PROPOSING A NEW ACOUSTIC EMISSION PARAMETER FOR BEARING CONDITION MONITORING IN ROTATING MACHINES

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1 PROPOSING A NEW ACOUSTIC EMISSION PARAMETER FOR BEARING CONDITION MONITORING IN ROTATING MACHINES Seyed Ali Niknam 1,2, Victor Songmene 1 and Y.H. Joe Au 2 1 Department of Mechanical Engineering, École de Technologie Supérieure (ÉTS), Montreal, QC, Canada 2 School of Engineering and Design, Brunel University, Uxbridge, U.K. seyed-ali.niknam.1@ens.etsmtl.ca; victor.songmen@etsmtl.ca; joe.au@brunel.ac.uk Received August 2012, Accepted February 2013 No. 12-CSME-87, E.I.C. Accession 3407 ABSTRACT Bearings are important machine parts and their condition is often critical to success of an operation or process, hence there is a great need for periodic knowledge of their performance. According to reported research works in the past several years, it is believed that the extracted information from acoustic emission (AE) signals can be used for bearing condition monitoring. In this work, a novel parameter based on using the ratio of AE mean (µ) and AE standard deviation (σ), formulated as µ/σ is proposed to distinguish between lubricated and dry bearings. A heavy duty test rig was used in experimental work. Various levels of radial loads and rotational speed (ω) were applied to rotating shaft, which is connected to rolling element bearings. It was found that, except few cases, regardless of various levels of radial loads used, at higher levels of rotational speed, dry and lubricated bearings can be clearly distinguished when using proposed parameter. Keywords: acoustic emission; condition monitoring; bearing; low rotating speed machine. PROPOSER UN NOUVEAU PARAMÈTRE D ÉMISSION ACOUSTIQUE PORTANT SUR LA SURVEILLANCE DE L ÉTAT DE LA MACHINE TOURNANTE RÉSUMÉ Les roulements sont parmi les pièces critiques de la machine et leur état est indispensable à la réussite d une opération ou d un procédé, d où la nécessité de connaître périodiquement leur performance en cours de fonctionnement. Les travaux de recherche effectués au cours des dernières années ont montré que les informations émises sur les signaux acoustiques (AE) peuvent être utilisées pour la surveillance de l état des paliers à roulements des machines. Dans ce travail, un nouveau paramètre basé sur l utilisation du rapport des AE moyenne (µ) et AE écart-type (σ), formulée comme µ/σ est proposé. Durant les expériences, plusieurs niveaux de charges radiales et de vitesse de rotation (ω) ont été appliqués sur l arbre qui est relié aux paliers à roulements. Il a été constaté que le paramètre proposé a permis de distinguer les deux types de paliers testés (à sec et lubrifié) à des niveaux élevés de vitesse de rotation indépendamment de différents niveaux de charges radiales utilisées. Mots-clés : émission acoustique ; surveillance ; palier à roulement ; machine à faible vitesse de rotation. Transactions of the Canadian Society for Mechanical Engineering, Vol. 37, No.4,

2 1. INTRODUCTION Rolling element bearings are perhaps the most omnipresent machine elements that can be found in almost all rotating machines. If a manufacturing plant performs efficiently, the bearings need to be kept in acceptable conditions. Considering that the bearing failure may occur from a variety of causes [1], having periodic knowledge of their health status is desirable. There are two approaches for bearing maintenance: (1) statistical bearing life estimation and (2) bearing condition monitoring [2]. The latter approach is more reliable, because it provides up to date information of bearing condition. The most popular bearing condition monitoring techniques use vibration and AE signal analysis. Comparative studies of various vibration and AE signal acquisition methods were reported in [3 5]. Previous studies [6 8] confirmed that AE monitoring is more reliable than vibration monitoring, as the former can detect the subsurface crack growth, whereas the latter can at best detect a defect only when it emerges on the surface of a structure. Yashioka [9] described an AE system for source location. Bashir et al. [10] studied the variation of AE energy during fatigue crack growth. Further investigations reported in [11 16] stated that AE is an effective condition monitoring method for early fault detection in bearings. This article investigates the effectiveness of proposed AE parameter (µ/σ) to distinguish between lubricated and dry bearings in a rotating machine. The µ/σ is formulated as a function of arithmetic mean (µ) and standard deviation (σ), where σ = 1 N µ = 1 N N i=1 N i=1 X i (1) (X i µ) 2 (2) where X i, i = 1,...,N are data points recorded from each AE signal. This article is organized as follows: In Section 2, bearing and AE are briefly introduced. In Section 3, methodology and experimental devices are described, followed by results in Section 4. Finally, the paper is concluded in Section BACKGROUND 2.1. Bearing Bearing are mainly classified into rolling element bearing (see Fig. 1) and plain bearing (see Fig. 2). The classification is based on the friction mechanism that occurs in the contacting interface. Rolling element bearings include all forms of bearing that utilize the rolling action of balls or rollers to provide minimum friction in the constrained motion of one body relative to another. Both ball and roller bearings can be used as a part of rotating machine elements. The common types of bearing failures are flaking (see Fig. 3), fatigue, faulty installation, crack, contamination, wear, corrosion, electrical discharge damage and indentations. There are various solutions to improve the bearing performance, including the use of lubricant in bearing surfaces. Lubrication is mainly used to reduce the friction between two contacting surfaces sliding friction between the rolling elements on the one hand and the cage and raceways on the other. Moreover, the lubricant can protect raceways and rolling elements against corrosive pitting and rust, and it can also be used as a heat transfer medium to maintain an even temperature in the bearing. It must also be chemically neutral and non-corrosive. The choice of lubricant lies between grease and oil Acoustic Emission Acoustic emission is a natural phenomenon of sound generation applied to the spontaneously generated elastic waves produced within a material under stress [4]. Nowadays the technology of AE as a non-destructive 1106 Transactions of the Canadian Society for Mechanical Engineering, Vol. 37, No. 4, 2013

3 Fig. 1. Example of a rolling element bearing [17]. Fig. 2. Example of plain bearing [17]. Fig. 3. Inner rings with initial flaking and spreading flaking [17]. technique (NDT) is very well established and highly sophisticated AE systems are commercially available. A distinct feature of AE is its ability to monitor an entire structure often without taking it out of service. This may offer better expense reduction as compared to other NDT approaches, such as ultrasonic. AE signals are high frequency transients in the range from 20 KHz to 1 MHz. Typically in order to eliminate the noise effect, the threshold is used. The AE events that rise above the threshold level are counted for further investigations. In order to capture AE events, an AE sensor is required to convert very small surface displacement Transactions of the Canadian Society for Mechanical Engineering, Vol. 37, No.4,

4 Table 1. Experimental parameters. Level Rotational speed (Hz) Pressure (bar) Radial load (N) Fig. 4. Schematic diagram of the experimental test rig [20]. (on the order of picometers) into electrical signals that can be amplified and recorded. Most of AE sensors have a sensing element in the form of a thin disc of piezoelectric material; normally lead zirconate titanate (PZT) that can convert mechanical deformation into electrical voltage [18]. It has an excellent sensitivity and relative immunity to a wide range of industrial applications [19]. 3. METHODOLOGY AND APPARATUS 3.1. Experimental Plan In this study, eight levels of rotational speeds ( Hz), and four levels of radial loads (see Table 1) were applied to the test rig rotating shaft (see Figs. 4 and 5), when using dry and lubricated bearings. This led to 32 tests with respect to each bearing mode as presented in Tables 2 and 3. The average values of recorded AE parameters are presented in Tables 2 and Experimental Test Rig A schematic diagram of the experimental test rig is shown in Fig. 4. In order to construct the shaft and base plate, steel was used as a raw material. The design of the test rig comprises a rotating shaft supported at three positions: a double-row self-aligned ball bearing SKF 2206 ETN9 (third bearing) at the drive-end, a single row self-aligned ball bearing SKF 1206 E (second bearing) near the non-drive, and a spherical roller bearing SKF E (testing bearing) near to applied load position. They are mounted in the bearing housings that in turn were attached to the base plate. The radial load could be applied to the rotating shaft when using hydraulic pressure. The shaft rotational speed can be varied by using an inverter and motor controller Transactions of the Canadian Society for Mechanical Engineering, Vol. 37, No. 4, 2013

5 Applied load Supported frame Pressure gauge Hydraulic hand pump Fig. 5. Overview of loading system [20]. 38 mm Diameter of area of contact Hydraulic cylinder Fig. 6. Sketch of applied hydraulic cylinder [20]. The motor is foot-mounted onto the base plate. The connection of the three-stator phase windings of the motor is delta ( ). Therefore, it would accept a three-phase input voltage range of Volts for a 50 Hz supply frequency. The power of the motor is 0.55 kw (or 0.75 hp). An inverter (or A.C motor speed controllers) was used to enable the 3-phase induction for voltage deviation by adjusting the rotational speed. The control range is 0 10 Volts or 4 20 ma. The power of the selected inverter was 0.75 kw. It can be set up and operated using the integral keypad. The shaft was driven by a controllable inverter running at 0.23 rev/sec. A worm wheel reduction gearbox was used to decrease the shaft speed using a three-phase A.C motor. The flexible mechanical couplings were used on both input and output shafts of the gearbox to incorporate with the drive shaft from electric motor and the rotating shaft. They are drilled to fix with keyways on both shafts. At the lower bottom of the base plate, the two U-shape steels are mounted to the bed to prevent the plate s deformation, when exposed to an excessive load. As depicted in Fig. 5, the hydraulic loading system consists of a hydraulic cylinder, hydraulic hand pump, high-pressure hydraulic hose (1 meter), gauge mounting block and hydraulic pressure gauge. The hydraulics system can provide enough radial force to the non-drive end of the shaft when using a low height hydraulic cylinder as shown in Fig. 6. Considering the radius of the area of contact (r) is 19 mm (see Fig. 6) and the maximum applied pressure is 80 bars, the maximum radial load applied downwards to the non-drive end of the shaft is 9073 N (see Table 1). Transactions of the Canadian Society for Mechanical Engineering, Vol. 37, No.4,

6 Fig. 7. Overview of experimental test rig, testing bearing and AE sensor [19]. Radial load (N) Table 2. The µ/σ values when using dry bearing. Average of µ/σ 30 Hz 40 Hz 50 Hz 60 Hz 70 Hz 80 Hz Hz Hz Radial load (N) Table 3. The µ/σ values when using lubricated bearing. Average of µ/σ 30 Hz 40 Hz 50 Hz 60 Hz 70 Hz 80 Hz 90 Hz Hz Data Acquisition The data acquisition system consisted of a Pentium PC with a 1 GHz-CPU that was fitted with a high performance National Instruments NI 6110 data acquisition card. The NI 6110 card comprises four 12-bit input resolution channels, simultaneously sampled analogue-to-digital (A/D) input channels with scalable input limits and adjustable sampling rates up to 5M samples/s, both controllable by software. The detected signals from AE sensors were recorded on the data acquisition card simultaneously with a sampling frequency of 3 MHz. The signals were then converted to digital format that can be stored in the PC for further data processing. The software used was LABVIEW (version 5.1). No satisfactory results were obtained during preliminary tests when using the threshold levels of 2σ and 3σ. Therefore, 4σ was used as threshold level to avoid noise effects. In addition, in each test, six trial points were recorded for each AE parameter. The average values of these points were recorded for further analysis. The overview of the experimental test rig, bearings and AE sensors mounting place are shown in Fig. 7. According to pencil lead break tests, the signal attenuation extracted from second and third bearings has insignificant effects on AE power. This 1110 Transactions of the Canadian Society for Mechanical Engineering, Vol. 37, No. 4, 2013

7 Fig. 8. The µ/σ values when using all levels of rotational speeds and radial load of 2268 N. Fig. 9. The µ/σ values when using all levels of rotational speeds and radial load of 4537 N. means that the extracted AE signal information is related to the testing bearing (SKF E) presented in Figs. 5 and RESULTS The computed values of µ/σ for dry and lubricated bearings are presented in Tables 2 and 3. As shown in Figs. 8 11, when using rotational speeds Hz, regardless of using various levels of radial loads, variation of µ/σ values cannot be interpreted scientifically. In this interval, the proposed AE parameter Transactions of the Canadian Society for Mechanical Engineering, Vol. 37, No.4,

8 Fig. 10. The µ/σ values when using all levels of rotational speeds and radial load of 6805 N. Fig. 11. The µ/σ values when using all levels of rotational speeds and radial load of 9073 N. cannot be used to distinguish between lubricated and dry bearings. When using a rotational speed of Hz (see Figs. 8 11), regardless of any level of radial load used, larger resulting values of µ/σ are obtained for lubricated bearing. According to confidence interval of 95% and the error bars presented in Figs. 8 11, at the highest radial load used (F = 9076 N) the difference between the computed values of µ/σ from lubricated and dry bearings are not within error limits. This exhibits that when the test rig shaft is rotated with Hz, the proposed AE parameter can clearly distinguish between dry and lubricated bearings. In this operational condition, due to lack of lubrication, more friction occurs in the bearing surfaces which leads to lower values of µ/σ in dry bearing. When decreasing radial loads, except few cases as shown in Figs. 8 11, a similar 1112 Transactions of the Canadian Society for Mechanical Engineering, Vol. 37, No. 4, 2013

9 trend is observed for other cases. According to Fig. 11, at the speed interval Hz, the difference between computed values of µ/σ from lubricated and dry bearings are not within error limits. The sensitivity of proposed parameter (µ/σ) becomes weaker when a lower value of radial load is used (see Figs. 8 10). Based on Fig. 8, within the speed interval of Hz, the difference between computed values of µ/σ from lubricated and dry bearings are not within error limits when using rotational speeds 60 and 80 Hz. It is anticipated that the proposed parameter may show more efficient performance when using higher levels of radial load and rotational speeds. The results of this study can be useful for subsequent studies dealing with real time bearing health condition monitoring. 5. CONCLUSION This study presented the effectiveness of a proposed AE parameter (µ/σ) that can be used to distinguish between lubricated and dry bearings in a rotating machine under similar operating conditions. Irrespective to radial load used, at higher levels of rotational speeds ( Hz), larger resulting values of µ/σ were obtained for lubricated bearing. It was found that the proposed parameter (µ/σ) can clearly distinguish between dry and lubricated bearings at higher levels of radial load and rotational speed ( Hz), while less sensitivity was observed when smaller radial loads were used. This can be due to higher friction effects at higher levels of radial load and rotational speed. However irrespective to radial load used, the proposed AE parameter had a poor performance in lower level of rotational speed (30 50 Hz). The experimental data in this article are limited to the maximum rotational speed of 100 Hz. More consistence results can be obtained in further experiments at higher levels of rotational speeds. This study can be expanded in wider scopes for real time bearing condition monitoring. ACKNOWLEDGMENTS The authors would like to acknowledge Brunel University and École de Technologie Supérieure of Montreal for providing experimental facilities and financial support in the course of this study. The constructive comments and contribution of Dr. Chao Wang is also greatly appreciated. REFERENCES 1. Nisbet, T.S. and Mullet, G., Rolling Bearings in Service: Interpretation of Types of Damage, Hutchinson, Li, Y., Billington, S., Zhang, C., Kurfess,T., Danyluk, S. and Liang, S.., Adaptive prognostics for rolling element bearing condition, Journal Mechanical Systems and Signal Processing, Vol. 13, No. 1, pp , Rogers, L., The application of vibration signature analysis and acoustic emission source location to on-line condition monitoring of anti-friction bearings, Tribology International, Vol. 12, No. 2, pp , Holroyd, T. and Randall, N., Use of acoustic emission for machine condition monitoring, British Journal of Non-Destructive Testing, Vol. 35, No. 2, pp , Choudhury, A. and Tandon, N., Application of acoustic emission technique for the detection of defects in rolling element bearings, Tribology International, Vol. 33, No. 1, pp , Tandon, N. and Nakra, B., Comparison of vibration and acoustic measurement techniques for the condition monitoring of rolling element bearings, Tribology International, Vol. 25, No. 3, pp , James, Li C. and Li, S., Acoustic emission analysis for bearing condition monitoring, Wear, Vol. 185, No. 1, pp , Kaewkongka, T., Au, Y.H.J., Rakowski, R. and Jones, B., Continuous wavelet transform and neural network for condition monitoring of rotodynamic machinery, in Proceedings of 18th IEEE Instrumentation and Measurement Technology Conference, Vol. 3, pp , Centre for Manuf. Metrol., Brunel University, Uxbridge, UK, Yoshioka, T. and Fujiwara, T., A new acoustic emission source locating system for the study of rolling contact fatigue, Wear, Vol. 81, No. 1, pp , Transactions of the Canadian Society for Mechanical Engineering, Vol. 37, No.4,

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