Airborne Acoustic Signature Analysis for Fault Diagnosis of Reciprocating Compressors Using Modulation Signal Bi-spectrum

Debanjan Mondal, Usama Haba, Fengshou Gu, Andrew Ball

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Reciprocating compressors are the most important part of a petrochemical industry. In order to monitor the conditions of this complex machine, a remote fault diagnosis technique based on airborne acoustic signature analysis has been proposed. However, as there are many rotating and reciprocating components involved, extracting the characteristic features from the non-stationary and non-linear acoustic signals resulted by those machine components are very difficult. The presence of structural or acoustic resonance may further contribute to the signal modulation which makes the acoustic signal of the compressor very complex. In this paper, the modulation signal bi-spectrum method has been applied to the compressor sound signals with the capabilities of suppressing random noise, demodulating non-modulation components, and estimating modulation degrees. It allows an in depth representation of the non-linear effects of the modulation signals due to the repetitive valve impacts, airflow fluctuations, resonance phenomenon, and thereby providing a more accurate diagnosing feature to identify the root cause of the faults. The experimental study examines various kind of reciprocating compressor (RC) faults including intercooler leakage, discharge valve leakage and filter blockage. The analysis results show the effectiveness of the proposed method in diagnosis of these faults based on the airborne acoustic signature analysis.

Original languageEnglish
Title of host publication2019 25th IEEE International Conference on Automation and Computing, ICAC 2019
Subtitle of host publicationImproving Productivity through Automation and Computing
EditorsHui Yu
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages5
ISBN (Electronic)9781861376657
ISBN (Print)9781728125183
DOIs
Publication statusPublished - 11 Nov 2019
Event25th IEEE International Conference on Automation and Computing: Improving Productivity through Automation and Computing - Lancaster University, Lancaster, United Kingdom
Duration: 5 Sep 20197 Sep 2019
Conference number: 25
http://www.research.lancs.ac.uk/portal/en/activities/25th-ieee-international-conference-on-automation-and-computing-icac19-57-september-2019-lancaster-university-uk(679d94ff-4efb-46b5-9c80-c6d34a13bae4).html

Conference

Conference25th IEEE International Conference on Automation and Computing
Abbreviated titleICAC 2019
CountryUnited Kingdom
CityLancaster
Period5/09/197/09/19
Internet address

Fingerprint

Bispectrum
Reciprocating compressors
Compressor
Fault Diagnosis
Failure analysis
Acoustics
Modulation
Signature
Fault
Compressors
Leakage
Nonlinear Acoustics
Machine components
Acoustic noise
Petrochemicals
Random Noise
Nonlinear Effects
Acoustic waves
Experimental Study
Rotating

Cite this

Mondal, D., Haba, U., Gu, F., & Ball, A. (2019). Airborne Acoustic Signature Analysis for Fault Diagnosis of Reciprocating Compressors Using Modulation Signal Bi-spectrum. In H. Yu (Ed.), 2019 25th IEEE International Conference on Automation and Computing, ICAC 2019: Improving Productivity through Automation and Computing [8895097] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.23919/IConAC.2019.8895097
Mondal, Debanjan ; Haba, Usama ; Gu, Fengshou ; Ball, Andrew. / Airborne Acoustic Signature Analysis for Fault Diagnosis of Reciprocating Compressors Using Modulation Signal Bi-spectrum. 2019 25th IEEE International Conference on Automation and Computing, ICAC 2019: Improving Productivity through Automation and Computing. editor / Hui Yu. Institute of Electrical and Electronics Engineers Inc., 2019.
@inproceedings{1c51986fb12b43739c447accdb6dd039,
title = "Airborne Acoustic Signature Analysis for Fault Diagnosis of Reciprocating Compressors Using Modulation Signal Bi-spectrum",
abstract = "Reciprocating compressors are the most important part of a petrochemical industry. In order to monitor the conditions of this complex machine, a remote fault diagnosis technique based on airborne acoustic signature analysis has been proposed. However, as there are many rotating and reciprocating components involved, extracting the characteristic features from the non-stationary and non-linear acoustic signals resulted by those machine components are very difficult. The presence of structural or acoustic resonance may further contribute to the signal modulation which makes the acoustic signal of the compressor very complex. In this paper, the modulation signal bi-spectrum method has been applied to the compressor sound signals with the capabilities of suppressing random noise, demodulating non-modulation components, and estimating modulation degrees. It allows an in depth representation of the non-linear effects of the modulation signals due to the repetitive valve impacts, airflow fluctuations, resonance phenomenon, and thereby providing a more accurate diagnosing feature to identify the root cause of the faults. The experimental study examines various kind of reciprocating compressor (RC) faults including intercooler leakage, discharge valve leakage and filter blockage. The analysis results show the effectiveness of the proposed method in diagnosis of these faults based on the airborne acoustic signature analysis.",
keywords = "Airborne acoustic analysis, Fault diagnosis, Modulation signal bi-spectrum (MSB), Reciprocating compressor",
author = "Debanjan Mondal and Usama Haba and Fengshou Gu and Andrew Ball",
year = "2019",
month = "11",
day = "11",
doi = "10.23919/IConAC.2019.8895097",
language = "English",
isbn = "9781728125183",
editor = "Hui Yu",
booktitle = "2019 25th IEEE International Conference on Automation and Computing, ICAC 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",

}

Mondal, D, Haba, U, Gu, F & Ball, A 2019, Airborne Acoustic Signature Analysis for Fault Diagnosis of Reciprocating Compressors Using Modulation Signal Bi-spectrum. in H Yu (ed.), 2019 25th IEEE International Conference on Automation and Computing, ICAC 2019: Improving Productivity through Automation and Computing., 8895097, Institute of Electrical and Electronics Engineers Inc., 25th IEEE International Conference on Automation and Computing, Lancaster, United Kingdom, 5/09/19. https://doi.org/10.23919/IConAC.2019.8895097

Airborne Acoustic Signature Analysis for Fault Diagnosis of Reciprocating Compressors Using Modulation Signal Bi-spectrum. / Mondal, Debanjan; Haba, Usama; Gu, Fengshou; Ball, Andrew.

2019 25th IEEE International Conference on Automation and Computing, ICAC 2019: Improving Productivity through Automation and Computing. ed. / Hui Yu. Institute of Electrical and Electronics Engineers Inc., 2019. 8895097.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

TY - GEN

T1 - Airborne Acoustic Signature Analysis for Fault Diagnosis of Reciprocating Compressors Using Modulation Signal Bi-spectrum

AU - Mondal, Debanjan

AU - Haba, Usama

AU - Gu, Fengshou

AU - Ball, Andrew

PY - 2019/11/11

Y1 - 2019/11/11

N2 - Reciprocating compressors are the most important part of a petrochemical industry. In order to monitor the conditions of this complex machine, a remote fault diagnosis technique based on airborne acoustic signature analysis has been proposed. However, as there are many rotating and reciprocating components involved, extracting the characteristic features from the non-stationary and non-linear acoustic signals resulted by those machine components are very difficult. The presence of structural or acoustic resonance may further contribute to the signal modulation which makes the acoustic signal of the compressor very complex. In this paper, the modulation signal bi-spectrum method has been applied to the compressor sound signals with the capabilities of suppressing random noise, demodulating non-modulation components, and estimating modulation degrees. It allows an in depth representation of the non-linear effects of the modulation signals due to the repetitive valve impacts, airflow fluctuations, resonance phenomenon, and thereby providing a more accurate diagnosing feature to identify the root cause of the faults. The experimental study examines various kind of reciprocating compressor (RC) faults including intercooler leakage, discharge valve leakage and filter blockage. The analysis results show the effectiveness of the proposed method in diagnosis of these faults based on the airborne acoustic signature analysis.

AB - Reciprocating compressors are the most important part of a petrochemical industry. In order to monitor the conditions of this complex machine, a remote fault diagnosis technique based on airborne acoustic signature analysis has been proposed. However, as there are many rotating and reciprocating components involved, extracting the characteristic features from the non-stationary and non-linear acoustic signals resulted by those machine components are very difficult. The presence of structural or acoustic resonance may further contribute to the signal modulation which makes the acoustic signal of the compressor very complex. In this paper, the modulation signal bi-spectrum method has been applied to the compressor sound signals with the capabilities of suppressing random noise, demodulating non-modulation components, and estimating modulation degrees. It allows an in depth representation of the non-linear effects of the modulation signals due to the repetitive valve impacts, airflow fluctuations, resonance phenomenon, and thereby providing a more accurate diagnosing feature to identify the root cause of the faults. The experimental study examines various kind of reciprocating compressor (RC) faults including intercooler leakage, discharge valve leakage and filter blockage. The analysis results show the effectiveness of the proposed method in diagnosis of these faults based on the airborne acoustic signature analysis.

KW - Airborne acoustic analysis

KW - Fault diagnosis

KW - Modulation signal bi-spectrum (MSB)

KW - Reciprocating compressor

UR - http://www.scopus.com/inward/record.url?scp=85075779162&partnerID=8YFLogxK

U2 - 10.23919/IConAC.2019.8895097

DO - 10.23919/IConAC.2019.8895097

M3 - Conference contribution

AN - SCOPUS:85075779162

SN - 9781728125183

BT - 2019 25th IEEE International Conference on Automation and Computing, ICAC 2019

A2 - Yu, Hui

PB - Institute of Electrical and Electronics Engineers Inc.

ER -

Mondal D, Haba U, Gu F, Ball A. Airborne Acoustic Signature Analysis for Fault Diagnosis of Reciprocating Compressors Using Modulation Signal Bi-spectrum. In Yu H, editor, 2019 25th IEEE International Conference on Automation and Computing, ICAC 2019: Improving Productivity through Automation and Computing. Institute of Electrical and Electronics Engineers Inc. 2019. 8895097 https://doi.org/10.23919/IConAC.2019.8895097