Predictive Health Management System for Heavy-Duty Vehicles
The development of predictive health management is a novel and actively researched topic within the automotive industry. These systems aim to enhance the reliability of heavy-duty vehicles by predicting and preventing component failures and make the maintenance of the vehicles more cost effective. This role focuses on identifying component failure modes of automotive components, the modelling of these failures, and evaluating measurements performed on them.
Objectives: Understand the potential failure modes and critical failure points of automotive components. Create accurate predictive models and perform measurements to validate and verify the models.
- Understand the potential failure modes of automotive components.
- Develop sophisticated predictive models for component failures. Utilize advanced data analytics, machine learning, and statistical methods to create models that can anticipate potential failures based on various parameters, such as usage patterns, environmental conditions, and component aging.
- Investigate and assess different measurement techniques and sensors to gather real-time data from vehicle components. Evaluate the accuracy, precision, and reliability of these measurements for use in the predictive health management system.
- Collaborate closely with experts in vehicle design, data science, and sensor technology.
- Catalogize the potential failures, document the developed models and the evaluation results from the measurements.
In this project as the automotive engineer of the team, you will contribute to the development of a cutting-edge predictive health management system for heavy-duty vehicles.
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