Classification of Ball Bearing Faults Using Vibro-Acoustic Sensor Data Fusion

Gunerkar, Rohit S.; Jalan, A. K.

doi:10.1007/s40799-019-00324-0

Cited by 49 publications

(28 citation statements)

References 17 publications

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“…The main steps of the methodology are according to the following ones:Realization of a representative kinematic and dynamic model of the system of interest. The theoretical model developed for the system has been extensively described in [10]. Realization of experiments, corresponding to different operating conditions.Multiple runs of the model, considering as input different quantities, theoretical or measured ones [17,28].…”

Section: Methodsmentioning

confidence: 99%

“…Realization of a representative kinematic and dynamic model of the system of interest. The theoretical model developed for the system has been extensively described in [10].…”

Section: Methodsmentioning

confidence: 99%

“…For clarity, PRC98_TD is the 98th percentile of the amplitude of TD. Similarly to TD features, there are some features calculated on the basis of the measured current of the servomotor, which result as particularly significant with respect to the lubrication and setting conditions, as described in [10]. It has to be pointed out that this approach is not trivial, due to the need for taking into account also the operating modus of the mechatronic system.…”

Section: Methodsmentioning

confidence: 99%

“…A remarkable role for the success of the CM application is connected to the variation of specific features, defined to synthetize the phenomena of interest. The feature’s behavior depends on many factors, like, for instance, the quantity of interest, the type of sensor used and the way data from different sensors are merged [9,10], the part of the operating cycle taken into account and, of course, sensor positioning throughout the kinematic chain [1].…”

Section: Introductionmentioning

confidence: 99%

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Physical and Metrological Approach for Feature’s Definition and Selection in Condition Monitoring

D’Emilia

Gaspari

Natale

2019

Sensors

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In this paper, a methodology is described aiming at emphasizing physical and metrological criteria in feature selection for condition monitoring of a real scale mechatronic system. The device is used for packaging applications according to the movements of its end effector, driven by a couple of brushless servomotors and a kinematic mechanical linkage. The approach is hybrid, meaning that the starting feature set is built with reference to both experimental data from different sensors and to the indication of a simplified kinematic and dynamic model of the mechanical linkage itself. A critical comparison and mixing of theoretical and experimental data, based also on a physical interpretation of differences, suggests some more features, with respect to the classical ones, of hybrid type, which could be mostly correlated to the effects of statuses and defects of the system to be identified. The whole procedure is step by step validated, in order to evaluate the variability of features, throughout the whole procedure. The variability is analyzed depending on the actions that are realized in order to define, select, and use the proposed features for data processing by advanced algorithms, like the most typically used classifiers and artificial neural networks. A comparison with the state-of-the-art automatic feature’s selection procedure is also presented. Experimental results show that the proposed methodology is able to classify with high accuracy many statuses of the mechatronic system, which are only slightly different as for set-up settings and/or mechanical wear and lubrication conditions of mechanical parts of the mechatronic system. Issues to be pursued to a more effective generalization of the method are also discussed.

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Section: Methodsmentioning

confidence: 99%

“…Realization of a representative kinematic and dynamic model of the system of interest. The theoretical model developed for the system has been extensively described in [10].…”

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Physical and Metrological Approach for Feature’s Definition and Selection in Condition Monitoring

D’Emilia

Gaspari

Natale

2019

Sensors

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“…Amarnath et al [7] employed the acoustic signal analysis in conjunction with machine learning algorithm for the fault diagnostics of roller bearings. Gunekar and Jalan [8] diagnosed the bearing defects of a bearing test rig operating at stationary speeds by using vibration and acoustic signatures. Vamsi et al [9] have compared the fault diagnosing abilities of vibration, lubrication oil and acoustic analyses while diagnosing the local defects of a wind turbine gearbox.…”

Section: Introductionmentioning

confidence: 99%

Bearing Fault Severity Analysis on A Multi-stage Gearbox Subjected to Fluctuating Speeds

2020

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Early detection of bearing defects may prevent the occurrence of catastrophic failures of the whole associated system. Condition monitoring strategies such as vibration and acoustic signal analyses are employed for incipient fault diagnosis of bearings. The current investigation attempts to compare the fault diagnostic capabilities in terms of their effectiveness in early detection of local bearing defects. Experiments are performed on a three-stage gearbox under constant and fluctuating operating conditions of speed. Wavelet coefficients are achieved from the acquired raw signals by discrete wavelet transform and various statistical features are obtained. Most contributing features among them are chosen by decision tree. Further, the extracted features are classified based on their fault severity levels using support vector machine algorithm. The experimental investigation revealed that vibration signal analysis outperformed the acoustic signal analysis under the experimental operating conditions.

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Multi-channel sensor fusion for real-time bearing fault diagnosis by frequency-domain multilinear principal component analysis

Mamun

Bappy

Mudiyanselage

et al. 2022

Int J Adv Manuf Technol

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Classification of Ball Bearing Faults Using Vibro-Acoustic Sensor Data Fusion

Cited by 49 publications

References 17 publications

Physical and Metrological Approach for Feature’s Definition and Selection in Condition Monitoring

Physical and Metrological Approach for Feature’s Definition and Selection in Condition Monitoring

Bearing Fault Severity Analysis on A Multi-stage Gearbox Subjected to Fluctuating Speeds

Multi-channel sensor fusion for real-time bearing fault diagnosis by frequency-domain multilinear principal component analysis

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