AI-BASED PREDICTIVE MAINTENANCE OF AUTOMOTIVE HYDRAULIC BRAKING SYSTEMS USING SENSOR DATA ANALYTICS

Engineering notes

The findings indicate that AI-enabled predictive maintenance systems significantly outperform conventional preventive maintenance approaches by enabling real-time condition monitoring, fault prediction, and intelligent decision-making.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

The automotive industry is rapidly transitioning toward intelligent mobility systems characterized by advanced electronics, autonomous driving capabilities, electrification, and connected vehicle technologies. Among all vehicle subsystems, the hydraulic braking system remains one of the most critical safety components because it directly influences vehicle controllability, stopping performance, passenger safety, and operational reliability. Traditional maintenance approaches for hydraulic braking systems primarily rely on periodic inspections and corrective maintenance practices. While such approaches have been effective for decades, they often fail to detect early-stage component degradation, resulting in unexpected failures, increased maintenance costs, vehicle downtime, and safety risks. Recent advances in Artificial Intelligence (AI), machine learning, sensor technologies, cloud computing, and automotive diagnostics have enabled the development of predictive maintenance frameworks capable of forecasting failures before they occur. This research investigates AI-based predictive maintenance methodologies for automotive hydraulic braking systems using sensor data analytics. The study focuses on integrating pressure sensors, temperature sensors, vibration sensors, fluid quality sensors, wheel speed sensors, brake pad wear sensors, and vehicle telemetry systems with machine learning algorithms to create intelligent maintenance platforms. Mathematical models describing hydraulic pressure dynamics, braking force generation, degradation behavior, failure probability, and predictive analytics are developed to evaluate system performance and maintenance effectiveness. The paper explores the use of supervised learning, unsupervised learning, deep learning, neural networks, support vector machines, decision trees, random forests, and anomaly detection techniques for fault diagnosis and remaining useful life prediction. Particular emphasis is placed on hydraulic pump degradation, brake fluid contamination, actuator wear, seal leakage, pressure fluctuations, and sensor failure detection. Industrial case studies demonstrate how predictive maintenance can reduce downtime, improve vehicle safety, enhance operational efficiency, and optimize maintenance scheduling. The findings indicate that AI-enabled predictive maintenance systems significantly outperform conventional preventive maintenance approaches by enabling real-time condition monitoring, fault prediction, and intelligent decision-making. The study concludes that predictive analytics combined with sensor data fusion will become a fundamental component of future intelligent braking systems used in electric vehicles, hybrid vehicles, connected vehicles, and autonomous transportation platforms.

Links and sources

Comments