Online Current and Vibration Signal Monitoring Based Fault Detection of Bowed Rotor Induction Motor M. Nasir Uddin* and Md. Mizanur Rahman* * Department of Electrical Engineering Lakehead University, Thunder Bay, Canada E-mails:
[email protected],
[email protected]
Abstract – Regular condition monitoring of rotating machines using advanced spectrum analysis reduces the unexpected breakdown and excessive maintenance of the machines. If the irregularities are not identified in the early stage, the reliable operation of the machines are affected which may become catastrophic to the operation of the rotating machines. Therefore, this paper presents an online condition monitoring based fault detection of induction motor (IM). Characteristic features of motor current and vibration signals are analyzed in time domain as a fault diagnosis technique which is a key parameter to the fault threshold. Motor current and vibration signals are analyzed using Fast Fourier Transform (FFT) and Hilbert Transform (HT) to detect the severity of the fault and its possible location under different load conditions. The effectiveness of the proposed FFT and HT based analysis to predict the fault is verified using experimental data and its rate of success under different load conditions is also recorded. It is found that the HT can more precisely identify the fault using vibration signal as compared to the conventional FFT method. The magnitudes of the spectral components are extracted for the pattern reorganization of the fault. Spectrum analysis techniques are used under normal and bowed rotor condition to a 3-phase, 2 pole, 1/3 hp, 60 Hz, 2950 rpm IM drive. Keywords— Bowed rotor induction motor, fault detection and diagnosis, fast fourier transform, hilbert transform, current & vibration spectrum analysis.
I. INTRODUCTION Electric motors consume more than 50% of the total power generated in the world. Due to robustness and simplicity of construction, induction motors (IMs) have been widely used as a variable speed drives motor in the industry to assure the continuity of the process and production chains [1]. Motors are used not only for general purpose, but also in hazardous locations and severe environments [2]. High degree of performance of the motor depends on several condition monitoring techniques so that the motor does not fail during the operation. To minimize the maintenance cost and unwanted downtimes, conditions of the motor are continuously monitored to find out early signs of the fault [3]. Among all the techniques, electric current and vibration spectrum analysis are the two most classic condition monitoring techniques to detect the fault of the motor. Generally, condition monitoring techniques are used to
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identify the specific failure modes in motor components: the stator, rotor, or bearings [4]. Rotor failures account almost 5%-10% of total IM failures. If these unwanted conditions are not identified in early stage then it can cause the IM to go into a premature failure [5]. Condition based monitoring requires expert persons to differentiate between healthy and faulty conditions and to analyze the data. Otherwise, sometimes meaningless current or vibration spectrum appears from number of sources, including those related to normal operating conditions and load variations [6]. In the case of current spectrum analysis of IM, multiple of harmonics can exist due to the design and construction variation of the motor from ideal condition. Sometimes, noise and nonlinear behavior of the machine also makes it difficult to distinguish between healthy and faulty conditions of the motor. Thus, the expert knowledge is required to verify the conditions. Over the last two decades a lot of research on condition monitoring techniques has been carried out for fault detection and diagnosis of IM [3-10]. Chromatographic analysis, temperature analysis, noise analysis, current and vibration analysis are the most common methods used to detect the healthy and faulty condition of a motor [7-8]. Most of the researchers concentrate on FFT and wavelet transformations (WT) to decide the operating conditions of the motor [10]. Although researchers mostly concentrate on motor current signature analysis (MCSA) to detect the fault but sometimes to determine the existence of incipient faults vibration signals also need to be monitored in spite of the expensive vibration sensors. Major drawback of MCSA analysis is its performance depends on the motor speed and load conditions. Thus, the FFT doesn’t provide accurate result using MCSA as the speed and load torque is not constant. In order to overcome that problem wavelet decomposition methods are used by the researchers [6],[11]. Researchers mainly concentrated on broken rotor bars, voltage unbalancing, air gap eccentricity and bearing damage fault detection [12-13]. A limited research has been done on bowed rotor IM fault detection. The phenomenon of bowed rotor is exactly same as bent rotor but it may not be seen externally. Therefore, this paper presents a fault detection technique for bowed rotor IM based on both time domain and frequency domain analysis of current and vibration signals. In time domain analysis kurtosis, crest factor, standard deviation and other characteristics features are analyzed from the current and vibration signals but they cannot define the precise nature of the faults. Therefore, for fault detection both FFT and HT based frequency domain analysis are used
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and found that both analyses can easily identify the spectral components of the signals during both healthy and faulty conditions of the motor under varying load. In the case of HT, fault location and its severity are identified introducing an imaginary part in real valued-signal, which can easily identify the fault severity and location of the fault. The effectiveness of the proposed FFT and HT based analyses to predict the fault and their rate of successes are verified using experimental data under different load conditions. It is found from the results that the HT can more precisely identify the fault using vibration signal as compared to the conventional FFT method. II. BOWED ROTOR IM Dynamic eccentricity occurs due to bowed rotor which defines the condition when minimum air gap revolves with the rotor. During dynamic eccentricity rotor runs upon the stator bore centre but not on its own centre [15]. Dynamic eccentricity produces air gap field components which rotates at ݂௦ ±݂ Hz with corresponding p±1 pole-pairs where ݂௦ is the supply frequency, ݂ is the rotational frequency of the rotor and p is the pole-pair number of the motor. Bowed rotor defects occur inside the machines. When the defects are in stationary condition, the weight of the rotor causes the shaft to deflect and it becomes dominant after a long time. Distance between the length of the stator bore and rotor is not equal throughout the circumference in bowed rotor motor. As a result imbalance appears as a defect which causes the variation in air-gap flux and thus, the defects can be easily identified using the spectrum analysis of current and vibration signals. III. EXPERIMENTAL SETUP Bowed rotor fault detection bench for experimental verification is available in the power electronics and drives research lab at Lakehead University. The experimental setup is assembled in the laboratory to collect and analyze the current and vibration sensors output data, which is shown in Fig. 1. In the experimental set up current sensor consists of 3 magnetic fields to voltage transducers and output represents the current. Sensors data are collected through the data acquisition board (DAQ) which has 14 bit analog input resolution of quanser Q4 model. To limit the rotational speed of the encoder a gearbox 10.11:1is used. ArduinoUno model microcontroller is used to collect the speed encoder’s digital output. A general purpose computer is used to interface the MATLAB 2013a with the data acquisition board. The configuration allows analyzing the data. A 3phase, 2 pole, 1/3 hp, 60 Hz, 2950 rpm induction motor is used as a test motor in the experimental setup. Three phase motor currents and vibration signals are obtained from the respective sensors and collected for analysis purpose. Data samples are collected for healthy and bowed rotor at 10% rated load, 50% rated load and rated load conditions to analyze the operating conditions of the motor.
Fig. 1: Experimental setup.
IV. FAULTS DETECTION TECHNIQUE: TIME DOMAIN ANALYSIS Healthy and bowed rotor conditions are identified using some feature extractions where input patterns are transformed into representative features [16]. Raw data’s are collected and then time domain analysis is performed on stator current and vibration signal which provides the characteristics values of the motor to determine the changes by trend setting [14]. Statistical results are obtained by characteristics values including kurtosis, crest factor, standard deviation analysis which does not provide the precise nature of the fault but provide the information about the variation of the signals in bowed rotor IM as compared to healthy rotor. Tables I and II show the different characteristic values of both healthy and bowed rotor IM for stator 3-phase currents and vibration signals at 10% rated load conditions. From Tables I and II it is clearly seen that when bowed rotor occurs characteristics values are spread out for both current and vibration signals in compared to healthy motor. Therefore, characteristic values show the indication of the air-gap flux vibration due to imbalance whereas harmonics components due to eccentricity can be observed in the spectrum of current and vibration signal. Table -I: KURTOSIS, CREST FACTOR & STANDARD DEVIATION (S.D.) ANALYSIS FOR HEALTHY MOTOR AT 10% RATED LOAD Healthy motor at 10% rated load
Kurtosis Crest Factor S.D.
For Current signal of phase 1 1.5508
For Current signal of phase 2 1.5085
For current signal of phase 3 1.4974
For Vibration signal 2.9717
1.1538
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19.3651
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For the fault detection and analysis purpose, 2000 experimental sample data’s are taken into account. Analysis simply indicates during fault characteristics values of the extracted current and vibration signal increases but it does not specify the severity and location of the fault. In Table-I, first three columns represent the characteristic values for current sensors data of phase 1, 2 and 3 and fourth column represents the vibration sensor data. During fault condition peaks in the spectrum increases which result in an increase in the crest factor value and changes are detected as a fault of the rotating machines.
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Table -II: KURTOSIS, CREST FACTOR & STANDARD DEVIATION (S.D.) ANALYSIS FOR BOWED ROTOR IM AT 10% RATED LOAD Bowed rotor at 10% rated load
Kurtosis Crest Factor S.D.
For Current signal of phase 1 1.5685
For Current signal of phase 2 1.5444
For current signal of phase 3 1.5210
For Vibration Signal 2.9794
1.1561
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At 50% rated load condition IM rotes at 2925 rpm. Tables III and IV show the different characteristic values of both healthy and bowed rotor IM for stator 3-phase currents and vibration signals at 50% rated load. Both tables show that, although vibration signal is analyzed to detect the bowed rotor fault but also currents signal can be used to detect the fault but vibration signal specifies the correct nature and location of the faults. Although standard deviation does not provide the information where the fault occurs but it is effective in detecting a major out-of-balance in rotating systems.
V. FAULTS DETECTION TECHNIQUE: FAST FOURIER TRANSFORM (FFT) Salient features of the rotating machines are extracted using the frequency domain analysis. In frequency domain analysis, mechanical oscillations like vibration can be characterized by amplitude and frequency. Amplitude represents the strength of the signal and frequency represents the oscillation rate. The FFT is employed to convert the time domain signals into frequency domain signals. For fault detection FFT can be effectively used which is well known algorithm and useful technique for signal analysis. Three phase stator current and vibration signals plot of 2000 samples are shown in Figs. 2 and 3 for healthy and bowed rotor IM, respectively. The tests were done at 10% rated load while the motor rotates at 2950 rpm. Current Signal of Phase 1 3 2.5 2
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The sharp peaks at the onset affect the tails of the probability density function (pdf), and techniques sensitive to such pattern changes are sometimes of interest in fault detection. Kurtosis is related to the 4th moment and moments of the pdf are sensitive to changes occurring at its tail, that’s why kurtosis is the important parameter of machinery fault detection techniques. As kurtosis and crest factor describe the peakness of the function therefore result obtained from these two analyses are used as an indication of fault.
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Fig.3: Collected 3-phase current and vibration signals plot (2000 samples) at 10% rated load for bowed rotor IM.
It is clearly seen from Figs. 2 and 3 that the current signals of phase current 2 and 3 are greatly affected during fault condition whereas vibration signal is also affected. Although a current signal provides the fault identity but also vibration signal is analyzed to get the precise nature of the fault & its location. Therefore, bowed rotor condition can be characterized by the vibration signals. Figs. 4 and 5 show the
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collected three phase current and vibration signal plot of 2000 samples for healthy & bowed rotor IM at rated load when motor operates at 2850 rpm. At rated load condition fault can be easily identified by vibration signal.
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FFT is used to extract the frequency components of any signal. Amplitude spectrum of three phase currents and vibration signal for healthy motor at 10% rated load is shown in Figs. 6(a), (b), (c) and (d) respectively. Current spectrum components are shown at fundamental 50 Hz frequency to compare the healthy and bowed rotor condition. To specify faulty condition precisely vibration signal spectrum is shown up to 200 Hz.
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frequency 10Hz. Expert can identify that spectral components at 10 Hz does not indicates the faulty condition of the motor as magnitudes remains same in both healthy and bowed rotor condition. Amplitude spectra: vibration signal 450 400
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Vibration signal analysis for healthy and bowed rotor IM at 50% rated load condition are shown in Figs. 8(a) and 8(b), respectively when motor rotates at 2925 rpm. At 50% rated load condition, for bowed rotor IM fundamental spectral magnitude at 48.9 Hz increases almost 5 times as compared to healthy motor. At ͵݂௦ spectral component increases almost 2 times whereas at ʹ݂௦ spectral magnitude is decreased and after 160 Hz spectral components are missing in bowed rotor IM which clearly indicates the faulty condition of the motor. The vibration spectrum clearly shows that during fault condition defect caused on the rotor is more significant after݂௦ . Spectral component presents at 10 Hz can be removed using high pass filter of cutoff
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fundamental frequency is modified by the fault where the amplitudes is increased significantly. HT spectrum analysis 500 X: 49.36 Y: 493.2
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VI. FAULTS DETECTION TECHNIQUE: HILBERT TRANSFORM (HT) Signal processing is a fast growing area and a desired effectiveness in utilization of bandwidth and energy makes the progress even faster. With the Hilbert transform, the intrinsic mode functions (IMF) yield instantaneous functions of time that gives clear identification of embedded structures [17]. HT is a useful tool of fault detection where firstly, an analytic signal is introduced by adding imaginary part with real valued signal [14]. Secondly, absolute value of the analytic signal is used to find out the envelope and finally, FFT is used to determine the spectrum of the envelope. HT gives an energy-frequency-time distribution of the signal. This transformation methodology is used to analyze of vibration signals for both bowed rotor and healthy rotor induction motor. In both cases it is found that the frequency spectrum of the envelope is shifted in 50 Hz. Although only the failure associated with a bowed rotor is analyzed in this paper, the methodology can be used for many other types of failures through the analysis of the steady state stator current and vibration signal, such as unbalanced rotor, broken rotor bar, bearing failures, saturation and eccentricities. Figures 10(a) and 10(b) show the HT analysis of vibration signals for healthy and bowed rotor IM at 10% rated load, respectively. Zoom-in-view of the vibration spectrum clearly shows that failures in the rotor (bowed) can be easily identified through the spectral components around the fundamental frequency. When bowed rotor condition occurs there is sideband spectrum near the 50 Hz whereas for healthier motor just single spike at fundamental frequency.
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Figures 12(a) and 12(b) show the HT analysis of the vibration signal for healthy and bowed rotor IM at rated load respectively. HT spectrum analysis 500
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(b) Fig.12: Magnitude spectra of vibration signal at rated load (HT analysis), (a) healthy motor (b) bowed rotor IM. (b) Fig.10: Magnitude spectra of vibration signal at 10% rated load (HT analysis): (a) healthy motor (b) bowed rotor IM.
Figures 11(a) and 11(b) show the HT analysis of vibration signals for healthy and bowed rotor IM, respectively at 50% rated load, when motor rotes at 2925 rpm. Spectrum analysis shows that for bowed rotor condition amplitude at
At rated load, HT of vibration signal represents a straight line at fundamental frequency for healthy motor. But for bowed rotor IM, HT gives the distorted signal and amplitude increases at fundamental frequency. From Fig. 12(b), it is clearly seen that sidebands near the 50Hz which is considered as the best fault indicator. For bowed rotor IM
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current signal also deviates from the healthier condition but vibration signal specifies the faulty condition precisely. The total amplitudes from each frequency values are given through the HT. Faulty motor condition can be clearly identified by the zoom-in-view of the vibration signal. VII. CONCLUSION In this paper, an online fault detection of bowed rotor IM operating at different load condition is carried out using both time and frequency domain analysis of motor current and vibration signals. For time-domain analysis several statistical characteristic parameters of current and vibration signals are determined for both healthy and bowed rotor IM. Comparative characteristic parameters indicate the motor fault. For frequency domain analysis fast Fourier transform and Hilbert transform of three-phase stator currents and vibration signals have been used. The techniques can be implemented online by analyzing the signals in both time and frequency domains. It is found that the vibration signal analysis is dominant to the current signal analysis in the detection and distinction of faults for bowed rotor IM. The proposed methodology has been implemented and tested in the laboratory. The fault detection methods used in the paper could be used for other type of faults in the motor such as unbalanced rotor, broken rotor bar under different load conditions. VIII. RE F E R E N C E S
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