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Statistical Analysis Of Vibration Signals For Condition Monitoring Of Defects In Exhaust Fan Bearings

Exhaust fan bearing defects and vibration frequency solutions analysis

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Authors Name/s per 2nd Affiliation (Author)

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Identification of exhaust fan bearing defects has its root foundations in the analysis of frequency modulation and sensing using mostly online techniques.

Centrifugal fans are the most commonly used fans in the industry today. They are preferred to axles because they are cheaper and constructed with simplicity. They are used in wind systems in vehicles and buildings and also in delivering gas or minerals. They are suitable for industrial and air pollution processes and systems.

Fans play a key role in the production of re-circulating applications, in the supply of combustion air and the movement of air through processes and equipment for the protection of the environment. As they are indispensable components in these and other fields, it is essential that they always work reliably and efficiently. Reliability prevents interruptions that result in downtime and product damage. While engine failures or bearings sometimes contribute to fan failure, bearings are the most common failure point. Bearings are the crucial link between the rotating fan shaft and the stationary drive base, two of the strongest components of the fan.

Mechanics form the difference between the direct drive fan and the belt drive. In design, the motor in the direct drive discharge fans is directly connected to the fan board. This system is generally more efficient and has fewer moving parts, which reduces the likelihood of repair. The belt conveyor fans use a belt and motor to control the motor shaft. While the vibration friction of this system might cause inefficiency, the lower price and the quiet operation make this fan a popular choice. Direct drive and belt drive will provide the necessary suction air to heat the heat, smoke and smoke of your commercial kitchen.

The focus of this research study is the identification of exhaust fan bearing defects and requisite solutions to the problems caused by such defects. The use of harmonic and frequency modulations analysis in relation to revolutions per minute is critical in the determination of the extent of such defects. Identification of a clear algorithm to be applied to achieve the underlying objectives of the research study is of crucial importance.

KEYWORDS: Bearings, Exhaust fan defects, harmonics, frequencies.


Important is the determination of the defects of rolling elements of the bearing components. Vibration diagnostics is one of the most effective analytical tools for defect analysis in fans, pumps, compressors, turbines, motors, etc.

Sound pattern recognition forms the basis of fault diagnosis. Unlike the time analysis, frequency analysis is able to isolate specific vibrational frequencies. Frequency analysis aids in the easy maintenance and value addition to adding to the life of machines for increased profitability through maintenance cost reduction.

This has been possible with the recent advent and move from preventive measures maintenance to condition based maintenance of bearings. Therefore bearing condition monitoring is preferably carried out through vibration analysis. Condition based maintenance has advanced in health management of fan bearings.

Health assessment and behavior analysis prediction focused on data processing improves the overall performance of data driven frameworks. Provision of better services, cost and risk reduction and ensured smooth and efficient running of the machines are the results of well-focused data frameworks.

Results from the data analysis of the velocity and frequency vibrations are most effective when used in conjunction with a health-based system of evaluating the results. The outcome should be generation of benchmark figures for the comparison of the data in question. Another important paradigm is the classification of the fan bearing vibrations data and defects to the bearings identified to enable the most effective method of systematic maintenance and residual life determination for the exhaust fan bearing.


Vibration analysis is a technique used to evaluate the operating conditions of the exhaust fan bearing and the deterioration trends, such as maintenance costs and the idle time. The vibration analysis technique consists of the measurement of vibration and its interpretation. Vibration measurement is recording the signal from the machine by measuring the vibrations.

Vibration is the movement of a machine or part of the machine or mechanical vibration. It may be normal, eg pendulum movement or occasional movement if. Vibrations can be expressed in units (m / s2) or gravitational “g” 1 g = 9.81 m / s2. The subject can vibrate in two ways: free vibrations and forced vibrations. Free vibration occurs when the object or structure is moved or shaken and then a natural vibration is obtained, Felten d 2003.

For example, if you lose a laptop computer, it will ring and will eventually turn off. At natural frequency, it is often the frequency in which a structure wants to “vibrate” after a stroke or change.

Resonance is the tendency of the system to oscillate at certain frequencies of others. Forced oscillation at or near the natural frequency of the object causes the structure of the energy structure. Over time, vibrations can become very important even if the vibration of the input force is very poor. If the structure has natural frequencies suitable for conventional vibrations, the structure loses noise and fails prematurely.

A Fast Fourier Transform Spectrum (FFT) is a very useful tool for machine vibration analysis. If there is a problem, the FFT spectrum will provide information to determine the cause of the problem and how long it will take to verify the problem, Allen Bradley 2016.

FFT spectra allow the analysis and amplitudes of frequencies and frequencies of different components in the FFT spectrum. This way we can detect and monitor the vibrations that occur at certain frequencies. Since we know that some machine problems generate vibrations at certain frequencies, we can use this information to diagnose the cause of excessive vibration.

The signal is then processed with an FFT analyzer to get the frequency Spectrum. The result is mainly played by parents Frequency measured with relevant causes such as imbalance, Misalignment, bad bearing and resonance.

The overall level is an estimation of the total vibration amplitude over a wide frequency dimension. The measurement of global vibration, also called broadband, is one relatively simple and inexpensive unique value for collecting, processing, analyzing, setting and trending it.


The general objectives of the study are determining the effects of application of frequency analysis in determination of exhaust fan bearing defects.

The specific objectives include;

  1. To determine the effect of vibration frequency and speed calculations on the identification of fan bearing faults and defects.
  2. To determine the effect of fan bearing defects on the performance of the bearing
  3. To determine the impact of the algorithm chosen in solution finding and implementation to solve problems identified



The research study entailed capturing frequency data from the exhaust fan bearing industry. To be practicable mechanical information and other technical information for exhaust fan bearing vibration defect analysis availed to the industry used in the study.

The information which includes the harmonics and revolutions per minute, fundamental train frequency (FTF), the ball spin frequency (BSF), ball passing frequency-outer race (BPFO), and the ball passing frequency inner race (BPFI) are analyzed using Minitab, a statistical application program for the determination of defect and extent of remaining life of the exhaust fan bearing under investigation.

An inquest was then made to find out the statistical parameters and outputs necessary for determining the health of the fan bearings. The inputs into the computer algorithm aid in the determination the health of exhaust fan bearings.

The inputs include the harmonics and revolutions per minute, which are then used to calculate the velocity against which all the frequencies are compared in statistical analysis.

The outputs from the system are statistical frequency graphs and histograms, statistical parameters for vibration data studied and data analysis involving the frequency of vibration signals and velocity (Harmonics multiplied by revolutions per minute).

Further, the industry-collected data analyzed in the algorithm is subjected to health monitoring by comparing it with collected experimental accelerometer frequency and bearing revolutions benchmark standards developed in the algorithm over time.

This being a concise way of determining the possible defects in specific exhaust fan bearings and the relevant life expectancy of the bearing over its remaining useful life.

This is possible by storing analyzed data file and exhaust fan bearing defect analysis information within the system for future reference and analysis.

The nearest neighbor anomaly detector is then applied to detect data, which is placed far from the clustered data, which indicates an anomaly. To decide whether new data X is an anomaly it is compared to its nearest neighbor Y after normalization of the distance between Y and its nearest neighbor Z.

Distance = || X – Y || / || Y – Z || …………(1)


The methodology of inquest employed was online vibration monitoring which is about constantly buying vibration signals and the use of this reduced data as indicators of machine health in near real time.

Factory and field test and vibration data collection can be greatly undermined by the level of grease and oil in the fan bearing being tested. Several precautions are undertaken to ensure these factors are reduced significantly.



Figure 1 Ball bearing pillow blocks

Figure 2 set screw collar



Figure 4





Grease hardening and insufficient lubrication can be caused by mixing the grease with different bases and thus should be completely avoided. To change from the lithium-based grease used in factory manufacture of exhaust fan bearings, continually add the new grease to the bearing until only the new grease built with another base such as sodium or synthetic base completely replaces the factory lithium based grease.

The galvanized steel base bracket of the exhaust fan is welded to provide the strength and support needed for it’s cleaning, preventing spillage and loss into the building. A factory-covered aluminum grease plug is welded to the fan housing to ensure that it does not spill oil and grease.

Low oil levels do not provide adequate lubrication. The level meters are used to control the oil level must be adjusted to read correctly. Since it has been indicated when the fan is on, it’s better to check the oil levels when the fan is off.

Maintenance of the bearing while ensuring proper lubrication ensures the long life of the bearing. To prevent damage to the bearing a sufficient supply of lubrication should be ensured to avoid metal to metal contact between the bearing and the fan.

Static or circulating oil should only be used with separation High speed or high temperature cushion bearings applications where grease is inadequate. Lubricated with oil bearings require more rigorous maintenance.

During testing air inside the chamber should be avoided at all costs and at all times. High oil levels can prevent the bearing from venting the air inside rolling while the bearing warms up in revolutions. The accumulated pressure can blow all the oil on landing in a few minutes.


Identification of fan bearing defaults involved the collection of fault frequencies, which include the harmonics and revolutions per minute, fundamental train frequency (FTF), the ball spin frequency (BSF), ball passing frequency-outer race (BPFO), and the ball passing frequency inner race (BPFI).

Data analysis of the ball passing frequency-outer race (BPFO), and the ball passing frequency inner race (BPFI) in conjunction with the revolutions per minute was applied to identify the fan-bearing defect.


The fan bearing chosen for this research study is the

UCP204-12-AH-SP4 – Bearing 3/4″ pillow block bearing with cast housing and grease fitting. (8B0750) 1/4-28 x 1/8 Socket Set Screw (2@62°). Serrated Tip Set Screw With Loctite Patch. Set Screw Torque = 85 inch lbs. Grease fitting on opposite side of standard.

The UCP204-12-AH-SP4 bearing has been used in many applications including those mentioned below:

Used in Qty Used
DD7FA Centrifugal downblast belt drive exhaust fan with a 11.75″ wheel. 2
DD7HPFA HP Centrifugal downblast belt drive exhaust fan with a 11.75″ wheel. 2
DD8FA Centrifugal downblast belt drive exhaust fan with an 11.75″ wheel. 2
DD8HPFA HP Centrifugal downblast belt drive exhaust fan with an 11.75″ wheel. 2
DD9FA Centrifugal downblast belt drive exhaust fan with a 11.75″ wheel. 2
DD9HPFA HP Centrifugal downblast belt drive exhaust fan with a 11.75″ wheel. 2
DD11FA Centrifugal downblast belt drive exhaust fan with an 11.75″ wheel. 2
DD11HPFA HP Centrifugal downblast belt drive exhaust fan with an 11.75″ wheel. 2
DD13FA Centrifugal downblast belt drive exhaust fan with a 13.75″ wheel. 2
DD13HPFA HP Centrifugal downblast belt drive exhaust fan with a 13.75″ wheel. 2
DD15FA Centrifugal downblast belt drive exhaust fan with a 15.75″ wheel. 2
DD15HPFA HP Centrifugal downblast belt drive exhaust fan with a 15.75″ wheel. 2
NCA8FA Belt Drive Centrifugal Upblast Exhaust Fan with 11.75″ wheel 2
NCA8HPFA HP Belt Drive Centrifugal Upblast Exhaust Fan with 11.75″ wheel 2
NCA10FA Belt Drive Centrifugal Upblast Exhaust Fan with 13.75″ wheel 2
NCA10HPFA HP Belt Drive Centrifugal Upblast Exhaust Fan with 13.75″ wheel 2
NCA14FA Belt Drive Centrifugal Upblast Exhaust Fan with 15.75″ wheel 2
NCA14HPFA High Pressure Belt Drive Centrifugal Upblast Exhaust Fan with 15.75″ wheel 2


Exhaust fans are built to work efficiently. Evident failure in new and old exhaust fan bearings is testimony enough that manufacture and reliance on long life usage is nowhere near perfection. Detecting the causes of such failures or the problems with the bearing is important in predicting, gauging and guarding against eminent loss in usage of the exhaust fan bearing.

A series of high-energy pulses at a rate equal to the frequency of passing the ball in relation to the inner raceway result from a small fault on the inner raceway. Because of the inner ring rotating, the defect will enter and leave the load area causing a variation in contact raceway-bearing raceway force, so deflections.

Contamination is a very common source of bearing damage and premature failure and is due to the entry of strange element either because of improper treatment or during operation. Naturally the size of the vibration caused by the contamination varies and in the beginning steps can be difficult to recognize, but depends very much on the type and nature of impurities. Contamination can cause wear and tear damage to the bearing contact surfaces and generate vibrations over a wide range of frequencies.

Other causes of failure exhaust fan bearings include rusting of the bearing result from existence of moisture, broken bearings, low quality wrapping, acidic wear and tear and high temperatures. Buildup of metal in front of the rollers resulting in flaking, contamination due to defective bearing seals, arcing and burning at the point between the races and the rolling elements in electric currency damage of the bearing, mis-alignment and improper mounting and existence of foreign matter in the exhaust fan bearing are reasons, which also cause bearing damage and defects.

Accelerometers placed at keys positions on the motor and fan bearings are used to measure vibrations caused by the exhaust fan bearing during live factory testing and diagnosis. Because the bearings are the support part, mechanical train accelerometers should be placed in the entrance and exit of rolling bearings for vibration measurement levels.

Permanent accelerometers are set Fan and fan bearings Vibration sensors must be placed radial (vertical and horizontal) and axial positions on the motor and exhaust fan bearings. This provides the best recognition to every vibration components, including bearing vibration, imbalance failure, electrical disturbance, wings flow (aerodynamic disturbances) and frequency bands.

The piezoelectric accelerometer is widely regarded as a standard vibration sensor for vibration measurement of the machine. System configuration the piezoelectric crystal and the seismic mass depend on the frequency range of the sensor desired.

The prufteck vibroexpert does not only offers frequency information, but also general amplitudes for data analysis. It is useful for tendency or comparison measurements on similar machines but for diagnosis problems on machines. The other two large vibration instruments are tunable filters and FFT (Fast Fourier Transform) analysts. Analyzer is a default because analysis is a human function. One electronic analyzer “analyzed”. It only measures and display electrical signals.

The electrical signals of Accelerometers and speed transducers are very small AC voltages, typically millivolts. Therefore, the tunable filters and FFT instruments are nothing but fancy AC voltmeters with a frequency display axis. Any of these two large vibration-measuring instruments, of any one manufacturer, can be used effectively to diagnose machine vibrations.

The balancing and alignment method can be used to correct the blower vibration problems. This entails disassembly, manual sight inspection and the reassembling the bearing. It might result in replacement of the fan bearing or the defective parts, balancing and realignment and lubrication.

Clear and repeatable vibration measurements and data capture keys are not difficult to understand and set up. They are based on sound reason, care, organization and consistency. Precise vibration measurement requires the selection of the best measuring stations fan assembly. It also requires the best vibration converter specific frequencies or frequencies of general interest.

Last years, piezoelectric accelerometers have become the most widely used sensor type because of their general frequency characteristics, size, reliability and overall stability. Unless low frequency vibration measurement is required, the unusually low amplitude accelerometer is usually the best choice for accurate data.

The latest vibration measuring devices have the option of selecting a transition, speed or acceleration vibration measurement parameters, regardless of the type of sensor used. It is desirable, for the sake of accuracy, to use the parameter that provides the best (lowest) interest rate.

It turns out that speed is usually the best choice parameter for measuring vibration of the machine. The most common exception to this rule is using higher frequency accelerations to detect problems with certain components such as roller bearings.

How to use vibration converters to the measuring point is critical aspect vibration measurement accuracy. The stronger the sensor is connected the machine is your best answer. Machine vibration has different categories of causes that we’ve discovered after so many repairs, though it is now useful to check them to get more confidence about diagnosis, B.A Kardile, 2012.


There are four phases of classifying exhaust fan bearing defects. These phases include the phase one of wear and tear of the fan bearing where the possibility of complete failure of the bearing is quite low. At this phase, further monitoring for defect extents can be carried out on the fan bearing.

In the second phase most damage to the fan bearing can be seen with the naked eye and at the same time it can be sensed in the increased quantity and amplitude of fault frequency harmonics. In this second phase, outer race fault frequency is detected in the first bearing band. Inner race fault frequency is detected in second bearing band. Repair should continue at scheduled time intervals

In the third phase three, the ball spin frequencies appear in the outer bearing band. Harmonics of bearing fault frequencies should be easy to detect in a velocity spectrum. The possibility of catastrophic failure is imminent and repair should be more periodical.

In the fourth phase, the discrete frequency indicator will shows the extent of fundamental train frequency FTF (cage frequency) indicates that the bearings life is almost over or already over.


Analytical tests conducted using the chosen exhaust fan bearing data yielded various results, which enabled the process of decision-making. The determination of defect and lifetime remaining for a fan bearing enabled the compilation of specific benchmark predictors. In this section of the report follows the discussion of the results from data analysis.

TABLE 1 : HARMONIC AND FREQUENCY TABLE AT 179.99O WITH 50,000 REVOLUTIONS PER MINUTE FOR UCP204-12-AH-SP4 – Bearing 3/4″ pillow block bearing

Harmonic FTFI FTFO
1 306.564.8047 193.435.1953
2 613,129.6093 386.870.3907
3 919.694.4140 580.305.5860
4 773.740.7813
5 1,532.824.0233 967,175.9767
6 1.839.388.8280 1,160,611.1720
7 2.145.953.6327 1,354.046.3673
8 2.452,518.4373 1.547.481.5627
9 2,759.083.2420 1,740.916.7580
10 3.065.648.0466 1.934.351.9534
11 3,372.212.8513 2.127.787.1487
12 3.678.777.6560 2.321.222.3440
13 3.985.342.4606 2.514.657.5394
14 4.291.907.2653 2,708.092.7347
15 4.598.472.0700 2.901.527.9300
16 4.905,036.8746 3.094.963.1254
17 5,211.601.6793 3.288.398.3207
18 5,518.166.4840 3.481.833.5160
19 5.824.731.2886 3.675,268.7114
20 3.868.703.9067
21 6.437.860.8980 4.062,139.1020
22 6.744.425.7026 4.255,574.2974
23 7,050.990.5073 4.449.009.4927
24 7,357,555.3120 4.642.444.6880
25 7.664.120.1166 4.835.879.8834
26 7.970.684.9213 5.029.315.0787
27 8.277,249.7259 5.222.750.2741
28 8.583.814.5306 5.416.185.4694

TABLE 2 HARMONIC AND FREQUNCY TABLE AT 179.99O WITH 50,000 REVOLUTIONS PER MINUTE FOR UCP204-12-AH-SP4 – Bearing 3/4″ pillow block bearing

Harmonic BPFO BPFI
1 2,452,518.4373 1.547.481.5627
2 4.905,036.8746 3,094.963.1254
3 7,357,555.3120 4.642.444.6880
4 9.810,073.7493 6,189.926.2507
5 12,262,592.1866 7,737,407.8134
6 14,715,110.6239 9.284.889.3761
7 17,167,629.0612 10.832,370.9388
8 19.620,147.4985 12,379.852.5015
9 22.072.665.9359 13,927,334.0641
10 24,525,184.3732 15,474.815.6268
11 26.977,702.8105 17,022,297.1895
12 29,430.221.2478 18,569,778.7522
13 31.882,739.6851 20,117,260.3149
14 34,335,258.1225 21.664,741.8775
15 36,787,776.5598
16 39,240,294.9971 24,759,705.0029
17 41,692.813.4344 26,307,186.5656
18 44,145,331.8717 27,854.668.1283
19 46,597,850.3091 29,402,149.6909
20 49,050,368.7464 30.949.631.2536
21 51,502.887.1837 32,497,112.8163
22 53.955,405.6210 34.044,594.3790
23 56,407,924.0583 35,592.075.9417
24 58.860.442.4956 37,139,557.5044
25 61,312.960.9330 38.687,039.0670
26 63,765,479.3703 40.234,520.6297
27 66,217,997.8076 41,782.002.1924
28 68.670.516.2449 43.329.483.7551




SOURCE: Author



0.385 0.615 2. 0614 3.08 4.92 1

48.138 76.862 257.6750 385.13 614.88 125000

96.275 153.725 515.3600 770.25 1229.75 250000

192.550 307.450 1030.7000 1540.50 2495.50 500000

Results of multiplying RPM with all frequencies to get velocities


1 1 3 5

6017188 9607813 48140625 76859375

24068750 38431250 192562500 307437500

96275000 153725000 770250000 1247750000

SOURCE: Author


The scatter plot for BPFO RPM (or velocity) v/s frequency of vibration outer race defect vibration BPFI shows anomaly in the fan bearing because the data is not linearly co-related, but significant variances are recorded in the data points plotted.

SOURCE: Author


Source: Author

The plot shows deviations from the densely located data thus indicating anomalies in both defect analyzing fault frequencies and therefore possible defects in the exhaust fan bearing. Figure 9 below clearly illustrates the deviations.


Source: Author


Source: Author

Figure 10 shows that more points vary at wider gaps from the densely distributed data indicating more defect possibilities at a contact angle of fan bearing of 360O as opposed to the distribution in figure 9, which shows less possibility of defects.

The possible causes would be looseness of the fan bearing due to the extent of defect causing variations in the frequency vibrations or a technical error in mounting the fan bearing during lab testing.


The KNN classifier requires a metric d and a positive integer K. In KNN Euclidean distance computes distances in multidimensional input space. The Euclidean distance between point p and q is the length of the line between them. In Cartesian coordinates, if pi and qi are two points in Euclidean n-space, then the distance from p to q is given by dE = (pi – qi)2. Position of training samples and input sample can be visualized on 2D and 3D Cartesian coordinates.



50000 1030704 2.459,499.2636

100000 2061407 4,918,998.5271

150000 3092111 7,378,497.7907

200000 4122814 9.837,997.0542

250000 5153518 12,297,496.3178

300000 6184222 14,756,995.5813

350000 7214925 17,216,494.8449

400000 8245629 19.675,994.1084

450000 9276333 22,135,493.3720

Source: Author


Source: Author


1.540,500.7364 50000 50000

3,081,001.4728 50000 100000

4,621,502.2091 50000 150000

6,162,002.9455 50000 200000

7,702,503.6819 50000 250000

9,243,004.4183 50000 300000

10,783,505.1546 50000 350000

12,324,005.8910 50000 400000

13.864,506.6274 50000 450000

15,405,007.3638 50000 500000

16.945,508.1001 50000 550000

18,486,008.8365 50000 600000

20,026,509.5729 50000 650000

21,567,010.3093 50000 700000

23,107,511.0457 50000 750000

24.648.011.7820 50000 800000

26,188,512.5184 50000 850000

27,729,013.2548 50000 900000

29,269,513.9912 50000 950000

30.810,014.7275 50000 1000000

32,350,515.4639 50000 1050000

33.891,016.2003 50000 1100000

35,431,516.9367 50000 1150000

36.972,017.6730 50000 1200000

The distance between incoming velocities, frequency is compared to the distance between harmonics and revolutions per minute for the two scenarios of Ball Pass frequency Outer BPFO outer race defects.

The distance is greater for the 360 degrees bearing data as compared to the 179.99 degrees frequencies, thus revealing more defects in the 360-degree dimension.


Vibration frequency analysis is an effective method of ascertaining exhaust fan bearing defects. It is a meticulous technique, which when coupled with the K – nearest neighbor analysis combines to form a formidable force in exhaust fan bearing defect detection through analysis of vibration frequency data and finding of requisite solutions for enhanced bearing health and maintenance.


  1. Lacey s,“An overview of bearing vibration analysis” Maintenance and asset management, 2008,Vol 23 No. 6
  2. Felten d, “Understanding bearing vibration frequencies.” Schofield, Wisconsin 2003, pp. 1–3.
  3. Granney b, “Rolling element bearing analysis,” ME technical paper, 2011, pp. 78-85.
  4. Allen Bradley, “applying condition monitoring to various machinery” Application techniques, 2016, Publication 1444-AT001A-EN-P. , pp. 147–149.
  5. Professor Samir N.Y. Gerges, Gustav A. Sehrndt and Wolfgang Parthey “Noises sources,” Muenster, Germany, Brazil, 2011, pp. 108, 121.
  6. B.A Kardile, “Bearing life improvement of centrifugal blowers by vibration analysis,” international journal of modern engineering research, 2012, Vol.2, Issue.6, pp. 1-2

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