Method for estimating the state of wear of a tire

A method for estimating a state of wear of a tire of a mounted assembly of a vehicle traveling on a road surface, comprising the following steps:

• installing a vibroacoustic sensor in a wheel housing of the tire to detect noises and running vibrations generated by the tire;
• performing a learning campaign including:
• selecting a specific vehicle with a known tire assembly, including a level of wear of a tire of the known tire assembly according to multiple pressure and static load conditions of the known tire assembly having nominal conditions;
• and
• running the specific vehicle on a predetermined road having known meteorological conditions;
• obtaining matrices associated with a vibroacoustic signature during performance of the learning campaign;
• measuring, during a measuring step, a vibroacoustic signal produced by the tire running on the road surface during a given time frame to obtain a completed measurement and a time signal;
• converting, during a converting step, the time signal into a frequency signal over a frequency range;
• segmenting, during a segmenting step, the frequency range into at least one frequency band with a predetermined width and associating at least one datum representing the frequency signal in the at least one frequency band with the at least one frequency band, with the at least one representative datum derived from the measurement forming at least one variable of a matrix associated with the completed measurement;
• predicting the state of wear of the tire corresponding to the matrix associated with the completed measurement, by performing machine learning from data based on a learning database made up of a set of matrices associated with measurements recorded and carried out according to the measuring step, the converting step, and the segmenting step, under known running conditions, according to modalities each representing a state of wear of the tire;
• determining a ground surface state category, where the ground surface state category is a condition for predicting the state of wear of the tire by the machine learning step if it is a specific ground state category containing a dry ground surface state category or the dry ground surface state category and a wet ground surface state category;
• determining a ground nature category, where the ground nature category is a condition for predicting the state of wear of the tire by the performing machine learning if it is a specific ground nature category comprising an open ground nature category;
• and
• determining the state of wear of the tire after a number N of identical predictions in a series M of consecutive predictions.