Affiliation:
1. Predictive Lab, Department of Industrial Engineering, Universidad Técnica Federico Santa María, Avenida Santa María 6400, Santiago 7630000, Chile
Abstract
The Proportional Hazards Model (PHM) under a Condition-Based Maintenance (CBM) policy is used by asset-intensive industries to predict failure rate, reliability function, and maintenance decisions based on vital covariates data. Cox’s partial likelihood optimization is a method to assess the weight of time and conditions into the hazard rate; however, parameter estimation with diverse covariates problem could have multiple and feasible solutions. Therefore, the boundary assessment and the initial value strategy are critical matters to consider. This paper analyzes innovative non/semi-parametric approaches to address this problem. Specifically, we incorporate IPCRidge for defining boundaries and use Gradient Boosting and Random Forest for estimating seed values for covariates weighting. When applied to a real case study, the integration of data scaling streamlines the handling of condition data with diverse orders of magnitude and units. This enhancement simplifies the modeling process and ensures a more comprehensive and accurate underlying data analysis. Finally, the proposed method shows an innovative path for assessing condition weights and Weibull parameters with data-driven approaches and advanced algorithms, increasing the robustness of non-convex log-likelihood optimization, and strengthening the PHM model with multiple covariates by easing its interpretation for predictive maintenance purposes.
Subject
Electrical and Electronic Engineering,Industrial and Manufacturing Engineering,Control and Optimization,Mechanical Engineering,Computer Science (miscellaneous),Control and Systems Engineering
Reference49 articles.
1. Aircraft routing with generalized maintenance constraints;Safaei;Omega,2018
2. Maletič, D., Maletič, M., Al-Najjar, B., and Gomišček, B. (2020). An analysis of physical asset management core practices and their influence on operational performance. Sustainability, 12.
3. Godoy, D.R., Álvarez, V., and López-Campos, M. (2023). Optimizing Predictive Maintenance Decisions: Use of Non-Arbitrary Multi-Covariate Bands in a Novel Condition Assessment under a Machine Learning Approach. Machines, 11.
4. A comparison of strategic mine planning approaches for in-pit crushing and conveying, and truck/shovel systems;Nehring;Int. J. Min. Sci. Technol.,2018
5. Galar, D., and Kans, M. (2017, January 28). The impact of maintenance 4.0 and big data analytics within strategic asset management. Proceedings of the Maintenance Performance and Measurement and Management 2016 (MPMM 2016), Luleå Tekniska Universitet, Luleå, Sweden.