Cost-Benefit Analysis using Modular Dynamic Fault Tree Analysis and Monte Carlo Simulations for Condition-based Maintenance of Unmanned Systems
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Published
Oct 12, 2024
Joseph Southgate
Katrina Groth
Peter Sandborn
Shapour Azarm
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.
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Keywords
Cost-benefit analysis, Condition-based maintenace, Prognostics and health management, Modular dynamic fault tree analysis, Unmanned systems, Unmanned surface vehicles, Return on investment
References
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Aslansefat, K., Kabir, S., Gheraibia, Y., & Papadopoulos, Y. (2020). Dynamic fault tree analysis: state-of-the-art in modeling, analysis, and tools. Reliability Management and Engineering, 73-112.
Berdinyazov, A., Camci, F., Baskan, S., Sevkli, M., & Eldemir, F. (2011). Economic Analysis of Condition Based Maintenance. International Journal of Industrial Engineering, 18(8).
Chang, C. H., Kontovas, C., Yu, Q., & Yang, Z. (2021). Risk assessment of the operations of maritime autonomous surface ships. Reliability Engineering and System Safety, 207, 107324.
Compare, M., Antonello, F., Pinciroli, L., & Zio, E. (2022). A general model for life-cycle cost analysis of Condition-Based Maintenance enabled by PHM capabilities. Reliability Engineering and System Safety, 224, 108499.
David, M., William, F., & John, R. (2014). Electronic Parts Reliability Data (EPRD2014). Reliability Information Analysis Center, Utica, NY.
David, M., William, F., & John, R. (2016). Non-electronic Parts Reliability Data (NPRD2016). Reliability Information Analysis Center, Utica, NY.
De Carlo, F., & Arleo, M. A. (2013). Maintenance cost optimization in condition based maintenance: a case study for critical facilities. International Journal of Engineering and Technology, 5(5), 4296-4302.
De Jonge, B., & Scarf, P. A. (2020). A review on maintenance optimization. European journal of Operational Research, 285(3), 805-824.
Dreyer, L. O., & Oltedal, H. A. (2019). Safety challenges for maritime autonomous surface ships: a systematic review. In The Third Conference on Maritime Human Factors. Haugesund.
Dui, H., Xu, H., Zhang, L., & Wang, J. (2023). Cost-based preventive maintenance of industrial robot system. Reliability Engineering and System Safety, 240, 109595.
Gao, C., Guo, Y., Zhong, M., Liang, X., Wang, H., & Yi, H. (2021). Reliability analysis based on dynamic Bayesian networks: a case study of an unmanned surface vessel. Ocean Engineering, 240, 109970.
Gascard, E., & Simeu-Abazi, Z. (2018). Quantitative analysis of dynamic fault trees by means of Monte Carlo simulations: Event-driven simulation approach. Reliability Engineering and System Safety, 180, 487-504.
Guillén, A. J., Crespo, A., Gómez, J. F., & Sanz, M. D. (2016). A framework for effective management of condition based maintenance programs in the context of industrial development of E-Maintenance strategies. Computers in Industry, 82, 170-185.
Gulati, R., & Dugan, J. B. (1997). A modular approach for analyzing static and dynamic fault trees. In Annual Reliability and Maintainability Symposium (pp. 57-63). IEEE.
Hazra, I., Chatterjee, A., Southgate, J., Weiner, M. J., Groth, K. M., & Azarm, S. (2024). A Reliability-Based Optimization Framework for Planning Operational Profiles for Unmanned Systems. Journal of Mechanical Design, 146(5), 051704.
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Komianos, A. (2018). The autonomous shipping era. operational, regulatory, and quality challenges. TransNav: International Journal on Marine Navigation and Safety of Sea Transportation, 12(2).
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Poppe, J., Boute, R. N., & Lambrecht, M. R. (2018). A hybrid condition-based maintenance policy for continuously monitored components with two degradation thresholds. European Journal of Operational Research, 268(2), 515-532.
Prajapati, A., Bechtel, J., & Ganesan, S. (2012). Condition based maintenance: a survey. Journal of Quality in Maintenance Engineering, 18(4), 384-400.
Rao, K. D., Gopika, V., Rao, V. S., Kushwaha, H. S., Verma, A. K., & Srividya, A. (2009). Dynamic fault tree analysis using Monte Carlo simulation in probabilistic safety assessment. Reliability Engineering and System Safety, 94(4), 872-883.
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Rausand, M., & Hoyland, A. (2003). System reliability theory: models, statistical methods, & applications (Vol. 396). John Wiley & Sons.
Ruan, D., Ma, L., Yang, Y., Yan, J., & Gühmann, C. (2024). Improvement by Monte Carlo for Trajectory Similarity-based RUL Prediction. IEEE Transactions on Instrumentation and Measurement.
Sandborn, P., & Lucyshyn, W. (2023). System Sustainment: Acquisition and Engineering Processes for the Sustainment of Critical and Legacy Systems.
Singh, V., & Verma, N. K. (2020). Intelligent condition-based monitoring techniques for bearing fault diagnosis. IEEE Sensors Journal, 21(14), 15448-15457.
Teixeira, H. N., Lopes, I., & Braga, A. C. (2020). Condition-based maintenance implementation: a literature review. Procedia Manufacturing, 51, 228-235.Torti, M., Venanzi, I., Laflamme, S., & Ubertini, F. (2022). Life-cycle management cost analysis of transportation bridges equipped with seismic structural health monitoring systems. Structural Health Monitoring, 21(1), 100-117.
Torti, M., Venanzi, I., Laflamme, S., & Ubertini, F. (2022). Life-cycle management cost analysis of transportation bridges equipped with seismic structural health monitoring systems. Structural Health Monitoring, 21(1), 100-117.
Verma, N. K., & Subramanian, T. S. S. (2012). Cost benefit analysis of intelligent condition based maintenance of rotating machinery. In 2012 7th IEEE Conference on Industrial Electronics and Applications (ICIEA) (pp. 1390-1394). IEEE.
Verma, N. K., Khatravath, S., & Salour, A. (2013). Cost benefit analysis for condition based maintenance. In 2013 IEEE Conference on Prognostics and Health Management (PHM) (pp. 1-6). IEEE.
Yang, R., Vatn, J., & Utne, I. B. (2023). Dynamic maintenance planning for autonomous marine systems (AMS) and operations. Ocean Engineering, 278, 114492.
Yoon, J. T., Youn, B. D., Yoo, M., Kim, Y., & Kim, S. (2019). Life-cycle maintenance cost analysis framework considering time-dependent false and missed alarms for fault diagnosis. Reliability Engineering and System Safety, 184, 181-192.
Zhang, T., Li, Q., Zhang, C. S., Liang, H. W., Li, P., Wang, T. M., & Wu, C. (2017). Current trends in the development of intelligent unmanned autonomous systems. Frontiers of Information Technology and Electronic Engineering, 18, 68-85.
Zhao, H., Xu, F., Liang, B., Zhang, J., & Song, P. (2019). A condition-based opportunistic maintenance strategy for multi-component system. Structural Health Monitoring, 18(1), 270-283.
Aslansefat, K., Kabir, S., Gheraibia, Y., & Papadopoulos, Y. (2020). Dynamic fault tree analysis: state-of-the-art in modeling, analysis, and tools. Reliability Management and Engineering, 73-112.
Berdinyazov, A., Camci, F., Baskan, S., Sevkli, M., & Eldemir, F. (2011). Economic Analysis of Condition Based Maintenance. International Journal of Industrial Engineering, 18(8).
Chang, C. H., Kontovas, C., Yu, Q., & Yang, Z. (2021). Risk assessment of the operations of maritime autonomous surface ships. Reliability Engineering and System Safety, 207, 107324.
Compare, M., Antonello, F., Pinciroli, L., & Zio, E. (2022). A general model for life-cycle cost analysis of Condition-Based Maintenance enabled by PHM capabilities. Reliability Engineering and System Safety, 224, 108499.
David, M., William, F., & John, R. (2014). Electronic Parts Reliability Data (EPRD2014). Reliability Information Analysis Center, Utica, NY.
David, M., William, F., & John, R. (2016). Non-electronic Parts Reliability Data (NPRD2016). Reliability Information Analysis Center, Utica, NY.
De Carlo, F., & Arleo, M. A. (2013). Maintenance cost optimization in condition based maintenance: a case study for critical facilities. International Journal of Engineering and Technology, 5(5), 4296-4302.
De Jonge, B., & Scarf, P. A. (2020). A review on maintenance optimization. European journal of Operational Research, 285(3), 805-824.
Dreyer, L. O., & Oltedal, H. A. (2019). Safety challenges for maritime autonomous surface ships: a systematic review. In The Third Conference on Maritime Human Factors. Haugesund.
Dui, H., Xu, H., Zhang, L., & Wang, J. (2023). Cost-based preventive maintenance of industrial robot system. Reliability Engineering and System Safety, 240, 109595.
Gao, C., Guo, Y., Zhong, M., Liang, X., Wang, H., & Yi, H. (2021). Reliability analysis based on dynamic Bayesian networks: a case study of an unmanned surface vessel. Ocean Engineering, 240, 109970.
Gascard, E., & Simeu-Abazi, Z. (2018). Quantitative analysis of dynamic fault trees by means of Monte Carlo simulations: Event-driven simulation approach. Reliability Engineering and System Safety, 180, 487-504.
Guillén, A. J., Crespo, A., Gómez, J. F., & Sanz, M. D. (2016). A framework for effective management of condition based maintenance programs in the context of industrial development of E-Maintenance strategies. Computers in Industry, 82, 170-185.
Gulati, R., & Dugan, J. B. (1997). A modular approach for analyzing static and dynamic fault trees. In Annual Reliability and Maintainability Symposium (pp. 57-63). IEEE.
Hazra, I., Chatterjee, A., Southgate, J., Weiner, M. J., Groth, K. M., & Azarm, S. (2024). A Reliability-Based Optimization Framework for Planning Operational Profiles for Unmanned Systems. Journal of Mechanical Design, 146(5), 051704.
Kim, N. H., An, D., & Choi, J. H. (2017). Prognostics and health management of engineering systems. Switzerland: Springer International Publishing.
Komianos, A. (2018). The autonomous shipping era. operational, regulatory, and quality challenges. TransNav: International Journal on Marine Navigation and Safety of Sea Transportation, 12(2).
Márquez, F. P. G., Lewis, R. W., Tobias, A. M., & Roberts, C. (2008). Life cycle costs for railway condition monitoring. Transportation Research Part E: Logistics and Transportation Review, 44(6), 1175-1187.
Modarres, M., Kaminskiy, M. P., & Krivtsov, V. (2016). Reliability engineering and risk analysis: a practical guide. CRC Press.
O'Rourke, R. (2019). Navy large unmanned surface and undersea vehicles: Background and issues for congress. Congressional Research Service.
Peng, Y., Dong, M., & Zuo, M. J. (2010). Current status of machine prognostics in condition-based maintenance: a review. The International Journal of Advanced Manufacturing Technology, 50, 297-313.
Poppe, J., Boute, R. N., & Lambrecht, M. R. (2018). A hybrid condition-based maintenance policy for continuously monitored components with two degradation thresholds. European Journal of Operational Research, 268(2), 515-532.
Prajapati, A., Bechtel, J., & Ganesan, S. (2012). Condition based maintenance: a survey. Journal of Quality in Maintenance Engineering, 18(4), 384-400.
Rao, K. D., Gopika, V., Rao, V. S., Kushwaha, H. S., Verma, A. K., & Srividya, A. (2009). Dynamic fault tree analysis using Monte Carlo simulation in probabilistic safety assessment. Reliability Engineering and System Safety, 94(4), 872-883.
Rastegari, A., & Bengtsson, M. (2014, June). Implementation of Condition Based Maintenance in manufacturing industry-A pilot case study. In 2014 International Conference on Prognostics and Health Management (pp. 1-8). IEEE.
Rastegari, A., & Bengtsson, M. (2015). Cost effectiveness of condition based maintenance in manufacturing. In 2015 Annual Reliability and Maintainability Symposium (RAMS) (pp. 1-6). IEEE.
Rausand, M., & Hoyland, A. (2003). System reliability theory: models, statistical methods, & applications (Vol. 396). John Wiley & Sons.
Ruan, D., Ma, L., Yang, Y., Yan, J., & Gühmann, C. (2024). Improvement by Monte Carlo for Trajectory Similarity-based RUL Prediction. IEEE Transactions on Instrumentation and Measurement.
Sandborn, P., & Lucyshyn, W. (2023). System Sustainment: Acquisition and Engineering Processes for the Sustainment of Critical and Legacy Systems.
Singh, V., & Verma, N. K. (2020). Intelligent condition-based monitoring techniques for bearing fault diagnosis. IEEE Sensors Journal, 21(14), 15448-15457.
Teixeira, H. N., Lopes, I., & Braga, A. C. (2020). Condition-based maintenance implementation: a literature review. Procedia Manufacturing, 51, 228-235.Torti, M., Venanzi, I., Laflamme, S., & Ubertini, F. (2022). Life-cycle management cost analysis of transportation bridges equipped with seismic structural health monitoring systems. Structural Health Monitoring, 21(1), 100-117.
Torti, M., Venanzi, I., Laflamme, S., & Ubertini, F. (2022). Life-cycle management cost analysis of transportation bridges equipped with seismic structural health monitoring systems. Structural Health Monitoring, 21(1), 100-117.
Verma, N. K., & Subramanian, T. S. S. (2012). Cost benefit analysis of intelligent condition based maintenance of rotating machinery. In 2012 7th IEEE Conference on Industrial Electronics and Applications (ICIEA) (pp. 1390-1394). IEEE.
Verma, N. K., Khatravath, S., & Salour, A. (2013). Cost benefit analysis for condition based maintenance. In 2013 IEEE Conference on Prognostics and Health Management (PHM) (pp. 1-6). IEEE.
Yang, R., Vatn, J., & Utne, I. B. (2023). Dynamic maintenance planning for autonomous marine systems (AMS) and operations. Ocean Engineering, 278, 114492.
Yoon, J. T., Youn, B. D., Yoo, M., Kim, Y., & Kim, S. (2019). Life-cycle maintenance cost analysis framework considering time-dependent false and missed alarms for fault diagnosis. Reliability Engineering and System Safety, 184, 181-192.
Zhang, T., Li, Q., Zhang, C. S., Liang, H. W., Li, P., Wang, T. M., & Wu, C. (2017). Current trends in the development of intelligent unmanned autonomous systems. Frontiers of Information Technology and Electronic Engineering, 18, 68-85.
Zhao, H., Xu, F., Liang, B., Zhang, J., & Song, P. (2019). A condition-based opportunistic maintenance strategy for multi-component system. Structural Health Monitoring, 18(1), 270-283.
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Technical Papers