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CHAPTER 5
VIBRATION ANALYSIS
5.1
INTRODUCTION
Monitoring the vibration characteristics of rotating machinery
helps to detect problems that might be developing (Li et al 2005). The Steam
Turbo-Generator machine is one of the most important machines for
generating electricity. The real-time fault diagnosis is more important for
steam turbine generator and various rotating machineries like generators,
motors, pumps, cooling fans, etc. in a thermal power plant. One of the widely
used and effective methods for fault detection and diagnosis of various
rotating machineries is vibration analysis. Vibration analysis is useful for
diagnosing the unbalance, bent and misalignment in shaft and rotor, damages,
distortion and loss of lubricant in bearing, tooth meshing fault, worn teeth,
misalignment in gear and mechanical looseness (Kumar et al 2012).
5.2
PROBLEM DESCRIPTION
Mechanical vibration fault such as imbalance, no orderliness, oil-
membrane oscillation, misalignment, and rotor crack is one of the common
incipient faults in the Turbine-generator. Vibration is an important indicator
for monitoring mechanical condition and avoiding defect development.
Vibration signals are collected from data acquisition system (DAS). FFT
extracts frequency-domain features from the time-domain signals and thus
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obtained frequency-domain features are used to perform diagnostic analysis to
detect faults in characteristic frequencies associated with various fault types.
The faults are classified based on spectral values and some of the
faults and their associated magnitudes are reproduced as given by (Lin et al
2007) in Figure 5.1.
Figure 5.1 Spectral values for some of the typical faults
The problem is to detect the type of fault based on spectral values
as given in Figure 5.1. In order to estimate the efficacy and applicability of
MSVM algorithm, the spectral values corresponding to 20 generator sets
have been taken from (Lin et al 2007) and reproduced in Table 5.1 for ready
reference and use. Using the table values, it is to be found / detected one of
the four types of faults as shown in Figure 5.1.
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Table 5.1 Spectral values for different generators
Generator
No.
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
5.3
< 0.4f
1f
2f
3f
>3f
3.35
4.43
3.29
5.72
3.24
6.32
0.54
1.51
2.43
0.54
0.81
1.24
1.78
0.92
0.65
1.13
0.92
1.08
0.54
0.27
12.15
11.02
11.61
12.31
42.66
15.23
37.80
52.92
54.49
48.82
52.00
49.79
22.46
30.08
21.98
24.46
26.08
20.52
8.10
8.64
1.94
3.20
1.24
3.62
2.16
3.56
2.70
6.59
4.64
6.64
6.43
4.64
23.8
22.0
26.2
22.3
26.0
25.4
2.70
1.08
2.30
1.30
0.90
1.50
1.10
2.30
2.70
2.50
0.80
3.90
3.60
1.00
19.0
16.0
18.0
15.0
20.0
17.0
2.70
1.10
3.67
2.43
1.30
0.59
0.54
3.19
0.00
2.54
1.78
1.51
1.89
2.27
8.59
5.67
11.1
15.8
11.4
11.9
1.08
0.54
Faults
OilMembrane
Imbalance
No
Orderliness
Normal
Condition
SCHEMATA OF FAULT DIAGNOSIS
The architecture of the M-SVM for fault diagnostics with the
desired input and output parameters is schematically illustrated in Figure 5.2.
The algorithm uses RBF kernel to analyze the performance of four different
classes (oil-membrane, imbalance, no orderliness, and normal condition). The
efficacy of the MSVM algorithm in detecting the abnormalities (defects) is
investigated in the thesis. Fault diagnosis system of detecting the deviations in
steam turbine generator initially requires the collection of training data set
models for training and testing.. The data set models represent the collection
of data made from the steam turbine generator. According to the field records,
diagnostic information can be provided to monitor mechanical condition by
the spectrum of the vibration signal. Frequency-based features are computed
by fast Fourier transformation (FFT), the frequency ranges are <0.4f, 1f, 2f, 3f,
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and >3f. The maximum and minimum values of power spectrum indicate
mechanical vibration fault at a particular frequency, and frequency patterns
are applied to diagnose faults.
Figure 5.2 Schemata of Fault Diagnosis analysis using MSVM
The spectral graph obtained from vibration analysis of a signal
provides a complete fault diagnosis data to monitor mechanical condition
of a steam turbine generator.
5.4
PERFORMANCE RESULTS
This section used the spectral data values available in the literature,
and applied MSVM classification method in order to detect the possible faults
in the turbo generator. Out of 20 sets of data,
6 sets belong to turbo generators with oil-membrane
oscillation
6 sets belong to turbo generators with imbalance
6 sets belong to turbo generators with no orderliness
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2 sets belong to turbo generators with normal condition (no
fault)
Table 5.2 Confusion matrix evaluation
Training
Oil Membrane
Oscillation
Imbalance
No
Orderliness
Normal
Oil Membrane
Oscillation
6
0
0
0
Imbalance
0
6
0
0
No Orderliness
0
0
6
0
Normal
0
0
0
2
Accuracy
rate
100%
Testing
Oil Membrane
Oscillation
4
1
0
0
Imbalance
0
6
0
0
No Orderliness
0
0
6
0
Normal
1
1
0
0
80%
Table 5.2 shows the confusion matrix and its values where each
cell contains input data classified for the corresponding combination of
desired outputs and is simulated using matlab. The ratio of the samples
classified correctly and the total number of samples gives the accuracy of
classification. The accuracy rate of 100% is obtained with Multi-Class SVM
classification during training and 80 % is obtained during testing. Considering
many factors of problem complexity, the data points in the set of data is
classified into the four different classes (oil-membrane, imbalance, no
orderliness and normal condition). However, in the absence of 100% accuracy
in any of the algorithm, one cannot resort to these techniques for online fault
detection.
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5.5
VIBRATION
ANALYSIS
USING
VB5
VIBRATION
ANALYZER
To get the hands on experience, the author performed vibration analysis in a
steam turbine generator of a 40MW power plant. This machine is monitored
during startup, normal operation and shut down by a sophisticated vibration
monitoring system using vb5 instrument along with ascent software. The
instrument is used to perform the tasks such as taking free run measurements
of vibration spectra and waveforms for onsite analysis, recording routes and
store vibration data for transferring to a personal computer for offsite analysis
and using keypad entry to enter additional machine information and process
inputs. The type of sensor is accelerometer to measure velocity and
displacement. The vibration spectra and waveforms are measured, and
analyzed onsite which is suitable for one-off investigations. ISO standard
10816 classifies the condition of bearing into four zones – based on vibration
amplitude and velocity (Michael and Eng 2011).The classification details are
given in Table 5.3.
Table 5.3 Health status according to ISO 10816 standards
Zone
1
2
3
4
5.6
Vibration Amplitudes (rms)
Up to 23 microns 1.8 mm/sec
Up to 56 microns 4.5 mm/sec
56 microns & 4.5 mm/sec to 140
microns & 11.20 mm/sec
Above 140 microns & 11.20 mm/Sec
Health Status
Good
Satisfactory
Just satisfactory
Unsatisfactory
VIBRATION SPECTRUM OF TURBINE GENERATOR
WITH VB5 INSTRUMENT
The rise in vibration is a direct result of unsound elements in the
machine and the information about the level of vibration guides to the
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machine’s health condition. Thus the primary operation of vibration
diagnostics is to assess the machine condition. To support this operation,
standards are used for the estimation of the overall RMS value of vibration
velocity or of the peak amplitude. The author has collected vibration spectral
signals related to turbine thrust bearing and turbine rear bearing (the pickup
locations are shown in Figure 5.3) – as shown in Figure 5.4 (a) and Figure 5.4
(b) - using vb5 analyzer.
Turbine rear bearing
Turbine thrust bearing
Figure 5.3 Pickup locations of Turbine
Figure 5.4 (a) Vibration signal associated with turbine thrust bearing
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Figure 5.4 (b) Vibration signal associated with turbine rear bearing
For training purpose, the RMS values of velocity in three
directions and displacement in two directions for various components in a
40 MW plant (Madras Cements, Ariyalur) have been collected using VB5
analyzer. These values are shown in Appendix 4. Using these values, the
MSVM architecture shown in Figure5.5 has been trained.
Figure 5.5 Training of MSVM and subsequent evaluation of fault zone
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Subsequently, the author has collected velocity and displacement
values at two locations, namely turbine thrust bearing and turbine rear
bearing. The collected values are tabulated in Table 5.4.
Table 5.4 Vibration Amplitude Records of Turbine and Alternator at
40 MW load condition
Turbine And Alternator
Vibration Amplitude (rms) Records At 40 Mw Load Condition
Location
Turbine Thrust Bearing
Turbine Rear Bearing
Velocity (mm/sec) - rms
Horizontal
2.167
1.898
Vertical
1.476
1.489
Displacement (microns) - rms
Axial
2.107
2.357
Axial
21.00
10.55
Radial
39.37
9.209
The two sets of readings captured were tested for the health
condition of the thrust and rear bearing. As expected, the network correctly
predicted the health zone related to thrust and rear bearing based on ISO
10816 standards. The qualitative predictions are:
Health condition of turbine thrust bearing: Good
Health condition of turbine rear bearing: Satisfactory
This kind of zone level prediction (Good / Satisfactory / Just Satisfactory/ Un
Satisfactory) helps the operators and operation and maintenance (O&M)
personnel to jointly plan an immediate action or a scheduled maintenance
plan.
5.7
SUMMARY
Motivated by the successful application of MSVM to detect very
slowly developing faults, the author ventured to apply MSVM architecture to
detect the vibration related faults.
As a first step, the spectral values available in the literature have
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been used for training MSVM to detect the possible faults. The
algorithm yielded good results.
Secondly, the author has collected a good amount of the velocity
and displacement values pertaining to different locations of
rotating parts from a working plant. These values have been used
for training. Subsequently, velocity and displacement values
pertaining to turbine thrust bearing and turbine rear bearing have
been collected and tested for healthiness of the two said
components. The predictions are good as per ISO standard 10816.
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