High-Frequency Data Acquisition, Real-Time Analytics, and AI-enhanced Predictive Maintenance for Industrial Equipment Enabled by 5G Connectivity

Abstract

Predictive maintenance of industrial equipment is shifting from periodic inspection to continuous online monitoring. With high bandwidth, low latency, network slicing, non-public network support, and Multi-access Edge Computing (MEC), 5G provides a flexible communication foundation for high-frequency industrial data acquisition. This paper analyzes a 5G-enabled predictive maintenance pipeline covering high-frequency sensing, uplink transmission, MEC-based analysis, cloud training, and maintenance decision-making. The results show that a single vibration node is usually not the main transmission bottleneck; the major pressure comes from multi-node concurrency, event-triggered raw waveform transmission, and limited uplink resources. To reduce uplink load and improve real-time response, an edge-cloud collaborative strategy is proposed. During normal operation, MEC extracts and uploads low-dimensional features such as root mean square (RMS), kurtosis, peak-to-peak value, and spectral peaks, while short-time raw waveforms are transmitted only when anomalies occur. The paper further suggests a hybrid networking scheme in which sub-6 GHz ensures wide-area coverage, millimeter-wave communication supplements hotspot capacity, and Time-Sensitive Networking (TSN) or Industrial Ethernet supports deterministic control.