Enhancing Sensor Reliability in Intelligent Fields Using a Low/No Code Predictive Analytics Approach

Abstract This paper presents a low-code/no-code (LCNC) predictive analytics framework designed to forecast sensor failures in intelligent fields using advanced machine learning (ML) techniques. The framework focuses on sensors that monitor critical environmental variables, including temperature, pressure, and water level, which are essential for ensuring operational safety and efficiency. By providing real-time predictive insights, the proposed solution enables proactive identification of potential issues before they escalate, going beyond the capabilities of traditional monitoring systems. Domain experts can leverage this framework without extensive programming knowledge, allowing them to implement and manage predictive maintenance strategies directly. This approach minimizes unplanned downtime, improves equipment reliability, and enhances overall operational resilience. Furthermore, the framework facilitates informed, data-driven decision-making, empowering industrial teams to respond to emerging risks efficiently. Overall, the study demonstrates that integrating LCNC platforms with advanced machine learning techniques provides a practical and accessible solution for maintaining reliable and safe operations in complex industrial environments.