Condition Monitoring of a Reciprocating Air Compressor Using Vibro-Acoustic Measurements

被引:1
作者
Mondal, Debanjan [1 ]
Gu, Fengshou [1 ]
Ball, Andrew D. [1 ]
机构
[1] Univ Huddersfield, Ctr Efficiency & Performance Engn, Huddersfield HD1 3DH, W Yorkshire, England
来源
PROCEEDINGS OF INCOME-VI AND TEPEN 2021: PERFORMANCE ENGINEERING AND MAINTENANCE ENGINEERING | 2023年 / 117卷
关键词
Vibro-acoustic analysis; Sound source analysis; MSB analysis; Reciprocating compressor; Fault diagnosis; FAULT-DIAGNOSIS; SIGNAL; VALVE; MACHINES;
D O I
10.1007/978-3-030-99075-6_50
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Fault diagnosis in reciprocating compressor (RC) requires time-consuming feature-extraction processes due to the complexity of the compressor operation and fluid-solid interaction. This causes the useful information to be corrupted and difficulty in accurately diagnosing the faults with traditional methods. The aerodynamic phenomenon has a large impact on acoustics signal compared to the vibration. Thus, this paper presents analytical modelling of compressor sound highlighting the important sound sources and their generation. The additional contribution of this paper is the application of a state-of-the-art signal processing technique: Modulation Signal Bispectrum (MSB) which overcomes the challenges by showing good noise suppression capability and characterising the modulating components present in the signal, thereby resulting in stable modulation components for accurate diagnostics. The result reveals that the fault diagnosis based on airborne acoustics using MSB method outperformed the vibration-based method.
引用
收藏
页码:615 / 628
页数:14
相关论文
共 34 条
  • [1] Ahmed M., 2012, 2012 UKACC International Conference on Control (CONTROL), P461, DOI 10.1109/CONTROL.2012.6334674
  • [2] Feature Selection and Fault Classification of Reciprocating Compressors using a Genetic Algorithm and a Probabilistic Neural Network
    Ahmed, M.
    Gu, F.
    Ball, A.
    [J]. 9TH INTERNATIONAL CONFERENCE ON DAMAGE ASSESSMENT OF STRUCTURES (DAMAS 2011), 2011, 305
  • [3] Automated valve fault detection based on acoustic emission parameters and support vector machine
    Ali, Salah M.
    Hui, K. H.
    Hee, L. M.
    Leong, M. Salman
    [J]. ALEXANDRIA ENGINEERING JOURNAL, 2018, 57 (01) : 491 - 498
  • [4] EARLY DETECTION OF LEAKAGES IN THE EXHAUST AND DISCHARGE SYSTEMS OF RECIPROCATING MACHINES BY VIBRATION ANALYSIS
    BARDOU, O
    SIDAHMED, M
    [J]. MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 1994, 8 (05) : 551 - 570
  • [5] Vibro-acoustic condition monitoring of Internal Combustion Engines: A critical review of existing techniques
    Delvecchio, S.
    Bonfiglio, P.
    Pompoli, F.
    [J]. MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2018, 99 : 661 - 683
  • [6] Object-Based Thermal Image Segmentation for Fault Diagnosis of Reciprocating Compressors
    Deng, Rongfeng
    Lin, Yubin
    Tang, Weijie
    Gu, Fengshou
    Ball, Andrew
    [J]. SENSORS, 2020, 20 (12) : 1 - 11
  • [7] Fengshou Gu, 2009, 2009 ICROS-SICE International Joint Conference. ICCAS-SICE 2009, P4890
  • [8] Electrical motor current signal analysis using a modified bispectrum for fault diagnosis of downstream mechanical equipment
    Gu, F.
    Shao, Y.
    Hu, N.
    Naid, A.
    Ball, A. D.
    [J]. MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2011, 25 (01) : 360 - 372
  • [9] Gu F., 2000, SAE TECHNICAL PAPER
  • [10] Haba U, 2018, CONDITION MONITOR DI, V2