Research on infrared thermal imaging sensor for overhead catenary hot spot detection

The overhead catenary system provides an essential component in the electric railway system, where its stable performance is necessary for continuous power transmission. Thermal variations, especially hot spots, often signal the onset of mechanical erosion or electrical faults, and they must be recognized quickly to verify operational safety and efficiency. This paper presents the design and study of a focused infrared thermal imaging sensor system engineered to identify such exceptions in overhead lines. Furthermore, the system features a simplified design and embedded processing architecture, which improve flexibility, responsiveness, and affordability compared to traditional imaging solutions. Unlike conventional thermal cameras that rely on full-frame imaging and external computation, the proposed method integrates on-device waveform analysis optimized for narrow targets and high-speed scanning. This approach enables the system to detect faults faster while consuming less power and reducing deployment costs significantly. The system includes a mid-wavelength infrared detector array with sensitivity across the 6–11 µm spectral band, allowing exact capture of thermal emissions from raised-temperature regions. Theoretical modeling reflects critical parameters such as target and ambient temperatures, detector geometry, and specified detectivity to enhance signal contrast and detection reliability. The hardware implementation includes a capacitive trans-impedance amplifier-based readout integrated circuit, a low-noise analog front-end, and a high-speed successive approximation register analog-to-digital converter. A digital processing module manages data obtaining and real-time thermal analysis. Simulation results, including waveform analyses of output voltage over time and signal-to-noise ratio against integration time, show maximum output at a 6 ms integration window. The proposed solution offers an efficient, reliable, and affordable framework for real-time fault monitoring in railway electrification systems, helping indicative maintenance and improved system recovery.