Fleet Prognostic Assessment Model for Reliability Optimization
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Solution Overview
Problem
The operation and maintenance of vehicle fleets are economically burdensome due to overlooked factors like reliability and maintainability, which are not adequately considered in cost assessments, leading to increased ownership costs over the service life of the vehicles.
Innovation Solution
A system health operations analysis model that provides a prognostic condition assessment decision aid by processing data from vehicle missions and maintenance activities to optimize fleet scheduling, increase reliability, and reduce operational costs through prognostic indicators and decision criteria, such as condition-based maintenance and 'fix or fly' decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional cost assessment methods are used for vehicle fleets, then initial costs (vehicle purchase, design, development) are considered, but reliability and maintainability are overlooked, leading to increased operational costs over service life
Solution Approach 1:
The system performs preliminary health management integration during the initial design phase of vehicles. By incorporating health monitoring systems, diagnostic capabilities, and maintenance scheduling algorithms before the vehicle enters service, the organization can predict and prevent failures before they occur, thereby improving reliability without requiring complex post-service interventions.
Solution Approach 2:
The system continuously monitors vehicle health parameters and provides feedback loops that trigger maintenance actions based on actual condition data. This closed-loop approach allows the organization to adjust maintenance schedules dynamically, improving reliability by responding to real-time vehicle status while keeping cost assessment manageable through automated decision-making.
2Productivity
If detailed benefit analysis is conducted for health management integration, then operational performance benefits are identified, but the complexity of analyzing all factors (OEM observations, mission operator requirements, fleet management needs) increases
Solution Approach 1:
The system segments the complex benefit analysis into distinct categories: OEM specifications, mission operator requirements, fleet management objectives, and maintenance operational needs. By dividing the analysis into manageable segments, the system can process each factor independently using specialized algorithms, thereby maintaining comprehensive analysis while reducing overall complexity.
Solution Approach 2:
The health management system is designed with multi-functionality to serve multiple purposes simultaneously: it monitors vehicle conditions, schedules maintenance, predicts failures, and optimizes fleet utilization. This universal approach consolidates multiple analysis functions into a single system, improving operational performance without proportionally increasing analysis complexity.
3Reliability
If condition-based maintenance and prognostic indicators are implemented, then fleet reliability increases and operational costs reduce, but the complexity of processing mission and maintenance data increases
Solution Approach 1:
The system employs self-service mechanisms where vehicles continuously self-monitor their own health parameters through onboard sensors and diagnostic systems. This autonomous self-monitoring reduces the need for complex external data collection and processing infrastructure, as the vehicles themselves generate and manage their own health data, thereby improving reliability without proportionally increasing data processing complexity.
Solution Approach 2:
The system dynamically adjusts monitoring parameters and data collection frequencies based on vehicle condition and operational context. By changing parameters such as sensor thresholds, sampling rates, and maintenance intervals in response to real-time conditions, the system optimizes reliability while minimizing unnecessary data processing, avoiding the complexity of continuous high-frequency monitoring.
Data Source
AI summary
Methods and systems for prognostic condition assessment decision aid of a fleet of vehicles are disclosed. In one embodiment, a method includes providing a schedule of missions and maintenance of a fleet of vehicles, comprising receiving data from the fleet of vehicles for missions and maintenance activity, determining mission and maintenance requirements, processing the received data and requirements to provide an operational allocation of the fleet of vehicles, determining an alternative allocation of the operational allocation of the fleet of vehicles, the alternative allocation satisfying at least one operational objective for the fleet of vehicles, and generating a schedule for the alternative allocation of the fleet of vehicles.