Integrating machine learning algorithms leveraged by advanced data acquisition systems is emerging as a pivotal approach in predictive maintenance. This paper presents the deployment of such an integration on an industrial air compressor unit. This research combines updated concepts from the Internet of Things, machine learning, multi-sensor data collection, structured data mining, and cloud-based data analysis. To this end, temperature, pressure, and flow rate data were acquired from sensors in contact with the compressor. The observed data were sent to the Structured Query Language database. Then, a Linear Regression model was fitted to the training data, and the optimized model was stored for real-time inference. Afterward, structured data were passed through the model, and if the data exceeded the determined threshold, a warning email was sent to an operator. Adopting the Internet of Things enhances surveillance for specialists, decreasing the failure and damage probabilities. The model achieved 98% accuracy in the Mean Squared Error metric for our regression model. By analyzing the gathered data, the implemented system demonstrates the capabilities to predict potential equipment failures with promising accuracy, facilitating a shift from reactive to proactive maintenance strategies. The findings reveal substantial potential for improvements in maintenance efficiency, equipment uptime, and cost savings.

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.