MULTI-SENSOR PREDICTIVE ANALYTICS FOR EARLY FAULT DETECTION IN SMART MANUFACTURING SYSTEMS

Abstract

The rapid advancement of Industry 4.0 technologies has transformed traditional manufacturing environments into intelligent, interconnected, and data-driven smart manufacturing systems. Modern industrial equipment generates large volumes of heterogeneous data through multiple sensing devices, including vibration sensors, temperature sensors, acoustic sensors, pressure sensors, current monitoring sensors, and operational condition sensors. Effective utilization of these multi-sensor data streams enables early identification of equipment degradation, abnormal operating conditions, and potential failures before they result in unexpected downtime. This research presents a Multi-Sensor Predictive Analytics framework for early fault detection in smart manufacturing systems by integrating sensor data fusion techniques, machine learning algorithms, and predictive analytics approaches. The proposed framework collects real-time industrial sensor data, performs data preprocessing and feature extraction, applies multi-dimensional data fusion strategies, and utilizes intelligent prediction models for accurate fault classification and failure forecasting. The research focuses on developing an adaptive predictive maintenance architecture capable of analyzing complex relationships among multiple sensor parameters and identifying hidden fault patterns in industrial machinery. Machine learning techniques such as Random Forest, Support Vector Machine, Artificial Neural Networks, and Deep Learning models are evaluated for their ability to detect early-stage equipment abnormalities. The proposed approach improves fault detection accuracy by combining complementary information from different sensor sources rather than relying on individual sensor analysis. Experimental evaluation demonstrates that multi-sensor predictive analytics provides improved reliability, reduced false alarms, enhanced maintenance scheduling, and increased operational efficiency compared with conventional monitoring techniques. The framework supports real-time decisionmaking and contributes toward the development of autonomous smart factories with improved productivity and reduced maintenance costs.