Advanced Tire Looseness Identification through Algorithmic Gear Error Analysis and Probabilistic Assessment

At present, tire failures directly affect road safety, and the number of incidents caused by them is gradually increasing. Examining tire looseness on time is vital for vehicle safety. Tire-related incidents not only put people in peril but also have a detrimental effect on the economy. Therefore, the goal of this research is to develop a new and effective method for identifying tire looseness. A novel gear error reduction approach, distinct from traditional methods, combines advanced computing and probabilistic analysis. This paper involves three key components: extracting looseness eigenvalues, calculating ring gear errors, and computing the tire loosen probabilities. Gear errors derived from the Kalman filter and adjusted for speed, eigenvalues were calculated, and a tire loosening probability analysis was performed. Real-car trials across speeds and roads confirm its accuracy and reliability. This technology can improve automotive safety and maintenance, reducing accidents, claims, and pollution. It also fits autonomous and smart cars, where tire monitoring is key.