IoT Sensor-Based Vehicle Maintenance Scheduling
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Solution Overview
Problem
Current vehicle maintenance practices rely on owner knowledge and experience, leading to potential neglect of maintenance, which can result in costly repairs or accidents.
Innovation Solution
A computer-implemented method using IoT sensors and machine learning models to monitor vehicle subsystem performance and driving behavior, detecting issues, and scheduling maintenance accordingly, while considering user and repair shop availability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If vehicle maintenance is performed at regular intervals based on owner knowledge and experience, then maintenance costs are reduced, but vehicle safety and reliability may be compromised due to potential neglect
Solution Approach 1:
The system enables the vehicle to monitor and manage its own maintenance needs through IoT sensors that continuously collect data on subsystem performance and automatically detect issues, eliminating the need for owner knowledge and experience in maintenance management
Solution Approach 2:
The system implements continuous feedback loops where sensor data is collected, analyzed by machine learning models to detect issues, and then used to automatically schedule maintenance, creating a closed-loop system that continuously improves vehicle safety and reliability
2Reliability
If maintenance is scheduled proactively using IoT sensor data and machine learning analysis, then vehicle safety and performance are improved, but system complexity and initial costs increase
Solution Approach 1:
The system integrates multiple functions into a unified platform: IoT sensors monitor various subsystems, machine learning models analyze diverse data types, and the scheduling system coordinates maintenance across multiple dimensions (time, location, availability), creating a universal maintenance management solution
Solution Approach 2:
The system introduces an intelligent intermediary layer between the vehicle subsystems and the owner, where machine learning models act as mediators that translate complex sensor data into actionable maintenance schedules, simplifying the overall system interaction
3Ease of operation
If maintenance scheduling considers user availability and repair shop availability, then user convenience is improved, but scheduling complexity and processing time increase
Solution Approach 1:
The system performs preliminary actions by pre-collecting and storing availability data for both users and repair shops, and by pre-training machine learning models to rapidly process scheduling requests, reducing processing time when actual scheduling is needed
Solution Approach 2:
The system automatically manages the complex scheduling process without requiring user intervention, where the intelligent system independently coordinates between user availability, repair shop capacity, and vehicle maintenance needs, improving convenience despite increased processing complexity
4Reliability
If IoT sensors continuously monitor vehicle subsystems, then maintenance issues are detected earlier improving safety, but energy consumption and device complexity increase
Solution Approach 1:
The system implements periodic monitoring where IoT sensors collect data at optimized intervals rather than continuously, and machine learning models process data in batches, reducing energy consumption while maintaining effective issue detection capabilities
Solution Approach 2:
The system applies monitoring at different levels of intensity based on needs: critical subsystems are monitored more frequently while less critical systems use periodic sampling, optimizing the balance between detection accuracy and energy consumption
Data Source
AI summary
Dynamic vehicle maintenance management is provided. Performance of each subsystem of a plurality of subsystems corresponding to a vehicle and driving behavior of a user of the vehicle is monitored by performing an analysis of data collected from an IoT sensor system onboard the vehicle to detect any subsystem issues in the vehicle using a set of machine learning models. An issue is detected in a subsystem of the vehicle based on the analysis of the data collected from the IoT sensor system onboard the vehicle. Maintenance corresponding to the issue detected in the subsystem of the vehicle is scheduled at a date, time, and location based on availability of the user of the vehicle and a selected vehicle repair shop. A notification regarding the maintenance corresponding to the issue detected in the subsystem of the vehicle is sent to the user of the vehicle via a network.