As a modern technological trend, unmanned aerial vehicles (UAVs) are extensively employed in various applications. The core purpose of condition monitoring systems, proactive fault diagnosis, is essential in ensuring UAV safety in these applications. In this research, adaptive health monitoring systems perform blade balancing fault diagnosis and classification. There seems to be a bidirectional unpredictability within each, and this paper proposes a hybrid-based transformed discrete wavelet and a multi-hidden-layer deep neural network (DNN) scheme to compensate for it. Wide-scale, high-quality, and comprehensive soft-labeled data are extracted from a selected hovering quad-copter incorporated with an accelerometer sensor via experimental work. A data-driven intelligent diagnostic strategy was investigated. Statistical characteristics of non-stationary six-leveled multi-resolution analysis in three axes are acquired. Two important feature selection methods were adopted to minimize computing time and improve classification accuracy when progressed into an artificial intelligence (AI) model for fault diagnosis. The suggested approach offers exceptional potential: the fault detection system identifies and predicts faults accurately as the resulting 91% classification accuracy exceeds current state-of-the-art fault diagnosis strategies. The proposed model demonstrated operational applicability on any multirotor UAV of choice.

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.