial neural networks

Abstract

Industry 4.0 technologies need to plan reactive and Preventive Maintenance (PM) strategies for their production lines. This applied research study aims to employ the Predictive Maintenance (PdM) technology with advanced automation technologies to counter all expected maintenance problems. Moreover, the deep learning based AI is employed to interpret the alarming patterns into real faults by which the system minimizes the human based fault recognition errors. The Sensors Information Modeling (SIM) and the Internet of Things (IoT) have the potential to improve the efficiency of industrial production machines maintenance management. This research work provides a better maintenance strategy by utilizing a data-driven predictive maintenance planning framework based on our proposed SIM and IoT technologies. To verify the feasibility of our approach, the proposed framework is applied practically on a corrugated cardboard production factory in real industrial environment. The Fuzzy Logic System (FLS) is utilized to achieve the AI based PM while the Deep Learning (DL) is applied for the alarming and fault diagnosis in case the fault already occured.

Development of Multi-Objective Production Scheduling Fuzzy Algorithm in Manufacturing

“…However, a fuzzy system can be set up in many other ways. A widespread method for setting up a manufacturing machine-related fuzzy system is based on real-time sensor data 22 . The fuzzy system can also mathematically reproduce human decisions.…”

Strategies for overcoming data scarcity, imbalance, and feature selection challenges in machine learning models for predictive maintenance

“…In predictive maintenance, data is collected from the same components or system continuously over time to help under-stand the component's behavior and failure patterns, this way observations collected from the same sensor are temporally dependent 39,40 . For instance, the scenario where a bolt becomes loose within a machine:…”