Computational Intelligence for Predictive Maintenance in IoT-enabled Autonomous Vehicles
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 for those used in the automotive industry [1]. IoT offers comprehensive data monitoring and maintenance tools, including vibration monitoring systems, lubrication systems, and other equipment, to take preventive measures to improve equipment life and operational stability. However, various limitations exist when applying standard sensor or acquired network technologies, such as high operational costs, constant maintenance of network systems, and burn-out due to extreme sensor settings. In addition, each standard sensor collects data on a singular piece of equipment or item to reduce network-induced expenditures. The high costs that come with each standard sensor could pose financial hardships for equipment vendors in mass production, and high operational costs hinder operational profitability.
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References
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