Design and Implementation of ZigBee Based Vibration Monitoring and ...

Design and Implementation of ZigBee based Vibration Monitoring and Analysis for Electrical Machines Suratsavadee K. Korkua1 Student Member, IEEE

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 — This paper presents a method to monitor and

analyze the vibration of induction machine due to the rotor imbalance. A novel health monitoring system of electric machine based on wireless sensor network (ZigBee™) is developed in this paper. The communication protocol and software design for both wireless sensor network node and base station are also presented in detail. Moreover, the positioning scheme in ZigBee wireless network is also investigated. Based on the receiving strength signal indicator (RSSI), we can determine the distance of the sensed node by applying the distance-based positioning method. Experimental results of the proposed severity detection technique under different levels of rotor imbalance conditions are discussed and show the feasibility of this method for on-line vibrating monitoring system. ZigBee, wireless sensor network, RSSI, induction machines, health monitoring system, vibration detection Keywords:

1

Introduction

Predictive maintenance by condition based monitoring of electrical machine is a scientific approach that becomes new strategy for maintenance management [1]-[3]. Traditionally, monitoring system is realized in wired systems formed by communication cables and various types of sensors. The cost of installation and maintenance these cables and sensors are more expensive than the cost of the sensors themselves. To overcome the restrictions of wired networks, using wireless system for monitoring is proposed. ZigBee is a new wireless networking technology with low power, low cost and short time-delay characteristics. Based on ZigBee network communication technology, the system can deal with the various operating parameters of the remote transmission, real-time data collection, and real-time health monitoring system [4]-[5]. Moreover, ZigBee wireless technology enables to access the location of each node under the network with several types of positioning algorithms. Anyway, this study is based on the distance of the location algorithm.

Most recent research has investigated the electrical machines fault detection technique primarily based on the Motor Current Signature Analysis (MCSA) [6]-[7] with various DSP and signal processing techniques [8]-[9]. Since the rotor imbalance is mechanical related problem, the possibility of an analysis of machine vibrations is obvious [10]-[13]. This paper proposes and develops a ZigBee based wireless sensor network for health monitoring of induction motors. By observing the RSSI value and applying the distance-based positioning method, we can estimate the distance of the data collector node where fault happened. The vibration signals obtained from monitoring system are then processed and analyzed 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 information for induction machine health diagnostic.

2

Wireless sensor network system

2.1

The proposed wireless machine health monitoring system

The proposed wireless health monitoring system is shown in Fig. 1. In this proposed wireless sensor system, vibration signal from three-axis accelerometer are recorded and stored at the base station. Signal analysis is used to extract detailed information for induction machine health diagnostic. WSN 3-Phase 110 Volt 60Hz

Accelerometer

INDUCTION MOTOR

PWM Inverter

WSN

LOAD

Heath Monitoring System Base Station

Fig. 1 Schematic diagram of the wireless machine health monitoring system

ZigBee™/IEEE802.15.4 Standard

2.2

3.1

IEEE 802.15.4 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. The purpose is to provide a standard for ultra-low cost, ultra-low power consumption and low data. ZigBee technology may be used in various applications in industrial controls, embedded sensors, medical devices, and others. Based on these features provided by IEEE 802.15.4/ZigBee, the ZigBee technology is very suitable for our application.

2.3

ZigBee based System Framework

The ZigBee system framework for data collecting system based on wireless sensor network is shown in Fig.2. It is made up of data collection nodes and PAN network coordinator. We are able to set up a network node in several nearby collection points. The nodes can carry out desired functions such as detecting the current/vibration signals, signal quantizing, simple processing, and the ZigBee/IEEE802.15.4 Standard package framing to transmit data to the PAN network coordinator. In the star topology used in this application, every device in the network can communicate only with the PAN coordinator.

CC2430/31 Introduction

The CC2430/31 is a true System-On-Chip [14] for ZigBee/IEEE802.15.4 solutions for 2.4GHz wireless sensor network. 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. Both the embedded 8051 MCU and the radio components have very low power consumption. The CC2430/31 includes 12-bit ADC with up to eight inputs and configurable resolution. There are two powerful USARTs that support serial protocols. Combined with the ZigBee protocol stack from TI, the CC2430 is one of the most competitive ZigBee solutions among industry.

3.2

ADXL330 MEMS Accelerometer

The ADXL330 [15] is a complete 3-axis acceleration measurement system on a single monolithic IC. The ADXL330 has a measurement range of ±3 g minimum. The block diagram is illustrated in Fig. 4. It contains a micromachined sensor and signal conditioning circuit to implement the open loop acceleration measurement architecture. The output signals are analog voltages that are proportional to acceleration.

Fig.2 Structure of the wireless sensor network

3

Design of nodes and base station

The hardware framework is illustrated in Fig. 3. The main circuits include the power supply circuit, CC2430/31 external circuit, 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.3 Hardware framework

PC Base Station

Fig.4 Block diagram of ADXL330 MEMS accelerometer

3.3

Node & Base station

The sensor nodes (shown in Fig. 5) are in charge of collecting information such as vibration signal. All vibration signals are digitized through a 12-bit ADC convert up to 2000 samples per second. Storage unit has 128kb flash memory and 8kb RAM to be chosen. Controlled by the MCU, the data from the ADC can be temporarily stored in the storage unit 8kB RAM and then transmitted to the PAN network coordinator node through the ZigBee module. The base station includes PAN network coordination (shown in Fig. 5) and a PC. The network coordination primarily takes charge to distribute network address, and notarize the physical address, transmit test data. The network coordination can connect, and send the data and information to PC through RS232 or USB port.

4.2

Field Test Results

In order to observe the accuracy of the distance-based positioning method, we do conduct experiments at different distance of the location node varied from 0-15 meters. We performed three measurements in an open space environment. By sending 1000 packets from a transmitter, the results of RSSI at different testing distance can be shown in Table 1. Fig.5 A data collection node board (left side) and a PAN network coordinator (right side) developed in this study

4

RSSI based method

distance

Range (m.)

positioning

In order to determine the location of unknown nodes, most positioning studied in sensor networks is based on the Received Signal Strength Indication (RSSI) of the signal received from nodes with known locations. By applying a mathematical model, the position of nodes can be estimated by analyzing the RSSI signals. In this work, we use distance based localization algorithms. We use CC2431 as a wireless transceiver. ZigBee protocol stack and host-computer program can build a local area wireless positioning system. Basing on the RSSI value, these algorithms can find the distance of the transmitting node. From the test, the localization of sensor network can be conducted in a feasible and efficient way.

4.1

TABLE I: RSSI VALUE AT DIFFERENT DISTANCE TESTS

Received signal strength indicator (RSSI)

Test #1 RSSI Packets (dB)

Test #2 RSSI Packets (dB)

Test #3 RSSI Packets (dB)

0

1000

-39

1000

-40

1000

-37

1

1000

-47

1000

-50

1000

-53

4-5

1000

-64

1000

-64

1000

-65

9-10

999

-70

997

-71

995

-72

10-12

997

-79

1000

-76

998

-78

14-15

999

-81

999

-79

999

-85

The RSSI measurements were carried out in a real environment in order to analyze the RF propagation behavior of ZigBee modules in real working condition. To fit the experiment results with the theoretical propagation model, we choose the parameter of A=40 and n=2.5. The result in Fig.6 shows that the distance based positioning method of the proposed ZigBee wireless sensor network can provide satisfying accuracy.

The received signal strength is a function of the transmitted power and the distance between the sender and the receiver. The theoretical propagation model as the equation below shows. RSSI   (10n log10 d  A) (1) Where n is signal propagation constant which indicates the decreasing rate of signal strength in an environment, d is the distance between the transmitter and receiver, and A is the received signal strength at a distance of one meter. Given this model, we can first measure the receiving strength in different distances, and then adjust the value of n to let the model fit the actual data. We use CC2431 as the core of the location. CC2431's location uses received signal strength indicator (RSSI). When CC2430/31 receives a packet it will automatically add an RSSI value to the received packet. After system startup, the location node first sends a certain sequence of RSSI Blast information broadcast. The node has been configured to wait for the completion of positioning at specified intervals. Then the location node sent RSSI request broadcast to the reference nodes. When the request is received, the reference node will sent back its location and the RSSI value.

Fig.6 RSSI value (dB) versus Distance: comparison between the averaged tested data (Blue line) with the propagation model (Black line).

5

Determination of rotor imbalance severity

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 of the load torque. When a mechanical fault happens, it will result in a rotating eccentricity at the rotating frequency [16]. 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. 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. Therefore, the harmonics of rotor imbalance can be modeled as an integer multiple of rotating frequency [16];

f Where

 1 s   k.  f ( ) vib  s p 

f s is supply frequency, k

(2) is the integer

number, p is the number of pole pair, and s is the slip. 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. In this paper, all the techniques used here for signal analysis and processing have been implemented by MATLAB software. Block diagram of the proposed severity detection of rotor imbalance is shown in Fig. 7. Sample vibration signals and determine the RMS value

exceed the baseline data ?

N

occurring in a waveform. Crest factor can be used to differentiate the bearing fault and rotor imbalance fault. Fast Fourier Transform (FFT) algorithm is used to perform discrete Fourier transform (DFT) for all vibration signals. The amplitude of the FFT spectrum at the rotational 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.

7

Experimental setup and results

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 signals. Vibration signals were collected by using ADXL330 tri-axial accelerometer mounted on the motor housing (in Fig. 8). Axes of acceleration sensitivity corresponding to machine vibration are shown as in Fig. 9.

Healthy Case

Y Determine the Crest factor

exceed the baseline data ?

Y

Check Bearing fault

Fig. 8 A accelerometer mounted on the motor housing

N Calculation the spectrum amplitude at the rotational freq.

Determine the rotor imbalance indicator

Interpret the severity level Low: Medium: High

The most important element of this test-bed is the flywheel which has holes drilled in it (Fig. 9.) 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.

Fig. 7 Block diagram of the proposed severity detection technique of rotor imbalance Z

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. The crest factor is the ratio of the peak value of the vibration signal to the RMS value. The purpose of this calculation is to give an analyst a quick idea of how much impacting is

X Y

Fig. 9 Fly wheel design used in rotor imbalance test

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

X-axis Vibration Spectrum Y-axis Vibration Spectrum

TABLE II CREST FACTOR OF THREE-AXIS VIBRATION SIGNALS FOR DIFFERENT IMBALANCE MASS TESTS

Z-axis Vibration Spectrum

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 threeaxis vibration data were calculated the RMS values. As shown in Fig. 10, the RMS values are increasing corresponding to the level of imbalance mass. However, the change in crest factor is small as represented in Table 2. It implies that the degree of impacting is relatively small from bearing faults.

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

90

100

0

10

20

30

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. 11 3-axis vibration spectrum component amplitude at rotating frequency from different weights of imbalance mass

3

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. 12. 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.

2

1.5

1

0.5

0

X-axis Y-axis Z-axis

100 90

0

5

10 15 Weight of imbalance mass (g.)

20

Fig. 10 RMS value of vibration signals from different imbalance mass tests In order to observe the frequency component amplitude of vibration signals, FFT algorithm is used to perform the vibration signals. The spectrum component amplitude at rotating frequency is shown in Fig. 11. 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.

80 70

Rotor Imbalance Indicator

RMS Vibration Signal (V.)

2.5

60 50

T1 T0

40 30

Prognostic Region 20 X-axis Y-axis Z-axis

10 0

1

1.5

2

Low

2.5

3

3.5

Severity level

4

4.5

5

High

Fig. 12 Rotor imbalance indicator of different levels of imbalance severity

8

Conclusion

The method of rotor imbalance fault diagnostic through vibration analysis has been analyzed and determined on their ability to detect the induction motor abnormalities. Both hardware and software design of a ZigBee based wireless health monitoring system for induction machine is discussed in detail in this paper. By using MEMS accelerometer which is low cost, light in weight, compact in size and consume low power, this leads to the proposed vibration detection method. It is a non physical contact type which is free from electrical hazards. Moreover, it is more flexible because of tri-axial vibration measurement. Vibration signals have been analyzed and determined 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. RSSI of CC2431 provides the distance between the base station and monitored devices. This feature gives operator the ability to identify the location of the equipment that requires immediate attention. The proposed wireless health monitoring system is tested under various operating conditions and is found to work satisfactorily.

9

References

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