Deep Learning for Enhanced Fault Diagnosis of Monoblock Centrifugal Pumps: Spectrogram-Based Analysis

被引:16
|
作者
Chennai Viswanathan, Prasshanth [1 ]
Venkatesh, Sridharan Naveen [1 ]
Dhanasekaran, Seshathiri [2 ]
Mahanta, Tapan Kumar [1 ]
Sugumaran, Vaithiyanathan [1 ]
Lakshmaiya, Natrayan [3 ]
Paramasivam, Prabhu [4 ]
Nanjagoundenpalayam Ramasamy, Sakthivel [5 ]
机构
[1] Vellore Inst Technol, Sch Mech Engn, Vandalur Kelambakkam Rd, Chennai 600127, India
[2] UiT Arctic Univ Norway, Dept Comp Sci, N-9037 Tromso, Norway
[3] Saveetha Sch Engn, SIMATS, Dept Mech Engn, Chennai 602105, India
[4] Mattu Univ, Coll Engn & Technol, Dept Mech Engn, Mettu 318, Ethiopia
[5] Amrita Vishwa Vidyapeetham, Amrita Sch Engn, Dept Mech Engn, Coimbatore 641112, India
关键词
monoblock centrifugal pumps; spectrogram; MEMS accelerometer; pre-trained networks; deep learning;
D O I
10.3390/machines11090874
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
The reliable operation of monoblock centrifugal pumps (MCP) is crucial in various industrial applications. Achieving optimal performance and minimizing costly downtime requires effectively detecting and diagnosing faults in critical pump components. This study proposes an innovative approach that leverages deep transfer learning techniques. An accelerometer was adopted to capture vibration signals emitted by the pump. These signals are then converted into spectrogram images which serve as the input for a sophisticated classification system based on deep learning. This enables the accurate identification and diagnosis of pump faults. To evaluate the effectiveness of the proposed methodology, 15 pre-trained networks including ResNet-50, InceptionV3, GoogLeNet, DenseNet-201, ShuffleNet, VGG-19, MobileNet-v2, InceptionResNetV2, VGG-16, NasNetmobile, EfficientNetb0, AlexNet, ResNet-18, Xception, ResNet101 and ResNet-18 were employed. The experimental results demonstrate the efficacy of the proposed approach with AlexNet exhibiting the highest level of accuracy among the pre-trained networks. Additionally, a meticulous evaluation of the execution time of the classification process was performed. AlexNet achieved 100.00% accuracy with an impressive execution (training) time of 17 s. This research provides invaluable insights into applying deep transfer learning for fault detection and diagnosis in MCP. Using pre-trained networks offers an efficient and precise solution for this task. The findings of this study have the potential to significantly enhance the reliability and maintenance practices of MCP in various industrial settings.
引用
收藏
页数:17
相关论文
共 50 条
  • [31] Hybrid Deep Learning Model for Fault Diagnosis in Centrifugal Pumps: A Comparative Study of VGG16, ResNet50, and Wavelet Coherence Analysis
    Zaman, Wasim
    Siddique, Muhammad Farooq
    Ullah, Saif
    Saleem, Faisal
    Kim, Jong-Myon
    MACHINES, 2024, 12 (12)
  • [32] Fault detection in water pumps based on sound analysis using a deep learning technique
    Nguyen, Minh T.
    Huang, Jin H.
    PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART E-JOURNAL OF PROCESS MECHANICAL ENGINEERING, 2022, 236 (02) : 298 - 307
  • [33] Motor Current Signal Analysis Based on Machine Learning for Centrifugal Pump Fault Diagnosis
    Alabied, Samir
    Daraz, Alsadak
    Rabeyee, Khalid
    Alqatawneh, Ibrahim
    Gu, Fengshou
    Ball, Andrew D.
    2019 25TH IEEE INTERNATIONAL CONFERENCE ON AUTOMATION AND COMPUTING (ICAC), 2019, : 35 - 41
  • [34] The DMF: Fault Diagnosis of Diaphragm Pumps Based on Deep Learning and Multi-Source Information Fusion
    Meng, Fanguang
    Shi, Zhiguo
    Song, Yongxing
    PROCESSES, 2024, 12 (03)
  • [35] Deep transfer learning architecture for suspension system fault diagnosis using spectrogram image and CNN
    Balaji, Parameshwaran Arun
    Venkatesh, Sridharan Naveen
    Sugumaran, Vaithiyanathan
    Mahamuni, Vetri Selvi
    ADVANCES IN MECHANICAL ENGINEERING, 2024, 16 (06)
  • [36] Review on Deep Learning Based Fault Diagnosis
    Wen Chenglin
    Lu Feiya
    JOURNAL OF ELECTRONICS & INFORMATION TECHNOLOGY, 2020, 42 (01) : 234 - 248
  • [37] Fault Evolution Testability Modeling and Analysis for Centrifugal Pumps
    Tan Xiaodong
    Qiu Jing
    Liu Guanjun
    Li Qing
    PROCEEDINGS OF 2014 PROGNOSTICS AND SYSTEM HEALTH MANAGEMENT CONFERENCE (PHM-2014 HUNAN), 2014, : 469 - 473
  • [38] Diagnosis of Centrifugal Pumps using Vibration Analysis
    Minescu, M.
    Pana, I.
    Stan, M.
    OIL GAS-EUROPEAN MAGAZINE, 2015, 41 (04): : 215 - 218
  • [39] A deep neural network based fault diagnosis method for centrifugal chillers
    Li, G. N.
    Hu, Y. P.
    Mao, Q. J.
    Zhou, C. H.
    Jiao, L. Z.
    4TH ASIA CONFERENCE OF INTERNATIONAL BUILDING PERFORMANCE SIMULATION ASSOCIATION, 2019, 238
  • [40] A Spectrogram-Based Deep Feature Assisted Computer-Aided Diagnostic System for Parkinsons Disease
    Zahid, Laiba
    Maqsood, Muazzam
    Durrani, Mehr Yahya
    Bakhtyar, Maheen
    Baber, Junaid
    Jamal, Habibullah
    Mehmood, Irfan
    Song, Oh-Young
    IEEE ACCESS, 2020, 8 : 35482 - 35495