Journal of Applied Mechanical Engineering

Artificial intelligence applied to predictive maintenance in industrial machinery focuses on estimating equipment condition and forecasting potential operational issues before they lead to functional interruptions. This approach relies on continuous monitoring of machine behavior through embedded sensors and data acquisition systems that capture vibration patterns, thermal readings, acoustic signals, electrical load variations, and lubrication conditions. The collected data is processed through analytical models that identify deviations from normal operating patterns and estimate remaining operational lifespan of components. The objective is to support maintenance planning based on actual equipment condition rather than fixed service intervals, reducing unnecessary maintenance actions and minimizing unexpected stoppages in production environments [1-2].

Industrial machines operate under varying mechanical and thermal stresses, which gradually affect component performance over time. Traditional maintenance approaches often rely on scheduled inspections or reactive repairs after failure occurs. These methods can result in inefficiencies either by replacing parts that still have usable life or by allowing equipment degradation to progress until failure interrupts operations. Artificial intelligence introduces a data-driven alternative by continuously analyzing operational signals and identifying subtle changes that indicate wear, misalignment, imbalance, or material fatigue. Machine learning models are trained using historical datasets that include both normal operating conditions and recorded failure events. Through this training process, the system learns patterns associated with degradation and can estimate future behavior under similar conditions [3-5].

Sensor integration forms the foundation of predictive maintenance systems. Vibration sensors are commonly used to detect mechanical irregularities in rotating equipment such as motors, pumps, and turbines. Changes in vibration frequency or amplitude can indicate bearing wear or shaft misalignment. Temperature sensors provide information about thermal stress in electrical systems and mechanical assemblies, while acoustic sensors capture sound variations that may signal friction changes or structural anomalies. Electrical sensors monitor current and voltage fluctuations that reflect load imbalances or inefficiencies in power consumption. These multiple data streams are synchronized and transmitted to centralized or distributed computing systems for analysis [6-7].

Artificial intelligence models used in predictive maintenance include regression algorithms, classification systems, and deep learning networks. Time-series analysis plays a significant role in interpreting sequential sensor data, allowing the system to recognize trends over extended operational periods. Recurrent neural networks and long short-term memory architectures are often applied to capture dependencies in temporal data. In addition, anomaly detection algorithms identify deviations from established baseline behavior without requiring explicit failure labels. These models continuously refine their accuracy as more operational data becomes available, improving their ability to distinguish between normal variations and early signs of degradation [8-9].

Edge computing has become an important component of modern predictive maintenance systems. Instead of transmitting all raw sensor data to centralized servers, preliminary processing is performed closer to the equipment itself. This reduces communication delays and allows faster identification of abnormal conditions. Only relevant features or summarized data are transmitted to cloud-based platforms for deeper analysis and long-term storage. This distributed structure improves system responsiveness and reduces the load on communication networks in large industrial environments with extensive machinery deployments [10].