Automation of low-speed bearing fault diagnosis based on autocorrelation of time domain features

Nikula, Riku-Pekka; Karioja, Konsta; Pylvänäinen, Mika; Leiviskä, Kauko

doi:10.1016/j.ymssp.2019.106572

Cited by 56 publications

(18 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Many researchers have taken the time-domain features as the input for fault diagnosis methods of rotation machinery [ 31 , 32 ]. Similarly, the present work also selects some time-domain features for fault diagnosis, namely, mean root square, peak value, crest factor, kurtosis, and skewness, and they are listed in Table 3 .…”

Section: Entropy-based Fault Diagnosis Methodsmentioning

confidence: 99%

Experimental Investigation and Fault Diagnosis for Buckled Wet Clutch Based on Multi-Speed Hilbert Spectrum Entropy

Xue

Chen

et al. 2021

Entropy

View full text Add to dashboard Cite

The multi-disc wet clutch is widely used in transmission systems as it transfers the torque and power between the gearbox and the driving engine. During service, the buckling of the friction components in the wet clutch is inevitable, which can shorten the lifetime of the wet clutch and decrease the vehicle performance. Therefore, fault diagnosis and online monitoring are required to identify the buckling state of the friction components. However, unlike in other rotating machinery, the time-domain features of the vibration signal lack efficiency in fault diagnosis for the wet clutch. This paper aims to present a new fault diagnosis method based on multi-speed Hilbert spectrum entropy to classify the buckling state of the wet clutch. Firstly, the wet clutch is classified depending on the buckling degree of the disks, and then a bench test is conducted to obtain vibration signals of each class at varying speeds. By comparing the accuracy of different classifiers with and without entropy, Hilbert spectrum entropy shows higher efficiency than time-domain features for the wet clutch diagnosis. Thus, the classification results based on multi-speed entropy achieve even better accuracy.

show abstract

Section: Entropy-based Fault Diagnosis Methodsmentioning

confidence: 99%

Experimental Investigation and Fault Diagnosis for Buckled Wet Clutch Based on Multi-Speed Hilbert Spectrum Entropy

Xue

Chen

et al. 2021

Entropy

View full text Add to dashboard Cite

show abstract

“…Other indicators in the literature are specifically designed (but not limited) for bearings, such as shape factor (SF), crest factor (CF), impulse factor (IF), and margin factor (MF) [ 37 , 86 ]. By definition, SF is related to the shape of the signal independent of its time extension.…”

Section: Theoretical Background: Classic and Entropy Indicatorsmentioning

confidence: 99%

“…The kurtosis, covariance, root mean square (RMS), and skewness are indicators that are widely used in the literature [ 34 , 35 ]. In addition, other indicators have been developed in the time domain, such as factors [ 36 , 37 , 38 ], and Hjorth parameters [ 39 , 40 , 41 ]. In the frequency domain, several statistical indicators have their counterparts from the time domain, which are helpful for frequency analysis [ 42 ].…”

Section: Introductionmentioning

confidence: 99%

Entropy Indicators: An Approach for Low-Speed Bearing Diagnosis

Sandoval

Leturiondo

Vidal

et al. 2021

Sensors

View full text Add to dashboard Cite

To increase the competitiveness of wind energy, the maintenance costs of offshore floating and fixed wind turbines need to be reduced. One strategy is the enhancement of the condition monitoring techniques for pitch bearings, because their low operational speed and the high loads applied to them make their monitoring challenging. Vibration analysis has been widely used for monitoring the bearing condition with good results obtained for regular bearings, but with difficulties when the operational speed decreases. Therefore, new techniques are required to enhance the capabilities of vibration analysis for bearings under such operational conditions. This study proposes the use of indicators based on entropy for monitoring a low-speed bearing condition. The indicators used are approximate, dispersion, singular value decomposition, and spectral entropy of the permutation entropy. This approach has been tested with vibration signals acquired in a test rig with bearings under different health conditions. The results show that entropy indicators (EIs) can discriminate with higher-accuracy damaged bearings for low-speed bearings compared with the regular indicators. Furthermore, it is shown that the combination of regular and entropy-based indicators can also contribute to a more reliable diagnosis.

show abstract

“…The symptoms can sometimes be enhanced with various signal processing techniques [21,31] and the features that reveal changes together could be selected based on some search algorithm [32] that optimizes the prediction accuracy. However, the computationally optimized signal processing parameters [33] and features [34] are prone to become case-dependent to the data sets analyzed, and then, the selections may not be useful for other data sets. In practical applications, the new data may reflect new symptoms which are not known by the models trained [10,35].…”

Section: Feature Extractionmentioning

confidence: 99%

“…The envelope spectrum is commonly used in the bearing diagnosis conducted by an expert [39]. However, the automated selection of an appropriate frequency band for demodulation is complicated [33,40], and therefore, the amplitude spectrum was used in this study instead. The computational bearing frequencies (see Table 1) exhibit some uncertainty due to the measurement precision, skidding and variations in the rotational speed.…”

Section: No Feature Details Generalized Norm (L 10 )mentioning

confidence: 99%

Probabilistic Condition Monitoring of Azimuth Thrusters Based on Acceleration Measurements

et al. 2021

Self Cite

View full text Add to dashboard Cite

Drill ships and offshore rigs use azimuth thrusters for propulsion, maneuvering and steering, attitude control and dynamic positioning activities. The versatile operating modes and the challenging marine environment create demand for flexible and practical condition monitoring solutions onboard. This study introduces a condition monitoring algorithm using acceleration and shaft speed data to detect anomalies that give information on the defects in the driveline components of the thrusters. Statistical features of vibration are predicted with linear regression models and the residuals are then monitored relative to multivariate normal distributions. The method includes an automated shaft speed selection approach that identifies the normal distributed operational areas from the training data based on the residuals. During monitoring, the squared Mahalanobis distance to the identified distributions is calculated in the defined shaft speed ranges, providing information on the thruster condition. The performance of the method was validated based on data from two operating thrusters and compared with reference classifiers. The results suggest that the method could detect changes in the condition of the thrusters during online monitoring. Moreover, it had high accuracy in the bearing condition related binary classification tests. In conclusion, the algorithm has practical properties that exhibit suitability for online application.

show abstract

Automation of low-speed bearing fault diagnosis based on autocorrelation of time domain features

Cited by 56 publications

References 44 publications

Experimental Investigation and Fault Diagnosis for Buckled Wet Clutch Based on Multi-Speed Hilbert Spectrum Entropy

Experimental Investigation and Fault Diagnosis for Buckled Wet Clutch Based on Multi-Speed Hilbert Spectrum Entropy

Entropy Indicators: An Approach for Low-Speed Bearing Diagnosis

Probabilistic Condition Monitoring of Azimuth Thrusters Based on Acceleration Measurements

Contact Info

Product

Resources

About