Deep learning for fault diagnosis of monoblock centrifugal pumps: a Hilbert–Huang transform approach

IF 1.6 Q2 ENGINEERING, MULTIDISCIPLINARY
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.

Abstract Image

用于单体离心泵故障诊断的深度学习:希尔伯特-黄变换方法
单体离心泵的故障检测在确保机械设备的安全和高效使用方面发挥着重要作用。本研究提出了一种基于深度学习的方法,利用迁移学习对单体离心泵进行故障检测。利用 MEMS 传感器从实验装置中获取振动信号,然后将这些信号处理并存储为 Hilbert-Huang 变换图像。利用 15 个预训练网络,如 InceptionResNetV2、DenseNet-201、GoogLeNet、ResNet-50、VGG-19、Xception、VGG-16、EfficientNetb0、ShuffleNet、InceptionV3、ResNet101、MobileNet-v2、AlexNet、NasNetmobile 和 ResNet-18,对获取的数据进行故障诊断。为了达到较高的分类精度,系统地改变和优化了各种超参数,包括批量大小、学习率、训练-测试分割比和优化器。目的是为深度学习模型确定最合适的配置。通过利用迁移学习并将获取的振动信号预处理为希尔伯特-黄变换图像,分类准确率得到了显著提高。事实证明,通过大量实验优化超参数有助于提升模型性能。经过全面试验和细致调整,GoogleNet 架构成为最佳设置,达到了 100.00% 的峰值分类准确率,同时保持了 80 秒的计算效率。
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来源期刊
CiteScore
4.30
自引率
10.00%
发文量
252
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.
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