Sensor Network Maintenance Scheduling Using Failure Distribution
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Solution Overview
Problem
Existing maintenance schedules for assets, such as vehicle parts and industrial equipment, are not optimized due to their non-normal failure distributions, leading to either premature failure or wastage of asset lifespan, as they rely solely on Mean Time to Failure (MTTF) calculations that do not account for deviation and probability density.
Innovation Solution
A sensor network using kernel density estimation (KDE) to generate probability density functions (PDF) and cumulative density functions (CDF) from historical failure data, determining an optimized maintenance schedule by setting a reliability rate threshold to calculate the minimum Time to Failure (TTF) for each asset, thereby creating a more precise maintenance program.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If Mean Time to Failure (MTTF) calculations are used for maintenance scheduling, then the maintenance process is simple to implement, but the maintenance schedule is not optimized and does not account for failure distribution variations
Solution Approach 1:
The patent transforms the single-parameter MTTF approach into a multi-parameter statistical model using probability density functions and cumulative density functions. By incorporating failure rate thresholds and analyzing failure distribution patterns, the system optimizes maintenance scheduling while maintaining computational feasibility through automated statistical analysis.
2Device complexity
If traditional fixed maintenance schedules are used, then the maintenance program is easy to manage, but assets may experience premature failure or wastage of asset lifespan
Solution Approach 1:
The patent transitions from static fixed maintenance schedules to dynamic optimized schedules based on actual failure distribution patterns. The system continuously analyzes sensor data to generate probability density functions and determines optimal maintenance timing that adapts to the specific failure characteristics of each asset, preventing both premature failure and unnecessary replacements.
3Measurement precision
If sensor networks collect detailed historical failure data, then the failure distribution analysis is more accurate, but the data processing and analysis complexity increases
Solution Approach 1:
The patent introduces statistical tools (probability density functions and cumulative density functions) as intermediaries between raw sensor data and maintenance decisions. These mathematical models transform complex historical failure data into actionable insights through standardized statistical analysis, reducing the complexity of interpreting raw sensor data while maintaining high measurement precision.
Data Source
AI summary
Embodiments determine an optimized maintenance schedule for a maintenance program that includes multiple levels, each level including at least one asset (i.e., asset type) and at least one of the levels including a plurality of assets. Embodiments receive historical failure data for each of the assets, the historical failure data generated at least in part by a sensor network. For each asset, embodiments generate a probability density function (“PDF”) using kernel density estimation (“KDE”). For each asset, based on a reliability rate threshold, embodiments determine a cumulative density function (“CDF”) using the PDF. For each asset, embodiments determine an optimized time to failure (“TTF”) using the CDF. Embodiments then create the schedule for each level that includes a minimum TTF for the assets at each level.