Comparative analysis of response surface methodology and adaptive neuro-fuzzy inference system for predictive fault detection and optimization in beverage industry

Abstract

Maintenance is crucial for ensuring equipment reliability and minimizing downtime while managing associated costs. This study investigates a data-driven approach to predicting machine faults using Response Surface Methodology (RSM) and Adaptive Neuro-Fuzzy Inference System (ANFIS). RSM was employed to develop a mathematical model to analyze how operational parameters such as pressure, voltage, current, vibration, and temperature affect fault occurrence. Data were collected at three levels for each parameter using a central composite design. The model identified that faults peaked at a pressure of 28.38 N/m2, an operating voltage of 431.77 V, current consumption of 12.54 A, machine vibration of 47.17 Hz, and temperature of 25°C, with a maximum of 25 faults observed. Conversely, the lowest fault detection occurred at a pressure of 29.42 N/m2, an operating voltage of 441.04 V, current consumption of 12.04 A, machine vibration of 49.46 Hz, and temperature of 46.5°C. A strong correlation was found between these parameters and machine faults, with the model achieving high accuracy (R2 = 98.22%) and statistical significance (p-value <0.05), demonstrating its reliability in predicting faults. The study also compared RSM with ANFIS for fault detection and process optimization in the beverage industry. While RSM effectively optimized parameter relationships, ANFIS, with its adaptive learning capabilities, provided superior fault prediction accuracy. This comparative analysis highlighted the strengths of both methods and suggested that integrating them could enhance predictive maintenance strategies. The findings offer valuable insights for industry practitioners, recommending a combined approach to improve fault detection, optimize production processes, and enhance operational efficiency.