Estimation of brake pad wear and remaining useful life from fused sensor system, statistical data processing, and passenger car longitudinal dynamics

Jensen, Kenneth M.; Santos, Ilmar F.; Corstens, Harry J.P.

doi:10.1016/j.wear.2023.205220

Cited by 1 publication

(4 citation statements)

References 18 publications

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“…Table 10. Random components of the residuals of models ( 21), (22), and (24). An analysis of Table 11 shows that there is no basis for rejecting the hypothesis that the random components of the models have a normal distribution, for the N(0, 2583.179) linear model and the N(0, 0.048) power model.…”

Section: Results Of Random Component Analysis Of the Modelsmentioning

confidence: 99%

“…The current structure of the quasi-linear power model is correct. After simple transformations, we obtain a power model in the form (24) and geometric interpretation in Figure 4:…”

Section: Determination Of the New Power Modelmentioning

confidence: 99%

“…In the case of managing a fleet of multiple vehicles, the ability to estimate brake pad wear would make it possible to optimize the replacement schedule and identify the causes of rapid brake system wear. Currently, the most commonly used predictor is the vehicle distance traveled [24].…”

Section: Introductionmentioning

confidence: 99%

“…Kenneth Ma et al tried to estimate brake pad wear based on the Archard equation. The estimation error on a real-world car proved to be very large, and as the authors stated "…without access to the associated usage data, accurate validation of the prediction cannot be carried out" [24] (p. 12). Trucks carrying oversize loads experience frequent changes in the load carried.…”

Section: Introductionmentioning

confidence: 99%

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Prediction of Brake Pad Wear of Trucks Transporting Oversize Loads Based on the Number of Drivers’ Braking and the Load Level of the Trucks—Multiple Regression Models

Basista,

Hajos,

Francik

et al. 2024

Applied Sciences

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Brake pad wear forecasting, due to its complex nature, is very difficult to describe using engineering formulas. Therefore, the aim of this publication is to create high-quality brake pad wear forecasts based on three stochastic quantitative models based on multiple regression models (linear model, inverted linear model, and power model). The matrix of explanatory variables was extracted from the controllers of 29 vehicles: A—the driver’s style of using the brake pedal specified on a 4-point scale and B—the number of vehicle load ranges specified on a 5-point scale. Methodology: A matrix of explanatory variables was obtained over a 2-year period from trucks carrying oversize loads via OBD2 socket. The trucks operated under similar operating conditions. The created models were verified in terms of their fit to the source data and by analyzing the residuals of the models. It should be emphasized that only the linear model met all the required criteria. The inverted linear and power-law models were rejected. Results: The verified linear model is characterized by very small MAPE errors. The model was validated on 4 trucks and the brake pad wear prediction errors ranged from −0.39% to 7.03%.

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Section: Results Of Random Component Analysis Of the Modelsmentioning

confidence: 99%

“…The current structure of the quasi-linear power model is correct. After simple transformations, we obtain a power model in the form (24) and geometric interpretation in Figure 4:…”

Section: Determination Of the New Power Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations