## 1 Introduction
### Considerations in the Preparation of the Predictive Maintenance Plan  
This paper focuses on predicting the operational behavior of a hydrogen compressor in a petrochemical facility producing caprolactam, a precursor for nylon 6 fibers. Given the continuous nature of the manufacturing process, any component failure can lead to full production stoppages. Unplanned shutdowns entail significant risks, including collateral damage to critical components (e.g., sensors, control valves, pipelines, and  
Figure 1: Stages of transitioning from a preventive maintenance model to a predictive one (maintenance plan). Based on PDCA cycle. electrical systems), delayed customer deliveries, and increased costs for emergency spare parts and overtime labor. Although the compressor has experienced few failures in recent years, emerging electromechanical issues--such as bearing wear due to overheating and power supply instability--highlight the need for proactive solutions. Additionally, missing data values in other equipment records, attributed to aging communication infrastructure and lack of signal redundancy, further complicate predictive efforts. These challenges have been formally assessed by multidisciplinary teams, confirming the urgency of addressing both hardware and data reliability gaps. Implementing these enhancements in the hydrogen compression process is expected to yield the following benefits:  
* Reduction in Unplanned Downtime: Minimizing emergency stops caused by malfunctions or missing data values, thereby avoiding subsequent inspections, refurbishments, or replacements of damaged components. * Improved Decision-Making for Operators: Providing reliable, data-driven insights to operators enables controlled shutdowns when necessary. In optimal scenarios, this allows continued operation until scheduled maintenance can address the issue. * Enhanced Sensor Infrastructure: Rewiring sensors to ensure signal redundancy may require new hardware and software modifications to process duplicated data streams effectively. * Increased System Reliability: Evaluating the acquisition of a twin compressor could further enhance reliability through redundancy, ensuring uninterrupted operation. Sustainable maintenance management represents a critical strategy for achieving sustainable development [20], requiring organizational shifts in policies, strategies, and asset maintenance practices [21; 22]. This approach expands the traditional triple-bottom-line framework of sustainable maintenance [23] by incorporating a fourth, technology-driven dimension [24]. The resulting four dimensions are defined as follows:  
1. Economic Dimension: Focused on operational efficiency, downtime reduction, process quality, and overall productivity [25; 26]. 2. Environmental Dimension: Centered on resource optimization and waste minimization [27; 28; 29; 30]. 3. Social Dimension: Encompassing legal compliance regarding employee health/safety and customer obligations [31]. 4. Technological Dimension: Addressing the integration of advanced technologies into maintenance systems [32; 33; 34; 35]. The transition to sustainable maintenance necessitates adopting new performance metrics. Notably, the EN 15341 standard [36] outlines 183 maintenance management indicators, including, 3 directly tied to sustainability, 22 related to health, safety, and environment (HSE), 21 concerning personnel competence, and 20 focused on Industry 4.0 technologies. A comprehensive maintenance plan should integrate at least one indicator from each sub-function to enable holistic performance assessment. The adoption of condition-based maintenance is significantly enhanced by Industry 4.0 technologies, which are fundamentally characterized by cyber-physical systems (CPS). These CPSs are characterized by a tight integration of physical and computational systems, enabling actuators deployed in the real world to operate adaptively and autonomously based on signals from sensor inputs [37].