Change Management in Predictive Maintenance for IoT-Enabled Autonomous Vehicles: Harnessing Computational Intelligence for Enhanced Operational Efficiency
Keywords:
sensorAbstract
Efficient Predictive Maintenance (PdM) is critical for reducing maintenance costs, improving operational safety, minimizing unplanned downtime, and prolonging the life of rotating machinery, particularly within the automotive industry. In this context, IoT-enabled systems offer comprehensive data monitoring and maintenance tools, including vibration monitoring systems and lubrication solutions, which are essential for implementing preventive measures to enhance equipment lifespan and operational stability. However, challenges persist when applying standard sensor or network technologies, such as high operational costs, ongoing maintenance of network systems, and sensor burnout due to extreme operational conditions. Each standard sensor typically collects data on a singular piece of equipment, which can exacerbate network-related expenses. These high costs associated with individual sensors can pose significant financial challenges for equipment vendors in mass production, ultimately hindering operational profitability. This paper explores how leveraging computational intelligence can address these limitations and optimize predictive maintenance strategies in IoT-enabled autonomous vehicles, aligning them with effective change management practices.
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