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

The rapid adoption of Industry 4.0 technologies has transformed modern manufacturing by enabling intelligent monitoring and automation of industrial equipment. However, unexpected machine failures continue to cause production downtime, increased maintenance costs, and reduced operational efficiency. Predictive maintenance has emerged as an effective strategy to address these challenges by forecasting equipment failures before they occur. This paper proposes an Industrial Internet of Things (IIoT)-based Predictive Maintenance System that integrates smart sensors, edge computing, cloud analytics, artificial intelligence, and digital twin technology. The proposed framework continuously collects machine parameters such as vibration, temperature, pressure, current consumption, and acoustic signals through connected sensors. The collected data is analyzed using machine learning algorithms to identify anomalies, estimate remaining useful life (RUL), and generate maintenance recommendations. A digital twin model provides a virtual representation of industrial assets, enabling real-time simulation and performance evaluation. Experimental results demonstrate significant improvements in fault detection accuracy, equipment availability, and maintenance efficiency while reducing downtime and operational expenses. The proposed system 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

Predictive maintenance improves reliability and reduces downtime in modern manufacturing systems. However, many studies rely on laboratory datasets or single-component monitoring, limiting their applicability to complex industrial environments. This study proposes a predictive maintenance framework for a multi-pass wire drawing machine using vibration and motor current signals from a real industrial production line. A Composite Health Index (CHI) is developed to transform multi-motor sensor data into an interpretable machine-level degradation indicator. The extracted features are used to train ensemble machine learning models including Random Forest, Extra Trees, and XGBoost, whose outputs are combined through a weighted hybrid ensemble model. A decision-layer mechanism with smoothing and temporal filtering is applied to reduce false alarms while preserving detection capability. Experimental results show that the model achieves a recall of 0.90 and an F1-score of 0.75, demonstrating its effectiveness for industrial applications.

AN INTELLIGENT PREDICTIVE MAINTENANCE SYSTEM FOR INDUSTRIAL IOT APPLICATIONS

Predictive maintenance has become a key application of the Industrial Internet of Things (IIoT), helping industries improve equipment reliability and operational efficiency. Conventional maintenance approaches, such as reactive and preventive maintenance, often result in unexpected equipment failures, increased maintenance costs, and inefficient use of resources. By integrating real-time sensor monitoring with machine learning techniques, predictive maintenance enables early detection of potential faults, allowing maintenance activities to be performed only when necessary. This study presents a real-time predictive maintenance framework for Industrial IoT systems using machine learning. The proposed solution collects live sensor data, including temperature, vibration, and pressure, from industrial equipment. The data is preprocessed and analyzed using Python-based tools before being fed into machine learning models to identify anomalies and predict potential equipment failures. The system provides timely maintenance recommendations, minimizing unplanned downtime and improving overall equipment performance. 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. Keywords—Industrial Automation, Machine Learning, Predictive Maintenance, Sensor Networks, Equipment Failure Prediction.

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

It is widely recognized that modern industrial automation depends on real time condition monitoring of the equipment in order to avoid catastrophic failure and ensure that maintenance resources are used in the most efficient manner while minimising unscheduled downtime. In this research paper, an end-to-end predictive maintenance system is proposed for high stress mechanical drivetrain and rotating machinery. The architecture proposed combines an industrial Internet of Things (IoT) edge sensory network and hybrid machine learning and deep learning pipelines. These data include multi-modal sensor telemetry, including tri-axial vibration profiles, acoustic emissions, thermal imaging data, operational load metrics and electrical motor current signatures, all acquired at high sampling rates continuously. Advanced Wavelet Packet Decomposition and fast empirical mode extraction are used to remove signal artifacts and high frequency noise. These statistical, temporal and spectral attributes are reduced to a few-dimensional vector each and each is individually judged by a predictive suite of models including Random Forest regressors, Extreme Gradient Boosting (XGBoost), Support Vector Machines, Long Short-Term Memory (LSTM) recurrent networks, and Deep Residual Convolutional Neural Networks (ResNet-1D). Experimental validations conducted on standard bearing and gear testbeds prove the accuracy of optimized hybrid CNN-LSTM model in multi-class fault classification of 99.14% and Root Mean Square Error (RMSE) in Remaining Useful Life (RUL) prediction of 4.18 operating cycles. The edge-to-cloud telemetry infrastructure has been proven to provide an inference latency of less than 12 milliseconds per monitoring window, making real-time autonomous prognostics feasible in Industry 4.0 applications.