Failure Evaluation of Ball Bearing for Prognostics

Procedia Technology 23 ( 2016 ) 179 – 186

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2212-0173 © 2016 Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).Peer-review under responsibility of the organizing committee of ICIAME 2016doi: 10.1016/j.protcy.2016.03.015

3rd International Conference on Innovations in Automation and Mechatronics Engineering, ICIAME 2016

Failure Evaluation of Ball Bearing for Prognostics V. M. Nistane *, S. P. Harsha

Vibration & Noise Control Laboratory, Mechanical & Industrial Engineering Department, Indian Institute of Technology Roorkee, Roorkee—247667, India

Abstract

Vibration analysis is extensively used for ball bearing condition monitoring to monitor vibration signal. The measurement of vibration and their signal analysis plays a significant role for investigation of defect. Vibration signature divulges important information about development of defect within the system. Vibration signature of ball bearing for lifetime assessment in time domain analysis shows variation of features due to development of defects over the span of bearing. The trend extrapolation of features illustrates failure variation in the bearing. Every defect harvests characteristics vibration in the ball bearings. After examining vibration spectrum may provide valuable information about defects develop in the bearing. In this paper Bearing Prognostic Simulator (BPS) as test rig is used to examine vibration signature of bearing throughout the lifespan. The bearing failure evaluation is assessing to investigate variation of defect in bearing under radial load and constant speed. Acquired vibration signatures are scrutinized to extract statistical feature for proper representation of failure evaluation. When any irregularities are noticed in statistical measures, vibration spectra will provide enough information to express position of defect in the bearing. © 2016 The Authors, Published by Elsevier Ltd Selection and/or peer-review under responsibility of the Organizing Committee of ICIAME 2016

Keywords: Ball bearing, Condition monitoring, Vibrations signature, Statistical analysis, Defects, Prognostics

1. Introduction

It is widely admitted that Condition Monitoring System has been progressively utilized due to potential benefit of enhancing safety and reducing maintenance cost. It supports in early detection of faults and keenness of problems in time to avoid complete failure of mechanical components. Most of applications, entrenched accelerometers use to

* Corresponding author. Tel.: +919422541498;

E-mail address: [email protected] (V. M. Nistane), [email protected] (S.P. Harsha)

© 2016 Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).Peer-review under responsibility of the organizing committee of ICIAME 2016

180 V.M. Nistane and S.P. Harsha / Procedia Technology 23 ( 2016 ) 179 – 186

monitor the health of mechanical components. Bearing vibrations generate at applied radial load and high speed, it affects bearing life and degrade the quality of a components. The defects may be developed during longer run of bearing under certain condition. Severe vibrations of bearing can even cause the entire system to function incorrectly, result in downtime of the system and economic loss to the customer. Vibration signature analysis of machine components is a commonly used fault-detection technique employed in rotor-bearing systems [1–3]. There are two critical states in the condition monitoring of machine components: the first is the detection of the fault and the second is the determination of severity of the fault, that is, the lifetime of the machine component [4]. The different signal analysis techniques in time and frequency domains are applied to vibration measurements acquired from machines [2]. The current condition monitoring systems for ball bearing evaluate the threshold limit between warning and failure. In industry maintenance procedures may be strengthen to choose proper condition monitoring system to reduce minimum downtime. The monitoring of machine systems for failure is not only unbearable but will also cause unnecessary maintenance cost. So, enhanced the priority of failure in machine is essential to comprehend defect development stages and the prognostication of the lifetime of ball bearings. The vibration signatures of bearing shows fault patterns of bearing to initiation and progress of damage in the bearing. These vibration signatures can extract feature as a measure of component health and predict the remaining safe operating life of the component. These features provide warning of potential failure and allow maintenance to be performed as required as opposed to traditional scheduled maintenance. There are several vibration based methods to diagnose bearing faults and they can be broadly classified as time domain, frequency domain and time-frequency methods as expressed in [5, 6]. Time domain are used to descriptive statistics such as mean and kurtosis of the time-series signals, it identify bearing faults. It is well known that ball bearings produce characteristic vibrations [7, 8] and when a defect develops in ball bearings, vibration characteristics and levels change. Many researchers theoretically and experimentally studied defect development in ball bearings and its effects on vibration level. Williams et al. [9] investigated the failure progress by measuring the vibration of their test bearings during run-to-failure lifetime testing and analyzed the vibration signal with well-known time domain techniques. Kankar P.K., Harsha S.P., Pradeep K. & Sharma S.C., [10] have applied response surface methodology (RSM), to investigate the effects of various defects on the non-linear vibrations of rotor bearing system. For frequency domain methods, the use of the Fast Fourier Transform (FFT) of the vibration signal is widely used to identify fault frequencies in rotating machineries. The direct use of descriptive statistics of the fault frequencies within the FFT spectra has been shown to be a simple but effective method to diagnose faulty bearings in [11, 12]. Bearing fault frequencies, however, are often masked by more dominant gear mesh frequencies and envelope analysis is a popular technique used to identify them by demodulating the high frequencies impulses caused by the bearing faults [13-19]. Time-frequency methods are relatively recent where the vibration signal is no longer assumed to be stationary and techniques using wavelet analysis and Wigner-Ville distribution are applied to detect changes in the frequency content of the signal. Although many studies on condition monitoring of the ball bearing are presented in the literature, reliability of the methodologies employed ought to be increased.

In this paper, Bearing Prognostic Simulator (BPS) as test rig is used to investigate vibration signature of bearing throughout the lifespan. This paper is mainly focused on the actual service data gathered from bearing prognostics simulator for healthy commercial new ball bearings 6205, which is installed in test rig, run to failure assessment at constant speed and varying radial load. Vibration signatures are analyzed to investigate the nature and development of defects in the ball bearings. The selected features are strategized to graph throughout the running time to capture sudden variation in signals. This test was concluded as the statistical measures indicate failure evaluation in ball bearing. 2.Experimental Setup The bearing run-to-failure tests were conducted using a custom built bearing prognostic simulator test rig produced by Spectra Quest, Inc. The details of the test rig are displayed in Fig.1 and designed to investigate failure and vibration characteristics of ball bearings. Bearing Prognostics Simulator is connected to a computer system through data acquisition system. The output vibration signals can be monitor online and the same signals capture for further analysis. The speed of the stepper motor can be controlled by speed controller in range of 0-3500 rpm. The Hydraulic loading system is having capacity to apply pressure in range of 0-2000 psi in radial direction. The test bearings are fitted in the bearing housing to conduct run to failure tests. The standard 6205 deep groove ball bearings are investigated whose dimensions and characteristic frequencies are given in Fig. 2 and Table 1.


Fig 1 Bearing Prognostic Simulator (BPS)

Outer ring outside diameter (D) 52 mm

Inner bore diameter (d) 25 mm Pitch Circle diameter (P.C.D.) 39 mm Ball diameter 7.93 mm Width (B) 15 mm No of balls 9 Nos.

Fig.2 Bearing Dimensions

Table 1 Bearing characteristics frequencies

Characteristics Frequencies Hz

Ball pass frequency inner race 270.75

Ball pass frequency outer race 179.25

Ball spin frequency 117.9

Fundamental train frequency 19.9

An investigation is carried out for new healthy 6205 bearing under different radial load, i.e. 6.5KN, 8.7KN and 10.9KN and speed is kept constant i.e.3000 rpm. The piezoelectric accelerometer IMI 608 A11 model sensor is placed at vertical directions on the top of bearing housing. Photoelectric Tachometer Probe is used to record spindle speed. Acceleration and Tachometer signals are fed through compact spectra DAQ card to personal computer-based data acquisition system. The DAQ is set to sampling rate 20 kHz. 3.Results and discussion An initiation and development of localized defect in the bearing is investigated under applied condition, the test is conducted for new healthy ball bearings for run to failure under Bearing Prognostic Simulator test rig. The vibration signals are captured at every 10 min for 6.4 sec duration during the test. The test is run continuously till complete failure of new healthy bearing and stopped after failure. During the test, the temporary results are calculated periodically to monitor and analyses data for judgment of incipient fault develop in bearing. Vibration data are investigated to detect localized defect formation and propagation. Scalar measures such as peak-to-peak value, root mean square and spectrum are calculated during lifespan of the test. The location and severity of the defects in ball bearings detect during the running condition and also conform after plotting the feature. The vibration spectra are procured where the scalar measures indicate an anomaly in signal. 3.1 Peak to Peak Level Peak-to-peak amplitude history of vibrations along with running time for the test of 6205 bearing at speed of 3000rpm


and radial load of 6.5KN, 8.7KN and 10.9KN are given in Fig. 3. An initiation of wear in new healthy bearing are shown in Fig.3, for the radial load 6.5KN at 9hours (216min.), 8.7KN at 2.5hours (60min.) and 10.9 KN at 30min. It is noted that the vibration levels are inconsistent. In middle stage or after 20hours (480min.), it has indicated to extrapolate trend in peak-to-peak vibration levels till 84hours (2016min.). This trend increased suddenly to reach at high value for radial load 6.5KN and bearing fail. This trend indicates failure evaluation in the bearing to model for prognostics. For radial load 8.7KN, there is no observable trend in the graphics after 10hours (600min.). The peak-to-peak level continuously changes with sudden increase and bearing fail. For radial load 10.9KN, the peak-to-peak level continuously changes with sudden fail after 146min. From the observed data, the categorically pointed out that this failure evaluation model of bearing can be utilized for prognostics. The sudden increase/decrease and gradient fluctuations in the vibrations level have been also observed other researcher [9, 14] and may attribute to a portent known as healing, the localized defect may be continued till rolling contact of ball. When the failure time is reached, the vibration level suddenly increases. This has happened due to formation of defect in the bearing.

Fig. 3. Peak-to-Peak history of vibrations at speed of 3000rpm and radial load 6.5 KN, 8.7KN & 10.9KN 3.2 RMS The RMS history of vibrations with running time for the test of 6205 bearing at speed 3000rpm and radial load 6.5KN, 8.7KN and 10.9KN are shown in Fig. 4. It is very similar to the peak-to-peak amplitude history (see Fig. 3). The figure shows similar trends as in peak-to-peak levels, but sudden changes of the RMS values are not observed clearly.


Fig. 4. RMS history of vibrations at speed of 3000rpm and radial load 6.5 KN, 8.7KN & 10.9KN 3.3 Spectrum Scalar measures show failure evaluation of the bearing for run to failure analysis and indicate trend extrapolation for bearing prognostics. However, they do not provide accurate information about the size and the location of the localized defects. In order to understand the characteristics of the vibration signals, detect the location of defect and the relative size of the defect in the ball bearing can be captured through vibration spectra.

Fig. 5.Peak-to-Peak frequency at speed of 3000rpm and radial load 6.5 KN, 8.7KN & 10.9KN The vibration spectrum for radial load 6.5KN, 8.7KN and 10.9KN of the ball bearing are shows in Fig.5, where peak-to-peak frequency levels at running-in stage of the test rig for bearing are evaluated at every stage of observation.


Fig. 6.Power Spectrum history at speed of 3000rpm and radial load 6.5 KN, 8.7KN & 10.9KN

Fig. 7 .Power density history at speed of 3000rpm and radial load 6.5 KN, 8.7KN & 10.9KN The most dominant peaks in the spectrum are shown over entire running time and sudden changes are recorded. Prominent values of spectrum are noted. In order to analyse the response of the system, Fig. 6 and Fig.7 shows the power spectrum and power density for loads 6.5KN, 8.7KN and 10.9KN.


Fig. 8. FFT of frequency v/s amplitude at 3000rpm & 6.5KN load at 9 hours

Fig. 9 FFT of frequency v/s amplitude at 3000rpm & 6.5KN load at 84 hours.

Fig.10 FFT of frequency v/s amplitude at 3000rpm & 8.7KN load at 2.5 hours

Fig. 11 FFT of frequency v/s amplitude at 3000rpm & 8.7KN load at 10 hours

Fig.12 FFT of frequency v/s amplitude at 3000rpm & 10.9KN load at 30min

Fig.13 FFT of frequency v/s amplitude at 3000rpm & 10.9KN load at 146min

The scalar measures indicate changes in the condition of the ball bearing for radial load 6.5KN at 9hours (216 min.) and 84hours (2016 min.) peak of frequency and amplitude spectrum are shown in Fig.8 and Fig.9, the value of peak frequencies are 495 Hz and 0.789gRMS, and outer race defect develop in the bearing. Fig.10 and Fig.11 shows spectrum for radial load 8.7KN. The prominent peaks of frequency and spectrum amplitude are noted after 2.5hours (60 min.) and 10hours (240min.) and values are frequency (751Hz), spectrum amplitude (0.33gRMS) and frequency (495Hz), spectrum amplitude (0.95gRMS) respectively. It has observed defect develop in ball in the bearing. The spectrum for radial load 10.9KN is shown in the Fig. 12 and Fig.13, their peak values at 30min and 146min are recorded i.e. frequency (751.25Hz), spectrum amplitude (0.498gRMS) and frequency (931.25Hz), spectrum amplitude (0.40gRMS) respectively and observed bearing completely burnt. The defect frequencies and modulations in the spectra are also reported by other researchers theoretically and experimentally [9, 14-18].


4. Conclusions The quality of feature is extracted from vibrations signals for effective prognostics of bearing. These features explore information of forecasting the time of failure before it occurs. Scalar parameters illustrate damage of ball bearing but they do not provide information about the location of defect. The spectrum analyses are investigated at running test durations in order to envisage defect locations. The defect size can be depends upon vibration magnitude and its severity. This is not possible to locate a general correlation between the defect size and the amplitude of the vibration. It is somewhat because the characteristic of the vibrations varies from system to system. The development of the localized defects in the ball bearing can be monitored and feature extracted from bearing vibration data collected. These investigations divulge that the defects are formed and developed in the ball bearing associated with applied radial load on the bearing. The results obtained from the presented graphics are very promising and justified with analysis of the bearing failure analysis. Therefore, this paper accentuates an application steps to investigate the defect development using statistical and spectral methods to develop failure evaluation model for bearing prognostics. 5. References [1]Renwick JT. Condition monitoring of machinery using computerized vibration signature analysis. IEEE Trans Ind. Appl 1984; IA-20(3):519–27. [2]Barron R. Engineering condition monitoring UK: Longman; 1996. [3]Cempel C. Condition evolution of machinery and its assessment from passive diagnostic experiment MSSP1991;5(4):317–26. [4]Gerval JP, Morel G, Querre C. Behaviour modelling and vibration analysis applied to predictive maintenance: different approaches leading to the same conclusion. In: OCEANS, 94. ‘Oceans engineering for today’s technology and tomorrow’s preservation’ Proceedings, 1,, Fransa. I/544-I/546, 1994 [5]Jardine, A. K. S., Lin, D. and Banjevic D. (2006), "A review on machinery diagnostics and prognostics implementing condition-based maintenance", Mechanical Systems and Signal Processing,vol.20,no.7,pp.1483-1510. [6]Heng, A., Zhang, S., Tan, A. C. C. and Mathew, J. (2009), "Rotating machinery prognostics: State of the art, challenges and opportunities", Mechanical Systems and Signal Processing, vol. 23, no. 3, pp. 724-739. [7]Yang JP, Chen SX. Vibration predictions and verifications of disk drive spindle system with ball bearings. Compu. Struct. 2002; 80:1409–18 [8]Aini R, Rahnejat H, Gohar R. Vibration modelling of rotating spindles supported by lubricated bearings. J Tribol 2002; 124:158–65. [9]Williams T, Ribadeneria X, Billington S, Kurfess T. Rolling element bearing diagnostics in run-to-failure lifetime testing. MSSP 2001; 15(5): 979–93. [10]Kankar P. K., Harsha S. P., Pradeep K., & Sharma Satish C. (2009). Fault diagnosis of a rotor bearing system using response surface method. European Journal of Mechanics A/Solids, 28, 841–857. [11]Uluyol O., Kim K. and Hicken bottom, C. (2011), "Update to a Systematic Approach to Bearing Health Monitoring", 7th DSTO International Conference on Health & Usage Monitoring, 28th Feb - 3rd Mar, Ml Australia. [12]Dykas B., Krantz T., L., Decker, H. and Lewicki, D., G (2009), Experimental Determination of AH64 Apache Tailshaft Hanger Bearing Vibration Characteristics with Seeded Faults, ARL-TR-4865, A R.arch Laboratory, USA. [13]Randall, R. B. (2011), Vibration-based condition monitoring: industrial, aerospace, and automotive applications, Wiley, Chichester. [14]Tandon N, Choudhury A. An analytical model for the prediction of the vibration response of rolling element bearing due to localized defect. JSV 1997; 205(3):275–92. [15]Kiral Z, Karagulle H. Simulation and analysis of vibration signals generated by rolling element bearing with defects Tribol. Int. 2003; 36:667–78 [16]Tandon N, Choudhury A. A review of vibration and acoustic measurement methods for the detection of defects in rolling element bearings .Tribol Int 1999; 32:469–80 [17]McFadden PD, Smith JD., Vibration monitoring of rolling element bearings by the high-frequency resonance technique—a review Tribol., Int. 1983; 17(1): 3–10 [18]Alfredson RJ, Mathew J. Frequency domain methods for monitoring the condition of rolling element bearings. Inst. Eng. Austr. Mech. Eng. Trans 1985; 10:108–12 [19]Baillie, D. C. and Mathew, J. (1996), "A comparison of autoregressive modeling techniques for fault diagnosis of rolling element bearings", Mechanical Systems and Signal Processing, vol. 10, no. 1, pp. 1-17

Failure Evaluation of Ball Bearing for Prognostics

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