Detection of incipient gear failures using statistical techniques

Naim Baydar, Andrew Ball, Bradley Payne

Research output: Contribution to journalArticle

5 Citations (Scopus)

Abstract

Gears are important components in most power transmission mechanisms. Failures of gears can cause heavy losses in industry. Condition monitoring and fault diagnosis of gears is therefore important to improve safety and reliability of gearbox operations and reduce losses caused by gear failures. This research proposes a new diagnostic approach based on the statistical analysis of data. It investigates the use of Principal Components Analysis (PCA) to detect growing local faults in a two-stage industrial helical gearbox. In this research, the vibration signal is used to monitor fault conditions and a broken tooth is simulated as a local fault. Since the early detection of faults is a challenge, small fault conditions were tested first as well as severe fault conditions. In order to examine the ability of the PCA to detect fault conditions, first the PCA-based model was created for normal operating conditions. Any unexpected event such as a fault condition causes a significant deviation from the PCA model, which is obtained from the normal condition data of the gearbox. The Square Prediction Error (SPE) was calculated to detect the fault conditions. When the vibration signal from the gearbox is representative of normal operation, the value of the SPE shows very little fluctuation and remains under a certain threshold value. However, in the presence of the fault the SPE fluctuates considerably beyond the threshold value. It is shown that the PCA-based statistical approach cannot only be used to detect severe fault conditions, but that it also reveals small growing fault conditions at very early stage. The technique also provides information about the state of the fault such as the location of the fault as well as its severity.

LanguageEnglish
Pages71-79
Number of pages9
JournalIMA Journal of Management Mathematics
Volume13
Issue number1
DOIs
Publication statusPublished - 1 Jan 2002
Externally publishedYes

Fingerprint

Principal component analysis
Gears
Fault
Gearbox
Principal Component Analysis
Prediction Error
Condition monitoring
Power transmission
Failure analysis
Vibration Signal
Statistical methods
Threshold Value
Condition Monitoring
Industry
Fault Diagnosis
Statistical Analysis
Diagnostics
Monitor
Deviation
Safety

Cite this

@article{b8469ebc155a45eba20a16b7aa6d11a6,
title = "Detection of incipient gear failures using statistical techniques",
abstract = "Gears are important components in most power transmission mechanisms. Failures of gears can cause heavy losses in industry. Condition monitoring and fault diagnosis of gears is therefore important to improve safety and reliability of gearbox operations and reduce losses caused by gear failures. This research proposes a new diagnostic approach based on the statistical analysis of data. It investigates the use of Principal Components Analysis (PCA) to detect growing local faults in a two-stage industrial helical gearbox. In this research, the vibration signal is used to monitor fault conditions and a broken tooth is simulated as a local fault. Since the early detection of faults is a challenge, small fault conditions were tested first as well as severe fault conditions. In order to examine the ability of the PCA to detect fault conditions, first the PCA-based model was created for normal operating conditions. Any unexpected event such as a fault condition causes a significant deviation from the PCA model, which is obtained from the normal condition data of the gearbox. The Square Prediction Error (SPE) was calculated to detect the fault conditions. When the vibration signal from the gearbox is representative of normal operation, the value of the SPE shows very little fluctuation and remains under a certain threshold value. However, in the presence of the fault the SPE fluctuates considerably beyond the threshold value. It is shown that the PCA-based statistical approach cannot only be used to detect severe fault conditions, but that it also reveals small growing fault conditions at very early stage. The technique also provides information about the state of the fault such as the location of the fault as well as its severity.",
author = "Naim Baydar and Andrew Ball and Bradley Payne",
year = "2002",
month = "1",
day = "1",
doi = "10.1093/imaman/13.1.71",
language = "English",
volume = "13",
pages = "71--79",
journal = "IMA Journal of Management Mathematics",
issn = "1471-678X",
publisher = "Oxford University Press",
number = "1",

}

Detection of incipient gear failures using statistical techniques. / Baydar, Naim; Ball, Andrew; Payne, Bradley.

In: IMA Journal of Management Mathematics, Vol. 13, No. 1, 01.01.2002, p. 71-79.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Detection of incipient gear failures using statistical techniques

AU - Baydar, Naim

AU - Ball, Andrew

AU - Payne, Bradley

PY - 2002/1/1

Y1 - 2002/1/1

N2 - Gears are important components in most power transmission mechanisms. Failures of gears can cause heavy losses in industry. Condition monitoring and fault diagnosis of gears is therefore important to improve safety and reliability of gearbox operations and reduce losses caused by gear failures. This research proposes a new diagnostic approach based on the statistical analysis of data. It investigates the use of Principal Components Analysis (PCA) to detect growing local faults in a two-stage industrial helical gearbox. In this research, the vibration signal is used to monitor fault conditions and a broken tooth is simulated as a local fault. Since the early detection of faults is a challenge, small fault conditions were tested first as well as severe fault conditions. In order to examine the ability of the PCA to detect fault conditions, first the PCA-based model was created for normal operating conditions. Any unexpected event such as a fault condition causes a significant deviation from the PCA model, which is obtained from the normal condition data of the gearbox. The Square Prediction Error (SPE) was calculated to detect the fault conditions. When the vibration signal from the gearbox is representative of normal operation, the value of the SPE shows very little fluctuation and remains under a certain threshold value. However, in the presence of the fault the SPE fluctuates considerably beyond the threshold value. It is shown that the PCA-based statistical approach cannot only be used to detect severe fault conditions, but that it also reveals small growing fault conditions at very early stage. The technique also provides information about the state of the fault such as the location of the fault as well as its severity.

AB - Gears are important components in most power transmission mechanisms. Failures of gears can cause heavy losses in industry. Condition monitoring and fault diagnosis of gears is therefore important to improve safety and reliability of gearbox operations and reduce losses caused by gear failures. This research proposes a new diagnostic approach based on the statistical analysis of data. It investigates the use of Principal Components Analysis (PCA) to detect growing local faults in a two-stage industrial helical gearbox. In this research, the vibration signal is used to monitor fault conditions and a broken tooth is simulated as a local fault. Since the early detection of faults is a challenge, small fault conditions were tested first as well as severe fault conditions. In order to examine the ability of the PCA to detect fault conditions, first the PCA-based model was created for normal operating conditions. Any unexpected event such as a fault condition causes a significant deviation from the PCA model, which is obtained from the normal condition data of the gearbox. The Square Prediction Error (SPE) was calculated to detect the fault conditions. When the vibration signal from the gearbox is representative of normal operation, the value of the SPE shows very little fluctuation and remains under a certain threshold value. However, in the presence of the fault the SPE fluctuates considerably beyond the threshold value. It is shown that the PCA-based statistical approach cannot only be used to detect severe fault conditions, but that it also reveals small growing fault conditions at very early stage. The technique also provides information about the state of the fault such as the location of the fault as well as its severity.

UR - http://www.scopus.com/inward/record.url?scp=0036070603&partnerID=8YFLogxK

U2 - 10.1093/imaman/13.1.71

DO - 10.1093/imaman/13.1.71

M3 - Article

VL - 13

SP - 71

EP - 79

JO - IMA Journal of Management Mathematics

T2 - IMA Journal of Management Mathematics

JF - IMA Journal of Management Mathematics

SN - 1471-678X

IS - 1

ER -