Computer Graphics and Computer-Aided Design, Multidisciplinary, Mechanical Engineering, Artificial Intelligence
10
Scopus Publications
213
Scholar Citations
8
Scholar h-index
6
Scholar i10-index
Scopus Publications
Attention-Guided Generative Adversarial Networks for Enhancing MRI-based Alzheimer's Disease Diagnosis A. Sathiya, CH Hussaian Basha, A Balasupramani, P Arun Balaji, S. Rajan, N.N. Baalakumar Proceedings of 8th International Conference on Trends in Electronics and Informatics Icoei 2025, 2025 o date, Alzheimer's disease (AD) remains a major neurodegenerative disorder without a cure, and early and accurate diagnosis is critical for patient management. To this end, we propose an Attention Guided Generative Adversarial Network (AG-GAN) framework for improving MRI-based Alzheimer's disease diagnosis in this study. The dual-stream attention model is proposed, in which the deep learning process is refined by a dual-stream attention mechanism to extract refined MRI features and enhance its capability of discriminability to labels. The AG-GAN framework provides high-quality synthetic MRI scans that help with data augmentation and mitigate the class imbalance problem in AD datasets. Moreover, the attention module is designed to learn key pathological features using robust feature learning. Finally, we test the model on AD datasets and benchmark it against baseline CNN classifiers and standard GAN-based classifiers. The results of the proposed experiment show improved classification accuracy, sensitivity, and specificity of this approach for early AD detection. This work contributes to developing deep learning techniques in medical imaging applications to increase model interpretability and diagnostic precision.
Fault detection of automobile suspension system using decision tree algorithms: A machine learning approach P Arun Balaji, V Sugumaran Proceedings of the Institution of Mechanical Engineers Part E Journal of Process Mechanical Engineering, 2024 The study aims to detect multiple faults that are exhibited by suspension system components during prolonged usage. Faults such as strut worn out, strut external damage, strut mount fault, lower arm ball joint fault, lower arm bush worn out and tie rod ball joint fault were considered in this study. A novel approach is proposed in the present study that involves vibration signals and machine learning techniques to identify various suspension system faults. Vibration signals were acquired for different fault conditions (as mentioned above) at three different load conditions by a specially fabricated experimental setup. Statistical features were extracted from the acquired vibration signals from which the most significant features were selected using J48 decision tree algorithm. The selected features were provided as input to the tree-based family of algorithms to determine the best in class classification algorithm for suspension fault diagnosis. The results obtained enumerate that the random forest classifier produces the best classification accuracy for all the load conditions (no load, half load, and full load) with values of 95.88%, 94.88%, and 92.01%, respectively. Finally, the performance of the proposed classification model is compared with other state-of-the-art machine learning classifiers.
Deep transfer learning architecture for suspension system fault diagnosis using spectrogram image and CNN Parameshwaran Arun Balaji, Sridharan Naveen Venkatesh, Vaithiyanathan Sugumaran, Vetri Selvi Mahamuni Advances in Mechanical Engineering, 2024 The suspension system plays a critical role in automobiles, ensuring the safety and comfort of vehicle occupants. However, extended usage, varying road conditions, external forces, and heavy loads can result in damage and faults within the internal components of the suspension system. To mitigate the occurrence of suspension system failures, the development of an effective fault diagnosis system for suspension components becomes imperative. Traditional fault diagnosis techniques often heavily rely on human expertise, which comes with certain limitations. In response, researchers have embraced intelligent fault diagnosis techniques, with transfer learning-based fault diagnosis emerging as a highly effective approach. By leveraging transfer learning, it becomes possible to extract and select fault-specific features for classification purposes. Deep learning-based methods, with their capacity to extract significant features and essential information from raw data, offer notable advantages. Despite these advantages, the implementation of deep learning-based fault diagnosis in suspension systems remains relatively unexplored and limited. In this article, a deep transfer learning architecture specifically designed for fault diagnosis in suspension systems is proposed. The approach involves employing 12 pre-trained networks and tuning them to identify the optimal model for fault diagnosis. Time domain vibration signals obtained from suspension systems under seven fault conditions and one good condition are transformed into spectrogram images. These images are then pre-processed and used as input for the pre-trained networks in fault classification. The results demonstrate that among the 12 pre-trained networks, AlexNet outperforms the others in terms of classification accuracy while requiring the least amount of training time. Therefore, AlexNet network in conjunction with the spectrogram images of time domain vibration signals for applications in suspension system fault diagnosis is highly recommend.
Fault Diagnosis of Suspension System Based on Spectrogram Image and Vision Transformer Arun Balaji P, Naveen Venkatesh S, Sugumaran V Eksploatacja I Niezawodnosc, 2024 The suspension system plays a critical role in vehicles, providing both comfort and directional control. Therefore, it is essential to implement a monitoring system to ensure the proper functioning of suspension components, as a failure in any of these components can lead to accidents. Furthermore, monitoring the condition of the suspension system helps in maintaining its performance and minimizes maintenance costs. Traditionally, diagnosing faults in suspension systems has relied on specialized setups and vibration analysis. Alternatively, deep learning-based approaches for fault diagnosis in suspension systems offer a promising solution by enabling faster and more accurate real-time fault detection. This study investigated the use of vision transformers as an innovative approach to fault diagnosis in suspension systems, leveraging spectrogram images. Spectrogram images from vibration signals were extracted and used as inputs for the vision transformer model. Test results showcased a remarkable 99.39% accuracy in fault identification, affirming the system's effectiveness.
Transfer Learning Based Fault Detection for Suspension System Using Vibrational Analysis and Radar Plots Samavedam Aditya Sai, Sridharan Naveen Venkatesh, Seshathiri Dhanasekaran, Parameshwaran Arun Balaji, Vaithiyanathan Sugumaran, Natrayan Lakshmaiya, Prabhu Paramasivam Machines, 2023 The suspension system is of paramount importance in any automobile. Thanks to the suspension system, every journey benefits from pleasant rides, stable driving and precise handling. However, the suspension system is prone to faults that can significantly impact the driving quality of the vehicle. This makes it essential to find and diagnose any faults in the suspension system and rectify them immediately. Numerous techniques have been used to identify and diagnose suspension faults, each with drawbacks. This paper’s proposed suspension fault detection system aims to detect these faults using deep transfer learning techniques instead of the time-consuming and expensive conventional methods. This paper used pre-trained networks such as Alex Net, ResNet-50, Google Net and VGG16 to identify the faults using radar plots of the vibration signals generated by the suspension system in eight cases. The vibration data were acquired using an accelerometer and data acquisition system placed on a test rig for eight different test conditions (seven faulty, one good). The deep learning model with the highest accuracy in identifying and detecting faults among the four models was chosen and adopted to find defects. The results state that VGG16 produced the highest classification accuracy of 96.70%.
Robust Algorithm to Learn Rules for Classification - a Fault Diagnosis Case Study Arun Balaji, V. Sugumaran Fme Transactions, 2023 Machine learning algorithms are used for building classifier models. The rule-based decision tree classifiers are popular ones. However, the performance of the decision tree classifier varies with hyperparameter tuning. The optimum hyperparameter values are obtained using either optimization algorithms or trial and error methods. The present study utilizes the MODLEM algorithm to overcome the drawbacks accounted for by decision tree algorithms. Eliminating hyperparameter tuning and producing results closer to standard decision tree algorithms makes MODLEM a robust classification algorithm. The robustness of the MODLEM algorithm is illustrated with the fault diagnosis case study. The case study is faults diagnosis of an automobile suspension system using vibration signals acquired at various fault conditions.
Photovoltaic Module Fault Detection Based on Deep Learning Using Cloud Computing S. Naveen Venkatesh, P. Arun Balaji, Ganjikunta Chakrapani, K. Annamalai, S. Aravinth, P. S. Anoop, V. Sugumaran, Vetriselvi Mahamuni Scientific Programming, 2023 The performance of photovoltaic modules (PVMs) degrades due to the occurrence of various faults such as discoloration, snail trail, burn marks, delamination, and glass breakage. This degradation in power output has created a concern to improve PVM performance. Automatic inspection and condition monitoring of PVM components can handle performance-related issues, especially for installed capacity where no trained personnel are available at the location. This paper describes a deep learning-based technique involving convolutional neural networks (CNNs) to extract features from aerial images obtained from unmanned aerial vehicles (UAVs) and classify various types of fault occurrences using cloud computing and Internet of things (IoT). The algorithm used demonstrates a binary classification with high accuracy by comparing individual faults with good condition. Efficient and effective fault detection can be observed from the results obtained.
Transfer Learning-Based Condition Monitoring of Single Point Cutting Tool S. Naveen Venkatesh, P. Arun Balaji, M. Elangovan, K. Annamalai, V. Indira, V. Sugumaran, Vetri Selvi Mahamuni Computational Intelligence and Neuroscience, 2022 Machining activities in recent times have shifted their focus towards tool life and tool wear. Cutting tools have been utilized on a daily basis and play a vital role in manufacturing industries. Prolonged and incessant operation of the cutting tool can lead to wear and tear of the component, thereby compromising the dimensional accuracy. The condition of a tool is estimated based upon the surface quality of the machined component, condition of the machine, and the rate of production. Maintaining the tool health plays a vital role in enhancing the productivity of manufacturing industries. Numerous efforts were experimented by the researchers to maintain the tool health condition. The drawbacks of conventional diagnostic techniques include requirement of high level of human intelligence and professional expertise on the field, which led the researchers to develop intelligent and automatic diagnostic tools. There are many techniques suggested by researchers to detect the condition of single point cutting tool. This article proposes the use of transfer learning technology to detect the condition of single point cutting tool. First, the vibration signals were collected from the cutting tool and plots were made which will work as input to the deep learning algorithms. The deep learning algorithms have the capability to learn from the plots of vibration signals and classify the state of the single point cutting tool. In this work, the pretrained networks such as VGG-16, AlexNet, ResNet-50, and GoogLeNet were employed to identify the state of the cutting tool. In the pretrained networks, the effect of hyperparameters such as batch size, solver, learning rate, and train-test split ratio was studied, and the best performing network was suggested for tool condition monitoring.
RECENT SCHOLAR PUBLICATIONS
Misfire detection in internal combustion engine with MEMS accelerometer using decision-tree classifiers PABSV Vinod Vasan1, Naveen Venkatesh Sridharan2 Measurement and control, 1-15 , 2025 2025
Attention-Guided Generative Adversarial Networks for Enhancing MRI-based Alzheimer's Disease Diagnosis A Sathiya, CHH Basha, A Balasupramani, PA Balaji, S Rajan, ... 2025 8th International Conference on Trends in Electronics and Informatics … , 2025 2025 Citations: 3
Diagnosing faults in suspension system using machine learning and feature fusion strategy HL Karthikeyan, NV Sridharan, PA Balaji, S Vaithiyanathan Arabian Journal for Science and Engineering 49 (11), 15059-15083 , 2024 2024 Citations: 8
Deep transfer learning architecture for suspension system fault diagnosis using spectrogram image and CNN P Arun Balaji, S Naveen Venkatesh, V Sugumaran, VS Mahamuni Advances in Mechanical Engineering 16 (6), 16878132241258904 , 2024 2024 Citations: 4
Fault detection of automobile suspension system using decision tree algorithms: a machine learning approach P Arun Balaji, V Sugumaran Proceedings of the Institution of Mechanical Engineers, Part E: Journal of … , 2024 2024 Citations: 21
Fault diagnosis of suspension system based on spectrogram image and vision transformer B Arun, NS Venkatesh, V Sugumaran Eksploatacja i Niezawodność 26 (1) , 2024 2024 Citations: 4
Weightless Neural Network-Based Fault Diagnosis in Suspension System. R Shah, PA Balaji, V Sugumaran FME Transactions 52 (1) , 2024 2024 Citations: 2
Reducing cost with MEMS sensor and improving performance of classifier using probabilistic voting method PA Balaji, V Sugumaran Measurement Science and Technology 35 (1), 015134 , 2024 2024 Citations: 5
Transfer learning based fault detection for suspension system using vibrational analysis and radar plots SA Sai, SN Venkatesh, S Dhanasekaran, PA Balaji, V Sugumaran, ... Machines 11 (8), 778 , 2023 2023 Citations: 45
Efficacy of machine learning algorithms in estimating emissions in a dual fuel compression ignition engine operating on hydrogen and diesel V Sugumaran, V Thangavel, P Arun balaji, M Vijayaragavan, ... International Journal of Hydrogen Energy , 2023 2023 Citations: 43
Robust Algorithm to Learn Rules for Classification-a Fault Diagnosis Case Study. PA Balaji, V Sugumaran FME Transactions 51 (3) , 2023 2023 Citations: 3
Comparative study of machine learning and deep learning techniques for fault diagnosis in suspension system PA Balaji, V Sugumaran Journal of the Brazilian Society of Mechanical Sciences and Engineering 45 … , 2023 2023 Citations: 18
Research Article Photovoltaic Module Fault Detection Based on Deep Learning Using Cloud Computing SN Venkatesh, PA Balaji, G Chakrapani, K Annamalai, S Aravinth, ... 2023
Photovoltaic module fault detection based on deep learning using cloud computing S Naveen Venkatesh, P Arun Balaji, G Chakrapani, K Annamalai, ... Scientific Programming 2023 (1), 8805817 , 2023 2023 Citations: 9
Transfer Learning-Based Condition Monitoring of Single Point Cutting Tool VSM S. Naveen Venkatesh ,P. Arun Balaji,M. Elangovan,K. Annamalai,V. Indira ... Computational Intelligence and Neuroscience 2022, 14 , 2022 2022 Citations: 35
Research Article Transfer Learning-Based Condition Monitoring of Single Point Cutting Tool SN Venkatesh, PA Balaji, M Elangovan, K Annamalai, V Indira, ... 2022
A Bayes learning approach for monitoring the condition of suspension system using vibration signals PA balaji, V Sugumaran IOP Conference Series: Materials Science and Engineering 1012 (1), 012029 , 2021 2021 Citations: 13
MOST CITED SCHOLAR PUBLICATIONS
Transfer learning based fault detection for suspension system using vibrational analysis and radar plots SA Sai, SN Venkatesh, S Dhanasekaran, PA Balaji, V Sugumaran, ... Machines 11 (8), 778 , 2023 2023 Citations: 45
Efficacy of machine learning algorithms in estimating emissions in a dual fuel compression ignition engine operating on hydrogen and diesel V Sugumaran, V Thangavel, P Arun balaji, M Vijayaragavan, ... International Journal of Hydrogen Energy , 2023 2023 Citations: 43
Transfer Learning-Based Condition Monitoring of Single Point Cutting Tool VSM S. Naveen Venkatesh ,P. Arun Balaji,M. Elangovan,K. Annamalai,V. Indira ... Computational Intelligence and Neuroscience 2022, 14 , 2022 2022 Citations: 35
Fault detection of automobile suspension system using decision tree algorithms: a machine learning approach P Arun Balaji, V Sugumaran Proceedings of the Institution of Mechanical Engineers, Part E: Journal of … , 2024 2024 Citations: 21
Comparative study of machine learning and deep learning techniques for fault diagnosis in suspension system PA Balaji, V Sugumaran Journal of the Brazilian Society of Mechanical Sciences and Engineering 45 … , 2023 2023 Citations: 18
A Bayes learning approach for monitoring the condition of suspension system using vibration signals PA balaji, V Sugumaran IOP Conference Series: Materials Science and Engineering 1012 (1), 012029 , 2021 2021 Citations: 13
Photovoltaic module fault detection based on deep learning using cloud computing S Naveen Venkatesh, P Arun Balaji, G Chakrapani, K Annamalai, ... Scientific Programming 2023 (1), 8805817 , 2023 2023 Citations: 9
Diagnosing faults in suspension system using machine learning and feature fusion strategy HL Karthikeyan, NV Sridharan, PA Balaji, S Vaithiyanathan Arabian Journal for Science and Engineering 49 (11), 15059-15083 , 2024 2024 Citations: 8
Reducing cost with MEMS sensor and improving performance of classifier using probabilistic voting method PA Balaji, V Sugumaran Measurement Science and Technology 35 (1), 015134 , 2024 2024 Citations: 5
Deep transfer learning architecture for suspension system fault diagnosis using spectrogram image and CNN P Arun Balaji, S Naveen Venkatesh, V Sugumaran, VS Mahamuni Advances in Mechanical Engineering 16 (6), 16878132241258904 , 2024 2024 Citations: 4
Fault diagnosis of suspension system based on spectrogram image and vision transformer B Arun, NS Venkatesh, V Sugumaran Eksploatacja i Niezawodność 26 (1) , 2024 2024 Citations: 4
Attention-Guided Generative Adversarial Networks for Enhancing MRI-based Alzheimer's Disease Diagnosis A Sathiya, CHH Basha, A Balasupramani, PA Balaji, S Rajan, ... 2025 8th International Conference on Trends in Electronics and Informatics … , 2025 2025 Citations: 3
Robust Algorithm to Learn Rules for Classification-a Fault Diagnosis Case Study. PA Balaji, V Sugumaran FME Transactions 51 (3) , 2023 2023 Citations: 3
Weightless Neural Network-Based Fault Diagnosis in Suspension System. R Shah, PA Balaji, V Sugumaran FME Transactions 52 (1) , 2024 2024 Citations: 2
Misfire detection in internal combustion engine with MEMS accelerometer using decision-tree classifiers PABSV Vinod Vasan1, Naveen Venkatesh Sridharan2 Measurement and control, 1-15 , 2025 2025
Research Article Photovoltaic Module Fault Detection Based on Deep Learning Using Cloud Computing SN Venkatesh, PA Balaji, G Chakrapani, K Annamalai, S Aravinth, ... 2023
Research Article Transfer Learning-Based Condition Monitoring of Single Point Cutting Tool SN Venkatesh, PA Balaji, M Elangovan, K Annamalai, V Indira, ... 2022
