Condition Monitoring of Low Speed Slewing Bearings Based on Ensemble Empirical Mode Decomposition Method

Caesarendra, Wahyu; Park, J.H.; Kosasih, P.B.; Choi, Byeong-Keun

doi:10.5050/ksnve.2013.23.2.131

Cited by 4 publications

(2 citation statements)

References 16 publications

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“…Ž vokelj et al (2010) showed that, based on a principal component analysis (PCA) statistical indicator and by the use of the ensemble empirical mode decomposition (EEMD) of vibration and acoustic emission (AE) signals of a machine rotating at 8 rpm, a faulty bearing condition could be identified. Caesarendra et al (2013) then proposed to extract slewing bearing (rotating at 15 rpm) defect information in vibration signals via EEMD, and in Caesarendra et al (2014) the authors proposed to extract the slewing bearing defect information (at 1 rpm) from the vibration signal by reducing the number of samples using 'piecewise aggregate approximation' (PAA), elliptic shape identification (ellipse least-square fitting) of the neighborhood correlation plot and finally by the use of circular statistical indicators. According to the authors, these indicators gave a better estimation of the bearing condition than the ones resulting from Empirical Mode Decomposition (EMD) and wavelet transform.…”

Section: Low Speed Bearings Fault Diagnosismentioning

confidence: 99%

Low speed bearings fault detection and size estimation using instantaneous angular speed

Moustafa

Cousinard

Bolaers

et al. 2014

Journal of Vibration and Control

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The fault diagnosis and prognosis of low speed machines remains a difficult problem despite remarkable advances in the conditional monitoring domain. The Rolling-element bearing is a vital part of these machines and its failure is one of the main causes of machine breakdown. In order to have an efficient maintenance strategy, fault diagnosis of a bearing and time estimation of its remaining useful life is needed. However, conventional vibration analysis at very low speeds generally fails to detect vibrations issued from a faulty bearing due to its low energy, high and variable loading conditions and to the noisy environment generated by other mechanical components of low speed machines such as gearing systems. In this work, instantaneous angular speed (IAS)-based fault diagnosis is introduced in order to compensate for the shortcoming of conventional monitoring techniques since it is strictly synchronized to shaft rotation and much less dependent on the transfer path between the defect and the sensor contrary to vibration and acoustic emission analysis. At very low speeds and in the case of a seeded spall on the bearing’s race, the shaft IAS reveals the shaft dynamical behavior when the rolling element passes into the spall. It is proven that this behavior is different when entering the spall than when exiting. The determination of entrance and exit moments allows a precise fault size estimation which is a critical step for bearing prognosis. The proposed fault size estimation method is tested on different seeded spall widths at different low speeds. The results gave a satisfactory fault width estimation and show that IAS measurement is a promising tool for the health monitoring of very low speed machines.

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Section: Low Speed Bearings Fault Diagnosismentioning

confidence: 99%

Low speed bearings fault detection and size estimation using instantaneous angular speed

Moustafa

Cousinard

Bolaers

et al. 2014

Journal of Vibration and Control

View full text Add to dashboard Cite

show abstract

“…These results provide the contribution of EMD and EEMD application in real bearing case, where the faults are developed naturally whereas the most discussed literature were used EMD and EEMD for artificial bearing fault data. Earlier study which reported the application of EMD and EEMD for slewing bearing naturally damage data is presented in [19]. The rotational speed of slewing bearing was 15 rpm.…”

Section: Discussionmentioning

confidence: 99%

Condition Monitoring of Naturally Damaged Slewing Bearing Based on EMD and EEMD Methods

Caesarendra

Kosasih

Choi

et al. 2014

Lecture Notes in Mechanical Engineering

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Vibration-based condition monitoring and prognostic of rotating element bearings have been studied. Satisfactory methods on how identify bearing fault and lifetime prediction have been shown in literature. There are two characteristics of the investigated bearings in the literature: (1) The bearings were run in moderate and high rotating speed and (2) damage was artificially introduced, e.g., artificial crack and seeded fault. This paper deals with slewing bearing with very low rotational speed (1 and 4.5 rpm) with natural fault development. Two real vibration data are discussed, namely data obtained from laboratory slewing bearing test-rig and data from a sheet metal company. In this study, the result of EMD and EEMD applied in two real cases were shown superior compared to FFT method.

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Development of Acoustic Emission(AE) Sensor for Prognosis Detection of Bearing Fault

Lee¹,

Kim²,

Park³

2014

Transactions of the Korean Society for Noise and Vibration Engi

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Most mechanical systems are now operating consistently and getting faster due to the development of automation systems. Peoples' dependence on machines have increased as when problems occur within the mechanical system, personal injury and production loss may come as a result, as most of the mechanical system's malfunctions are caused by the failure of the rotational bearing. What we need now is a maintenance system that can warn us when it detects abnormal conditions before significant damage occurs to the bearing. In this study, we have developed an acoustic emissions sensor that can figure if the bearing works under the normal condition. With this acoustic emissions sensor, we can inspect the bearing for defects by using the Heterodyne technique, which converts the ultrasound signal into audio, as a signal conditioning process.

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Condition Monitoring of Low Speed Slewing Bearings Based on Ensemble Empirical Mode Decomposition Method

Cited by 4 publications

References 16 publications

Low speed bearings fault detection and size estimation using instantaneous angular speed

Low speed bearings fault detection and size estimation using instantaneous angular speed

Condition Monitoring of Naturally Damaged Slewing Bearing Based on EMD and EEMD Methods

Development of Acoustic Emission(AE) Sensor for Prognosis Detection of Bearing Fault

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