Chaozhi Cai^*, Tiexin Xu, Jianhua Ren, Yingfang Xue

Structural Durability & Health Monitoring, Vol.19, No.1, pp. 125-144, 2025, DOI:10.32604/sdhm.2024.052813 - 15 November 2024

Abstract A bearing fault diagnosis method based on the Markov transition field (MTF) and SEnet (SE)-IShufflenetV2 model is proposed in this paper due to the problems of complex working conditions, low fault diagnosis accuracy, and poor generalization of rolling bearing. Firstly, MTF is used to encode one-dimensional time series vibration signals and convert them into time-dependent and unique two-dimensional feature images. Then, the generated two-dimensional dataset is fed into the SE-IShufflenetV2 model for training to achieve fault feature extraction and classification. This paper selects the bearing fault datasets from Case Western Reserve University and Paderborn University… More >

Xinwei Wang^1,2,*, Ming Li¹, Hankun Bing¹, Dongxing Zhang¹, Yuanshu Zhang¹

Energy Engineering, Vol.121, No.12, pp. 3875-3898, 2024, DOI:10.32604/ee.2024.056237 - 22 November 2024

Abstract After long-term operation, the performance of components in the GTCC system deteriorates and requires timely maintenance. Due to the inability to directly measure the degree of component malfunction, it is necessary to use advanced exergy analysis diagnosis methods to characterize the components’ health condition (degree of malfunction) through operation data of the GTCC system. The dissipative temperature is used to describe the degree of malfunction of different components in the GTCC system, and an advanced exergy analysis diagnostic method is used to establish a database of overall operating condition component malfunctions in the GTCC system.… More >

Kusum Yadav¹, Yasser Alharbi¹, Eissa Jaber Alreshidi¹, Abdulrahman Alreshidi¹, Anuj Kumar Jain², Anurag Jain³, Kamal Kumar⁴, Sachin Sharma⁵, Brij B. Gupta^6,7,8,*

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2665-2683, 2024, DOI:10.32604/cmc.2024.053565 - 18 November 2024

Abstract In today’s world, image processing techniques play a crucial role in the prognosis and diagnosis of various diseases due to the development of several precise and accurate methods for medical images. Automated analysis of medical images is essential for doctors, as manual investigation often leads to inter-observer variability. This research aims to enhance healthcare by enabling the early detection of diabetic retinopathy through an efficient image processing framework. The proposed hybridized method combines Modified Inertia Weight Particle Swarm Optimization (MIWPSO) and Fuzzy C-Means clustering (FCM) algorithms. Traditional FCM does not incorporate spatial neighborhood features, making More >

Lei Wang^1,*, Blanca Rodriguez², Xiaoyu Luo³, Charles Augarde⁴

The International Conference on Computational & Experimental Engineering and Sciences, Vol.30, No.4, pp. 1-2, 2024, DOI:10.32604/icces.2024.013350

Abstract Cardiovascular disease is the leading cause of death worldwide. Disease-specific software, like FFRct from HeartFlow, and high-fidelity computational models within a general-purpose software, like Living Heart Project within Abaqus, are essential to revolutionise diagnosis and treatment of cardiovascular disease for clinicians and design of medical devices for industries. This talk presents our past researches on computational modelling of tear propagation in the aortic dissection [1-2] and of electromechanical coupling in the human heart with the finite element method [3], and our current exploration on high-fidelity and efficient computation and software development for diagnosis and treatment More >

Sumika Chauhan, Govind Vashishtha^*, Radoslaw Zimroz

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.3, pp. 1983-2020, 2024, DOI:10.32604/cmes.2024.055633 - 31 October 2024

Abstract Sensors, vital elements in data acquisition systems, play a crucial role in various industries. However, their exposure to harsh operating conditions makes them vulnerable to faults that can compromise system performance. Early fault detection is therefore critical for minimizing downtime and ensuring system reliability. This paper delves into the contemporary landscape of fault diagnosis techniques for sensors, offering valuable insights for researchers and academicians. The papers begin by exploring the different types and causes of sensor faults, followed by a discussion of the various fault diagnosis methods employed in industrial sectors. The advantages and limitations More >

Hussain AlSalman¹, Taha Alfakih², Mabrook Al-Rakhami², Mohammad Mehedi Hassan^2,*, Amerah Alabrah²

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.3, pp. 2575-2608, 2024, DOI:10.32604/cmes.2024.055011 - 31 October 2024

Abstract Breast cancer diagnosis through mammography is a pivotal application within medical image-based diagnostics, integral for early detection and effective treatment. While deep learning has significantly advanced the analysis of mammographic images, challenges such as low contrast, image noise, and the high dimensionality of features often degrade model performance. Addressing these challenges, our study introduces a novel method integrating Genetic Algorithms (GA) with pre-trained Convolutional Neural Network (CNN) models to enhance feature selection and classification accuracy. Our approach involves a systematic process: first, we employ widely-used CNN architectures (VGG16, VGG19, MobileNet, and DenseNet) to extract a… More >

Jing Geng^1,*, Huali Yang², Shengze Hu³

Intelligent Automation & Soft Computing, Vol.39, No.5, pp. 985-986, 2024, DOI:10.32604/iasc.2024.059591 - 31 October 2024

Abstract This article has no abstract. More >

Oncology Research Editorial Office

Oncology Research, Vol.32, No.11, pp. 1829-1829, 2024, DOI:10.32604/or.2024.056904 - 16 October 2024

Abstract This article has no abstract. More >

Md Naeem Hossain¹, Md Mustafizur Rahman^1,2,*, Devarajan Ramasamy¹

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.2, pp. 951-996, 2024, DOI:10.32604/cmes.2024.056022 - 27 September 2024

Abstract Conventional fault diagnosis systems have constrained the automotive industry to damage vehicle maintenance and component longevity critically. Hence, there is a growing demand for advanced fault diagnosis technologies to mitigate the impact of these limitations on unplanned vehicular downtime caused by unanticipated vehicle breakdowns. Due to vehicles’ increasingly complex and autonomous nature, there is a growing urgency to investigate novel diagnosis methodologies for improving safety, reliability, and maintainability. While Artificial Intelligence (AI) has provided a great opportunity in this area, a systematic review of the feasibility and application of AI for Vehicle Fault Diagnosis (VFD)… More > Graphic Abstract

Yanxue Ma¹, Xiaoling Liu^1,*, Bing Wang², Ying Liu¹

Structural Durability & Health Monitoring, Vol.18, No.6, pp. 871-889, 2024, DOI:10.32604/sdhm.2024.052683 - 20 September 2024

Abstract In the bridge technical condition assessment standards, the evaluation of bridge conditions primarily relies on the defects identified through manual inspections, which are determined using the comprehensive hierarchical analysis method. However, the relationship between the defects and the technical condition of the bridges warrants further exploration. To address this situation, this paper proposes a machine learning-based intelligent diagnosis model for the technical condition of highway bridges. Firstly, collect the inspection records of highway bridges in a certain region of China, then standardize the severity of diverse defects in accordance with relevant specifications. Secondly, in order… More >