Sensors for intelligent oil condition prediction system

Abstract

Lubricating oil degradation seriously impairs the operational stability and service life of industrial machinery. Traditional manual sampling and laboratory detection methods for oil condition monitoring suffer from low efficiency, high cost, and inability to capture early degradation anomalies. To solve this problem, this paper designs and implements a low-cost and scalable intelligent oil condition prediction system based on ESP32 microcontroller. The system uses a distributed telemetry framework to collect multi-dimensional oil parameters via temperature, torque, viscosity, and light sensors, transmits data wirelessly over MQTT, and runs moving average filtering, adaptive outlier detection, and real-time visualization on a Python server. This small article systematically expounds the research purpose, technical methods and experimental results of the system. The constructed multi-sensor monitoring scheme can realize continuous real-time perception of oil health status, which provides a feasible technical reference for industrial equipment predictive maintenance and intelligent oil condition monitoring.