Electromagnetic Interference Patterns and Measurement Accuracy in Smart Factory Sensors

Abstract

The transition toward Industry 4.0 has established smart factories as highly automated, data-driven environments where cyber-physical systems operate in continuous synchronization. At the foundation of these systems are smart sensors, which are expected to deliver high-fidelity measurements to facilitate real-time decision-making, predictive maintenance, and autonomous process control. However, modern industrial environments are characterized by dense arrays of heavy electrical machinery, variable frequency drives, and high-voltage power lines, all of which generate complex electromagnetic interference patterns. This study investigates the direct correlation between specific electromagnetic interference profiles and the resulting degradation in measurement accuracy across industrial sensor networks. By establishing a controlled, full-scale smart factory testbed, we systematically exposed a representative suite of temperature, pressure, and vibration sensors to synthesized and real-world radiated and conducted electromagnetic noise. The research methodology utilized high-frequency data acquisition systems to capture instantaneous signal deviations and long-term measurement drifts. Our findings demonstrate that distinct interference patterns yield highly specific error signatures, challenging the conventional assumption that electromagnetic noise merely induces uniform random variance in sensor outputs. The empirical evidence suggests that specific harmonic frequencies couple with sensor internal analog-to-digital conversion stages, leading to deterministic measurement offsets. These results provide a critical foundation for developing advanced, hardware-agnostic interference mitigation algorithms and robust sensor validation protocols.