85 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 86 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. 87 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, 88 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 89 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. 90 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 91 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 92 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 93 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 94 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.
