In the era of the fourth industrial revolution, several concepts have arisen in parallel with this new revolution, such as predictive maintenance, which today plays a key role in sustainable manufacturing and production systems by introducing a digital version of machine maintenance. The data extracted from production processes have increased exponentially due to the proliferation of sensing technologies. Even if Maintenance 4.0 faces organizational, financial, or even data source and machine repair challenges, it remains a strong point for the companies that use it. Indeed, it allows for minimizing machine downtime and associated costs, maximizing the life cycle of the machine, and improving the quality and cadence of production. This approach is generally characterized by a very precise workflow, starting with project understanding and data collection and ending with the decision-making phase. This paper presents an exhaustive literature review of methods and applied tools for intelligent predictive maintenance models in Industry 4.0 by identifying and categorizing the life cycle of maintenance projects and the challenges encountered, and presents the models associated with this type of maintenance: condition-based maintenance (CBM), prognostics and health management (PHM), and remaining useful life (RUL). Finally, a novel applied industrial workflow of predictive maintenance is presented including the decision support phase wherein a recommendation for a predictive maintenance platform is presented. This platform ensures the management and fluid data communication between equipment throughout their life cycle in the context of smart maintenance.

In an increasingly competitive industrial world, the need to adapt to any change at any time has become a major necessity for every industry to remain competitive and survive in their environments. Industries are undergoing rapid and perpetual changes on several levels. Indeed, the latter requires companies to be more reactive and involved in their policies of continuous improvement in order to satisfy their customers and maximize the quantity and quality of production, while keeping the cost of production as low as possible. Reducing downtime is one of the major objectives of these industries of the future. This paper aimed to apply machine learning algorithms on a TA-48 multistage centrifugal compressor for failure prediction and remaining useful life (RUL), i.e., to reduce system downtime using a predictive maintenance (PdM) approach through the adoption of Industry 4.0 approaches. To achieve our goal, we followed the methodology of the predictive maintenance workflow that allows us to explore and process the data for the model training. Thus, a comparative study of different prediction algorithms was carried out to arrive at the final choice, which is based on the implementation of LSTM neural networks. In addition, its performance was improved as the data sets were fed and incremented. Finally, the model was deployed to allow operators to know the failure times of compressors and subsequently ensure minimum downtime rates by making decisions before failures occur.

Industrial conveyor belt systems are critical infrastructure where unexpected failures cause major downtime, safety hazards, and economic losses, motivating growing research on AI-based fault detection. However, previous reviews have either focused on a single sensing modality, particularly computer vision, or considered conveyor systems within broader mining applications, without jointly assessing methodological quality, cross-modal sensing, deployment readiness, and the technical capabilities required for prognosis. Following PRISMA 2020 guidelines, this review analyzes 131 empirical studies on intelligent fault detection in industrial conveyor belt systems published between 2010 and 2026, with particular attention to sensing technologies, AI-based analytical methods, and deployment feasibility. The analysis considers the main fault types reported in the literature and the sensors and algorithms used for their detection. Deep learning was the dominant analytical approach (55.0% of studies), with CNN-based architectures such as YOLO, ResNet, and U-Net widely used for vision-based applications, while LSTM and GRU models were more commonly applied to acoustic and vibration signals. Vision/camera sensing accounted for 40.5% of studies, and mining represented 74% of applications. Despite substantial progress in automated fault detection, most studies remain focused on identifying existing faults rather than anticipating future degradation. Remaining useful life was estimated in only three studies (2.3%) and degradation trends in seven (5.3%); physics-informed machine learning and cross-site domain adaptation were absent, and principled multi-sensor fusion, uncertainty quantification, explainability, and digital-twin integration each appeared in no more than six studies. Predictive uncertainty was quantified in only one study (0.8%), and its calibration was not demonstrated. Future advances should therefore place greater emphasis on prognosis, cross-site generalization, uncertainty-aware inference, explainable models, and principled sensor fusion to support more reliable and transferable predictive maintenance systems.