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Design and Development of a Virtual Instrument for Hazardous Environment Monitoring and Control Using Lab View a
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A. Sureshkumar , S. Muruganand & P. Balakrishnan a
Research and Development Centre, Bharathiar University, Coimbatore, India
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Department of Electronics and Instrumentation, Bharathiar University, Coimbatore, India c
Department of Electronics and Communication, Hindustan College of Arts and Science, Coimbatore, India Published online: 23 Apr 2015.
Click for updates To cite this article: A. Sureshkumar, S. Muruganand & P. Balakrishnan (2015): Design and Development of a Virtual Instrument for Hazardous Environment Monitoring and Control Using Lab View, Intelligent Automation & Soft Computing, DOI: 10.1080/10798587.2015.1027508 To link to this article: http://dx.doi.org/10.1080/10798587.2015.1027508
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Intelligent Automation and Soft Computing, 2015 http://dx.doi.org/10.1080/10798587.2015.1027508
DESIGN AND DEVELOPMENT OF A VIRTUAL INSTRUMENT FOR HAZARDOUS ENVIRONMENT MONITORING AND CONTROL USING LAB VIEW A. SURESHKUMAR1, S. MURUGANAND2,
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P. BALAKRISHNAN3
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Research and Development Centre, Bharathiar University, Coimbatore, India Department of Electronics and Instrumentation, Bharathiar University, Coimbatore, India 3 Department of Electronics and Communication, Hindustan College of Arts and Science, Coimbatore, India
ABSTRACT—The increased high performance of personal computers and their reduced cost has made it possible for the development of computer based monitoring and control systems. Industry needs several measurement systems that can measure safety risk parameters of the hazardous area. Although wireless sensor networks have been widely used, combining virtual instrument technology to achieve the purpose of safety measurement has several benefits. These systems are efficient and cost-effective for acquiring and analyzing sensor signals. Utilizing virtual instrumentation to achieve safety measurement will largely decrease the cost and increase the flexibility of the instruments. This work aims at designing a virtual instrument for acquiring and processing of hazardous parameter signal. The software platform in terms of virtual instruments is developed under Lab VIEW programming environment and integrated with computer controlled system. Key Words: Computer control; Data acquisition; Lab VIEW; Virtual instrument; Wireless sensor network
1. INTRODUCTION Industrial safety monitoring needs several measurements that can measure hazardous factors of the industrial environment. Measurement and control systems should be able to identify the risk accurately and the factors like gas leakage, radiation, critical temperature, fire and smoke etc. These factors should be monitored at real time for industrial safety. Computer based signal acquisition, and analysis is an efficient and cost effective method for sensor based signal acquisition and monitoring. Isolation of the subject from the electronic circuitry is very important. Also, since the sensor signal level is very low, amplification of signals is important. Hence, a computer based system consists of additional circuits for isolation and amplification of the signals (Ahmad A.H 2013). Combining virtual instrumentation technology for industrial measurements is an upcoming technology that is currently rising up at a faster rate. The cost can be drastically brought down and the flexibility can be increased by using of virtual instrumentation. National Instrument’s Lab Views a platform and development environment for a visual programming. The purpose of such programming is automating the usage of processing and measuring equipment in any laboratory setup. Controls and indicators on the front panel allow an operator to input data into or extract data from a running virtual instrument. A key benefit of Lab VIEW over other development environments is the extensive support for accessing instrumentation hardware.
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2. HAZARDOUS ENVIRONMENTAL MONITORING Hazardous environmental monitoring is the most vital application for industrial safety. At present, wireless sensor networks have been applied for a number of applications as, e.g., gas monitoring, radiation mapping, aquatic monitoring, glacial control and climate change, forest fire alarm, landscape flooding alarm, and forecasting in rivers. The ability to place autonomous and low cost nodes in industrial hazardous environments without communication infrastructure enables accurate data collection directly observed from hazardous areas. With sensor networks, environmental data can be observed and collected in real-time, and used for forecasting upcoming phenomena and sending prompt warnings if required. Implementation of hazardous environmental sensor networks for data assimilation within model-based safety warning systems involves complex engineering and system challenges. These systems must withstand the event of interest in real-time, remain functional over long time periods when no events occur, cover large geographical regions of interest to the event, and support the variety of sensor types needed to detect the phenomenon (Barrenetxea 2008).
3. SYSTEM ARCHITECTURE The architecture of the precision industrial accident detection system based on wireless sensor networks consists of the monitoring station and wireless sensor nodes with coordinated module (Chaudhuri, A 2014 ). A PC attached with a NI WSN- 9791 gateway coordinator serves as a base station. Temperature, gas leakage, radiation and fire sensors with associated signal conditioners attached to NI WSN-3202 serves as a wireless sensor node. Figure 1 shows the block diagram of the system. The NI WSN devices use the ZigBee (IEEE 802.15.4) wireless communication protocol and operates in ISM band, 2.4 GHz frequency. NI WSN devices can communicate up to 300 m in open space and support data communication rate up to 250 kbps. The WSN-9791 coordinated gateway module can collect measurement data from up to 36 measurement nodes in a network and transfer data to a host PC through 10/100 Mbps Ethernet port. The measurement node NI WSN-3202 has four analog input channels and four digital I/O channels. Each analog input channel has 16-bit resolution. The NI WSN- 9791 design was developed to address the requirements of the described application. The block diagram of NI WSN- 9791 is illustrated in Figure 2. The critical components are a low-power microcontroller (uC) module that supervises the NI WSN- 9791 operation, multiple sensor interfaces (Pulse, SDI-12, RS-485, Analog) that enable to take measurements from different kinds of sensor devices, and a GPRS module for long-distance communication with the control center. Moreover, two communication modules (USB and Bluetooth) enable the in-situ interactions via a PC (Di Palma, D 2010).
Figure 1. System Design of Hazardous Area Safety Monitoring
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Figure 2. NI WSN System Operation Diagram
3.1 Design of Microcontroller Module The circuit schematic of the microcontroller module is shown in Figure 3. The central part of the schematic represents the low-power 16-bits microcontroller (MSP430F2274) manufactured by Texas instruments. The MSP430F2274 operating to 3.3 V is ideal for low power applications (nanoWatts) with 120 nW sleep mode and 25 W active mode. The MSP430F2274 microcontroller combines 16-MIPS performance with a 200-ksps 10-bit ADC and 2 op-amps, while the CC2500 multi-channel RF transceiver is designed for low-power wireless applications (Dixit, T. D 2013). The USB debugging interface enables eZ430-RF2500 to connect with a PC to send and receive data using the MSP430 application UART, which is recognized as the application backchannel (Hwang, J 2010).
3.2 Design of Sensor Interfaces The wireless measurement node uses RHT1000 transmitter module for measuring air temperature and relative humidity. The module has Pt100 RTD temperature sensor, HIH4000 humidity sensor and associated signal conditioners. The module provides outputs in the range of 0 –1 V for temperature 0 to 1008C.The outputs are further amplified with gain 5 using LM324 Op-Amp in non-inverting amplifier configuration. The op amp outputs are applied at AI0 and AI1 analog inputs of WSN3202. Silicon photodiode based instruments is used for solar radiation measurement. It measures radiation from 0 to 1800 W/m2 and provides voltage output in the range of 0 –3 V. It has the sensitivity of 1.67 mV/W/m2. LMP91051 gas sensor is used for gas measurement. It measures gas CO2 level 130 ppm to 5000 ppm with resulting in a slope of -0.0534 mV/ppm or -18.7 ppm/mV. The smoke detector samples the IR circuitry
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Figure 3. Microcontroller Module Circuit
for the presence of smoke every eight seconds (Mamduh, S 2012). A smoke detector is a device that detects smoke, typically as an indicator of fire to implement an ultra-low-power photo-diode-based smoke detector. An infrared (IR) diode and IR receiver are used inside a smoke chamber to detect the presence of smoke. The IR diode is pulsed periodically, and the IR receiver signal is examined to determine if smoke is present in the chamber. An optical detector is a light sensor. In the absence of smoke, the light passes in front of the detector in a straight line. When smoke enters the optical chamber across the path of the light beam, some light is scattered by the smoke particles, directing it at the sensor. The circuit diagram is shown in Figure 4. NI WSN-3202 provides multi-sensor interfaces to take readings from a wide set of hydrologic instruments (Papadopoulos, Y 2001).
3.3 Design of ZigBee Module A RF module is used to transmit monitoring data from NI WSN -3202 to the control center. Figure 5 shows the RF module implementing all functions for wireless Circuit schematic of the long-distance communication (Sarguna Priya 2014). The CC2500 RF transceiver, used both in MSP430FG4618 and eZ430-RF2500 wireless development tool, plays a great roll in the wireless application. It works in 2400 –2483.5 MHz frequency range and has a data rate, which is typically 250 kBaud and configurable to be from 1.2 to 500 kBaud (Sazonov. E 2010). With the typical current consumption of 13.3 mA in receiving mode, 11.1 mA in transmitting mode, and 400 nA in sleep mode, the low-power feature of CC2500 is obviously shown. In addition, Wake-On-Radio (WOR) and fast start-up time, which is generally 240ms from sleep mode to receiving or transmitting mode contribute to the low-power feature too (Figure 6) (Singh. R 2012).
3.4 Design of Power Module The power module consists of two power sources and three regulating mechanisms to provide a secure supply of electronic components. The main energy source is a 12 V DC battery of 7,000 mAh power capacity, which can be rechargeable using an optional solar panel. We use a commutated DC/DC regulator in step-down mode, as shown in Figure 7 (a). The microcontroller turns on the DC/DC regulator when it detects that the battery has a low level according to a pre-established threshold (Figure 8).
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Figure 4. Circuit Diagram of the Sensor Interface
To reduce the power consumption, NI WSN keeps almost all electrical components deactivated, such as RF module, sensors, and ADC. Only the microcontroller circuit is always supplied at 3.3 V. At the same time, it has a good linearity and load regulation characteristics, and allows establishing the voltage supply between 3 V and 10 V. The chosen MOSFET is a FDC6330L, manufactured by Fairchild Semiconductor, which provides high performance for extremely low on-resistance (, 0.2 V). The DC/DC used is a MC34063AD, manufactured by Texas Instruments, which make possible high voltage transmission. This technique provides high power efficiency.
4. DESIGN OF VIRTUAL INSTRUMENTATION SOFTWARE This section explains the visual software design of lab VIEW for critical hydrological scenarios. In hazardous zones, channels are often ephemeral, i.e., they are often dry throughout the year as the result of infrequent and irregular precipitations, and flows can sharply increase during short storm events. Therefore, regular periodic monitoring provides little relevant information. Also, sensor acquisition and massive data transmission in dry period leads to a waste of energy (Sunkpho. J 2011).
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Figure 5. NI WSN Gateway Coordinated Operation
The dynamic measuring schedule uses two different observation frequencies; low and high. The energy-saving low frequency is used most of the time in semiarid areas, as channels are commonly dry. When a relevant event is detected, the high observation frequency is activated. Also, only when a number of stored sensor measurements are reached, the NI WSN 9791 activates the RF module to transmit data to the control server. This schedule takes useful measurements increasing the autonomy of the monitoring system (Surendran. E 2012). The development environment for the lab VIEW software is Embedded Workbench and the graphical programming language. The Lab VIEW software is divided into three main modules. First, the main loop module is responsible for initializing the configuration parameters and managing the low-consumption dynamic schedule for sensor reading and wireless communication. Second, the sensor reading program is in charge of the power supply to sensors and collection of sensor observations. Third, the wireless communication program is designed for receiving and sending data signal over a RF communication network, Figure 9, flowchart showing the main loop routine of node operation. The low-consumption schedule manages the activation of reading and communication tasks (Vinh, A. 2008).
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Figure 6. Typical Circuit of CC2500
The implementation of the schedule consists of a reading timer, a counter of stored sensor data, and a RF timer. By default, the reading timer is configured in low observation frequency, set to a measurement every 10 minutes. If the reading of a sensor exceeds a pre-established threshold, then a relevant event is assumed to happen, and the fast mode is activated, so the reading timer fires every minute. This way, the sensor data are saved until there is a pre-configured amount to transmit over wireless sensor network.
Figure 7. (a) Battery Modules, (b) Secure Power Control for Battery, and Sensor
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Figure 8. Circuit Schematic of the Power Supply Module. (a) Power Supply for RF Sensors, and ADC Converter, (b) Power Supply for Microcontroller
In addition, a RF timer is used to guarantee a maximum time among transmissions, currently set to one hour. This schedule reduces the power consumption in reading and transmission, providing a reliable keepalive system.
4.1 Sensor Reading Routine The reading routine must manage a wide range of wireless sensors with different output types: Temperature, radiation, gas leakage, fire etc. So, the microcontrollers supply the sensor devices during the measurement time and request the sensor for the physical parameter. The reading of analog sensors (differential, singleend) needs the use of an analog-digital converter (ADC) to obtain the physical measurements. Moreover, we group the analog reading of several sensors with common features in order to save supplying energy. Below, we explain in detail the reading process for analog devices according to the periodic task of reading several sensors with different power supplies. In analog readings, the microcontroller divides all active sensor inputs into several sets with the same supplying voltage and the same output voltage, so each sensor set is measured simultaneously. First, the microcontroller selects a set of sensor inputs and activates the ADC converter. The sensor devices and the ADC converter is supplied with the specific voltage. Then the microcontroller waits until the output voltages are stable which is determined for the maximum measurement time of sensors selected. When the measurement time finishes, the ADC converts each read voltage output to the corresponding physical value. So, the microcontroller stores the measured values along with the time sequence when they are collected, and it switches off the sensors and the ADC converter.
4.2 RF Communication Routine The main operation of RF communication is to transmit sensor data collected by the NI WSN 9791. In addition, the RF communication is in charge of sending safety-status information and receiving software changes from the control server. To communicate with the control server, the NI WSN 9791 activates the RF module. Once the RF module is registered in the monitoring; the NI WSN 9791 sends a synchronization signal to the control server and waits for the respective response. During the data transmission, the NI WSN 9791 sends sensor historical and safety status signals to the control server. When the transmission finishes, the microcontroller switches off the RF signal and repeat the process again (Wang, S 2012).
4.3 Remote Monitoring System The monitoring system enables to gather sensor information from remote coordinated module and supports data analysis and decision-making. To administrate the monitoring system, a complete end-user
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Figure 9. Software Flow Control
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Figure 10. Performance Analysis
interface carries out historical data queries and alarm messages for critical status. The monitoring system provides data analysis for alarm messages by the RF module interface. The Lab VIEW server analyzes and processes the hazardous factor level and lets us know, whether it is safe, normal or critical. Its functionality provides reliable data gathering and fine-grained control without human presence in the field.
5. RESULTS AND DISCUSSIONS The multisensory signals acquired from the hazardous environmental are critical very small, often in the millivolt/microvolt range, and each has its own processing needs. Multisensory output signals are in the microvolt range and have many frequency components. These sensor signals require processing before they can be analyzed by using Lab VIEW tools. In order to facilitate this type of analysis, Lab VIEW comes with a built in data acquisition system that makes the process of component separation quick and easy. We can effectively acquire real time sensor signals, measure the risk value, and convert the results to real-world units and displays as shown on graphical analysers. For each digital interface, DatalogV1 can supply and read multiple sensors. The goal of our field development is to provide a long life-time monitoring system, to obtain continuous accurate hazardous level information to be assimilated into a model-based monitoring system. Here we focus on two tests conducted to assess the success of the monitoring system. The first test was done to validate the functional operation in terms of robustness and reliability. The second was performed in order to assure the critical value identification during a complete cycle shown in Figure 10.
6. CONCLUSION This paper is reported on an industrial safety application of wireless sensor networks on hazardous areas. It was designed to meet the requirements of providing real-time data on hazardous environment monitoring parameters and remote controlled. In a single sensor, there are disadvantages like misinterpretation, and reduce the uncertainty, so as to improve the safety performance of the safety system, ZigBee wireless communication technology and data fusion method is used in hazardous safety and which is feasible. Its flexibility enables a wide application span for autonomous data collection with reliable transmission in few sparse points over large areas.
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ACKNOWLEDGEMENTS The is work has been motivated and supported by the Dr. G. Ganesan Director In- Charge, Research and Development center, Bharathiar University and Dr. S. Muruganand, Assistant Professor, Department of Electronics and Instrumentation, Bharathiar University Coimbatore, India.
DISCLOSURE STATEMENT No potential conflict of interest was reported by the author(s).
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REFERENCES Ahmad, A. H., & Mahmood, A. K. (2013). A design of personal monitoring system for hazardous work. Research Notes In Information Science, 11, 1 –12. Barrenetxea, G., & Ingelrest, F. (2008). Sensor scope: Out-of-the-box environmental monitoring. In International Conference on Information Processing in Sensor Networks, IEEE Computer Society, pp. 332–343. ISSN: 7695-3157. Chaudhuri, A., & Roy, J. K. (2014). A remote monitoring system for river barrage Gates and allied structures using virtual Instrumentation technology. In Eighth International Water Technology Conference, IWTC8, Alexandria, Egypt, pp. 701 –714. Di Palma, D., & Bencini, L. (2010). Distributed monitoring systems for agriculture based on wireless sensor network technology. International Journal on Advances in Networks and Services, 3, 18–28. Dixit, T. D., & Dawande, N. A. (2013). Review of wireless Zigbee communication for hazardous application (coal mine). International Journal of Computer Science and Management Research, eETECME, 18– 23. ISSN 2278-733X. Hwang, J., Shin, C., & Yoe, H. (2010). Study on an agricultural environment monitoring server system using wireless sensor networks. Sensors, 10, 11189– 11211. ISSN 1424-822010.3390/s101211189 Mamduh, S. M., & Shakaff, A. Y. (2012). Odour and hazardous gas monitoring system for swift let farming using wireless sensor network (WSN). Chemical Engineering Transactions, 30, 331 –336. ISBN 978-88-95608. Papadopoulos, Y., & McDermid, J. (2001). Automated safety monitoring: A review and classification of methods. International Journal of Condition Monitoring and Diagnostic Engineering Management, 4, 1– 32. ISSN 1362-7681. Sarguna Priya, N., & Mani, J. (2014). Monitoring and control system for industrial parameters using can bus. International Journal of Engineering Trends And Technology (IJETT), 9, 479–484. ISSN: 2231-5381. Sazonov, E., & Krishnamurthy, V. (2010). Wireless intelligent sensor and actuator network – a scalable platform for timesynchronous applications of structural health monitoring. Structural Health Monitoring, 9, 465 –476. Singh, R., Kaundal, V., & Jain, A. (2012). Wireless personal area network design and simulation to locate accidental information using 2.4 GHz transceiver module. International Journal of Engineering Science & Advanced Technology, 2, 297–300. Sunkpho, J., & Ootamakorn, C. (2011). Real-time flood monitoring and warning system. Songklanakarin Journal of Science Technology, 227–235. Surendran, E., & Natarajan, M. (2012). Deployment of wireless sensor network for the measurement of exhaled nitric oxide in in-home healthcare. Sensors & Transducers Journal, 142, 87–94. ISSN 1726-5479. Vinh, A. (2008). Computer-based monitoring for decision support systems and disaster preparedness in buildings. IMETI Proceedings, 2, 1 –6. Wang, S., & Li, M. (2012). Gas monitoring system based on ZigBee Pro and a new method for safety grade evaluation. International Journal of Computer Science Issues, 9, 501–506. No 3, ISSN: 1694-0814.
NOTES ON CONTRIBUTORS Mr. Suresh Kumar A received Bachelor degree in Electronics Science, Master of Science in Applied Electronics from Bharathiar University, South India, Master of Philosophy in Electronics from Vinayaka Mission Research Foundation, South India and pursing PhD from the Research and Development center Bharathiar University South India. Currently, his research areas include the development of the wireless sensor network for safety and security in industrial hazardous monitoring area using virtual instrumentation.
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Dr. S. Muruganand received Ph.D. in Physics from Bharathiar University Coimbatore, South India. He has more than 20 years of experience in Teaching, Research and Industry. His field of research is nanoelectronics, MEMS/NEMS, embedded systems, image processing, biomedical instrumentation and thin films.
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Dr. P. Balakrishnan received Ph.D. in Electronics and Instrumentation from Bharathiar University Coimbatore, South India. He has two years experience in Teaching, Research and Industry. His field of research is MEMS/NEMS, embedded systems, image processing, biomedical and virtual instrumentation.
