Development of A Predictive Maintenance Model for Industrial Motor Drives and VFD Systems Using PLC/DCS Operational Telemetry

Abstract

This study examined the problem of reliability, safety, and compliance gaps in stationary hydrogen storage and distribution infrastructure, where reactive or time-based maintenance may fail to detect early leakage, pressure abnormality, compressor degradation, valve malfunction, sensor drift, pipeline stress, and emergency shutdown delays. The purpose of the study was to quantitatively evaluate how AI-driven predictive maintenance, supported by SCADA/PLC integration and operational telemetry, contributes to operational reliability, real-time fault detection, hydrogen safety risk reduction, and U.S. pipeline safety policy readiness in enterprise hydrogen infrastructure cases, including storage vessels, pipelines, compressors, valves, regulators, sensors, control systems, and cloud-assisted monitoring environments. A quantitative, cross-sectional, case-based design was adopted, using a structured five-point Likert-scale questionnaire. Data were obtained from 185 valid respondents out of 230 distributed questionnaires, representing an 80.4% valid response rate. The sample included engineers, maintenance managers, SCADA/PLC specialists, safety and risk officers, energy-sector professionals, and policy or compliance professionals, with 76.8% reporting high or very high familiarity with hydrogen infrastructure. The key variables were AI-driven predictive maintenance adoption, SCADA/PLC integration, data quality, sensor reliability, real-time fault detection capability, operational reliability, hydrogen safety risk reduction, policy compliance readiness, and predictive maintenance readiness. The analysis plan included descriptive statistics, Cronbach’s alpha reliability testing, Pearson correlation, multiple regression, hypothesis testing, Hydrogen Infrastructure Predictive Maintenance Readiness Index analysis, and hydrogen safety-critical fault detection and policy alignment assessment. The headline findings showed high ratings for SCADA/PLC integration, M = 4.22, real-time fault detection, M = 4.20, hydrogen safety risk reduction, M = 4.19, and AI-driven predictive maintenance, M = 4.18. Reliability was strong, with Cronbach’s alpha values ranging from 0.80 to 0.91. Correlation results showed strong positive relationships, including SCADA/PLC integration and real-time fault detection, r = 0.72, p < 0.01, and real-time fault detection and hydrogen safety risk reduction, r = 0.70, p < 0.01. Regression results showed that predictive maintenance, SCADA/PLC integration, data quality, and sensor reliability explained 57.4% of operational reliability, while real-time fault detection, predictive maintenance, and SCADA/PLC integration explained 61.2% of hydrogen safety risk reduction. The HPMRI score was 4.17, indicating high readiness. The study implies that hydrogen infrastructure operators should strengthen integrated telemetry, sensor reliability, data quality, predictive analytics, digital maintenance records, and policy-aligned safety reporting to improve reliability and risk-based compliance.