Acceleration Enveloping

 

 Abstract and Biography Technical Association of the Pulp and Paper Industry … … Optimized Application Application of Feature Extraction Techniques by Andre J. Smulders • SKF Condition Monitoring 

Abstract

Biography

Recent advancements in envelope enhancement techniques as applied to acceleration and acoustics emissions signals have led to new measurement solutions for many vibration problems. This paper discusses the theory of enveloping and how it is implemented in practice. It presents a paper

 ANDRE J. SMULDERS SMULDERS SKF Condition Monitoring

machine case study that illustrates how a rolling element bearing defect develops. Also some cas casee studies showing the strength of analysis in a modulating environment will be discussed. Measurement setups are very important for good analysis and ease of recognition of symptoms. symptoms. This will be illustrated with a case study too.

SKF Condition Monitoring

Andre J. Smulders holds a master degree in Electrical Engineering and a bachelor degree in Mechanical Engineering. He worked in the computer industry, the semi-conductor industry and the sensor industry before joining SKF in 1981. Has developed the Condition Monitoring technologies as applied by SKF Condition Monitoring today. today. He is the co-inventor of SEE technology and holds patents in the fields of semiconductors, sensor technologies, measurement techniques and in the field of signal analysis. He has been a part time professor at a technical high school for a number of years. He was involved of of the start up of SKF Condition Monitoring in 1989. He has been involved in the development of techniques and applications in the field of Condition Monitoring and Quality Monitoring.

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Technical Associat Association ion of the Pulp and Paper Industry … … Optimized Application of Feature Extraction Techniques by Andre J. Smulders • SKF Condition Monitoring  Copyright © 2000 by SKF Condition Monitori ng, Inc. ALL RIGHTS RESERVED

Abstract Recent advancements in envelope enhancement techniques as applied to acceleration and acoustics emissions signals have led to new measurement solutions for many vibration problems. problems. This paper discusses the theory of enveloping and how it is implemented in practice. It presents a paper machine case study that illustrates how a rolling element bearing defect develops. Also some case studies showing the strength of analysis in a modulating environment will be discussed. Measurement setups are very important for good analysis and ease of recognition of symptoms. This will be illustrated with a case study too.

Condition Monitoring – An Historical Review

visits to every machine based on a critical machine priority that not only measures the assigned vibration points, but to perform visual and acoustic inspections as well. These machine conditions that are subjective evaluations are entered as maintenance notes to be reviewed later in the “machine history” file. In a sense, sense, the periodic data logging sequence as defined by the predictive maintenance schedule serves inherently as a “watchman’s clock” to assure a prioritized organized “watchman’s visitation by experienced personnel to every machine. The assessment of machine conditions, conditions, operating performance and status of auxiliary components – valves piping, packing, loose bolts, flange leaks – are then considered in total for recommended corrective maintenance actions.

The main reason for condition monitoring is to prolong machinery life with the least overall cost. There are several measurement parameters that contribute to the evaluation of machinery health –

Condition monitoring has always existed where engine room personnel have felt, smelled or listened to machine sounds as symptomatic of abnormal machine performance. In these times of higher speeds, design limit operations, complex processes

such as vibration, bearing temperature, lubrication, oil conditions, pressure – that are measured on a periodic basis to assess the long term prognosis of  the operating machine life. These machine condition parameters as applied to a monitoring program, are not the only factors in the attempt to achieve maximum reliability with with minimum cost. The simplistic periodic visual inspection and the experienced technician’s ear are more often equally important as diagnostic measures to augment the predictive maintenance program and avert catastrophic failures. An important as aspect pect of the data loggers programmed route is to assure periodic

involving large populations of finite life machine components, more automatic controls resulting in minimum operations staff – combined with spiralling maintenance costs and extreme down time production loss – the need was created for warning diagnostic systems employing hardware and software sophisticated technology. technology. These modern condition monitoring systems now include new measurement techniques which were untried and unproven in the immediate past. These modern methods are known to be viable as evidenced by the case studies that are incorporated in the last section of this article.

SKF Condition Monitoring

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Technical Association of the Pulp and  Paper Industry  Optimized Application  of Feature Extraction Techniques  Techniques  …

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The Ultimate Goal

recognition have been developed and continue to be refined for both manually derived and automatic

In simple terms the main aim of the monitoring system is to first significantly reduce unexpected mechanical failures, thereby minimizing downtime production losses. This objective has been achieved generally by predictive maintenance programs that rely principally on a periodic data logging schedule, involving instruments measuring overall vibration data. These instruments often operate in conjunction with computer based programs that will trend the historical data of each measurement point and also allows the development of routes that can be downloaded to field instruments. The next level of  the program goal is to recognize problems early enough to schedule repairs with minimum disruption to the operations.

diagnostic decisions. The probable accuracy of  these maintenance recommendations that is derived from these problem recognition methods, increases with more available detailed information concerning specific machine characteristics and its associated mechanical components.

Such maintenance decisions require a sufficient assessment of the problem to assume a risk of failure with the consequences of an a n unscheduled shutdown. Today’s predictive maintenance programs with the Today’s many vibration measurement tools available and the various analysis methods inherent in the associated software, give the maintenance engineer the opportunity to corrective measures that prolong machine service life, improve product quality and reduce production costs by running process speeds closer to the design limits. Although multiparameter measurements are required for a complete assessment of operating characteristic, vibration is the best measurement parameter for evaluating machine dynamic conditions that affect machine performance and service life. The effects of imbalance, misalignment, mechanical looseness, bearing defects, ineffective lubrication, shaft rubs are revealed as vibration characteristics that are often identified by some spectrum signature. The methods of feature extraction for problem

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Oil condition monitoring provides an estimate of the deteriorating lubricating properties that can contribute to machine damage. Viscosity changes, contaminants and metal particles are some of lube oil detectable trends, that will over time affect, the wear of bearing surfaces. In addition to automatic vibration measurements and data transposition to organized files of data trending – diagnostic analysis and new software extensions allow for expert expert analysis. These software additions to the traditional predictive maintenance software include programs that scan machine historical data with feature extraction algorithms to generate symptom files. These files are are compared against resident diagnostic rules that are used to estimate the probable machine failure modes. The expert system system then recommends maintenance actions based on these severity estimates.

Traditional Vibrati Vibration on Parameter Velocity In the past field vibration measurements were usually performed using velocity transducers that did not require excitation. The electronic instrumentation measured overall values and the data was manually recorded. Vibration trends were plotted manually to determine the machine health status. Velocity measurements remain today an important measurable parameter since it is essentially related to vibration energy. energy.

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Technical Association of the Pulp and  Paper Industry  Optimized Application  of Feature Extraction Techniques  Techniques  …

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Overall vibration velocity measurements are often compared to standardized alarm levels based on

of small harmonic amplitudes with a frequency separation equal to the repetitive rate. Compare the

accumulated experience. These velocity alarms are constant levels applied over a wide frequency range. Velocity is a parameter linear to vibration and is proportional to sound pressure so correlated with the sound impression generated in a machine environment.

different amplitude/frequency relationships between a sinusoidal pure tone signal and a repetitive impulse.

The more universal transducer in use today is the piezoelectric accelerometer. accelerometer. The velocity measurement parameter is obtained by simple integration of acceleration.

Feature Extraction T Techniques echniques For Optimized Analysis and Ease of Diagnostics capabilities the strongest techniques known today is Acceleration Enveloping (or Demodulation in general as it can also be applied on other signals like motor currents or pressure signals). Enveloping addresses the problem of isolating small but significant impulse perturbations that are summed, during measurement, with larger, low frequency,, stationary vibration signals, such as frequency imbalance and misalignment. These small impulse signals come from the accelerometer response to impulsive forces from bearing race defects, from roller flat spots, from gear teeth interaction. Specifically related to a paper machine press sections, these signals may come from felt joint connectivity and/or felt dewatering anomalies. Although normal FFT spectrum analysis separates these signals into their fundamental and harmonics, the individual amplitudes are often too small to see above the instrumentation noise level. Because of  this low signal-to-noise ratio, these small spectral components are not generally measurable in the early onset of a bearing or other machine fault. A small, narrow, narrow, repetitive impact signal, when converted to the frequency domain, results in a plot

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The impulse signal amplitude is proportional to the pulse width (-t) and and pulse cycle interval (T). The smaller this ratio is – that is, the narrower the pulse width – the smaller are the spectrum amplitudes. This ratio is, of course, related to the width of the bearing defect. Initially, an accelerometer response signal is small in amplitude and narrow in time as each ball rolls over a newly developing developing fault. An acceleration spectrum spectrum plot at this early stage of defect growth would probably not show the defect as its amplitude is below the dynamic range of the measuring instrument. Vibration Vibration components identifying an incipient bearing failure are then not seen in an acceleration spectrum plot. However, However, enveloping technology,, now implemented in many dataloggers technology and on-line systems that incorporate FFT analysis, has proven to be an effective measurement tool because it modifies the raw vibration signal so as to enhance the rolling element bearing defect signal and other comparable signal.

The Basics of Enveloping The envelope method separates a repetitive impulse from a complex vibration signal by using a band pass filter that rejects low frequency components that are synchronous with vibration. Although there are signal enhancements that result from structural resonances, the envelope method is completely independent on local resonance to isolate rolling element defect signals. This is very very important as resonance frequencies and the damping at resonance are often not stable so not useful for a trend type analysis.

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Filter criteria selection is based on suitable rejection of the low frequency sinusoids while optimizing the passband of the defect harmonics. This also creates the possibility for separation of phenomenon. This is illustrated in the following figure. figure. The figure provides the table of filter selections based on rotational speeds and shows the optimal band of  analysis. After filtering the vibration signal, the resultant signal is enveloped by means of a circuit that approximates the squaring process of the signal. The enveloping process demodulates the signal which approximates a squaring function. This

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baseband display of the repetition rate harmonic components, where the component amplitude versus frequency is equivalent to a sin x over x distribution. These displays would only be seen if there are repetitive impulse components in a part of the overall raw vibration signal. Another way of understanding this translation to baseband is to consider the bandpass filtered signal as only comprising the higher frequency harmonic components of the repetitive impulse. When this harmonic series is squared, sum and difference components are created. The difference components fold back into the analysis range while all of the summed components are outside the analysis range.

translates the signal in the frequency domain to a

Enveloping Settings Microlog/Multil Microlog/Multilog og

 P APER M  ACHINE P RESS /F ELT  M   M ONITORING ONITORING Filters

Enveloping Frequency

Speed Range

Analyzing Range

1

Felts/Press Rolls 5 – 100 H z

0 – 50 RPM

0 – 10 H z

2

ROLL BEARINGS 50 – 1,000 Hz

25 – 500 R P M

0 – 100 H z

3

ROLL BEARINGS / GEARS 500 – 10,000 Hz

250 – 5,000 RPM

0 – 1,000 Hz

4

GEARS 5,000 – 40,000 Hz

2,500 – … RPM

0 – 10,000 Hz

High Pass High Pass Filte Filter: r: 24 dB/o dB/octa ctave ve B Bess essel el Low Pass Pass Filte Filter: r: 12 dB/o dB/octa ctave ve B Bess essel el where gears are involved sometimes sometimes a lower envelope filter needs to be chosen REMARK:  In a application where to suppress the noise from gearmesh frequencies that are commonly very dominant.

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Technical Association of the Pulp and  Paper Industry  Optimized Application  of Feature Extraction Techniques  Techniques  …

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Fundamental Properties of  Acceleration Enveloping/  Demodulation/Rectification This feature extraction technique has a number of  principles advantages and properties that make it ideal for signal extraction of non-sinusoidal signals and signals that are modulated by some carrier phenomenon. 1. S ELECTIVE F ILTERING so excludes specific sinusoidal signals. 2.  D ISCRIMINATION  OF P HENOMENON  by energy estimation in a specific selected frequency band. 3.  PULSE E NHANCEMENT  V  ERSUS S INUSOIDAL S IGNALS . Energy estimation focuses on peak phenomenon with correlated phase characteristics versus ‘wavy’ type phenomenon. OISE I   MPROVEMENT  4. S IGNAL-TO-N OISE  . An energy estimation enhances localized l ocalized energy, concentrating FFT distributed peaks into its basebands.

5. Speed varying varying compens compensation ation as small small phase shifts shifts during rotation (non-constant rotational velocity) will be averaged-out.  NSTANTANEOUS ANEOUS SYNCHRONOUS T   IME AVERAGING . 6.  I  NSTANT Bringing energy to baseband frequency components enables the time record to be longer and so inherently does better synchronous averaging.

Preconditions for Optimal Enveloping

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2. Pre-filterin Pre-filtering g with Constant Constant Time Time Delay filters filters for good Peak reproduction. 3. Large Large bandwi bandwidth dth for optimal optimal ‘summation ‘summation’’ o of  f  energy. 4. Signal Signal source se separati paration on by Optimal Optimal Pre-filter Pre-filter selection. 5. Time Time domain analys analysis is so ext extractio raction n is done without separation of coherent frequency components. 6. Low pass pass filter selectio selection n after Envelop Enveloping ing for rejection of Out-of-Band components.

Conclusion The acceleration enveloping technique is emerging as a very practical measurement tool for assessing initial problems associated with bearings, rollers, and felt rotation. The very low speeds speeds at which these measurements occur are often at sensitivity limits of transducers transducers and electronics. In the past, past, synchronous time averaging over very long intervals was required to isolate problems to a particular roll by establishing external trigger references. Enveloping has proven its capabilities to extract impact force signals developed by roll eccentricity, flat spots, rolling element bearing defects and many other impulse type or modulating type signals. Although enveloping is not the panacea for diagnosing all machine problems, it is proving to be an adaptable and effective measurement method in the tool box of analysis techniques.

1. Sufficient Sufficient signalsignal-to-no to-noise ise ratio in the the measuring measuring chain before Enveloping is performed.

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Technical Association of the Pulp and  Paper Industry  Optimized Application  of Feature Extraction Techniques  Techniques  …

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Case Studies  B EARING T  EST  R IG  D EMONSTRATION  Figure 1. Standard Velocity Measurement with defective bearing. Although bearing defect frequencies noticeable no clear indication as still many other frequency components are of  the same level.

Figure 1.

Figure 2. Zoomed Velocity Velocity spectrum with rotational components visible but no significant bearing defect pattern.

Figure 2.

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Technical Association of the Pulp and  Paper Industry  Optimized Application  of Feature Extraction Techniques  Techniques  …

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Case Studies  B EARING T  EST  R IG  D EMONSTRATION  Figure 3. Enveloped Acceleration showing a clear discriminative spectrum of an Inner-race Defect Pattern.

Figure 3.

Figure 4.

SEE ™  spectrum (Enveloped Acoustic Emission spectrum) also showing the bearing defect pattern as indicating friction (progress of wear). The extra sidebands around the bearing defect modulation frequency peaks indicate a modulation by a low-frequency phenomenon likely uneven coupling loading.

Figure 4.

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Technical Association of the Pulp and  Paper Industry  Optimized Application  of Feature Extraction Techniques  Techniques  …

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Case Studies  B EARING D EFECT  D EVELOPMENT  ON   A  A D RYER F ELT   R ROLL Figure 5. Trend Plot of the Standard Velocity elocit y Measurement. Measurem ent. No indication of a bearing defect visible.

Figure 5.

Figure 6. Velocity Spectrum showing a number of harmonic patterns but no clear indication of an Inner-race Defect Pattern.

Figure 6.

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Technical Association of the Pulp and  Paper Industry  Optimized Application  of Feature Extraction Techniques  Techniques  …

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Case Studies  B EARING D EFECT  D EVELOPMENT  ON   A  A D RYER F ELT   R ROLL Figure 7. Trend Plot of the Acceleration Enveloping Measurement. Good indication of a bearing defect development.

Figure 7.

Figure 8. Enveloped Acceleration Spectrum showing a clear discriminative spectrum of an Innerrace Defect Pattern.

Figure 8.

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Case Studies  EASUREMENT  S ETUP O PTIMAL M  ON   D D RYER C  AN 

Figure 9. Spectrum Plot of the Acceleration Enveloping Measurement. Although the bearing defect is visible the pattern is not extremely extremel y clear. The measurement TIMELENGTH  was  was too short. This is defined by the selected Bandwidth versus the chosen  RESOLUTION (LINES). (LINES). Timelength = Lines / Bandwidth Optimal timelength is 10 – 15X the time for one shaft rotation.

Figure 9.

Figure 10. Time plot belonging to Figure 9. The measurement Timelength does not contain sufficient revolutions of the shaft to built a clear spectral pattern.

Figure 10.

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Technical Association of the Pulp and  Paper Industry  Optimized Application  of Feature Extraction Techniques  Techniques  …

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Case Studies  EASUREMENT  S ETUP O PTIMAL M  ON   D D RYER C  AN 

Figure 11. Spectrum Plot of the Acceleration Enveloping Measurement. The bearing defect is clearly visible with a clear sideband pattern so indicative for an innerrace defect pattern. The selected measurement Timelength is optimally chosen.

Figure 11.

Figure 12. Time plot belonging to Figure 11. The measurement Timelength does contain sufficient revolutions of the shaft (modulation) to built a clear spectral pattern.

Figure 12.

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Technical Association of the Pulp and  Paper Industry  Optimized Application  of Feature Extraction Techniques  Techniques  …

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Case Studies  P RESS S ECTION  F ELT  A NOMALY  ODULATION   D W  ITH  M   M ODULATION  D RIVE T  RAIN   P ATTERN  Figure 13. Time plot indicating the Felt repetition pattern (see also Figure 14) modulated by a  DRIVE TRAIN control loop problem.

Figure 13.

Figure 14. Zoomed Time plot indicating the Felt repetition pattern. These patterns are indicative of  uneven dewatering characteristics in the felt.

Figure 14.

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Technical Association of the Pulp and  Paper Industry  Optimized Application  of Feature Extraction Techniques  Techniques  …

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Case Studies  P RESS S ECTION   F F ELT   A A NOMALY  W  ITH  M   M ODULATION  ODULATION   D D RIVE T  RAIN   P ATTERN  Figure 15. Spectrum Plot of the Acceleration Enveloping Measurement. The FELT pattern is clearly visible. The sideband pattern indicative for a modulation pattern becomes clearer after zooming (see Figure 16).

Figure 15.

Figure 16. Zoomed Spectrum Plot of the Acceleration Enveloping Measurement. The modulation caused by the drive train driving the Fourth press is clearly positioned around the spectral Felt Pattern.

Figure 16.

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