Vaithiyanathan Sugumaran

Bio: Vaithiyanathan Sugumaran is an academic researcher from VIT University. The author has contributed to research in topics: Fault (power engineering) & Reliability (statistics). The author has co-authored 1 publications.

A combined approach of convolutional neural networks and machine learning for visual fault classification in photovoltaic modules

Abstract: Fault diagnosis plays a significant role in enhancing the useful lifetime, power output, and reliability of photovoltaic modules (PVM). Visual faults such as burn marks, delamination, discoloration...

Automatic Inspection of Photovoltaic Power Plants Using Aerial Infrared Thermography: A Review

Abstract: In recent years, aerial infrared thermography (aIRT), as a cost-efficient inspection method, has been demonstrated to be a reliable technique for failure detection in photovoltaic (PV) systems. This method aims to quickly perform a comprehensive monitoring of PV power plants, from the commissioning phase through its entire operational lifetime. This paper provides a review of reported methods in the literature for automating different tasks of the aIRT framework for PV system inspection. The related studies were reviewed for digital image processing (DIP), classification and deep learning techniques. Most of these studies were focused on autonomous fault detection and classification of PV plants using visual, IRT and aIRT images with accuracies up to 90%. On the other hand, only a few studies explored the automation of other parts of the procedure of aIRT, such as the optimal path planning, the orthomosaicking of the acquired images and the detection of soiling over the modules. Algorithms for the detection and segmentation of PV modules achieved a maximum F1 score (harmonic mean of precision and recall) of 98.4%. The accuracy, robustness and generalization of the developed algorithms are still the main issues of these studies, especially when dealing with more classes of faults and the inspection of large-scale PV plants. Therefore, the autonomous procedure and classification task must still be explored to enhance the performance and applicability of the aIRT method.

Detection of Compound Faults in Ball Bearings Using Multiscale-SinGAN, Heat Transfer Search Optimization, and Extreme Learning Machine

Abstract: Intelligent fault diagnosis gives timely information about the condition of mechanical components. Since rolling element bearings are often used as rotating equipment parts, it is crucial to identify and detect bearing faults. When there are several defects in components or machines, early fault detection becomes necessary to avoid catastrophic failure. This work suggests a novel approach to reliably identifying compound faults in bearings when the availability of experimental data is limited. Vibration signals are recorded from single ball bearings consisting of compound faults, i.e., faults in the inner race, outer race, and rolling elements with a variation in rotational speed. The measured vibration signals are pre-processed using the Hilbert–Huang transform, and, afterward, a Kurtogram is generated. The multiscale-SinGAN model is adapted to generate additional Kurtogram images to effectively train machine-learning models. To identify the relevant features, metaheuristic optimization algorithms such as teaching–learning-based optimization, and Heat Transfer Search are applied to feature vectors. Finally, selected features are fed into three machine-learning models for compound fault identifications. The results demonstrate that extreme learning machines can detect compound faults with 100% Ten-fold cross-validation accuracy. In contrast, the minimum ten-fold cross-validation accuracy of 98.96% is observed with support vector machines.

Deep learning-based ensemble model for classification of photovoltaic module visual faults

Abstract: ABSTRACT Fault occurrences in photovoltaic (PV) modules can hinder the performance of the system, resulting in reduced lifetime and performance of the modules. PV module (PVM) faults if unmonitored can affect the power transmission through the system, thereby creating short circuits that can be hazardous. Unmanned aerial vehicle (UAV)-based monitoring is one of the most common and widely adopted techniques to detect faults in PVM. Visual images of PVM contain the necessary information about the faults, but sometimes, it becomes difficult even for expert professional to work on large amount of image data. Automatic classification of PVM faults using deep learning techniques can help in providing improved analysis and instantaneous results. The present study adopts renowned deep convolution neural network (CNN) models such as MobileNet V2, Inception V3, and Xception for the classification of PVM. The aforementioned models were trained individually, and the classification performances of the models were observed to be 97.03%, 95.55%, and 92.27%, respectively. A hybrid deep ensemble model is proposed in the study that merges all the aforementioned models. The proposed model produced classification accuracy higher than each of the individual model with a value of 99.04%. Automatic classification using deep ensemble model can help in the accurate identification of faults in PVM from images acquired through UAV. Consequently, this computer-aided and quick diagnosis can eliminate the downtime and fire hazards.

A Novel Approach for Disaster Victim Detection Under Debris Environments Using Decision Tree Algorithms With Deep Learning Features

Abstract: Search and Rescue operations for victim identification in an unstructured collapsed building are high-risk and time-consuming. The possibility of saving a victim is high only during the first 48 hours, and then the prospect tends to zero. The faster the response and identification, the sooner the victim can be taken to medical assistance. Combining mobile robots with practical Artificial Intelligence (AI) driven Human Victim Detection (HVD) systems managed by professional teams can considerably reduce this problem. In this paper, we have developed a Transfer Learning-based Deep Learning approach to identify human victims under collapsed building environments by integrating machine learning classification algorithms. A custom-made human victim dataset was created with five class labels: head, hand, leg, upper body, and without the body. First, we extracted the class-wise features of the dataset using fine-tuning-based transfer learning on ResNet-50 deep learning model. The learned features of the model were then extracted, and then a feature selection was performed using J48 to study the impact of feature reduction in classification. Several decision tree algorithms, including decision stump, hoeffiding tree, J48, Linear Model Tree (LMT), Random Forest, Random Tree, Representative (REP) Tree, J48 graft, and other famous algorithms like LibSVM, Logistic regression, Multilayer perceptron, BayesNet, Naive Bayes are then used to perform the classification. The classification accuracy of the abovementioned algorithms is compared to recommend the optimal approach for real-time use. The random tree approach outperformed all other tree-based algorithms with a maximum classification accuracy of 99.53% and a computation time of 0.02 seconds.