Research Papers: Applications

Experimental-Based Fault Diagnosis of Rolling Bearings Using Artificial Neural Network

[+] Author and Article Information
R. A. Kanai

Department of Mechanical Engineering,
Ashta 416 301, Maharashtra, India
e-mail: profrak@rediffmail.com

R. G. Desavale

Design Engineering Section,
Department of Automobile Engineering,
Ashta 416 301, Maharashtra, India
e-mail: ramdesavale@rediffmail.com

S. P. Chavan

Department of Mechanical Engineering,
Walchand College of Engineering,
Sangli 416 415, Maharashtra, India
e-mail: chavan.walchand@gmail.com

1Corresponding author.

Contributed by the Tribology Division of ASME for publication in the JOURNAL OF TRIBOLOGY. Manuscript received August 24, 2015; final manuscript received December 8, 2015; published online April 27, 2016. Assoc. Editor: Xiaolan Ai.

J. Tribol 138(3), 031103 (Apr 27, 2016) (9 pages) Paper No: TRIB-15-1316; doi: 10.1115/1.4032525 History: Received August 24, 2015; Revised December 08, 2015

In this paper, an innovative system for condition-based monitoring (CBM) using model-based estimation (MBE) and artificial neural network (ANN) is proposed. Fault diagnosis of deep groove ball bearings (DGBB) is a key machine element for stability of rotating machinery. MBE model is proposed to demonstrate and estimate the vibration characteristics of bearings. It is realized that it may be worth mentioning that the vibration analysis of damaged bearings at all the positions of a structure is difficult to obtain. For this purpose, methods have been discussed to get the utmost information to notify bearing faults. The ANN approach enables us to determine the effects of various parameters of the vibrations by conducting the experiments. The results point out that defect size, speed, load, unbalance, and clearance influence the vibrations significantly. Experimental simulated data using the MBE and ANN models of rotor–bearing are used to identify the damage diagnosis at a reasonable level of accuracy. The results of the experiments consist in constantly evaluating the performance of the bearing and thereby detecting the faults and vibration characteristics successfully. The effects of faults and vibration characteristics obtained using the experimental MBE and ANN are studied.

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Fig. 1

Experimental test rig

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Fig. 3

Photographs of defected bearings: (a) ball effect, (b) inner race effect, and (c) outer race effect

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Fig. 4

Structure of ANN detectors of bearing failures

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Fig. 5

FFT spectrum of vibration signal for 2.5 mm ball defect

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Fig. 6

FFT spectrum of vibration signal for 2.5 mm outer race defect

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Fig. 7

Effect of outer race defect on vibration amplitude

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Fig. 8

Effect of ball defect on vibration amplitude

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Fig. 9

Effect of inner race defect on vibration amplitude

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Fig. 10

Effect of speed on vibration amplitude of ball having single point defect

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Fig. 11

FFT spectrum of vibration signal in healthy bearing

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Fig. 12

FFT spectrum of vibration signal for 2.5 mm inner race defect




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