Diagnosis of the Pneumatic Wheel Condition Based on Vibration Analysis of the Sprung Mass in the Vehicle Self-Diagnostics System

• Published in: Sensors (Basel, Switzerland) Vol. 23; no. 4; p. 2326
• Main Authors: Prażnowski, Krzysztof, Mamala, Jarosław, Deptuła, Adam, Deptuła, Anna M., Bieniek, Andrzej
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 20.02.2023; MDPI
• Subjects: Algorithms; Classification; data classification; Decision trees; diagnostics; Discriminant analysis; Machine learning; pneumatic wheel logic tree; Tires; Vehicles; Vibration; vibroacoustics; diagnostics; vibroacoustics; pneumatic wheel logic tree; data classification; decision trees
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s23042326

This paper presents a method for the multi-criteria classification of data in terms of identifying pneumatic wheel imbalance on the basis of vehicle body vibrations in normal operation conditions. The paper uses an expert system based on search graphs that apply source features of objects and distances from points in the space of classified objects (the metric used). Rules generated for data obtained from tests performed under stationary and road conditions using a chassis dynamometer were used to develop the expert system. The recorded linear acceleration signals of the vehicle body were analyzed in the frequency domain for which the power spectral density was determined. The power field values for selected harmonics of the spectrum consistent with the angular velocity of the wheel were adopted for further analysis. In the developed expert system, the Kamada–Kawai model was used to arrange the nodes of the decision tree graph. Based on the developed database containing learning and testing data for each vehicle speed and wheel balance condition, the probability of the wheel imbalance condition was determined. As a result of the analysis, it was determined that the highest probability of identifying wheel imbalance equal to almost 100% was obtained in the vehicle speed range of 50 km/h to 70 km/h. This is known as the pre-resonance range in relation to the eigenfrequency of the wheel vibrations. As the vehicle speed increases, the accuracy of the data classification for identifying wheel imbalance in relation to the learning data decreases to 50% for the speed of 90 km/h.