Rotary Machine Health Diagnosis Based on Empirical

Ruqiang Yan Member ASME e-mail: [email protected]

Robert X. Gao1 Fellow ASME e-mail: [email protected]

Department of Mechanical and Industrial Engineering, University of Massachusetts, Amherst, MA 01003

Rotary Machine Health Diagnosis Based on Empirical Mode Decomposition This paper presents a signal decomposition and feature extraction technique for the health diagnosis of rotary machines, based on the empirical mode decomposition. Vibration signal measured from a defective rolling bearing is decomposed into a number of intrinsic mode functions (IMFs), with each IMF corresponding to a specific range of frequency components contained within the vibration signal. Two criteria, the energy measure and correlation measure, are investigated to determine the most representative IMF for extracting defect-induced characteristic features out of vibration signals. The envelope spectrum of the selected IMF is investigated as an indicator for both the existence and the specific location of structural defects within the bearing. Theoretical foundation of the technique is introduced, and its performance is experimentally verified. 关DOI: 10.1115/1.2827360兴 Keywords: empirical mode decomposition, intrinsic mode function, health diagnosis of rotary machine components, rolling bearings

1

Introduction

Growing demand for high quality production requires that deviation of machine conditions from its normal setting should be identified and fixed promptly to reduce costly machine downtime and maintain high productivity. As a result, research on effective machine health diagnosis has been gaining increasing attention 关1,2兴. Since vibration signals sensed from a rotary machine is directly related to the structural dynamics and its current working status, vibration analysis has been adopted widely as a means for machine failure identification 关3–5兴. Of the various techniques applied to rotary machine health diagnosis, demodulation and enveloping-based methods have been investigated extensively 关3,6–8兴, due to its ability in identifying defect-induced frequency in the rotary machine component, e.g., a rolling bearing. Such ability is based on the fact that structure impacts caused by a localized defect 共e.g., a crack on the inner raceway of a bearing兲 would excite resonance in the high frequency region, represented as impulses that are amplitude modulated by the defect characteristic frequency. This means that a structural defect can be identified by demodulating impulsive vibrations using spectral analysis. Traditional enveloping based on bandpass filtering, while able to extract defect-induced frequency components, suffers from the drawback of having to determine proper filtering band a priori, in order to obtain consistent results under varying operating conditions or continued progression of the defect severity. Such variations and progressions may result in different resonance modes to be excited by defect-induced impacts. A common practice is to use hammer strike to identify the structural modes excited, from which the applicable bandwidth of the bandpass filter is decided. However, the accuracy of such technique is subject to the experience of a human operator. These drawbacks motivate the development of new techniques to remove reliance on human and automate the defect identification process. In recent years, empirical mode decomposition 共EMD兲 has 1 Corresponding author. Contributed by the Technical Committee on Vibration and Sound of ASME for publication in the JOURNAL OF VIBRATION AND ACOUSTICS. Manuscript received December 14, 2006; final manuscript received June 31, 2007; published online February 4, 2008. Review conducted by Jean-Claude Golinval. Paper presented at the 2004 Japan USA Symposium on Flexible Automation.

Journal of Vibration and Acoustics

gained increasing attention for its application to monitoring civil and mechanical structures 关9,10兴. It was found that physical parameters such as the natural frequency and damping ratio of a multiple-degree-of-freedom 共MDOF兲 linear system can be identified by the EMD method, in association with the Hilbert transform. Further study on a four-story building has shown that the change of natural frequencies and damping ratios before and after damage occurrence can be identified using the EMD technique 关11兴. For machining process monitoring, tool breakage was detected through changes of the relative energy of characteristic frequencies in the Hilbert spectra generated by EMD 关12兴. When applied in conjunction with an autoregressive 共AR兲 model, the EMD method has shown to identify fault patterns of a roller bearing 关13兴. Using EMD, incipient tooth crack was identified in a gearbox 关14兴. In essence, the EMD method extracts the intrinsic oscillation of the signal being analyzed through their characteristic time scales 共i.e., local properties of the signal itself兲 and decomposes the signal into a number of intrinsic mode functions 共IMFs兲, with each IMF corresponding to a specific range of frequency components contained within the signal. Given such nature, EMD can be viewed as a self-adaptive filtering technique. Since vibration signals measured from rotary machines are inherently oscillatory, the EMD provides a versatile tool for vibration analysis. This paper investigates the utility of EMD as an effective tool for rotary machine condition monitoring and health diagnosis. Specifically, the EMD process serves as a preprocessing operation through which a suitable signal filtering band is selected. Such an approach is shown to be able to replace the traditional fixed bandpass filtering scheme that has been widely applied so far. After introducing the theoretical framework of EMD in Sec. 2, details of EMD-based feature extraction process are presented in Sec. 3, where two criteria based on the energy content and correlation coefficient between the extracted IMFs and the original signal are introduced, for purpose of filter band selection. An analytically formulated synthetic signal is then introduced to serve as a quantifiable test signal for evaluating the effectiveness of the developed EMD-based signal decomposition and feature extractiontechnique. In Sec. 4, experimental case studies on four defectseeded rolling bearings of various types and dimensions are

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presented, and the results verified the performance and advantage of the EMD technique. Finally, conclusions are drawn in Sec. 5.

2 Theoretical Framework of Empirical Mode Decomposition

Fig. 1 Procedure for performing the EMD

EMD is a direct, posteriori, and adaptive method for signal decomposition. It is derived from the signal to be analyzed itself, and based upon three assumptions 关15–17兴: 共a兲 the signal has at least two extrema 共maximum and minimum兲; 共b兲 the characteristic time scale is defined by the time lapse between successive alternations of local maxima and minima of the signal; and 共c兲 if the signal has no extrema but contains inflection points, then it can be differentiated once or more times to reveal the extrema. A signal satisfying such assumptions is decomposed into a number of IMFs, with each IMF being independent of the others, due to the different characteristic time scales of the EMD. As an example, the first IMF represents the shortest time scale of the signal, corresponding to the signal components in the highest frequency range. Conversely, the last IMF is associated with the longest time scale of the signal, and contains frequency components in the lowest frequency range. The procedure for EMD-based signal decomposition is illustrated in Fig. 1, where the local maxima and minima of the signal

Fig. 2 IMFs extracted from a bearing vibration signal, together with their corresponding spectra

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Fig. 3 IMFs as a filter

x共t兲 are first identified by means of peak value detection. Then the upper envelope and lower envelope are constructed from the maxima and minima, respectively, through cubic spline curve fitting. The mean values m1共t兲 of the upper and lower envelopes are subsequently calculated, and the difference between the signal and mean values, x共t兲 − m1共t兲, is designated as a new time series h1共t兲. The time series h1共t兲 represents the first IMF of the signal x共t兲, if it satisfies the following two constraints: 共a兲 within the whole data set, the numbers of extrema and zero crossings are either equal to each other or differ by at most 1; 共b兲 at any point, the mean value between the envelopes defined by the local maxima and local minima is zero. If these two constraints are not satisfied, the above iteration process is repeated by taking the time series h1共t兲 as the signal itself until it becomes an IMF. Through the iteration process, the first IMF component, denoted as c1共t兲, is extracted from the signal x共t兲. The residue r1共t兲 is obtained by separating c1共t兲 from the signal x共t兲, and is treated as a new signal for extracting the next group of IMFs embedded in the signal x共t兲, through a new iteration process.

The above signal decomposition process is terminated when the residue of the signal becomes a monotonic function, from which no further IMF can be extracted. As a result of the EMD process, the original signal x共t兲 is expressed as n

x共t兲 =

兺 c 共t兲 + r 共t兲 j

n

共1兲

j=1

where c j共t兲 represents the jth IMF, and rn共t兲 is the residue of the signal. As an example, a total of eight IMFs are illustrated in Fig. 2, which were extracted from the vibration signal measured on a ball bearing 共Timken 1100KR兲 with a 0.27 mm groove across the outer raceway. The left side of the figure shows each of the eight IMFs 共c1 – c8兲, representing the oscillation mode of the bearing signal, with r8 being the residue. These IMFs are characterized by their respective time scales, from the shortest to the longest, corresponding to the respective frequency ranges shown on the right side of the figure. As shown in Eq. 共1兲 and Fig. 2, the EMD process is essentially a filtering operation, through which the original vibration signal is represented by the sum of a series of frequency bands. Each frequency band corresponds to a specific IMF. The low-pass filtered components of the signal x共t兲L, as illustrated in Figs. 3共a兲 and 3共b兲, are represented by n

x共t兲L =

兺 c 共t兲 j

共2兲

j=l

Fig. 4 Procedure for EMD-based signal decomposition and feature extraction


and contain 共n − l + 1兲 IMFs. The symbols n and l represent the order of IMFs 共e.g., n = 8 and l = 5 for the low-pass filtered components illustrated in Fig. 3共a兲兲. Similarly, the bandpass filtered components of the signal x共t兲B, shown in Figs. 3共c兲 and 3共d兲, are represented by APRIL 2008, Vol. 130 / 021007-3


Fig. 5 Impulse response of a rolling element bearing „Type 2214… l

x共t兲B =

兺 c 共x兲 j

共3兲

j=h

where h and l determine the lower and upper limits of the bandwidth of the filter. Lastly, the high-pass filtered components of the signal x共t兲H, shown in Figs. 3共e兲 and 3共f兲, are represented by h

x共t兲H =

兺 c 共t兲 j

共4兲

j=1

These components correspond to the first h IMFs. By nature of the filtering operation, the EMD process enables decomposition of a vibration signal into a series of constituent frequency components. Each of these components can be viewed as a characteristic “feature” embedded within the signal. Such a “feature extraction” operation and signal decomposition technique identify both the existence of a structural defect and its location within a rotary machine component, thus provide an effective tool for defect diagnosis. The details of such a signal processing technique are described in the following sections.

3

Signal Decomposition

When structural defects occur on a rotary component in a machine system 共e.g., spalling on the surface of raceway in a rolling bearing兲, impacts are generated each time when a rolling element

Fig. 6 A constructed synthetic signal with its spectrum

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strikes the defects. Such defect-induced vibrations excite the bearing assembly and the structure being supported, as reflected in the resonance modes across the signal’s spectrum. The rotating nature of the machine leads to periodical excitation of such modes, which are amplitude modulated at the repetition frequency of the impacts 共e.g., ball-pass frequency on the inner or outer raceway of the bearing兲. Since such repetitive frequencies are indicative of the existence as well as location of defect in a bearing, accurate identification of their existence becomes the first step for bearing condition monitoring and diagnosis. The overall defect identification process is achieved through a multistep signal decomposition and feature extraction, as illustrated in Fig. 4. Given that bearing misalignment and unbalance typically generate high magnitude but low frequency vibrations that overwhelm defect-induced signals 共especially at the defect inception stage兲, high-pass filtering is first performed to remove them from the signal 关18兴. Subsequently, the signal is decomposed into a series of IMFs through the EMD process, as described in Fig. 2. The envelope of the selected IMF is then extracted, before spectral analysis is performed on it to identify the repetitive frequency of the impacts caused by the rolling element-defect interactions. While envelope extraction is generally realized through rectification and low-pass filtering of the jth component of the IMF, c j共t兲, the Hilbert transform was explored in the presented study for en-

Fig. 7 Results from traditional envelope spectrum analysis of the synthetic signal



be used to characterize the signal. The energy contained in all the IMFs of a signal produced by the EMD process is given by n

E x共t兲 =

兺E j=1

共7兲

c j共t兲

where the symbol n represents the number of all the IMFs. The term Ec j共t兲 represents the amount of energy contained in the jth IMF, and is calculated as T

E c j共t兲 =

Fig. 8 Repetition frequency detection in the synthetic signal using wavelet transform

兺兩c 共t兲兩 j

2

共8兲

t=0

with T representing the time duration of the signal being analyzed. Given the nature of data sampling, each IMF component c j共t兲 has a discrete value, thus the energy contained in the jth IMF can be estimated from the sampled data as N

Eˆc j共i兲 = velope extraction 关19兴. Analytically, performing the Hilbert transform on a signal, e.g., c j共t兲, is equivalent to formulating a corresponding analytic signal whose real and imaginary parts are the original signal c j共t兲 itself and its Hilbert transform, respectively. Such an analytic signal is expressed as A共t兲 = c j共t兲 + jcˆ j共t兲

共5兲

with cˆ j共t兲 indicating the Hilbert transform of the IMF c j共t兲. The modulus of such an analytic signal represents the original signal’s envelope, defined as e共t兲 = abs共A共t兲兲 = 冑c j共t兲2 + cˆ j共t兲2

共6兲

Compared with fixed bandpass filtering, each IMF extracted from the vibration signal serves as a filter and is self-adaptive in that it is directly originated from the signal and defined by its inherent characteristic time scale. To ensure that from all the IMFs produced during the EMD process, the one that best correlates with the vibration signal generated by the bearing defect is chosen and its corresponding frequency range is subsequently considered as the representative filter band. Two criteria on the IMF selection were developed, as discussed below. 3.1 Energy-Based Intrinsic Mode Function Selection. The energy content is a direct measure for a signal’s strength, thus can

兺兩c 共i兲兩 j

2

共9兲

i=1

where N is the number of data samples. Since bearing resonance caused by defect-induced impacts is characterized by comparatively high-amplitude vibrations with high energy content contained in the corresponding IMF, the energy-based criterion for IMF selection can be formulated as follows. Criterion 1. The IMF containing the highest amount of energy among all the IMFs extracted from the signal should be chosen as the representative IMF. 3.2 Correlation-Based Intrinsic Mode Function Selection. Correlation between two signals describes their similarity to each other or, in general term, their interrelationship. In the presented study, since the IMFs were extracted from the bearing vibration signal, they are inherently correlated with each other. The degree of similarity between an IMF and the original signal can be measured in terms of a correlation coefficient, defined as 关20兴

␳ x共t兲c j共t兲 =

C x共t兲c j共t兲

␴ x共t兲␴ c j共t兲

共10兲

where ␴x共t兲 and ␴c j共t兲 are the standard deviations of the signal x共t兲 and the jth IMF c j共t兲, and Cx共t兲c j共t兲 is the covariance of x共t兲 and c j共t兲. In practice, the signal x共t兲 and IMF c j共t兲 are sampled as

Fig. 9 Energy and correlation coefficient for IMFs extracted from the synthetic signal


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Fig. 10 Result from the EMD-based envelope spectrum of the synthetic signal

Fig. 11 Signal measured from a Type 6207 bearing and its spectrum

discrete values; thus the correlation coefficient is estimated from the sampled data as N

␳ˆ xc j =

冋

兺共x共i兲 − ¯x兲共c j共i兲 − ¯c j兲

i=1 N

N

兺共x共i兲 − ¯x兲兺共c j共i兲 − ¯c j兲 2

i=1

i=1

2

册

1/2

共11兲

where N is the number of data samples, and ¯x and ¯c j are the mean values of x共i兲 and c j共i兲, respectively. If an IMF contains high frequency resonant components, the correlation strength between that IMF and the signal will be relatively high. Thus, the correlation-based IMF selection criterion can be defined as follows. Criterion 2. The IMF that has the highest correlation coefficient with the signal should be chosen as the representative IMF. 3.3 Evaluation Using a Synthetic Signal. To quantitatively evaluate the applicability of the EMD for signal decomposition and feature extraction, a synthetic signal was analytically constructed, based on the actual impulse response measured on a bearing test bed. Figure 5 illustrates three impulse responses of a ball bearing 共Type 2214兲 to hammer strikes, with the sampling

Fig. 12 Results from traditional envelope spectrum analysis, for Type 6207 bearing

Fig. 13 Energy and correlation coefficient for IMF extracted from Type 6207 bearing

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Fig. 14 First five IMFs extracted from the signal with corresponding spectra

frequency being 20 kHz. The dominant frequency component of the signal was identified at 3242 Hz. Through an inverse Fourier transform and by adding white noise, a synthetic signal simulating the actual bearing vibration signals was generated for the rotational speed of 300 rpm. For the Type 2214 bearing with 17 rolling elements, such a rotational speed would generate eight impacts per revolution, corresponding to an impact interval of 25 ms, or a signal repetition frequency of 40 Hz. Such a repetitive ball passing frequency can occur when a localized defect exists on the outer raceway of the bearing, and is expressed as f BPFO = 40 Hz. Figure 6 illustrates the synthetic signal in the time and frequency domains, with a signal-to-noise ratio 共SNR兲 of −15 dB.

Because of the noise corruption, no signal repetition frequency of 40 Hz could be identified. In fact, no apparent pattern could be identified in either the time or frequency domain, except for spectral components of elevated amplitude in the frequency range of 2500– 3500 Hz, which are, however, not indicative of structural defects. Applying the traditional envelope spectrum analysis technique to the synthetic signal resulted in the identification of several peaks 共Fig. 7兲 within the frequency range of 0 – 200 Hz, including a peak at 40 Hz. However, the 40 Hz component is not clearly differentiated from other components 共e.g., at f 2 = 18 Hz or f 3 = 52 Hz兲 in the envelope spectrum, because the magnitude of the 40 Hz component appears to be weaker than that of the adjacent peaks. This illustrates the limitation of traditional envelope spec-

Fig. 15 Envelope spectrum of the first IMF, for Type 6207 bearing

Fig. 16 Vibration signals from a Type N205 bearing and its spectrum


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decomposition process. Figure 9 illustrates all the IMFs resulting from the decomposition process, arranged according to their respective magnitude in energy 共J兲 and correlation coefficient 共%兲. The most representative IMF with the highest energy content and correlation coefficient 共the first IMF兲 was then automatically chosen by the developed algorithm as the subsequent filter to extract the envelope of the vibration signal. As shown in Fig. 10, the defect characteristic frequency of 40 Hz could be clearly identified. Compared to the traditional enveloping technique, the EMDbased technique enabled a higher SNR 共defined as the ratio of the amplitude at 40 Hz to the next highest amplitude兲 in identifying the defect characteristic frequency. While the SNR is approximately −2 dB for traditional enveloping, it is 2.5 dB for the EMDbased technique. The 4.5 dB improvement can be attributed to the fact that the IMF is derived from the signal itself, and thus can adaptively capture the defect-induced vibration features more effectively.

4

Experimental Evaluation

Fig. 17 Results from traditional envelope spectrum analysis, for Type N205 bearing

Four experimental case studies have been performed to verify the effectiveness of the EMD-based signal demodulation technique, as described below.

trum technique when strong background noise is present. The synthetic signal was then analyzed by a continuous wavelet transform with the complex Morlet wavelet as the base wavelet. A series of equally spaced scales ranging from 1 to 6 with an increment of 0.2 was applied to stretch the base wavelet for signal feature extraction. The scale increment of 0.2 was selected based on the result of a series of tests aimed at identifying a good tradeoff between the decomposition accuracy and computational load. The lower and upper limits of the scales correspond to the wavelet center frequency at 10,000 Hz and 1667 Hz, respectively, ensuring full coverage of the signal range 共with dominant frequency at about 3242 Hz兲. As the three-dimensional time-scale-power spectrum map 共calculated from the wavelet coefficient matrix兲 shows in Fig. 8, repetitive, high-power components can be identified at scales ranging from 2 to 5, with an averaged time interval of 24 ms. Such a time interval corresponds to a signal repetition frequency of 42 Hz, which can be interpreted as indicative of an outer-raceway defect, given its numerical closeness to the defectrelated repetition frequency 共f BPFO = 40 Hz兲. However, it is not an accurate identification, and reading of the 3D map is subject to individual interpretations. The same vibration signal was then analyzed using the EMD

4.1 Outer-Raceway Defect Diagnosis. The first experiment was performed on a type DGBB 6207 ball bearing 共72 mm outer diameter兲 rotating at 3000 rpm, under a 1000 N radial load. The sampling frequency used for data acquisition was 16 kHz. The bearing has a defect in the form of 0.2 mm groove across the outer raceway. Based on the geometry of the bearing and the shaft speed, the ball passing frequency f BPFO was calculated to be 182 Hz. This frequency component could not be identified directly by the Fourier transform, as Fig. 11 confirms. While the traditional envelope spectrum was able to detect this frequency and its harmonics, as shown in Fig. 12, experience of the human operator is needed to first identify the appropriate resonance region 共e.g., within the range of approximately 6 – 8 kHz兲 from the spectrum, in order to establish the proper bandwidth of the passband filter. The same signal was then analyzed using the EMD-based technique, from which a total of 14 IMFs were extracted, as shown in Fig. 13. Since the first IMF possessed both the highest energy content and the highest correlation coefficient, it was chosen automatically by the developed algorithm for the subsequent enveloping. In Fig. 14, the wave forms of the first five IMFs and their corresponding spectra are shown. It is evident that the first IMF

Fig. 18 Energy and correlation coefficient of each IMF, for Type N205 bearing

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Fig. 19 Envelope spectrum of the first IMF for Type N205 bearing

covers the resonance region of the highest frequency. The corresponding envelope spectrum in Fig. 15 shows that the 182 Hz frequency component and its harmonics could be clearly identified, indicating the existence of a bearing outer-raceway defect. To further confirm the effectiveness of the EMD-based technique, a different bearing 共Type N205 ECP with 52 mm outer diameter兲 was tested at approximately 3.6 times the radial load 共3665 N兲 and 40% the rotational speed 共1200 rpm兲 as compared to the first bearing. The defect was in the form of a through hole of 0.1 mm diameter on the outer raceway. The ball passing frequency over the defect was calculated to be 105 Hz. Once again, the Fourier transform could not identify the existence of the defect, as f BPFO is not seen in the spectrum of Fig. 16. From the result of traditional enveloping in Fig. 17, the defect frequency can be identified, but only when proper bandwidth is identified through human involvement. After the signal was decomposed using the EMD method, the energy content associated with each IMF 共Fig. 18共a兲兲 and the correlation coefficient between the respective IMFs and the signal 共Fig. 18共b兲兲 indicated that the first IMF would be automatically chosen by the algorithm for signal

Fig. 20 Signal measured from a Type 2MM9104WI bearing and its spectrum

Fig. 21 Results from traditional envelope spectrum analysis, for 2MM9104WI bearing

Fig. 22 Energy and correlation coefficient for each IMF extracted from 2MM9104WI bearing


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Fig. 23 First five IMFs extracted and their corresponding spectra, for Type 2MM9104WI bearing

feature extraction. Subsequent enveloping clearly revealed the existence of the 105 Hz signal, together with its harmonics, as demonstrated in Fig. 19. 4.2 Inner-Raceway Defect Diagnosis. The third experiment was conducted on a ball bearing 共Type 2MM9104WI, outer diameter 42 mm兲. The defect is a 0.2 mm wide groove cut across the width of the inner raceway. The ball passing frequency over the defect, referred to as f BPFI, was calculated to be 528 Hz. The bearing was tested under 1000 N radial load and 4800 rpm, and the sampling frequency was 20 kHz. As shown in Fig. 20, Fourier spectrum analysis could not identify the existence of the innerraceway defect. To apply the traditional enveloping technique, the bandwidth of the passband filter needs to be determined first. Given the existence of a major peak at around 3500 Hz, and ad-

Fig. 24 Envelope spectrum of the second IMF for Type 2MM9104WI bearing

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jacent components are of much lower amplitude, the bandwidth of the passband filter was chosen as 1500 Hz. Subsequent enveloping revealed the defect frequency at 528 Hz, as shown in Fig. 21. Applying the EMD technique to the signal has resulted in 13 IMFs. As seen in Fig. 22, the second IMF has shown to possess both the highest energy content and the highest correlation coefficient. Subsequently, it was chosen for the envelope spectrum analysis. The wave forms of all the IMFs and their corresponding spectra are shown in Fig. 23, where the second IMF is seen to be located at the bottom half of the frequency spectrum, with the highest frequency being about 3500 Hz. The corresponding envelope spectrum in Fig. 24 shows that the 528 Hz frequency component could be clearly identified, and with a 50% higher signal strength 共6 mW兲 when compared to that identified by the tradi-

Fig. 25 Signal measured from a Type 6620 bearing and its spectrum



Fig. 26 Results from traditional envelope spectrum analysis, for Type 6620 bearing

Fig. 28 Envelope spectrum of the first IMF for Type 6220 ball bearing

tional enveloping technique 共4 mW兲. It is further noted that a frequency component at 608 Hz was also identified, which reflects upon the combined effect of bearing rotation 共80 Hz兲 and structural defect 共528 Hz兲. To confirm the reliability of the EMD technique, a fourth experiment was conducted on a ball bearing of Type 6220 with an outer diameter of 180 mm and a hole with 0.25 mm diameter on its inner raceway. The bearing was subject to a radial load of 10 kN, rotating under 740 rpm. The ball passing frequency was calculated to be f BPFI = 72.3 Hz. Based on the Fourier spectrum 共Fig. 25, which did not identify the 72.3 Hz frequency component兲, the bandwidth for the passband filter was estimated to be 2500 Hz. Subsequent application of the traditional enveloping technique identified this frequency, at a strength of 4.8 mW 共Fig. 26兲. Using the EMD-based technique, 13 IMFs were extracted from the signal 共Fig. 27兲. Based on the energy and correlation criteria, the first IMF was selected by the algorithm for envelope extraction. As shown in Fig. 28, the frequency component of f BPFI = 72.3 Hz could be clearly identified at a signal strength that is 2.8 times stronger than that identified by the traditional enveloping technique. This result confirms the effectiveness of the

EMD-based filtering technique for bearing defect identification. To quantify the computational load of the developed EMD technique, the time needed for the decomposition process to complete in each of the four experiments was recorded. It was found that the average time taken was 1.85 s, when the algorithm was performed on a laptop computer with 2.0 GHz CPU and 1 GByte memory. Such a result indicates the general applicability of the EMD technique for applications where on-line monitoring is needed.

5

Conclusions

An EMD-based vibration signal processing and feature extraction technique has been developed for the condition monitoring and health diagnosis of rotary machine components, using rolling bearings as an example. The proposed energy and correlationbased criteria for intrinsic mode function selection have shown to be effective in identifying the most-suited IMF as a bandpass filter for subsequent signal enveloping and feature extraction. The advantage of the developed EMD algorithm over traditional enveloping lies in its ability to automatically pinpoint the appropriate bandwidth of the bandpass filter, without relying on the experi-

Fig. 27 Energy and correlation coefficient for each IMF extracted from a Type 6220 ball bearing


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ence from human operators to determine this parameter. Analyses using an analytically formulated synthetic signal simulating a realistic bearing vibration signal and systematic experiments using realistic bearings have both confirmed the effectiveness of the EMD technique in differentiating defect-induced feature components from strongly contaminated background noise. The developed technique is computationally efficient and can be coded readily to automate the process of signal decomposition and feature extraction. Further study will be conducted to systematically investigate the performance as well as limitation of the EMD technique for applications to other types of machines and manufacturing processes.

Acknowledgment The authors gratefully acknowledge funding provided to this research by the National Science Foundation under Award No. DMI-0218161. Experimental support from SKF and Timken companies is appreciated.

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