Cost-Benefit Analysis using Modular Dynamic Fault Tree Analysis and Monte Carlo Simulations for Condition-based Maintenance of Unmanned Systems

ArXiv ID: 2405.09519 “View on arXiv”

Authors: Unknown

Abstract

Recent developments in condition-based maintenance (CBM) have helped make it a promising approach to maintenance cost avoidance in engineering systems. By performing maintenance based on conditions of the component with regards to failure or time, there is potential to avoid the large costs of system shutdown and maintenance delays. However, CBM requires a large investment cost compared to other available maintenance strategies. The investment cost is required for research, development, and implementation. Despite the potential to avoid significant maintenance costs, the large investment cost of CBM makes decision makers hesitant to implement. This study is the first in the literature that attempts to address the problem of conducting a cost-benefit analysis (CBA) for implementing CBM concepts for unmanned systems. This paper proposes a method for conducting a CBA to determine the return on investment (ROI) of potential CBM strategies. The CBA seeks to compare different CBM strategies based on the differences in the various maintenance requirements associated with maintaining a multi-component, unmanned system. The proposed method uses modular dynamic fault tree analysis (MDFTA) with Monte Carlo simulations (MCS) to assess the various maintenance requirements. The proposed method is demonstrated on an unmanned surface vessel (USV) example taken from the literature that consists of 5 subsystems and 71 components. Following this USV example, it is found that selecting different combinations of components for a CBM strategy can have a significant impact on maintenance requirements and ROI by impacting cost avoidances and investment costs.

Keywords: condition-based maintenance, cost-benefit analysis, unmanned systems, reliability engineering, Monte Carlo simulation, Infrastructure/Engineering

Complexity vs Empirical Score

• Math Complexity: 7.0/10
• Empirical Rigor: 6.5/10
• Quadrant: Holy Grail
• Why: The paper employs advanced probabilistic methods like modular dynamic fault tree analysis (MDFTA) and Monte Carlo simulations, requiring significant mathematical modeling. It is highly data-driven, validated with a real-world unmanned surface vessel case study with 71 components, demonstrating strong empirical backing.

  flowchart TD
    A["Research Goal<br>Determine ROI for implementing<br>Condition-Based Maintenance<br>in Unmanned Systems"] --> B
    subgraph B ["Methodology"]
        direction LR
        B1["Data Collection<br>USV 5 Subsystems<br>71 Components"] --> B2["Modular Dynamic Fault Tree Analysis<br>MDFTA"] --> B3["Monte Carlo Simulations<br>MCS"]
    end
    B --> C["Key Processes<br>Reliability Assessment<br>Maintenance Requirement Analysis<br>Cost-Benefit Calculation"]
    C --> D{"Findings"}
    D --> E["CBM ROI is<br>Highly Sensitive to<br>Component Selection"]
    D --> F["Significant Savings<br>Achievable via<br>Strategic Implementation"]