Hybrid physics-infused 1D-CNN based deep learning framework for diesel engine fault diagnostics

被引：0
|
作者：
Singh, Shubhendu Kumar [1 ]
Khawale, Raj Pradip [1 ]
Hazarika, Subhashis [2 ]
Bhatt, Ankur [1 ]
Gainey, Brian [1 ]
Lawler, Benjamin [1 ]
Rai, Rahul [1 ]
机构：
[1] Department of Automotive Engineering, Clemson University, 4 Research Drive, Greenville,SC,29607, United States
[2] SRI International, 3333 Coyote Hill Road, Palo Alto,CA,94304, United States
来源：
Neural Computing and Applications | 2024年 / 36卷 / 28期
关键词：
Failure analysis;
D O I：
10.1007/s00521-024-10055-y
中图分类号：
学科分类号：
摘要：
引用
收藏
页码：17511 / 17539
相关论文
共 50 条
  • [41] Hybrid 1D-CNN and attention-based Bi-GRU neural networks for predicting moisture content of sand gravel using NIR spectroscopy
    Yuan, Quan
    Wang, Jiajun
    Zheng, Mingwei
    Wang, Xiaoling
    [J]. CONSTRUCTION AND BUILDING MATERIALS, 2022, 350
  • [42] Acoustic Modality Based Hybrid Deep 1D CNN-BiLSTM Algorithm for Moving Vehicle Classification
    Mohine, Shailesh
    Bansod, Babankumar S.
    Bhalla, Rakesh
    Basra, Anshul
    [J]. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2022, 23 (09) : 16206 - 16216
  • [43] A Hybrid 1D-CNN-Bi-LSTM based Model with Spatial Dropout for Multiple Fault Diagnosis of Roller Bearing
    Choudakkanavar, Gangavva
    Mangai, J. Alamelu
    [J]. INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS, 2022, 13 (08) : 637 - 644
  • [44] Hemorrhage Detection Based on 3D CNN Deep Learning Framework and Feature Fusion for Evaluating Retinal Abnormality in Diabetic Patients
    Maqsood, Sarmad
    Damasevicius, Robertas
    Maskeliunas, Rytis
    [J]. SENSORS, 2021, 21 (11)
  • [45] 1D CNN-Based Transfer Learning Model for Bearing Fault Diagnosis Under Variable Working Conditions
    Hasan, Md Junayed
    Sohaib, Muhammad
    Kim, Jong-Myon
    [J]. COMPUTATIONAL INTELLIGENCE IN INFORMATION SYSTEMS (CIIS 2018), 2019, 888 : 13 - 23
  • [46] Fault Diagnosis of Double Bridge Parallel Excitation Power Unit Based on 1D-CNN-LSTM Hybrid Neural Network Model
    Yang, Yanjie
    Dong, Zhe
    Yao, Fang
    Shi, Guangxi
    Sun, Ruoyu
    [J]. Dianwang Jishu/Power System Technology, 2021, 45 (05): : 2025 - 2032
  • [47] Smart Aggregate-Based Concrete Stress Monitoring via 1D CNN Deep Learning of Raw Impedance Signals
    Ta, Quoc-Bao
    Pham, Quang-Quang
    Pham, Ngoc-Lan
    Huynh, Thanh-Canh
    Kim, Jeong-Tae
    [J]. STRUCTURAL CONTROL & HEALTH MONITORING, 2024, 2024
  • [48] A comparative analysis between deep neural network-based 1D-CNN and LSTM models to harness the self-sensing property of the shape memory alloy wire actuator for position estimation
    Singh, Samarth
    Bhargaw, Hari N.
    Jadhav, Mahendra
    John, Preetesh
    [J]. SMART MATERIALS AND STRUCTURES, 2024, 33 (08)
  • [49] Deep-Learning Method Based on 1D Convolutional Neural Network for Intelligent Fault Diagnosis of Rotating Machines
    Chuya-Sumba, Jorge
    Alonso-Valerdi, Luz Maria
    Ibarra-Zarate, David I.
    [J]. APPLIED SCIENCES-BASEL, 2022, 12 (04):
  • [50] An efficient approach based on a novel 1D-LBP for the detection of bearing failures with a hybrid deep learning method
    Kaya, Yilmaz
    Kuncan, Melih
    Akcan, Eyyup
    Kaplan, Kaplan
    [J]. APPLIED SOFT COMPUTING, 2024, 155
12345