Wind turbines generate clean and renewable energy for the international market. The most ‎‎important aspect of wind turbine maintenance is reducing failures, downtime, and operating and maintenance expenses. ‎This study aims to detect multiple faults exhibited by wind turbine blades; failures such as cracks (tip crack, mid-span crack, and crack ‎near the root) were observed in the blades at different locations. The research suggests a new approach, incorporating vibration signals and machine learning techniques to identify various failures in wind turbine blades. The technology of ranking features such as ReliefF algorithms, chi-squares, and information gains was adopted to discuss a method framework to diagnose several problems in wind turbine blades, such as cracks in different locations. The k-nearest neighbors (KNNs), support vector machines, and random forests are used to classify data based on measured vibration signals. The eight main time-domain features are calculated from the vibration signals. The proposed methodology was validated using four databases. The results showed good classification accuracy in four databases, with at least three non-conventional features in each database’s top nine features of the three classification techniques. The results also showed that when the ReliefF selection algorithm is applied with the KNN classification algorithm, it generates the highest classification accuracy under all failure conditions, and the value is 97%. Finally, the performance of the proposed classification model is compared with other machine learning classification models, and a promising result is obtained. ‎

The incorporation of fiber reinforcements into polymer matrices has emerged as an effective strategy to enhance the mechanical properties of composites. This study investigated the tensile and fracture behavior of 3D-printed polylactic acid (PLA) composites reinforced with chopped carbon fibers (CCFs) through experimental characterization and finite element analysis (FEA). Composite samples with varying CCF orientations (0°, 0°/90°, +45°/−45°, and 0°/+45°/−45°/90°) were fabricated via fused filament fabrication (FFF) and subjected to tensile and single-edge notched bend (SENB) tests. The experimental results revealed a significant improvement in tensile strength, elastic modulus, and fracture toughness compared to unreinforced PLA. The 0°/+45°/90° orientation exhibited a 3.6% increase in tensile strength, while the +45°/−45° orientation displayed a 29.9% enhancement in elastic modulus and a 29.9% improvement in fracture toughness (259.12 MPa) relative to neat PLA (199.34 MPa√m). An inverse correlation between tensile strength and fracture toughness was observed, attributed to mechanisms such as crack deflection, fiber bridging, and fiber pull-out facilitated by multi-directional fiber orientations. FEA simulations incorporating a transversely isotropic material model and the J-integral approach were conducted using Abaqus, accurately predicting fracture toughness trends with a maximum discrepancy of 8% compared to experimental data. Fractographic analysis elucidated the strengthening mechanisms, highlighting the potential of tailoring CCF orientation to optimize mechanical performance for structural applications.

Globally, wind turbines play a significant role in generating sustainable and clean energy. Ensuring optimal performance and reliability is crucial to minimize failures and reduce operating and maintenance costs. However, due to their conventional design, identifying faults in wind turbines is challenging. This dataset provides vibration data for faulty wind turbine blades, which covers common vibration excitation mechanisms associated with various faults and operating conditions, including wind speed. The introduced faults in the wind turbine blades include surface erosion, cracked blade, mass imbalance, and twist blade fault. This data article serves as a valuable resource for validating condition monitoring methods in industrial wind turbine applications and facilitates a better understanding of vibration signal characteristics associated with different faults.

Gas turbines play a crucial role in power generation and aviation, where effective maintenance strategies are essential to ensure reliability. Traditional condition monitoring methods often rely on scheduled inspections, leading to potential downtime and increased maintenance costs. This study presents an AI-driven approach for thermal condition monitoring and the predictive maintenance of gas turbines using machine learning. An Extreme Gradient Boosting (XGBoost)-based classification model was developed to distinguish between healthy and faulty operating conditions based on thermal load data. The dataset, collected over six months from strategically placed thermocouples in the exhaust gas section, was processed to extract key statistical features such as mean temperature, standard deviation, and skewness. The proposed XGBoost model achieved a classification accuracy (CA) of 97.2%, with an F1-score of 96.8%, precision of 97.5%, and recall of 96.1%, demonstrating its effectiveness in detecting anomalies. The results indicate that the integration of machine learning in gas turbine monitoring significantly enhances fault detection capabilities, enabling proactive maintenance strategies and reducing the risk of critical failures. This study provides valuable insights for data-driven maintenance strategies, optimizing operational efficiency and extending the lifespan of gas turbine components. Future work will focus on real-time deployment and further validation with extended datasets.

In a heavily-subjected-to-failure field of rotating machinery, the need for accurate and reliable detection methods is paramount. This paper aims to advance fault detection capabilities through a novel approach that integrates current–temperature–vibration data fusion analysis and also by employing a Random Forest (RF) artificial intelligence methodology. An experimental investigation was conducted to acquire temperature variations, vibration signals, and electrical current readings from a rotating machine subjected to various types of faults. These faults include conditions of normal operation, bearing faults (inner and outer races), shaft misalignment, and rotor unbalance, all monitored under a constant rotating speed of 680 RPM and zero load. Four ceramic shear ICP-based accelerometers, two thermocouples, and three current transformers, were collectively used to collect the data while adhering to the International Organization for Standardization (ISO) standards. To refine the data for the RF algorithm, the standard deviation of the three datasets was calculated at specific intervals, revealing a significant enhancement in diagnostic accuracy when combinedly used. The resulted accuracies varied as follows: 45.0% accuracy using current data alone, 19.4% with temperature data, 62.0% with vibration data, and a remarkable 96.0% when using data fusion. Thus, data fusion is promising in thermal, electrical, and mechanical condition monitoring. Integrating diagnostic approaches in control systems can significantly improve the reliability of rotating machinery.