## 5 Conclusions  
This paper provided a new and enhanced PHM-ML formulation to manage real operating time and condition monitoring data, facilitating optimal decision-making for predictive maintenance policies when diverse conditions are involved. A multi-covariate model  
Figure 9: Predictive maintenance policy for each model.  
was developed to address ample conditions heterogeneity and evaluate combined measurements that often lack a direct interpretation. Consequently, the proposed automated framework demonstrated high capability in selecting prioritized conditions, estimating covariate weights, determining state band ranges for reliability analysis, and evaluating predictive maintenance interventions under multi-condition scenarios.  
The framework underwent rigorous testing through two sensitivity analyses to discern their impact on the resulting predictive policy. The first analysis evaluated the framework's efficacy, highlighting that the traditional log-likelihood score did not automatically ensure feasible policies. It also demonstrated that the bound determination methods yielded identical results, the scaler significantly affected the dispersion of the state curves, and the sensitivity analysis aided in identifying optimal optimization parameters to produce feasible policies.  
The second analysis, focusing on the quality prediction of the framework, delved deeper into the covariate selection process. It revealed that the proposed methodology generated results similar to the reference case. Furthermore, it was shown that the proposed method allowed for the selection of optimal covariates to generate policies nearly identical to the reference. Thus, any model containing these covariates as a basis produced similar results, even when all covariates were selected, demonstrating the robustness of the covariate weight optimization process. Additionally, given the challenge of interpreting combined conditions, the scheme yielded enhanced results compared to expert criteria.  
In summary, the proposed PHM-ML framework offers significant advantages by seamlessly integrating multi-covariate scenarios under a CBM-predictive policy for asset management purposes across diverse industries. As further work, addressing the challenges observed with the MinMax scaler emerges as a key focus. While the proposed solution does not directly impact the framework's functionality, exploring alternative approaches and data-driven techniques could yield valuable insights for enhancing the policies generated during the implementation of this scaler. Moreover, future research could integrate the framework's outcomes with expert judgment elicitation techniques, thereby fostering a more comprehensive decision-making process in asset management.  
Conceptualization, D.R.G.; Methodology, D.R.G.; Validation, D.R.G., C.M., R.M., F.K. and P.V.; Formal analysis, D.R.G.; Investigation, D.R.G. and C.M.; Writing--original draft preparation, D.R.G. and C.M.; Writing--review and editing, D.R.G., C.M., R.M., F.K. and P.V.; Supervision, D.R.G.; Project administration and Funding acquisition, D.R.G. All authors have read and agreed to the published version of the manuscript. This research was supported by ANID through FONDEF - Concuso IDeA I+D (Chile). Grant Number: ID221I0348. Data Availability Statement: Data is contained within the article. The authors wish to acknowledge the financial support of this study by Agencia Nacional de Investigacion y Desarrollo (ANID) through Fondo de Fomento al Desarrollo Cientifico y Tecnologico (FONDEF) of the Chilean Government (Project FONDEF IDeA ID221I0348). The authors declare no conflict of interest.