Online estimation of driving events and fatigue damage on vehicles
Roza Maghsood, Jonas Wallin

TL;DR
This paper presents an online algorithm using a hidden Markov model and online EM to estimate driving events and fatigue damage in vehicles, adapting to changing conditions for durability assessment.
Contribution
It introduces a practical online EM-based method for real-time estimation of driving events and fatigue damage, adaptable to changing driving conditions.
Findings
The algorithm accurately estimates driving event counts in real-time.
It adapts to changing driving conditions using a fixed forgetting factor.
The method enables online fatigue damage computation.
Abstract
Driving events, such as maneuvers at slow speed and turns, are important for durability assessments of vehicle components. By counting the number of driving events, one can estimate the fatigue damage caused by the same kind of events. Through knowledge of the distribution of driving events for a group of customers, the vehicles producers can tailor the design, of vehicles, for the group. In this article, we propose an algorithm that can be applied on-board a vehicle to online estimate the expected number of driving events occurring, and thus be used to estimate the distribution of driving events for a certain group of customers. Since the driving events are not observed directly, the algorithm uses a hidden Markov model to extract the events. The parameters of the HMM are estimated using an online EM algorithm. The introduction of the online EM is crucial for practical usage, on-board…
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Taxonomy
TopicsTransport Systems and Technology · Vehicle Noise and Vibration Control · Vehicle Dynamics and Control Systems
