work on motor current signature analysis by using only stator current spectra has been well ... wireless sensor network for health monitoring of induction motors.
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Wireless Health Monitoring System for Vibration Detection of Induction Motors Suratsavadee Korkua1 Student Member, IEEE
Himanshu Jain1
Wei-Jen Lee1 Fellow, IEEE
Chiman Kwan2 Member, IEEE
1. Energy Systems Research Center, The University of Texas at Arlington, Arlington, TX 76019 2. Signal Processing, Inc. 13619 Valley Oak Circle, Rockville, MD 20850 Abstract — To avoid unexpected equipment failures and obtain higher accuracy in diagnostic for the predictive maintenance of induction motors, on-line health monitoring system plays an important role to improve the system reliability and availability. Among different techniques of fault detection, work on motor current signature analysis by using only stator current spectra has been well documented. In addition, the recent developments in MEMS technology shows increasing trend in integrating vibration analysis for fault diagnostic. Vibration-based detection by using the accelerometer is gaining popularity due to high reliability, low power consumption, and low cost. This paper presents the study of vibration due to the rotor imbalance. The technique of vibration detection and observation of vibration signal in the 3-phase induction machine is studied. A novel health monitoring system of electric machine based on wireless sensor network (ZigBee™/IEEE802.15.4 Standard) is proposed and developed in this paper. Experimental results of the proposed severity detection technique of rotor vibration under different levels of imbalance conditions are investigated and discussed. Index Terms – ZigBee, wireless sensor network, induction motors, health monitoring system, vibration detection
I. INTRODUCTION The focus in most industry is shifting from scheduled maintenance to the predictive maintenance by constantly observing and predicting the machine condition in advance [1]. Predictive maintenance by condition based monitoring of electrical machine is a scientific approach that becomes new strategy for maintenance management. Most industrial motors are being monitored using vibration, current, and temperature sensors which either provide warning signals or shut down the system before any catastrophic failure occurs. Though they are able to prevent permanent damage to the machine, they can neither predict the usable life of the equipment nor provide the severity level of the problem. This resulted in the need of an advance system called on-line health monitoring system. Traditionally, health monitoring system is realized in wired systems formed by communication cables and various types of sensors. The cost of installation and maintenance are difficult and expensive especially when the equipments are not at the same location. To overcome these restrictions, using wireless sensor networks for monitoring is proposed in this paper. Wireless sensor network [2]-[3] is a new control
network that integrates sensor, wireless communication and distributed intelligent processing technology. ZigBee is a new wireless networking technology with low power, low cost and short time-delay characteristics. These favorable features are suitable for our application. Most recent research has investigated the fault detection technique primarily based on the Motor Current Signature Analysis (MCSA) [4]-[5] with various DSP and signal processing techniques [6]. Although the fault detection by means of analysis of the current is quite simple, the main drawback is that the employed technique is only suitable for steady state operation. Since the rotor imbalance is mechanical related problem, the possibility of an analysis of machine vibrations is obvious. In [7], by using machine vibration analysis, the technique of detecting electrical fault such as voltage unbalance is explored but the mechanical fault and the level of the severity are not yet discussed. This paper proposes and develops a ZigBee based wireless sensor network for health monitoring of induction motors. The vibration signals obtained from monitoring system are processed with signal processing techniques. In order to predict the level of severity of rotor imbalance, the vibration detection techniques with suitably modified algorithms is used to extract detailed information for induction machine health diagnostic. II. THE PROPOSED WIRELESS HEALTH MONITORING SYSTEM As shown in Fig. 1, the proposed wireless health monitoring system comprises of PWM Inverter, Induction motor and load station. The three-axis accelerometer is used to measure the vibration of the motor.
Fig. 1 Schematic diagram of the wireless health monitoring system for vibration detection
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The machine health identification can be obtained with the on-line monitoring system. In this proposed system, vibration signal from three-axis accelerometer are recorded and stored at the base station. The recorded data are fed to monitoring system to evaluate the important machine condition. Signal analysis is used to extract detailed information for induction machine health diagnostic. For example, by calculating and comparing the actual recorded motor vibration signal with the baseline vibration quantity, the motor conditions can be obtained qualitatively. To improve the accuracy of health monitoring system, the voltage measurement, current sensor, speed and temperature measurement could be installed. III. WIRELESS NETWORK SYSTEM DESIGN A. ZigBee™/IEEE802.15.4 Standard IEEE802.15.4 standard defines the protocol and interconnection of devices via radio communication in a personal area network (PAN). It operates in the ISM (Industrial, Science and Medical) radio bands, at 868 MHz in Europe, 915 MHz in the USA and 2.4 GHz worldwide. The purpose is to provide a standard for ultra-low complexity, ultra-low cost, ultra-low power consumption, and low data rate wireless connectivity. The system framework for health monitoring system based on wireless sensor network is made up of data collection nodes and PAN network coordinator. The data collection nodes can carry out desired functions such as detecting the vibration signals, signal quantizing, simple processing, and the IEEE802.15.4 Standard package framing to transmit data to the PAN network coordinator. In addition, they can also receive data frames from other nodes, and then adding multi-hop information, package framing, and then transmit the new data frames to the network coordination in the same manner. Once receive the data, the PAN network coordinator will upload the receiving data to computer for further processing and analysis.
In the proposed system design, we adopt the IEEE 802.15.4 standard compliant transceiver CC2430 from Texas Instruments [8]. It combines the excellent performance of the leading CC2420 RF transceiver with an industry-standard enhanced 8051 microcontroller (MCU), with 128 KB flash memory and 8 KB RAM. The CC2430 also includes 12-bit ADC (Analog-to-Digital Converter) with up to eight inputs. The sensor nodes are in charge of collecting vibration data. Before being sent into ADC, the signals are filtered and amplified as presented in the signal conditioning circuit. The network coordination primarily takes charge to distribute network address to new rode, and notarize the physical address, transmit test data. The network coordination can connect, and send the data to PC through RS232 or USB port. C. Software Flow The software flowcharts describe the working process of the network coordinator and data collection node. They are shown in Fig. 3 and Fig. 4, respectively.
B. Hardware Design and Implementation The hardware framework is illustrated in Fig. 2. It is comprised of node and base station. The main circuits include the power supply, sensor and signal conditioning circuits, flash ROM and RAM memory, serial port interface, and three LEDs for status indication.
SENSOR
Signal Conditioning Circuit
Memory
USART Interface
CC2430
CC2430
LED
LED
Fig. 2 Hardware framework
PC Base Station
Fig. 3 The flow chart of network coordinator
As network coordinator, the CC2430 and the protocol stack is initialized firstly. Once the network is formed successfully, the network coordination will wait for user interrupt to send the “START” command to the node. If a node is ready to transmit the data, it will send “READY” command to the coordinator. Once all packages are received, the data will be sent to PC via USB port.
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of the load torque. When a mechanical fault happens, it will result in a rotating eccentricity at the rotating frequency [10]. These faults may also cause speed oscillations that have the effect on the stator current and finally lead to additional undesired harmonic components of power and torque at some particular frequencies in the spectra. A. Induction Motor under Rotor Imbalance To analyze the mechanical phenomena of the induction motor under rotor imbalance, the dynamic nature of a mechanical system can be modeled as shown in Fig. 5
θr
Fig. 5 Rotational model of horizontally mounted mechanical system under rotor imbalance
Fig. 4 The flow chart of data collection node
As data collection node, the CC2430 and protocol stack also initialize firstly. Once the node joins into network successfully, it will be put into monitoring state to monitor whether there are any commands coming from the network coordinator. It will perform the predefined task if data collection node receives the “START” command. After end of ADC conversion, node will store all of sampling data into RAM memory and then send command back to the network coordinator. After node finish transmitting all data, it will be returned back to monitoring state again. The debugging and programming can be done directly in the Integrated Development Environment IAR Embedded Workbench for 8051 [9].
With the assumption that the mechanical system with the unbalance is the horizontally installed along its rotational axis, the rotational dynamic for torque equation is derived from the free body diagram as Fig. 5. Rotor imbalance can be expressed by a mass of the total unbalance weight ( m) which is the main cause of the mechanical failure at the different speeds. This small mass is fixed on a disk mounted on the motor shaft. The different distances ( r ) from the center of the rotating part can be chosen. The gravity force by the unbalance mass produces the sinusoidal oscillating torque which is the function of the mechanical rotor position (θ r ) and the acceleration of the gravity ( g ) at the distance ( r ) . The electromagnetic torque
(Te ) is produced by
an induction machine and the rotational friction term is determined by the viscous damping constant and mechanical rotor speed that can be represented with the following equation; Jθ&&r + bθ&r = Te − mrg sin θr
(4.1)
We can see that the torque equation is as a function of time which is comprises of the constant component torque and the additional component varying at the mechanical rotor position. The total electromagnetic torque that is produced by the unbalance load makes it possible to detect the mechanical fault in induction machine.
IV. EFFECTS OF ROTOR IMBALANCE ON VIBRATION SIGNAL Rotor imbalances are common mechanical faults in induction motors. In general, a mechanical fault in the load part of the overall system can be observed from the variation
B. Vibration Signature In general, there are numerous sources such as external source with particular frequencies that can cause the vibration
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of rotating machinery Other vibrations do occur due to rotating asymmetries, such as rotor imbalance or misalignment. Among them, the largest low frequency vibration harmonic is normally due to the rotor imbalance of the rotational parts. From the vibration spectrum analysis, the low frequency harmonics are associated with the rotational frequency and can be distinguished from the lower range of the spectrum. The mechanical vibration due to rotor imbalance is a once per revolution force. Moreover, induction motors also vibrate at the twice of supply frequency due to their magnetic force between rotor and stator. This attraction force acts on the stator and induce the vibration on the motor frame. This also leads to a significant harmonic peak at twice of the supply frequency in the vibration spectrum. Therefore, the harmonics of rotor imbalance can be modeled as an integer multiple of rotating frequency [10]; 1− s ) fvib = k . f s ( p
(4.2)
f s is stator supply frequency, k is the integer number, p is the number of pole pair, and s is the slip. Where
Based on the vibration spectrum analysis, it is normally straightforward to locate the mechanical rotational frequency by monitoring the vibration spectrum and finding the most significant peak in the rotational frequency range expected. V. DETERMINATION OF ROTOR IMBALANCE SEVERITY Machine vibration analysis becomes one of the most important tools for machine health diagnostic. There are two types of analysis: time domain and frequency domain. In most research, the frequency domain analysis is more attractive because it is provide more detailed information about the status of the machine whereas the time domain analysis can give qualitative information about the machine condition. In general, machine vibration signal is composed of three parts, stationary vibration, random vibration, and noise. Traditionally, Fourier transform (FT) was used to perform such analysis. If the level of vibrations and the noise are high, inaccurate information about the machine condition may be obtained. In this paper, Fast Fourier Transform (FFT) is used to extract some useful features of the vibration signal i.e. root mean square (RMS) value, the crest factor. All the techniques used here for signal analysis and processing have been implemented by MATLAB software. Block diagram of proposed severity detection of rotor imbalance is shown in Fig. 6. A. Implementation of Root Mean Square In this study, the RMS value of the vibration signal is used for primary investigation of the machine health. The RMS values will be used to detect the severity of the abnormal condition. These values could also be used as input to training the neural network based fault classifier. All 3-axis vibration data are investigated.
Fig. 6 Block diagram of the proposed severity detection technique of rotor imbalance
B. Implementation of Crest factor The crest factor is the ratio of the peak value of the vibration signal to the RMS value. The purpose of the crest factor calculation is to give an analyst a quick idea of how much impacting is occurring in a waveform. It is meaningful where the peak values are reasonably uniform and repetitive from one cycle to another. Crest factor is often used to indicate the rolling element bearing faults. The rotor imbalance faults do not affect the value of crest factor. These values can give qualitative information about the machine condition if it is compared to the vibration signal standard and can be used to differentiate the bearing fault and rotor imbalance fault. The values of crest factor of the vibration signal for healthy case will be use as baseline condition in health monitoring system. Continuous monitoring of crest factor along with the historical record can provide useful information about bearing condition. Using both the RMS value and the crest factor for the machine health diagnostic may increase the overall system performance for health monitoring system. C. Implementation of spectrum analysis In the present work, Fast Fourier Transform (FFT) algorithm is used to perform discrete Fourier transform (DFT) for all three axis vibration signals. In order to achieve earliest possible recognition of the fault in the machine, a comparison of the spectrum of the machine with the spectrum of the healthy case must be performed. The most significant vibration peak at the rotational frequency will be determined. The amplitude of the FFT spectrum at the rotational
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frequency serves as the rotor imbalance indicator of the proposed monitoring system. Furthermore, the amplitude of this frequency component does reveal the severity level of the rotor imbalance fault.
In this test, rotor imbalance was created using four different weights namely, 5g, 10g, 15 and 20g. No fault data was also collected as baseline data and considered as 0 g. All 3-axis vibration data were calculated the RMS values. As shown in Fig. 8, the RMS values are increasing corresponding to the level of imbalance mass. However, the change in crest factor is small as represented in Table 1. It implies that the degree of impacting is relatively small from bearing faults. TABLE I CREST FACTOR OF THREE-AXIS VIBRATION SIGNALS FOR DIFFERENT IMBALANCE MASS TESTS Imbalance mass (g.)
Crest factor X-axis
Y-axis
Z-axis
0 g.
1.457313235
1.7216206
2.18396705
5 g.
1.451261113
1.77381552
1.82287472
10 g.
1.387397149
1.50183948
1.63720601
15 g.
1.303934686
1.40032313
1.40077586
20 g.
1.249204942
1.45098092
1.29577839
RMS Vibration Signal (V.)
1.5
1
0
X-axis Y-axis Z-axis 0
5
10 15 Weight of imbalance mass (g.)
20
Fig. 8 RMS value of vibration signals from different imbalance mass tests
In order to observe the frequency component amplitude of vibration signals related to rotor imbalance fault, FFT algorithm is used to perform for vibration signal. The 3-axis vibration spectrum component amplitude at rotating frequency (50 Hz) is shown in Fig. 9 and the amplitude of vibration spectra versus the different weights of imbalance mass are presented in Fig. 10. X-axis Vibration Spectrum
Fig. 7 Fly wheel design used in rotor imbalance test
2
0.5
Y-axis Vibration Spectrum
In order to validate the proposed vibration based health monitoring system, the test-bed for mechanical fault was set up. Rotor imbalance was created on a 3-phase, 2-pole and 1h.p squirrel cage induction motor. Induction motor is fed from the variable speed drive at 50Hz. The wireless sensor network is implemented and accelerometer is also integrated in the system for detecting the vibration signal. Vibration signals were collected using ADXL330 tri-axial accelerometer mounted on the motor housing. Axes of acceleration sensitivity corresponding to machine vibration are shown as in Fig. 7. The most important element of this test-bed is the flywheel which has holes drilled in it (Fig. 7.) The weights applied to these holes produce imbalance in the flywheel, thereby in the motor. The severity of the fault is determined by the weight.
2.5
Z-axis Vibration Spectrum
VI. EXPERIMENTAL SETUP AND RESULTS
3
60 0 g. 5 g. 10 g. 15 g. 20 g.
40 20 0
0
10
20
30
40 50 60 Frequency(Hz)
70
80
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100
0
10
20
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40 50 60 Frequency(Hz)
70
80
90
100
0
10
20
30
40 50 60 Frequency(Hz)
70
80
90
100
60 40 20 0
30 20 10 0
Fig. 9 3-axis vibration spectrum component amplitude at rotating frequency from different weights of imbalance mass
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It can be noticed that the rotational harmonic at 50 Hz has a dominant value. In addition, by increasing the weight of imbalance mass, the amplitude of this frequency component will apparently increase in the spectrum. Therefore, the spectrum amplitude can be used to specify the degree of fault for the certain operating condition. Vibration spectrum component amplitude at 50 Hz
60
50
40
VII. CONCLUSION The hardware and software design of a wireless health monitoring system for induction machine is presented in this paper. Vibration signals have been analyzed to detect the mechanical faults. The implementations of analysis technique in time and frequency domain are given. The proposed rotor imbalance detection technique is verified with different level of severity. Rotor imbalance indicator can be used to estimate the range of severity level which is very useful part of the predictive maintenance. The wireless health monitoring system is tested under various operating conditions and is found to work satisfactorily. REFERENCES
30
[1] 20
X-axis Y-axis Z-axis
10
0
0
5
10 15 Weight of imbalance mass (g)
20
Fig. 10 The 3-axis magnitude of vibration spectra at rotating frequency versus the different weights of imbalance mass
Furthermore, based on the analyzed vibration data and the linear approximation, the relationship between the rotor imbalance indicator and the severity level can be represented in Fig. 11. It is important to note that any estimation is subject to error. However, this relationship can be use as trend to determine the severity level of the fault. For example, the change of rotor imbalance indicator during the time interval T0-T1 can be used to predict the range of the severity level. The result from the prediction will be a very useful part of the condition monitoring system and the estimation on the usable life of the equipment.
B. Lu, T. G. Habetler, and R. G. Harley, “A survey of efficiency estimation methods of in-service induction motors with considerations of condition monitoring,” in Proc. 2005 International Electric Machine and Drive Conference (IEMDC), May 2005, pp.1365-1372. [2] J. A. Gutiérrez, E. H. Callaway, and R. L. Barrett, “Low-rate wireless personal area networks: enabling wireless sensors with IEEE 802.15.4,” IEEE Press, New York, 2003. [3] I. F. Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci, “Wireless sensor networks: a survey,” Computer Networks, vol. 38, no. 4, 2002, pp. 393-422. [4] T. G. Habetler, R. G. Harley, R. M. Tallam, S. B. Lee, R. Obaid, and J. Stack, “Complete current-based induction motor condition monitoring: stator, rotor, bearings, and load,” in Proc. 2002 VIII IEEE International Power Electronics Congress, Oct. 2002. [5] C. M. Riley, B. K. Lin, T. G. Habetler, and R. R. Schoen, “A method for sensorless on-line vibration monitoring of induction machines,” IEEE Trans. Industry Applications, Vol. 34, pp.1240-1245, Nov. 1998. [6] S. A. Saleh, A. Kazzaz and G. K. Singh, “Experimental investigations on induction machines condition monitoring and fault diagnosis using digital signal processing techniques,” in 2003 Electrical Power Systems Research, pp.197-221. [7] G. S. Maruthi and K. P. Vittal, “Electrical Fault Detection in three phase squirrel cage induction motor by vibration analysis using MEMS accelerometer,” in Proc. 2005 IEEE Power Electronics and Drive System (PEDS), pp.838-843 [8] Chipcon TI, “A True System-on-Chip solution for 2.4 GHz IEEE 802.15.4 /ZigBee® CC2430 DataSheet (Rev.2.1),” 2008 [9] IAR Systems, “IAR Embedded Workbench IDE User Guide,” ftp://ftp.iar.se/WWWfiles/guides/ide/ouew-3.pdf, December 2004. [10] C. Kral, T. G. Habetler, and R. G. Harley, “Detection of mechanical imbalances of induction machines without spectral analysis of time domain signal,” IEEE Trans. Industry Applications, Vol. 40, pp.11011106, July. 2004.
Fig. 11 Rotor imbalance indicator of different levels of imbalance severity
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