LIDAR Module Remaining Lifetime Estimation via Aging Data
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
LIDAR systems are often over-designed to withstand severe conditions, leading to higher costs and customer dissatisfaction due to premature failures, as they are not optimized for typical operating conditions, and there is a need for predictive maintenance strategies to extend their lifespan and reduce failures.
Innovation Solution
A LIDAR module with integrated processors that estimate remaining lifetime based on aging data from various components, using a 'bucket approach' to efficiently record and update aging events, allowing for timely maintenance and reducing design costs by identifying which components are likely to fail first.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If LIDAR system is over-designed to withstand severe conditions, then reliability under severe conditions is improved, but cost increases and customer satisfaction decreases due to premature failures under typical conditions
Solution Approach 1:
The patent implements dynamic monitoring of component aging through continuous collection of operational data (temperature, humidity, power consumption) and calculation of remaining lifetime using aging models. This allows the system to transition from static over-design to dynamic adaptive maintenance, where components are monitored and maintained based on actual aging state rather than conservative fixed specifications.
Solution Approach 2:
The system incorporates feedback mechanisms by continuously monitoring component performance parameters and comparing them against aging models to predict remaining lifetime. This feedback loop enables proactive maintenance decisions based on actual component condition, replacing the need for over-designed conservative specifications with data-driven maintenance timing.
2Ease of manufacture
If LIDAR system is designed for typical operating conditions, then cost is reduced, but reliability under severe conditions deteriorates
Solution Approach 1:
The patent applies preliminary action by implementing predictive maintenance that identifies component aging trends before actual failure occurs. By monitoring aging parameters continuously and predicting remaining lifetime in advance, the system can plan maintenance activities before components fail, ensuring reliability under severe conditions without requiring over-designed conservative specifications.
Solution Approach 2:
The system performs self-diagnosis and self-monitoring of component aging through integrated sensors and processing units that continuously assess component health status. This self-service capability allows the LIDAR system to track its own degradation and trigger maintenance alerts, replacing the need for expensive over-designed components with intelligent self-monitoring.
3Measurement precision
If predictive maintenance monitoring is implemented, then remaining lifetime estimation accuracy is improved, but device complexity increases
Solution Approach 1:
The patent implements multi-functionality by using the existing LIDAR processing unit and communication interface to also handle aging monitoring and remaining lifetime estimation tasks. Rather than adding dedicated complex monitoring hardware, the system repurposes existing components to perform multiple functions including operational data processing, aging model calculation, and maintenance prediction, thereby reducing overall system complexity.
Solution Approach 2:
The system creates a virtual copy of component aging state through digital twins and aging models that simulate component degradation without requiring physical monitoring of every component parameter. By using software-based aging models that process operational data to predict remaining lifetime, the system achieves accurate monitoring with minimal physical complexity additions.
Data Source
AI summary
According to various aspects a LIDAR module is provided, the LIDAR module including: one or more processors configured to: determine an estimate of a remaining lifetime of the LIDAR module as a function of aging data representative of aging events associated with one or more components of the LIDAR module.