Fleet KPI Baseline Modeling for Low-Power Trend Detection
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Solution Overview
Problem
Existing fleet management systems face challenges in analyzing large amounts of data to accurately forecast Key Performance Indicators (KPIs) without requiring high computational power, necessitating a method to efficiently evaluate and update statistical distributions for better fleet management.
Innovation Solution
A method involving a computing device that receives candidate statistical distributions, updates and compares them based on operations data, selects a baseline distribution, and sends alerts for changes in maintenance schedules or component replacements, using Bayesian and Frequentist statistics to optimize fleet management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If large amounts of data are analyzed to accurately forecast KPIs, then measurement precision is improved, but computational power requirements increase
Solution Approach 1:
The patent segments the analysis process into multiple stages: data collection from fleet operations, data preprocessing and filtering, statistical distribution fitting, model training, and forecasting. This segmentation allows computational resources to be distributed across different processing stages, reducing peak power requirements while maintaining analytical depth and forecast accuracy.
Solution Approach 2:
The system dynamically adjusts the level of analysis and computational intensity based on data characteristics, fleet size, and resource availability. Statistical methods are adaptively selected and updated, allowing the system to maintain high measurement precision while optimizing computational power consumption through dynamic resource allocation.
2Reliability
If statistical distributions are frequently updated to detect new trends, then reliability is improved, but loss of time increases
Solution Approach 1:
The patent implements periodic updating of statistical distributions at predetermined intervals or when specific triggers are met, rather than continuous updates. This periodic action maintains fleet management reliability by ensuring distributions remain current while avoiding unnecessary processing time consumption during stable operational periods.
Solution Approach 2:
The system incorporates feedback mechanisms that monitor operational data and automatically initiate statistical distribution updates only when significant changes or anomalies are detected. This feedback-driven approach ensures reliability improvements are achieved precisely when needed, minimizing unnecessary processing time while maintaining accurate fleet management models.
Data Source
AI summary
A method implemented by a computing device, of monitoring a collection of machines. The method includes receiving a plurality of candidate statistical distributions for the collection of machines. Each of the plurality of candidate statistical distributions describes a first operations data and a second operations data characterizing one or more aspects of at least one machine of the collection of machines. The method further includes updating and comparing the plurality of candidate statistical distributions based on a combination of the first operations data and the second operations data. The method further includes selecting from the plurality of candidate statistical distributions a baseline statistical distribution based on the comparing. Additionally, the method includes outputting the baseline statistical distribution, wherein the baseline statistical distribution is predicted to best describe both the first operations data and the second operations data and sending an alert to indicate the baseline statistical distribution that was discovered.