An Optimized Neuro Fuzzy System For Predictive Maintenance For Smart Manufacturing Systems

Abstract

Smart Manufacturing Systems Represent The Modern Evolution Of Industrial Automation Where Machines, Sensors, Communication Technologies, And Intelligent Algorithms Work Together To Improve Productivity And Operational Efficiency. Industries Are Increasingly Adopting Predictive Maintenance Techniques To Reduce Machine Downtime, Improve Reliability, And Minimize Maintenance Costs. Traditional Maintenance Approaches Such As Corrective Maintenance And Preventive Maintenance Often Fail To Provide Accurate Predictions Regarding Machine Failures. Corrective Maintenance Acts Only After A Failure Occurs, While Preventive Maintenance Follows Fixed Schedules That May Lead To Unnecessary Servicing. To Overcome These Limitations, Intelligent Predictive Maintenance Systems Based On Data Analytics And Machine Learning. This Paper Presents A Hybrid Neuro Fuzzy Inference Systems (ANFIS) Model For Automated Fault Prediction For Smart Manufacturing Systems Which Aim Predictive Maintenance. The Proposed Model Improves Upon The Error Performance Of Existing Work In The Domain

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