Cloud ML Maintenance Prediction for Multi-Component Vehicle Failures
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Solution Overview
Problem
Existing vehicle sensor systems are unable to detect component failures related to multiple combinations of components, leading to unexpected and unpredictable vehicle component failures.
Innovation Solution
A machine learning model is deployed on a cloud platform to predict equipment maintenance needs by analyzing data from multiple vehicle sensors and combining it with customer relationship management (CRM) data, allowing for proactive maintenance and prevention of component failures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional isolated sensor systems are used to monitor individual components, then the system complexity is low and ease of operation is maintained, but the reliability of detecting multi-component failures deteriorates
Solution Approach 1:
The patent combines multiple isolated sensor systems into a unified cloud-based monitoring platform that integrates data from sensors across different vehicle components. This merging enables the system to detect multi-component failure patterns that individual sensors cannot identify, thereby improving detection reliability while the cloud platform manages the complexity centrally.
Solution Approach 2:
The patent introduces a cloud-based machine learning model as an intermediary between isolated sensors and users. This intermediary collects, processes, and analyzes data from multiple sensors, identifying complex failure patterns across different vehicle components. The intermediary handles the analytical complexity while presenting simplified maintenance recommendations to users.
2Reliability
If isolated sensors monitoring single components are used, then the device complexity is low, but the ability to detect multi-component related failures deteriorates
Solution Approach 1:
The patent creates a universal cloud-based platform that serves multiple functions: collecting data from various sensor types, storing historical data, running machine learning models, and generating maintenance predictions. This multi-functional platform handles complexity centrally while enabling comprehensive multi-component failure detection through its integrated architecture.
3Loss of time
If reactive maintenance based on isolated sensor alerts is used, then the system is simple to operate, but the loss of time for unexpected failures increases
Solution Approach 1:
The patent implements preliminary action by using machine learning models to predict component failures before they occur. The system analyzes patterns in sensor data to identify early signs of degradation across multiple components, enabling maintenance to be scheduled in advance. This prevents unexpected failures and reduces downtime, while the automated predictive algorithms handle the complexity of analyzing multiple data sources.
Data Source
AI summary
A machine learning model hosted on a cloud platform may be used to proactively predict if a maintenance procedure should be performed for a vehicle. In some examples, to support the prediction, the machine learning model may be connected to a different cloud platform that includes a customer relationship management (CRM) system and receives data from sensors of the vehicle. As such, the cloud platform with the CRM data may transmit the CRM data and the sensor data of the vehicle to the cloud platform hosting the machine learning model to aid in generating the maintenance procedure predictions. Further, the maintenance procedure predictions may also include the generation of a prediction score associated with a maintenance procedure. In some examples, the prediction score may satisfy a prediction score threshold, thus a notification may be transmitted to a computing device that indicates the maintenance procedure to be performed for the vehicle.