C. V. Prasshanth, S. Naveen Venkatesh, Tapan K. Mahanta, N. R. Sakthivel, V. Sugumaran
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引用次数: 0
Abstract
Fault detection in monoblock centrifugal pumps plays an important role in ensuring the safe and efficient use of mechanical equipment. This study proposes a deep learning-based method using transfer learning for fault detection in monoblock centrifugal pumps. A MEMS sensor was used to acquire vibration signals from the experimental setup and these signals were subsequently processed and stored as Hilbert-Huang transform images. By leveraging 15 pretrained networks such as InceptionResNetV2, DenseNet-201, GoogLeNet, ResNet-50, VGG-19, Xception, VGG-16, EfficientNetb0, ShuffleNet, InceptionV3, ResNet101, MobileNet-v2, AlexNet, NasNetmobile and ResNet-18, fault diagnosis was performed on the acquired data. To achieve high classification accuracy, various hyperparameters including, batch size, learning rate, train-test split ratio and optimizer were systematically varied and optimized. The aim was to identify the most suitable configuration for the deep learning model. By leveraging transfer learning and preprocessing the acquired vibration signals into Hilbert–Huang transform images, the classification accuracy was significantly improved. Optimizing hyperparameters through extensive experimentation proved instrumental in elevating the models performance. Following thorough trials and meticulous tuning, the GoogleNet architecture emerged as the optimal setup, attaining a peak classification accuracy of 100.00%, all while upholding computational efficiency at 80 s.
期刊介绍:
This Journal is established with a view to cater to increased awareness for high quality research in the seamless integration of heterogeneous technologies to formulate bankable solutions to the emergent complex engineering problems.
Assurance engineering could be thought of as relating to the provision of higher confidence in the reliable and secure implementation of a system’s critical characteristic features through the espousal of a holistic approach by using a wide variety of cross disciplinary tools and techniques. Successful realization of sustainable and dependable products, systems and services involves an extensive adoption of Reliability, Quality, Safety and Risk related procedures for achieving high assurancelevels of performance; also pivotal are the management issues related to risk and uncertainty that govern the practical constraints encountered in their deployment. It is our intention to provide a platform for the modeling and analysis of large engineering systems, among the other aforementioned allied goals of systems assurance engineering, leading to the enforcement of performance enhancement measures. Achieving a fine balance between theory and practice is the primary focus. The Journal only publishes high quality papers that have passed the rigorous peer review procedure of an archival scientific Journal. The aim is an increasing number of submissions, wide circulation and a high impact factor.