Vibration-induced failures in industrial ventilation systems: A predictive maintenance approach

A Comprehensive Review on Real-Time Performance Assessment & Operational Fault Monitoring in Hydromachinery

Hydromachinery is vital for clean and sustainable power generation, where reliable and efficient operation directly supports the stability of hydropower plants. To achieve this, real-time performance tracking and fault monitoring are becoming increasingly important. This review summarizes recent techniques and technologies used for monitoring turbines and their components in operation. Key areas include sensor-based data collection, modern signal processing tools, and artificial intelligence methods for detecting issues such as cavitation, vibration irregularities, pressure fluctuations, and mechanical wear. Methods like wavelet analysis, principal component analysis (PCA), support vector machines (SVM), and digital twins are discussed for their roles in fault diagnosis and performance evaluation. Advances in IoT-enabled monitoring and predictive maintenance are also highlighted, demonstrating their potential to enhance reliability and minimize downtime. The paper further outlines challenges such as harsh operating conditions, large data handling, and the need for accurate predictive models. Future directions are suggested, focusing on hybrid machine learning approaches, adaptive monitoring strategies, and digital twins for smart, autonomous health management of hydro machinery.

A Smart Predictive Maintenance Architecture for Industrial Equipment Monitoring Using IIoT, Machine Learning, and Digital Twin Models

An Industrial Internet of Things (IIoT)-based Predictive Maintenance System that integrates smart sensors, edge computing, cloud analytics, artificial intelligence, and digital twin technology is proposed that contributes to the development of intelligent and self-optimizing industrial environments aligned with Industry 4.0 objectives.

An IoT-based System for Early Fault Detection and Predictive Maintenance in Induction Motors with Interpretable Generalized Additive Neural Networks

Machine Learning-Based Predictive Maintenance of a Wire Drawing Machine Using Vibration and Motor Current Signals

A Composite Health Index (CHI) is developed to transform multi-motor sensor data into an interpretable machine-level degradation indicator and is used to train ensemble machine learning models including Random Forest, Extra Trees, and XGBoost.

AN INTELLIGENT PREDICTIVE MAINTENANCE SYSTEM FOR INDUSTRIAL IOT APPLICATIONS

Experimental results demonstrate that the proposed framework achieves high fault prediction accuracy, enhances system reliability, reduces maintenance costs, and supports data-driven decision-making in industrial environments.

AI-DRIVEN PREDICTIVE MAINTENANCE AND INTELLIGENT MONITORING OF MECHANICAL SYSTEMS USING MACHINE LEARNING AND IOT

An end-to-end predictive maintenance system is proposed for high stress mechanical drivetrain and rotating machinery and the architecture proposed combines an industrial Internet of Things edge sensory network and hybrid machine learning and deep learning pipelines.