Monitoring UAV status and detecting insulator faults in transmission lines with a new classifier based on aggregation votes between neural networks by interval type-2 TSK fuzzy system

UAVs are commonly utilized for the detection of insulator faults in transmission lines. The successful execution of such missions depends on two pivotal factors; the detection of insulator faults, which reduces losses in transmission lines, and the monitoring of the status of the UAV during the mission. Due to the vulnerability of UAV wings to defects, particularly lagging defects, it is crucial for the UAV to promptly land if it has loose wings to prevent severe damage. This article employs vibration data acquired from a wing-mounted sensor on the UAV to monitor its status. To determine the state of the UAV, a novel classifier is introduced, which aggregates votes from support vector machines (SVM), probabilistic neural networks (PNN), and deep neural networks (DNN) using Type-1 and Interval Type-2 Takagi–Sugeno–Kang Fuzzy System. Furthermore, the UAV-captured images of insulator in transmission lines are utilized for insulator fault detection. A similar approach is adopted for insulator fault detection, except that Multilayer Perceptron (MLP) is used instead of PNN, and ResNet-50 (Residual Network) is employed for feature extraction from insulator images. The software's cell phone interface, designed specifically for mobile devices, presents the graphical representation of UAV status and insulator fault detection. Furthermore, the generalizability of the proposed classifier for other applications is evaluated using two test datasets sourced from the UCI Machine Learning repository. The findings indicate that the proposed method performs better than renowned classifiers such as PNN, MLP, ANFIS, Fuzzy classifier, SVM, and ResNet-50. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.