Design of a high-precision testing system for dynamic response of solenoid valves based on LabVIEW-Python collaboration

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Abstract
This paper proposes a high-precision automated testing system based on a LabVIEW-Python collaborative architecture for evaluating the dynamic response of solenoid valves. The system integrates LabVIEW’s high-precision data acquisition capabilities with Python’s automated feature extraction tools, thereby addressing the limitations of traditional single-platform solutions. During testing, a step voltage signal is applied to the solenoid valve while the drive current and output pressure responses are synchronously acquired at a 5 kHz sampling rate. Using the current signal as a time reference, the analysis algorithm automatically extracts seven dynamic parameters, including the opening time (T 2 ), closing time (t E1 ), pressure build-up gradient (G u ), and pressure release gradient (G d ). Experimental results demonstrate a 40% improvement in testing efficiency and a 60% improvement in parameter extraction completeness compared to conventional methods. The measured values (T 2 = 154.4 ms, G u = 46.4 bar/s, t E1 = 138.8 ms, G d = 46.83 bar/s) show high consistency with the acquired waveforms. The testing process strictly adheres to the ISO 12238:2023 standard. This system provides an efficient, reliable, and standardized solution for validating solenoid valve performance and establishes a foundation for integrating machine learning technologies to enable intelligent diagnostics and predictive maintenance.