## 1 Introduction  
Condition-Based Maintenance (CBM) is an approach that focuses on assessing asset conditions to establish a robust maintenance policy based on the vital signals of an asset [1]. Within CBM, the Proportional Hazards Model (PHM) has proven suitable for estimating the proportional risk generated by these critical signals, referred to as covariates, enabling the development of predictive maintenance policies [2]. This policy leverages real-time sensors and data analysis to predict equipment failure, scheduling maintenance only when needed to avoid equipment failures. While many studies utilize these techniques, the majority focus on academic or synthetic data, considering only a few covariates. This raises concerns about their applicability to real-world operational databases, where a multi-covariate scenario is more common [3, 4]. One significant challenge, due to the high variance in measurement units and magnitudes across covariates, is the need for more guidance on selecting optimal covariates, estimating their weights, and defining conditional states for use in the PHM. Two recent studies have independently addressed these challenges by leveraging machine learning (ML) tools. The authors of Ref. [3] introduced a novel technique for obtaining covariate weights in the PHM, while in Ref. [5], a sophisticated method is presented for determining state bandsusing automated clustering techniques. However, there is a lack of testing in real-world scenarios, and no further development has been made to define a decision rule for equipment intervention. In summary, the identified challenges are as follows:  
* Asset-intensive industries require a multi-covariate model for CBM predictive policies, capable of prioritizing diverse magnitudes and units when interpretations are lacking. * Assessing the impact of condition heterogeneity on decision-making sensitivity is crucial, especially in real-case scenarios with multiple-covariates. Thus, the objective of this research was to develop a framework capable of formulating predictive maintenance policies for multi-covariate scenarios where expert judgment alone is inadequate for determining the necessary parameters for the PHM. Consequently, the proposed framework is based on a PHM-machine learning (PHM-ML) model, structured into the following four phases to ensure its operation is autonomous:  
1. Optimize covariate selection for the model by integrating a machine learning cross-validation approach with statistical metrics to identify the most influential features. 2. The estimation of covariate weights for the PHM by combining ML genetic algorithms with a standard solver method. 3. The determination of covariate range bands for each state using K-means and Gaussian Mixture Model. 4. Obtaining ML reliability metrics to develop a maintenance policy for interventions on elements prone to failure through a predictive decision-making graph. The resulting policies undergo a performance evaluation by comparing them with an original solution developed for a Chilean energy distribution company using real operational data. Following this, a sensitivity analysis identifies aspects of the framework that induce positive changes in the model. In this context, the main contributions of the present work are summarized as follows:  
* Formulate a comprehensive PHM-ML framework that seamlessly integrates covariate selection, weight estimation, range band determination, and predictive maintenance models for multi-condition scenarios, with applicability across different industries. * Introduce sensitivity analyses to evaluate the impact of the framework's optimization methods, scalers, covariate constraint techniques, and selection criteria on the resulting predictive policy. * The assessment of the proposed PHM-ML model versus conventional solver methods and optimal log-likelihood scores to evaluate performance and coherence in prediction quality for maintenance purposes.