Research Article Efficient Wireless Vibration Data

Hindawi Publishing Corporation International Journal of Distributed Sensor Networks Volume 2014, Article ID 278560, 12 pages http://dx.doi.org/10.1155/2014/278560

Research Article Efficient Wireless Vibration Data Sensing and Signal Processing Technique Based on the Android Platform Chan-Seob Park,1 Ryong Baek,1 Woong Hoe,1 Byung-Gyu Kim,1 and Hyun-Jun Lee2 1 2

Department of Computer Engineering, SunMoon University, Asan 336-708, Republic of Korea RMS Technology, Asan 330-913, Republic of Korea

Correspondence should be addressed to Byung-Gyu Kim; [email protected] Received 19 May 2014; Accepted 25 July 2014; Published 11 September 2014 Academic Editor: Neil Y. Yen Copyright © 2014 Chan-Seob Park et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Recently, many researches on big-data sensing and analysis have been actively promoted. The big data, which is generated by the digital and networked environment, is referred to to form not only numerical data, but also large-scale data for storing image data and character. Usually, many data measured through sensors are very large in scale in various vibration measurements. Measurement methods for vibration analysis currently consist of a general sensing scheme using cabling to obtain vibration data. The system is difficult to use efficiently in a location where equipment installation is not easy. We proposed a novel vibration measurement system that includes a main hardware module and a wireless data transceiver module. The system is easy to use and field data are transmitted to a remote location using Bluetooth communication and the Android platform. Nonexpert personnel can obtain field vibration data for transport, even over long distances. Through experiments in field test, we verify that the stable remote sensing range reaches up to 150 m in real-time communication without any data loss.

1. Introduction Vibration is a natural phenomenon characterized by periodic oscillation with a magnitude of force and a frequency. If the frequency of vibration of an object and the natural vibrational frequency of a specific object are the same, the phenomenon known as resonance occurs, which can cause serious problems leading to destruction of a system or difficulty in precise control of a system. For example, in 1973 the largest suspension bridge in the United States, over the Tacoma Strait in the state of Washington, collapsed due to vibrational resonance in a high wind. In Korea, buildings are known to shake violently due to resonance when many people exercise together in a fitness center. Problems related to resonance have become increasingly important due to development of sophisticated modern machines used in industry and use of high-precision equipment in research, development, and production processes. Safety inspections for potential resonance problems have become essential. Industrial production and development of core technologies and products have evolved using precision equipment. Many modern buildings are complicated structures

encompassing different materials and mechanical systems, all with different natural resonant frequencies. Operation of the many high precision mechanical systems in modern buildings, laboratories, and factories requires high power. High precision vibration measuring equipment is used to evaluate the potential for vibrational resonance in these environments and vibration control for the normal operation of many systems is recognized as an important safety concern. Monitoring and suppression of vibration using vibration monitoring systems in both large and small projects and in research, construction, and production environments is essential. Vibration measurement generally uses time-based vibration displacement (m, cm, mm, um). The amount of displacement velocity per unit time, speed change, and the amount of change per unit time are all important vibration measurements. Displacement and velocity sensors and accelerometers are available for measuring the amount, speed, and strength of vibration in a system. A dynamic signal analyzer (DSA) [1] is used for collection and analysis of vibrational data in conjunction with a digital signal processor (DSP). A Fourier

2 transform is used to convert frequency data into time domain data with inclusion of autospectrum and power spectrum data. Cross-spectrum data are necessary for conversion of data using Fourier transformation. Monitoring is important for measurement of vibration. Tang et al. [2] proposed an application for monitoring and control of machine operations using vibration sensing in which a vibration analyzer continuously monitors and compares the actual vibration pattern against a known vibration signature, based on a fuzzy fusion technique. Similar systems have been proposed [3–5]. The frequency of vibration of monitoring data that is necessary in order to understand the transformation of a vibration will always be located in the front of an analyzer (Figure 1) as the analytical equipment is typically expensive and difficult to move. Vibration measurement in a railway environment requires large scale equipment in a big facility with a large amount of cabling and sensory equipment. It is difficult to build a general vibration measurement system for analysis of railway related structural problems. When the measuring point is the intersection of two railroads both the upper and lower branches of large vessels must be simultaneously measured. When making these types of measurements, equipment must be transported to the site, requiring a lot of manpower. Use of vibration measurement equipment must follow a detailed procedure and the operational personnel present at the time of measurement should be experts. In order to reduce the operational problems associated with transport of analytical equipment and manpower requirements, a vibration measurement system based on the Android platform is presented. This simple to use system can be easily operated by nonprofessional personnel in the field. Data for analysis can be sent to experts at a remote location. Similar systems have been proposed [6–9]. Kee-Yin Ng [10] presented a simple communication system between intelligent mobile terminals and control equipment using a Bluetooth serial adapter. In addition, the development of the Internet and various sensors is major issue for big-data analysis. References [11– 14] proposed a performed data aggregation on the basis of entropy of the sensors. The entropy has been computed from the proposed local and global probability models. The models provided assistance in extracting high precision data from the sensor nodes. In [15], a new energy-efficient routing protocol using message success rate has been proposed to resolve the node concentration problem. This paper is organized as follows: Section 2 presents development of hardware and software implementation. Simulation results and discussion are presented in Section 3 and concluding comments are presented in Section 4.

2. Proposed Technique An overview of the development system is shown in Figure 2. A Bluetooth module and a vibration sensor are used to transfer data from a data acquisition device. It is possible to collect field data for analysis in a remote location. Bluetooth components in a portable data reconstruction box and a portable data acquisition box are used

International Journal of Distributed Sensor Networks to configure the vibration measurements for a direct connection. Vibration measurements using an acceleration sensor generally use a connected DSP. After the signal processing, General Purpose Interface Bus (GPIB) software is used. Measured data are thus subjected to fast Fourier transform (FFT) [16] for conversion of time-domain sampling data to frequency-domain data prior to analysis. 2.1. Hardware System 2.1.1. Portable Data Acquisition Box. Figure 3 shows the structure of a portable data acquisition box that receives data from sensors. The box was designed with sending and receiving control circuits for Bluetooth communication and a microprocessor circuit design for integrated circuit piezoelectric (ICP) sensor signal conditioning using amplifiers and data acquisition devices. A typical ICP sensor BIASING to obtain power is supplied to the signal structure (Figure 4). All ICP sensors require a constant current power source for proper operation. The simplicity and the principle of 2-wire operation can be clearly seen from Figure 4. A parani-bcd110du chip that supports Bluetooth class 1 is employed [17]. The Bluetooth module is configured as shown in Figure 5. The microprocessor, signal conditioning amplifier, circuit diagram, and hardware will be discussed in Section 3. 2.1.2. Portable Data Acquisition Box. Figure 6 shows the structure of the portable data reconstruction box that receives data from remote locations. The portable data reconstruction box and the portable data acquisition box use the same CPU and Bluetooth module. The portable data reconstruction box receives Bluetooth data that are then sent to the CH via a digital-analog converter (DAC). Figure 7 shows a block diagram for the DAC, which has a characteristic in which the error does not exceed 0.5 LSB in the 16 bit area. The portable data reconstruction box and the portable data acquisition box have the same structure using a Bluetooth service employing thread and connect thread. 2.2. Developed Software Structure. Hardware and software to enable communication via Bluetooth used the Android platform. 2.2.1. Android Application. Figure 8 shows the structure of an Android application using the Bluetooth service accept thread and connect thread. Data are transmitted when the portable data acquisition box is connected. The Android program for processing the data received from a mobile device can produce a graph for confirmation. Information can be entered via a Configuration menu. A “Sensor Info” menu and data received from the “Measurement and RX Mode” menu allow access to a graph for expression of received data. In Figure 9, the overall configuration of application of Android platform is shown. We have implemented software that enables us to measure, analyze, and transform data in

International Journal of Distributed Sensor Networks

3

External vibration force c

Dynamic signal analyzer

M

Spring

Sensor amplifier

Accelerometer Damper

Base (Test equipment) GPIB

Data signal processing

Figure 1: Measuring scheme for vibrational data.

Portable data acquisition box

Bluetooth

Android mobile device

TCP/IP

Database server

DAC (pulse)

Analog

Portable data acquisition box

Bluetooth

Android mobile device

TCP/IP

Figure 2: The proposed system design of the overall process.

real time. It allows us to monitor in remote type. Also, it is possible to sense data and analyze it using FFT (fast Fourier transform) at the same time. Implementation and analyzed results for each item are discussed in Section 3. 2.2.2. Data Transport Protocol. In computer science and telecommunications, a communications protocol is a system of digital rules for data exchange within or between computers. When data are exchanged through a computer network, the rules of system are called a network protocol. In Figure 10, the designed protocol structure is displayed. An STX packet indicates the start of text and an ETX packet indicates the end of text. The structure is obtained by adding a longitudinal redundancy check (LRC) for detecting errors in transmitted data. The STX and ETX packets are not used as data. They are only markers. The packet receiving unit must know the beginning and end of the transmission time so that all packets can be accounted for with no problems. The STX packet indicates the beginning of a packet using the hexadecimal number 0X02 as shown in Figure 11(a). The ETX packet indicating the end of the packet uses the hexadecimal number 0X03. DATA is the actual data. LRC is the only DATA error check.

Table 1: Designed packet head. Msg. ID (communication message ID) Working sensor number (sensor number) Sampling freq. (sampling time) Sensitivity (sensitivity) Current time (current time) Wave data points (transmit data number) Amp gain (gain)

The LRC process checks all data (not the STX, LRC, and ETX packets). While processing data, a transmittance process is used because of the possibility of existing STX and ETX code in the data stream. In Figure 11(b), the transmittance process excludes the STX and ETX bytes. If STX appears during the transmittance process, STX and 0X08 are calculated using an OR operation as 0X82 to enable the packet to produce only one STX in the whole packet that is transmitted once. This process is necessary to determine the starting point of a packet in serial communication. On the receiver side, a packet structure is used to allow identification of the starting point. Table 1 describes the designed packet head. Packet head has the information necessary for common packet of one.

4


GPS receiver ICP sensor bias

Li-ion battery

Power management unit Precision timebase

Signal conditioning Signal conditioning

Simultaneous sampling ADC

Signal conditioning

DSP (embedded linux) Bluetooth transmitter

Digital signal processing Time-domain data (frequency-domain data)

Signal conditioning Vibration sensor

Figure 3: Portable data acquisition box.

Coaxial or tow conductor cable

Typical quartz ICP sensor

Constant current signal conditioner Decoupling capacitor Signal

Amplifier q + C

Current regulating diode

VM

R

+

Ground

18 to 30 VDO power

Figure 4: ICP sensor BIASING circuit.

Parani-BCD110

5 dBi dipole 3 dBi dipole 1 dBi stub 0 dBi chip

RF IN/OUT

USB RAM UART 24 GHz Baseband IO Radio DSP

LAN

24 GHz Antenna

RF amplifier circuit

PA

2402∼2480 MHz

MCU Bluecore 4 external

Crystal 26 MHz

Flash memory 8 Mb

Figure 5: Bluetooth block diagram.

SPI PIO PCM VCC 3.3 V

Interface pad or connector


5

Li-ion battery

Power management unit Signal conditioning Signal conditioning

Analog signal output

Digital signal processing

Precision DAC

Signal conditioning

Bluetooth transmitter

Signal conditioning

Figure 6: Portable data reconstruction box. IOVoo DGND DVoo AGND AVoo AVss

RFF-A

PEFGND-A

SDI SDO

SPI shift register

CS SCLK

Command register

LDAC GPIO-0 GPIO-1

DAC-0 User calibration Zero register-0 Gain register-0

RFB2 −1

Rfe2

VOUT −1

Latch-0

Power-on/ power-down control

AIN

SGND-0 RFB1 −1 RFB2 −1 VOUT −1 SGND-1

DAC-1

Internal trimming zero, gain, INL

VMON

RFB1 −1

Rfe1

Input data register

Control logic

UNI/BIP-B

AIN Rfe1-0 Rfe1-1 Rfe1-2 Rfe1-3

To DAC-0 DAC-1

RST UNI/BIP-A

Rerenence buffer A

MUX

Analog monitor

DAC8734

DAC-2

RFB1 −2 RFB2 −2 VOUT −2 SGND-2

DAC-3

RFB1 −3 RFB2 −3 VOUT −3 SGND-3

To DAC-2 DAC-3 Rerenence buffer B REF-B

Power-on/ power-down control PEFGND-B

Figure 7: DAC8734 function block diagram.

6


Configuration

Document Sensor Info Main Measurement

RX mode

Document

Document

CH1

CH3

CH2

CH4

Figure 8: Android application structure. Home

Sensor Info

Configuration

Measurement (RX mode)

Figure 9: Overall process of software.

STX DATA

···

DATA LRC ETX

Figure 10: Protocol structure.

STX DATA

1

0 × 10 0 × 02

0 × 10 0 × 03

2

0 × 10 0 × 10

LRC ETX

0 × 10 0 × 10

LRC ETX

3 (a) Before transmission

STX DATA

1

0 × 10 0 × 82

0 × 10 0 × 03

2

3 (b) After transmission

Figure 11: Transmittance process method.

Channel information, current time, sampling time, and the number of information data to be transmitted are defined in the buffer. In this study, the data is transmitted based on the serial communication. If the data loss is present, the lost packet is not used. 2.2.3. Remote Access Server. In this paper, it is to be sent to the server data measured in a remote location, analyzing

the measured data at the same time in the client and the server. DBMS (database management system) is on a remote server in order to synchronize the client and server. DBMS synchronizes data received from client with other clients. Then, it saves necessary data into database. Client uses WIFI network when it sends the sensed data. In Figure 12, the structure of data transmission is illustrated. Vibration data using the Bluetooth communication is


7

Vibration measurement

Mobile device

Data server

Pulse

Mobile device

Figure 12: Data transmission structure. ADS-OSC2M U1A

I2CSDA I2CSCL

ADS SCLK GPS RX X XAMP CS1 XAMP CS2 XAMP CS3 XAMP CS4 CANRX 1 XA16 XA14 XA12 XA10 XA8 XA6 XA4 XA2 XA0 XRD# XCS8#

A1 A3 A5 A7 A9 A11 A13 A15 A17 A19 A21 A23 A25 A27 A29 A31 A33 A35 A37 A39 A41 A43 A45 A47 A49 A51 A53 A55 A57 A59 A61 A63

BXTSOC1A

EXTSOC1B

BXTSOC2A

EXTSOC2B

BXTSOC3A

EXTSOC3B

BXTADCCLK

NC

NC

NC

NC

NC

NC

NC

-XRSIO

-XWE1

GPIO32/SDAA/EPWMSYNCIAADCSOCAO

+5 V

GPIO32/SCLA/EPWMSYNCOAADCSOBO

GND

PIO29/SCITXDAXA19

NC

NC

AGND

GPIO54/SPISIMOAXD25

GPIO55/SPISOMIAXD24

GPIO56/SPICLKAXD23

GPIO57/SPISTEA/XD22

GPIO35/SCITXDAXR-W

GPIO36/SCIR XDAZC50

GPIO19/-SPISTEA/SCIRXDB/CANTXA

GPIO18/SPICLKA/SCITXDB/CANRXA

GPIO22/EQEP1S/MCLKXA/SCITXDB

GPIO7/EPWM4B/MCLKRA/BCAP2

GPIO23/EQEP1MFSXA/SCIRXDB

GPIO5/EPWM3B/MFSRA/BCAP1

GPIO20/EQEP1AMDXA/SNATXB

GPIO21/EQEP1B/MDRA/CANR XB

GPIO9/EPWM5B/SCITXDB/ECAP3

GPIO11/EPWM6B/SCIRXDB/ECAP4

GPIO39/XA16

GPIO31/CANTXA/XA17

GPIO86/XA14

GPIO87/XA15

GPIO84/XA12

GPIO85/XA13

GPIO82/XA10

GPIO83/XA11

GPIO80/XA8

GPIO81/XA9

GPIO46/XA6

GPIO47/XA7

GPIO44/XA4

GPIO45/XA5

GPIO42/XA2

GPIO43/XA3

GPIO30/CANRXAXA18

GPIO41/XA1

GPIO40/XA0/XWE1

GPIO38/-XWE0

-XRD GPIO28/SCIRXDA/ZCS6

GPIO36/SCIR XDA/-XZCS0 GPIO37/ECAP2/XZCS7

A2 A4 A6 A8 A10 A12 A14 A16 A18 A20 A22 A24 A26 A28 A30 A32 A34 A36 A38 A40 A42 A44 A46 A48 A50 A52 A54 A56 A58 A60 A62 A64

C1 330 𝜇F/6.3V

C2 104 C3 103

U1B

R56 10 ADS-INT CPULED6 CPULED7 GPS-1PPS MSEN-A MSEN-B MSEN-C MSEN-D

X

ADS OUT1 SPID-SCK GPS TX SPIO-SOMI FrontLED CPULED1 CANTX 1 XA15 XA13 XA11 XA9 XA7 XA5 XA3 XA1 XWR#

MGND

X X GPS-RESET# BXD15 BXD13 BXD11 BXD9 BXD7 BXD5 BXD3 BXD1 232SINA

BT RXD

B1 GPIO0/EPWM1A B3 GPIO2/EPWM2A B5 GPIO4/EPWM3A B7 GPIO34/BCAP1/XREADY B9 GPID24/BCAP1/EQEP2AMDXB B11 GPIO26/BCAP3/EQEP2WMCLKXB B13 GPIO26/BCAP3/EQEP2WMCLKXB B15 GPIO13/-TZ2/CANRXB/MDRB B17 GPIO13/-TZ2/CANRXB/MDRB B19 GPIO6/EPWM4A/EPWMSYNCI/EPWMSYNCO B21 GPIO8/EPWM5A/CANTXB/-ADCSOCAO B23 GPIO10/EPWM6A/CANRXB/-ADCSOCBO B25 GPID27/BCAP4/EQEP2S/MFSXB B27 GPIO50/EQEP1A/XD29 B29 GPIO53/EQEP1/XD26 B31 GPIO49/BCAP6/XD30 B33 GPIO16/-TZ5/SPISIMOA/CANTXB B35 GPIO16/-TZ5/SPISIMOA/CANTXB B37 GPIO59/MFSRA/XD20 B39 XCLKOUT B41 GPIO64/XD15 B43 GPIO66/D13 B45 GPIO68/XD11 B47 GPIO70/XD9 B49 GPIO72/XD7 B51 GPIO74/XD5 B53 GPIO76/XD3 B55 GPIO78/XD1 B57 GPIO15/-TZ4/XHOLDA/SCIR XDB/MFSXB B59 GPIO34/BCAP1/XREADY B61 GPO60/MCLKRM/XD19/EPWM8A B63 GPIO62/SCIRXDC/XD17/EPWM9A

(a)

GPIO1/EPWM1B/ECAP6/MFSRB GPIO3/EPWM2B/ECAP5/MCLKRB GPIO5/EPWM3B/MFSRA/BCAP1 GPIO37/ECAP2/XZCS7 GPIO25/BCAP2/BQEP2B/MDRB GPIO27/ECAP4/EQEP2S/MFSXB GPIO12/-TZ1/CANTXB/MDXB GPIO14/-TZ3/-XHOLD/SCITXDB/MCLKXB GPIO32/SDAA/EPWMSYNCI/ADCSOCAO GPIO7/EPWM4B/MCLKRA/ECAP2 GPIO9/EPWM5B/SCITXDB/ECAP3 GPIO11/EPWM6B/SCIR XDB/ECAP4 GPIO48/ECAP5/XD31/SPISMOD GPIO51/EQEP1B/XD28/SPISTED GPIO52/EQEP1S/XD27 GPIO15/-TZ4/-XHOLDA/SCIRXDB/MFSXB GPIO17/-TZ6/SPISOMIA/CANR XB GPIO33/SCLA/EPWMSYNCO/ADCSOCBO GPIO58/MCLKRA/XD21 -XRS GPIO65/XD14 GPIO67/XD12 GPIO69/XD10 GPIO71/XD8 GPIO73/XD6 GPIO75/XD4 GPIO77/XD2 GPIO79/XD0 GPIO14/-TZ3/-XHOLD/SCITXDB/MCLKXB GPIO35/SCITXDA/XR-W GPIO61/MFSRB/XD18/EPWM8B GPIO63/SCITXDC/XD16/EPWM9B

B2 B4 B6 B8 B10 B12 B14 B16 B18 B20 B22 B24 B26 B28 B30 B32 B34 B36 B38 B40 B42 B44 B46 B48 B50 B52 B54 B56 B58 B60 B62 B64

CPULED0 CPULED1 CPULED2 CPULED3 CPULED4 CPULED5 CANTX 2 X

BT RTS BT CTS

X X SPID SIMO GPID51 GPID52 X BT RESET# BXD14 BXD12 BXD10 BXD8 BXD6 BXD4 BXD2 BXD0 232SOUTA

BT TXD

(b)

Figure 13: Microprocessor circuitry.

transmitted to the portable device. It is sent to the database servers at the request of the user and transferred to another mobile device from the database server.

3. Results and Discussion A wireless data acquisition system for hardware and software systems is proposed. Results for the proposed method were confirmed using the Android platform. 3.1. Results of Designed Hardware Platform. Figure 13 shows the microprocessor circuit diagram. A TMS320F28346 CPU was used (TMS320C2000 series). The C2000 series has good performance at a reasonable cost. A signal created from accurate ICP sensor signal control amplifier circuit should process ADC that converts analog to digital as shown Figure 14. In this study, we used ADS1274 assured 24 bit accurate and high speed accurate convert for composing ADC circuit. Figure 15 displays a circuit diagram for the signal conditioning amplifier. The signal supplied to the ICP sensor

generates a constant current with high accuracy using the voltage generated. In order to adjust the gain in accordance with the sensitivity of the sensor, a programmable gain amplifier was used with a fully differential amplifier for signal formation of low noise. In Figure 16, a power supply of portable data reconstruction box is shown. To input power of DSP and peripheral devices, we put the power supply at 5 V and then divide it into 5 V, 3.3 V, and 1.8 V. To reduce noise, we designed an analog circuit both ±15 V. It is advantageous to use both gate voltages instead of using general DC-DC converters. Because when we take advantage of a coil, voltage ripple gets smaller and it displays favorable attributes on usual noise. Figure 17 shows a complete portable data acquisition board. A total of 4 sensors can receive data. Figure 18 shows a complete portable data reconstruction board that has developed in the same manner as the above. 3.2. Results of Software Platform. Table 2 describes the information of the device used in the experiment. Basically, we used the device that supports Android OS. In the experiment, we have checked on data transmission distance and accuracy

8


VA50 C45

C46

C43

C44

104

104

104

104

C66 104

C42 104 MGND VDD18

AINP1 AINN1

PWDN4 # PWDN3 # PWDN2 # PWDN1 #

55 XREF25A

CLK CLKDIV

ADS1274

VREFP

57

+

−

R80

4

C111 104 3 MGND

X4 MGND

2.2 K

R67 NC OSC 2 M

MGND VD33 R129

10

DGND1 DGND2 DGND 3 DGND 4

AGND1 AGND2 AGND3 AGND4 AGND5

7 21 24 25

VD33

ADS OSC2M

R64 10K

R73 10 R74 10 R65 10 R71 10

ADS INT

ADS SCLK ADS OUT1

R133 10K

R72 10

12 DIN SYNC# 11

AINP8 AINN8 AINP7 AINN7 AINP6 AINN6 AINP5 AINN5

6 43 54 58 59

R66 10

OSC 2 M

27

20 DOUT5 19 DOUT6 18 DOUT7 17 DOUT8

VREFN

45 46 48 47 50 49 51 52

X

2

MGND

29 DRDY/FSYNC 28 SCLK

VCOM

56

39 40 41 42

1

10K

VD33

MGND VD33

R69 10 ADSTART

DOUT5 16 DOUT6 15 DOUT7 14 DOUT8 13 MODE1 33 34 MODE0 30 FMT2 31 FMT1 FMT0 32

PWDN 5# PWDN6# PWDN 7# PWDN8# TEST0 TEST1

ADINP4 ADINN4

2.2 K R79

38 37 36 35 8 9

ADINP3 ADINN3

2.2 K

2.2 K

2 AINP2 1 AINN2 63 AINP3 64 AINN3 61 AINP4 62 AINN4

ADINP2 ADINN2

R76

VD33

R81 NC

R78 NC

R77

22 23

DVDD18

AVDD1 AVDD2 AVDD3 AVDD4

3 4

ADINP1 ADINN1

VD33

VD33

VD33

VD33

26

5 44 53 60

VA50

VD33

MGND MGND

IOVD33 IOVD33

C114 22 𝜇F/10 V

C103 104

VD33

R136 10K

NC

R137 10K

VD33

R70 2.2 K

MGND

BD1

R139

R135

R68 NC

NC

NC

R75 2.2 K

MGND MGND

MGND MGND

MGND BD2

Figure 14: Delta-sigma ADC I/F circuitry.

VD33VA50

C29

R40

AMP CS1 C96

VA50

C102 10 𝜇F/10 V

ADINN1 C93 ADINP1

R112 50 502 R110

Vs +

4

+

+

8

10 K R119

−

2

1

R38

NC

100

10

+IN

+VOUT

6

11

−IN

−VOUT

−VIn (Att) 12

100

AK 100

XREF258

13

R107

U24 LM385-ADJ B

330

R49

U22 BD140

22 K

4 R106 C97

10 K

U21 4

1

MSEN-A R108 1K

3

2

100 22 uF/35 V (3216) MLCC

2

TLP620 MGND

C94

3 5

C32 502

502

C92 22 𝜇F/35 V (3216) MLCCJP3 R111 100 K

V0CM>

C99

BAT54S

R118

A 100

22 uF/10 V (2012) 22 uF/10 V (2012)

4

R7

8

104

U26

SDO

R113

C104 C95 R115

K

SCK

WP5845D-331

5

VA50

SDI

C139

C65 47 𝜇F/35 V

R53

CS #

+VIn (ATT)

R36 10 K

C98 103 10 pF 100 K

1

9

R39

R116

Vs − XREF258

7

C101 502

THS4531A

50

14

AMP SDI

3 7

5 −

R41

AMP SDO R37

AMP SCK

103

6

8

V+

U9 LMP7312 20 pFC100

VD33 L4

22 𝜇F/10 V 1 𝜇F/10

V10

R114

20 VA

C 19

V10

100 K

C31

1 uF/10

C27

C30

1 𝜇F/10

104

Figure 15: Signal conditioning amplifier schematic.

AIN 01P J P4 200 R109

AIN 01N BD9


9

5V 1N5819

D3 2

VI N

1

3

IN

1N5819

3

C4015 22 𝜇F/35 V

1

1 2 25nH

5

7

R388

2

COLB

R387

DUTY

6 RCSL

SHDN

6

5 10

NFB

VC

CT

GND 9

8

R39 C4014 18 K

3300 pF

12

7

R389

68 K

R395

8

D5 2 D6

13

FB

RVSL

SYNC

RT

4

SHDN

150K

MGND

BAT85 1

15

1N5819 1 1N5819

1

4 K TO 68 K

2

5V R392

C4018 22𝜇F/35V

C4016 22 𝜇F/35 V

D2

5 L46 100 𝜇H 1 2 7 8

VIN ILIM2 ILIM4 VIN U4016

324 K

4 SENSE

16

C4017 22 𝜇F/35V

4

GND

GND

2

332 K

R393

150 K

2

U4037

11

2

BAT85

LT1533/SO

1

R394

D1

3

OUT ∗

2

1 D4

2

LT1121CS8

GND

14

U4018

U4038 CTX02 137 16-X1

MGND COLA

VAP15

L45 100 𝜇H 8

22 𝜇F/10 V

U4028

1

NC/ADJ

C4013

OUTPUT LT1175CS8

3

VAP15

43 K

R390 10 K

MGND

Figure 16: Power control schematic. Table 2: Test conditions. Contents CPU OS memory Bluetooth

Galaxy Note 10.1 ARM holdings cortex A9 1.4 GHz quad-core Android 4.1 GB DDR SDRAM Bluetooth V2.0 class 1

Portable data acquisition box TMS320C2000/TMS320F28346 Embedded Linux — Bluetooth V2.0 class 1

DSP module

Vibration signal input

Bluetooth module

Figure 17: Portable data acquisition board. Figure 19: Shaker system.

Bluetooth module

DSP module

Vibration signal input

Figure 18: Portable data reconstruction board.

of data transmission. First, data were measured by using a shaker system to verify the accuracy of the data. Through a shaker system sent data with various frequencies, it was confirmed that the developed system could receive the exact data. Figure 19 shows a shaker system that has been used for our experiment. Further, when performing frequency conversion on the Android platform that has received the data from the sensor, we also checked data through receiving a distinct frequency to a client to verify the accuracy of data. Figure 20 shows data received from a shaker system to Android platform. Figure 20(a) shows a result in which we

10


(a)

(b)

Figure 20: Experiment of accuracy.

Figure 21: Configuration menu.

used the values that are frequency of 30 Hz and amplitude of 0.1 and Figure 20(b) shows the used the values that are frequency of 20 Hz and amplitude of 0.2. Using sine waves with periodic frequencies, we observed the received data and transformed characteristic of the received signal, accurately. Using the configuration of menu as shown in Figure 21, it is possible to enter a title for a measurement, along with a charge, a customer name, the number of channels, sampling rate, and data. The number of channels refers to the number of sensors (up to four possible). Information entered into the configuration menu can be confirmed by personnel at a remote location via the server. In Figure 22, the “Sensor Info” menu represents data in graphical form that is transmitted from the module. However, it is possible to enter the value of the gain and sensitivity to complement the data because an error may occur. By entering the IP address and port of the server and saving it through the “Save” button, the information for communication with the RX mode is set up. Also, we can click the button corresponding to each channel to show or hide the corresponding channel. Figure 22 shows that the state of the channel is on and all four series are shown in the Graph. Figure 23 shows results of the “Measurement” menu. “Graph Sensor Info” menu (in Figure 22) shows the same graph. When “Log Start” is pressed while receiving data, as shown in Figure 23(a), data are received and displayed on

Figure 22: Sensor Info menu.

Table 3: Experiment result of range test.

Measurement distance Data accuracy Frequency analysis Real-time transmission

Test status 100 m 150 m O O O O O O

160 m X O X

170 m X X X

the screen. FFT (fast Fourier transform) is used to convert time-domain data to frequency-domain data for analysis, as shown in Figure 23(b). In order to verify the performance of the developed technology, we took data sensing experiment outside the building. After installing the sensors on the road, it was tested that a mobile device could receive data that has been transmitted. When a person or the car passed through, there was no problem to transfer the data accurately. As a result of the experiment in Table 3, communication distance at the outside has been measured by up to 170 m. From almost 160 m of a remote distance, we can observe a little packet loss of the sensing data. From over 170 m of distance, we could not analyze the signal using some transformations because data was corrupted so much. Based on this result, we can induce that the stable communication range of the proposed system can be up to 150 m.


11

(a)

(b)

Figure 23: Measurement Info menu.

The accuracy of the transmitted data was guaranteed and real-time transmission and signal transformation were also operated normally. Vibration measurement data obtained in the field by nontechnical personnel can be transmitted via a wireless communication system for expert analysis in a laboratory. Analysis and receipt of data can occur simultaneously for quality checking and speed. This wireless data communication method reduces the amount of manpower necessary for transport, acquisition, and analysis of data. Further development of this wireless vibration measurement data system in conjunction with a wired system to deal with various field situations will be possible.

4. Conclusion A wireless sensing method is proposed for measurement and transmission of field derived vibration data to a remote location to reduce manpower requirements for equipment transport and data acquisition and analysis. A low cost of hardware is suitable for wireless transmission of vibration data measurements. Design, development, and software implementation were achieved to prevent data loss of packet. Development of wireless data measurement system can be commercialized to resolve the current need for expensive technologies.

Conflict of Interests The authors declare that there is no conflict of interests regarding the publication of this paper.

Acknowledgments This research was supported by the MSIP (Ministry of Science, ICT, and Future Planning), Korea, under the IT/SW Creative Research Program supervised by the NIPA (National IT Industry Promotion Agency) (NIPA-2013-0079-01).

References [1] T. J. Cavicchi, Digital Signal Processing, John Wiley & Sons, New York, NY, USA, 2000.

[2] K. Z. Tang, K. K. Tan, C. W. de Silva, T. H. Lee, K. C. Tan, and S. Y. Soh, “Application of vibration sensing in monitoring and control of machine health,” in Proceedings of the IEEE/ASME International Conference on Advanced Intelligent Mechatronics Proceedings, vol. 1, pp. 377–382, July 2001. [3] A. S. Weddell, G. V. Merrett, S. Barrow, and B. M. Al-Hashimi, “Vibration-powered sensing system for engine condition monitoring,” in Proceedings of the IET Conference on Wireless Sensor Systems, pp. 1–5, June 2012. [4] W. Wang and O. A. Jianu, “A smart sensing unit for vibration measurement and monitoring,” IEEE/ASME Transactions on Mechatronics, vol. 15, no. 1, pp. 70–78, 2010. [5] A. Awawdeh, S. T. S. Bukkapatnam, S. R. T. Kumara, C. Bunting, and R. Komanduri, “Wireless sensing of flow-induced vibrations for pipeline integrity monitoring,” in Proceedings of the 4th IEEE Sensor Array and Multichannel Signal Processing Workshop Proceedings (SAM ’06), pp. 114–117, IEEE Workshop, July 2006. [6] M. Cerny and M. Penhaker, “Bed vibration measurement and evaluation for maintanace health systems,” in Proceedings of the 2nd International Conference on Mechanical and Electronics Engineering, pp. V1372–V1373, August 2010. [7] W.-J. Yi, W. Jia, and J. Saniie, “Mobile sensor data collector using Android smartphone,” in IEEE Workshop IEEE 55th International Midwest Symposium on Circuits and Systems (MWSCAS ’12), pp. 956–959, August 2012. [8] C. Yuan, C. Gao, J. Dong, and W. Sun, “An optimizing scheme for wireless video transmission on Android platform,” in Proceedings of the IEEE International Conference on Transportation , Mechanical, and Electrical Engineering (TMEE ’11), pp. 970–973, December 2011. [9] S. Lee and J. W. Jeon, “Evaluating performance of android platform using native C for embedded systems,” in Proceedings of the International Conference on Control, Automation and Systems (ICCAS ’10), pp. 1160–1163, October 2010. [10] J. Kee-Yin Ng, “Ubiquitous healthcare: system and applications enabled by mobile and wireless technologies,” Journal of Convergence, vol. 3, no. 2, pp. 15–20, 2012. [11] J. Wu, M. Huo, J. Cai, M. Wu, and Y. Wang, “Research on Bluetooth expansion of communication based on android system,” in Proceedings of the World Automation Congress (WAC ’12), pp. 1–4, June 2012.

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