Commentary

Development of Remaining Useful Life Prediction
Technology for Rolling Bearings Under Flaking Progression

Masashi KITAI*In recent years, due to aging equipment and the lack of maintenance personnel, there is increasing
interest in advanced predictive maintenance, and rolling bearings are attracting attention as a target. In
general, rolling bearings are replaced when some kind of damage occurs. However, in some cases where
maintenance is not easy, they may continue to be used even after the damage has occurred as long as it
does not affect peripheral equipment. This paper introduces a developing AI method for predicting theremaining useful life of rolling bearings under flaking progression.

1. Introduction
Interest is growing for enhanced functionality andautomation of maintenance technologies amid the
backdrop of increased maintenance costs due tolong-term deterioration and the increasing burden
on workers due to the lack of maintenance personnel
in manufacturing, infrastructure and similar related
equipment. In recent years much attention has
been given to technology relating to “predictive
maintenance”, a method which involves detecting
abnormal signs of operation so that maintenance canthen be performed. This method is more efficient than
“preventive maintenance”, which involves regularly
maintaining equipment regardless of its condition or“corrective maintenance”, which involves maintaining
equipment after a failure has occurred. NTN has
increased activity in this area by introducing thesetechnologies in maintenance using IoT and AI1).
Rolling bearings are machine elements that is vitalto equipment and are a key element in supporting
machine rotation. In cases of equipment failure, it
is said that roughly 30 % of failures are caused byrolling bearings2). Based on this fact, it is desirable
to estimate the condition of rolling bearings andrepair or replace them at the proper time to reduce
maintenance costs for the entire machine.
Vibration acceleration is often used to diagnoserolling bearings because it can be measured without
interrupting the machine and is highly sensitive todamage3). Diagnostic methods that use vibration
acceleration predict damage conditions using statistics
such as the root mean square value and kurtosis, aswell as specifying areas of damage using frequency
analysis. There has been much recent activity in R&D
development to predict the damage conditions andremaining useful life of rolling bearings in line with thedevelopment of AI technology such as deeplearning4), 5), 6).

This paper introduces AI technology7) developed to
predict remaining useful life up to when it is necessary
to replace the bearings and is applicable to rolling
bearings after damage has occurred.

2. Development background
Generally, the life of a rolling bearing is often
considered to be when some type of damage occurs
on the bearing raceway surface, such as flaking or
indentation. However, depending on the environmentand conditions in which the bearing is used, it is noteasy to replace rolling bearings and these bearings
may continue to be used even after minor damage
has occurred because of the significant maintenancecosts involved.
If rolling bearing damage progresses, there will
be a sudden increase in vibration, which will cause
damage to the machine as well as other elements andwill likely lead to increased downtime. Therefore, it isdesirable to be able to predict the remaining usefullife up to when the bearing needs to be replacedby estimating the extent of damage on the rolling
bearing (hereafter, damage condition). However, mostresearch into remaining useful life prediction of rolling
bearings mentioned above does not describe thedamage condition of rolling bearings.
Therefore, this paper will introduce the technologythat was developed with the aim of predicting theremaining useful life with high accuracy, by expressinga regression model of the relationship between thedamage condition and remaining useful life for rolling
bearings where damage is in progress.

* Advanced Technology R&D Center

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Development of Remaining Useful Life Prediction Technology for Rolling Bearings Under Flaking Progression

4. Characteristics of development
technology

Fig. 1 shows the relationship between the operatingtime and vibration when using a cylindrical roller
bearing. Operation continued until the circumferential
length of flaking that occurred on the inner ring
raceway surface exceeded the rolling element pitch
length8). The horizontal axis shows the load count
while the vertical axis shows the change relative to theroot mean square (RMS) initial value of the vibration
acceleration (RMS relative value). The figure also
shows an external view of the flaking on the inner ring
raceway surface for a specific time. Flaking occurred
at a load count of approximately 900 thousand times.
Flaking on the raceway surface first develops mostly
in the axial direction (range A in Fig. 1), and when thelength of flaking in the axial direction reaches the rolling
element contact length, flaking mostly progresses
along the circumference after this (the direction inwhich the rolling element moves) (range B in Fig. 1).
RMS increases quickly while flaking progresses in theaxial direction, and while flaking mostly progresses
along the circumference, RMS increases slower
and fluctuations follow the same trend. As flaking
progresses further, if the length of flaking along thecircumference reaches the rolling element pitch length(range C in Fig. 1), RMS increases rapidly once again
and the fluctuation range also increases.
When this type of vibration increases, displacement
between the inner and outer rings of the rolling
bearing increases the risk of exceeding the range ofacceptable clearance for peripheral components,
which will result in damage to the peripheral
components. Consequently, it is best to stop
operation before the flaking circumferential length
reaches the rolling element pitch length. This study
regarded the bearing replacement time to be when
the circumferential flaking length reaches half of therolling element pitch length.

This chapter describes the overview for development
technology.
4.1 Development technology
Fig. 2 shows an overview of development technology.
Development technology consists of a combination of
Feature Fusion Network (FFN)7) as indicated in section
4.2 and Hierarchical Bayesian Regression, HBR9) as
indicated in section 4.3. Using a short time Fouriertransform (STFT) (see Fig. 3)10) spectrogram to input
vibration acceleration time series data, FFN can predict
maximum circumferential flaking length (hereafter,
flaking size) and remaining useful life (hereafter, SS
remaining life) as a snapshot (a single output for asingle input data set). Next, from the flaking size andSS remaining life predicted by FFN, HBR can be used
to output the remaining useful life regression equation,
remaining useful life and its distribution.
Measurement
data
Pre-processing
Spectrogram

Flaking size

Remaining useful life curve

Fig. 2 Development model overview

Traveling direction of rolling element

Fourier transform

A

30

B

C

20

0

50

100

150

200

Load count x 106 times

250

Fig. 1 Relationship between flaking progress andvibration acceleration8)

30

Time
Time series waveform

High

Low
Time
Spectrogram

Fig. 3 Illustration of short time Fourier transform

10
0

…

Frequency

Amplitude
RMS relative value

40

SS remaining useful life

Amplitude

3. Flaking progress and vibration
acceleration on rolling bearings

NTN TECHNICAL REVIEW No.89

4.2 Feature Fusion Network
FFN is a regression method based on a convolutional
neural network (CNN)11) in deep learning and is often
used for image recognition. Fig. 4 shows an illustrationof FFN. Normal CNN directly predicts corresponding
objective variables just from data input at the time of
the measurement. FFN calculates a deterioration index(a normalized index in a range of 0 to 1 for the state ofdeterioration) at each point in time from data input frommultiple times in the past. The deterioration index isthen vectorized with a measured permutation to createa deterioration index vector, which is then used as anintermediate variable with the aim of improving theprediction accuracy of the objective variables (flaking
size and SS remaining useful life).
Objective variables

Feature vector

Feature vector

Deterioration index vector

Deterioration index

Input data
(Measurement point)

5. Evaluation test
5.1 Test equipment and measurement data
A schematic drawing for test equipment used toevaluate development technology is shown in Fig. 5,
while test conditions are shown in Table. 17). A cylindricalroller bearing (number NU224, bore diameter 120 mm,
outer diameter 215 mm) was used as the test bearing.
Operation continued until the bearing reached its limit
of use after the initial flaking that occurred on the
bearing raceway surface, and both vibration accelerationand flaking size were measured at regular intervals.
Measurements were taken for a sample of 33 bearings.
Fig. 6 shows the relationship between the operatingtime and RMS from when the initial flaking occurred oneach bearing sample until the bearing reached its limit ofuse. Fig. 7 shows the relationship between the operatingtime and flaking size. At the end of flaking progress,
RMS fluctuated significantly, and this made it difficult
to accurately know the flaking conditions. Despite thefact that all 33 bearing samples were tested under thesame operating conditions, the remaining useful life, up
to when the bearing limit of use was reached, differedsignificantly among the bearing samples. Therefore, inorder to improve the accuracy of predicting remaining
useful life, it is necessary to consider fluctuations infeature quantities of vibration acceleration as well as
individual differences in remaining useful life.

Input data
(Measurement point + past)

Vibrational acceleration
sensorRolling bearing

Fig. 4 Feature fusion network overview
4.3 Hierarchical Bayesian regression
Since the previously mentioned SS remaining usefullife is predicted with a snapshot, the predicted valuechanges for each measurement time. In practical use,
the regression curve for remaining useful life is definedby a monotonically decreasing function because itis preferable for the remaining useful life predictedvalue to monotonically decrease as operating time
elapses. In addition to the above, HBR was used7) asa method to consider variations between individual
bearings during this development. HBR uses all pastdata before the measurement point to obtain theremaining useful life and regression curve. Morespecifically, differences in individual bearings are
assumed to vary based on a probability distribution
so that individual remaining useful life regression
curves can be handled for each bearing. This enables
relatively accurate predictions for remaining useful life
output at the end, even for individual bearings whose
SS remaining useful life greatly deviates from theaverage obtained from all learned data.

SpindleHousing

Load

Fig. 5 Test equipment7)
Table 1 Test conditions7)
Rolling bearing

Cylindrical roller bearing (number: NU224)

Main shaft
500 min-1
rotational speed
Radial load

90 kN

Measurement
data

Vibration acceleration (vertical direction)

Bearing sample
quantity

33 pieces

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Flaking size mm

Fig. 6 Relationship between operating time and RMS

Operating time hFig. 7 Relationship between operating time andflaking size
5.2 Evaluation metrics
2
The coefficient of determination R was used in the2evaluation metrics of the development method. R is
an indicator of the degree to which a predicted valueof the objective variables (flaking size or remaining
useful life in this case) matches the actual value(hereafter, true value), and is shown in equation (1)below.
R =1−
2

Σ nk −1(yk−ŷk)2Σ nk =1(yk−y)2(1)

Here, yk and ŷk denote the true value and predictedvalue of the objective variables at measurement time k,
respectively. Furthermore, y denotes the true value
average of the objective variables while n denotes the2number of data. R can be a value of 1 or less, and thelarger the value (closer to 1), the higher the prediction
2
accuracy. R was calculated for each bearing sample,
and both the average and variation of predicted values
calculated using leave-one-out cross-validation12)
were evaluated. Furthermore, the whole range,from when flaking occurred until it was necessary
to replace the bearing, was divided into two ranges
which were the early stage and late stage. Both ofthese stages were evaluated. Here, the range in which
flaking mostly progressed in the axial direction was
taken to be the early stage, while the range in which
flaking progressed along the circumference was taken
to be the late stage.

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NTN TECHNICAL REVIEW No.89

R2 Score (Coefficient ofdetermination)

Operating time h

5.3 Flaking size and SS remaining useful life
prediction results using FFN
This section provides a comparison of FFN with thevarious regression methods and evaluates the accuracyof predicting flaking size and SS remaining useful life.
Kernel Ridge (KR)13), Random Forest (RF)14), Support
Vector Regression (SVR)15), Neural Network (DNN)16)
with 4 hidden layers, and CNN were used as comparison
methods. Similar to the development method, CNN
inputs the vibration acceleration spectrogram and does
not consider past data. Moreover, for KR, RF, SVR and
DNN, statistics in the time domain, frequency domain
and cepstral domain (RMS, maximum value, peak factor,
kurtosis, skewness, RMS after envelope processing17))
were used as inputs after various band pass filtering wereapplied to vibration acceleration. Hyper-parameters ofthe development method and comparison methods
were then selected as optimal values using 5-fold crossvalidation12).
Fig. 8 shows a box plot of comparison results for theaccuracy of predicting flaking size, while Fig. 9 showsa box plot of comparison results for the accuracy of
predicting SS remaining useful life.

1.0：Early stage：Late stage：Average

0.5
0.0

-0.5
-1.0

KR

RF

SVR

DNN

CNN

FFN

Fig. 8 Flaking size prediction accuracy (w/o HBR)7), 17)

R2 Score (Coefficient ofdetermination)

RMS m/s2

Development of Remaining Useful Life Prediction Technology for Rolling Bearings Under Flaking Progression

1.0：Early stage：Late stage：Average

0.5
0.0

-0.5
-1.0

KR

RF

SVR

DNN

CNN

FFN

Fig. 9 SS remaining useful life prediction accuracy(w/o HBR)7), 17)
All four methods, excluding KR and DNN, had
2
an average R of 0.7 or higher for the flaking
size prediction during the early stage of flaking
progression. In particular, FFN prediction accuracy2
had the highest value. Additionally, the average R
for CNN and FFN was high during the late stage offlaking progression. However, because CNN prediction
accuracy worsened for specific samples, on average
CNN was significantly lower than FFN. Therefore, FFN
has the highest prediction accuracy for predicting theflaking size.

5.4 Remaining useful life curve using thedevelopment method
Fig. 10 shows the relationship for the median andprediction distribution of the remaining useful life
curve and damage progression using HBR. In thisfigure, measured data in the range used for HBR
learning is shown to the left of the red dotted line.
Measured data in the prediction range is shown to
the right of the red dotted line. The black line shows
the prediction curve using HBR. Furthermore, thedark grey area shows a 50 % level of confidence
range while the light grey area shows a 95 % level ofconfidence range. The three graphs in the figure show
prediction results for remaining useful life at points
where 10 %, 20 % and 50 % data was measured outof all measurement data for the bearing from the left,
respectively. A dashed line is also shown in the figure
to represent the limit of use (life criteria) as definedin chapter 3. The prediction curve approaches thetrue value and the range for the level of confidence
became narrower as measurement data increased
with the progression of damage. Therefore, therelationship between remaining useful life and damage
progression using HBR can be expressed as a curvewith a prediction distribution (reliability of predictedvalues). Furthermore, increasing measurement data
improved the remaining useful life prediction accuracy
and increased the reliability of the predicted value.
Fig. 11 shows a box plot of remaining useful life
prediction accuracy due to the development method(the combination of FFN and HBR). For comparison,
the figure shows the prediction results for RF and HBR
combined, CNN and HBR combined, and FFN by itself.
During the damage early stage, the development
method has improved accuracy in comparison withthe other methods. In particular, the interquartile
range becomes smaller, confirming that variation in
the bearing sample is smaller. This method also hasa high prediction accuracy compared with the othermethods during the damage late stage, with the2
only average R exceeding 0.5. Therefore, using thedevelopment method can predict remaining useful life
with high accuracy more than conventional methods.

Measurement Prediction

Remaining useful life

the early stage of flaking progression compared with
KR, RF and SVR. Among these three methods, FFN
had the highest prediction accuracy. All methods had
a lower prediction accuracy during the late stage offlaking progression compared with the early stage offlaking progression. However, RF and the development
method maintained a comparatively high accuracy
out of all the methods.
Based on these results, we can see that FFN
maintains a high prediction accuracy for both flaking
size and SS remaining useful life in comparison withthe other general regression methods.

Measurement Prediction Measurement Prediction

60

60

60

40

Limit of use 40
20

Limit of use 40
20

20
0

0
10 20
Number of measurement
data: 10 %

0

0
10 20
Number of measurement
data: 20 %0

Limit of use

0
10 20
Number of measurement
data: 50 %Flaking size

Fig. 10 Relationship between flaking progression
and remaining useful life prediction distribution7)

R2 Score (Coefficient ofdetermination)

The three methods DNN, CNN and FFN have a highR2 for the SS remaining useful life prediction during

1.0：Early stage：Late stage：Average

0.5
0.0

-0.5
-1.0

RF＋HBR

CNN＋HBRFFN
Development(w/o HBR)
methodFig. 11 Remaining useful life prediction accuracy7)

6. Summary
This paper compared and verified general machinelearning methods and their performance for aremaining useful life prediction method developed totarget rolling bearings after damage has occurred. It
confirmed that using the development method canpredict the remaining useful life with high accuracy
until the time the rolling bearing needs to be replaced
after damage has occurred. Therefore, use of thisdevelopment method enables us to obtain a guideline
for using rolling bearings in the range where thedisplacement between the inner and outer rings does
not exceed the acceptable clearance for peripheral
components even when operation is continued after
flaking has occurred on a rolling bearing. Equipment
such as pumps and fans used in special environments
are an example of equipment that continue to be
used even after the rolling bearings are damaged. In
the future, NTN will continue working on increasing
the versatility of this development technology while
contributing to the reduction of maintenance costs on
various equipment.

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Development of Remaining Useful Life Prediction Technology for Rolling Bearings Under Flaking Progression
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Photo of author

Masashi KITAIAdvanced
Technology
R&D Center

34

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