## 5 Conclusions  
By examining the intersection of ML techniques and maintenance strategies, this review highlights both significant advancements and persistent challenges across diverse industrial applications. Data-driven models, particularly ensemble methods and deep neural networks, have demonstrated superior performance compared with traditional maintenance protocols, especially in fault detection and RUL estimation. However, several critical barriers continue to limit real-world adoption. One of the foremost challenges is the dependency on labeled data in supervised learning approaches. In PdM, obtaining high-quality labeled datasets, where each instance is annotated with a known fault or failure type, is both time-consuming and expensive. This scarcity becomes especially problematic in high-risk sectors where failures are rare but impactful. Unsupervised learning can mitigate this issue by uncovering patterns, detecting anomalies, and discovering hidden structures in unlabeled sensor data. These capabilities make unsupervised methods well suited for maintenance environments where labeled datasets are incomplete or unavailable, offering scalable alternatives for early fault detection and health monitoring [78]. In addition, the unreliability and vulnerability of sensor data, whether caused by hardware faults or cyber threats, present further challenges to the robustness of PdM systems. To address these risks, ensuring the security and integrity of data within cyber-physical systems (CPSs) is essential. Industrial environments remain particularly exposed to deception, replay, and denial-of-service attacks, all of which can distort sensor readings and degrade model performance, leading to inaccurate or missed fault predictions in critical infrastructures such as smart grids and water treatment facilities [79]. Recent research demonstrates that ensemble deep learning methods can enhance the detection of such attacks, even when cyber incidents are infrequent and datasets are imbalanced [80]. In high-stakes sectors like healthcare and energy, the consequences of undetected tampering are especially serious. Techniques based on monitoring the behavior of physical systems, such as identifying real-time deviations, have proven effective in detecting advanced threats that bypass conventional information technology-based detection systems [81]. Another recurring theme is the lack of standardized frameworks that effectively integrate domain knowledge with algorithmic intelligence. While physics-informed learning and digital twins offer promising integration strategies, their implementation is constrained by edge computing limitations, communication overheads, and model interpretability concerns. To overcome these barriers, future research should prioritize cross-disciplinarycollaborations that unify data science with engineering domain expertise, alongside efforts to establish interoperable data standards and benchmarking practices. Ultimately, as organizations move toward proactive and intelligent maintenance ecosystems, attention must shift toward building explainable, scalable, and continuously evolving ML models. Emphasizing lifelong learning, transparent decision making, and cyber-resilience will be essential to accelerate the maturity of predictive maintenance technologies and to ensure operational continuity, safety, and efficiency in increasingly complex industrial systems. The following Supporting Information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app15094898/s1](https://www.mdpi.com/article/10.3390/app15094898/s1)]([https://www.mdpi.com/article/10.3390/app15094898/s1](https://www.mdpi.com/article/10.3390/app15094898/s1)), Table S1: Results of the systematic literature review. Conceptualization, C.T., P.P. and D.P. ; methodology, C.T., P.P. and D.P. ; validation, C.T., P.P., D.P. and R.A.M. ; formal analysis, C.T., P.P., D.P. and R.A.M. ; investigation, C.T., P.P. and D.P. ; resources, C.T. ; data curation, C.T.