Int. Journal of Electrical & Electronics Engg.
Vol. 2, Spl. Issue 1 (2015)
e-ISSN: 1694-2310 | p-ISSN: 1694-2426
Structural Health Monitoring System Using Wireless Sensor Network Kavita Kumari Student, Dept. of IT UIET,PU, Chandigarh Email: kavita06it16@gmail.com
Inderdeep Kaur Aulakh Asst. Professor, Dept.Of IT UIET, PU, Chandigarh Email: ikaulakh@yahoo.com
Abstract— The longevity and health monitoring of structure are important for their lifespan optimization and preservation. WSN technology has proven to be a boon for structural health monitoring in recent year due to its ease of installation, minimal structural intervention/damage and low cost. This paper provides a review on the recent developments in the area of SHM using WSNs. Keywords: wireless sensor network; structural health monitoring; scheduling approach; energy efficiency
I.
INTRODUCTION
Structural Health Monitoring (SHM) is referred as the process of implementing damage detection and characterization strategy for engineering structures. The changes to the material and/or geometric properties of a structural system, including changes to the boundary conditions and system connectivity which adversely affect the system’s performance, is defined as damage. In SHM process we observe system using periodically sampled dynamic response measurements from an array of sensors. Then the extraction of damage, damage-sensitive features from these measurements are carried out. To determine the current state of system health, the statistical analysis of the features is performed. There will be inevitable aging and degradation in the structure resulting from operational environment. Long term SHM is defined as output of this process that is periodically updated regarding the ability of the structure to perform its intended function. Regarding the integrity of the structure, SHM is used for rapid condition screening and to provide near real time reliable information, for example in case of extreme events such as earthquakes or blast loading [1]. To estimate the state of structure health, SHM detects the changes in structure that effects its performance. Time- scale of change and severity of change are two major factors. How quickly the change occurs is time- scale of change, and degree of change is severity of change. SHM has two major categories: disaster response (earthquake, explosion, etc.) and continuous health monitoring (ambient vibration, etc.). SHM has two approaches: direct damage detection (visual inspection, and X- ray, etc) and indirect damage detection (change in structural properties/behavior). A typical SHM system, in general, includes three major categories: a sensor system, a data
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Amol P Bhondekar Principal Scientst Agrionics,CSIO, Chandigarh Email:amol.bhondekar@gmail.com
processing system (including data acquisition, transmission, and storage), and health evaluation system(including diagnostic algorithms and information managements). II.
IMPORTANCE OF STRUCTURAL HEALTH MONITORING
There is a significant development in SHM due to major construction projects, such as large dams, long- span cable supported bridges and offshore gas/oil production installation. SHM infrastructure provides the means for society to function. It also includes buildings, pedestrian and vehicular bridges, tunnels, factories, conventional and nuclear power plants, offshore petroleum installations and heritage structures. A. Bridges For the purpose of understanding and eventually calibrating models of the load-structure-response chain, bridge monitoring programmes have historically been implemented. B. Buildings and towers The need to understand building performance during earthquakes and storms, the developments in monitoring of buildings werehistorically motivated. Originally, from vibration testing, the understanding of low-amplitude dynamic response was obtained. [3] C. Nuclear installations For one of the UK's civil nuclear reactors, Smith (1996) provided an overview of the inspection and monitoring regime. To validate and calibrate designs during performance testing, the safety- critical structural components of nuclear reactors, instrumentation were used. It also contributed to the condition monitoring during normal operation. [4] D. Tunnels and excavations In terms of stability and effects on or from adjacent structures, tunnel monitoring is aimed to ensure whether tunnel deformation is within limits. Hence, the emphasis is on deflections, while stresses and strains may also be measured.
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Int. Journal of Electrical & Electronics Engg.
Vol. 2, Spl. Issue 1 (2015)
During tunneling or mining, monitoring of heritage and other structures is a major concern. [5] III WIRELESS SENSOR NETWORKS (WSNS) The development of Wireless Sensor Network(WSNs) has originated from the need to continuously monitoring the physical phenomena coupled with the recent advances in sensing, computing and communication technologies. WSN consist of four main components: sensors, a processor, a radio, and a battery. In an application area, the WSN is formed through densely deployed sensors nodes. To collaboratively perform a particular task, in most deployments the sensor nodes have self- organizing capabilities to form an appropriate structure. WSNs are found suitable for applications such as surveillance, precision agriculture, smart homes, automation, vehicular traffic managements, habitat monitoring, and disaster detection. To revolutionize information and communication technology, WSN has great enabling technology. WSN connects the physical world to the Internet at fine granularity.WSN has power of creating a pervasive environment capable of remote sensing, monitoring and control. As a benefit, this technology offers fine granular tracking of actions in far away or inaccessible locations. WSN can also enable remote monitoring of components responsible for global warming.
B. Piezoelectric Sensors Piezoelectric materials exhibit simultaneous actuator/sensor behavior based on electrical-mechanical transformation. There are many types of piezoelectric materials: piezoelectric ceramics, piezoelectric polymers, and piezoelectric composites. Based on the measurement of electrical impedance and elastic waves piezoelectric sensors were recently introduced into SHM of civil engineering structures as an active sensing technology. C. Magnetostrictive Sensors Ferromagnetic materials are the materials which are mechanically deformed when placed in magnetic field. This phenomenon is known as the magnetostrictive effect. Inthe inverse magnetostrictive effect, the magnetic induction of the material changes when the material is mechanically deformed.Based on the above phenomena, Kwun and Bartels [10] invented a type of magnetostrictive sensor (MsS) without direct physical contact to the material surface which could generate and detect guided waves in the ferromagnetic materials under testing. Khazem et al. [11] also utilized MsS to inspect suspender ropes on the George Washington Bridge in New York. A pulse of 10 kHz longitudinal guided wave along the length of the suspenderdetected the reflected signals from geometric features and defects in the suspender. V. ROUTING ALGORITHMS FOR WIRELESS SENSOR NETWORKS
IV. SENSORS FOR SHM The sensing system in the SHM is formed by smart materials/sensors;Fibre optic sensors (FOS), piezoelectric sensors, magnetoresistive sensors, and self - diagnosing fibre reinforced structural composites. Thesesensorsare characterized with very important capabilities of sensing various physical and chemical parameters related to the health of the structures. A.FIBRE OPTIC SENSORS (FOSS) FOS can be classified by several methods. FOS can be classified based on the modulation of light characteristics (intensity, wavelength, phase, or polarization etc.) by the parameters to be sensed. It can also be classified by the methodthrough which the light in the sensing segments is modified inside or outside the fibre (intrinsic or extrinsic). FOS can also be classified based on the sensing range; local (Fabry- Perot FOS or long - gauge FOS etc.), quasidistributed(fibre Bragg grating) and distributed sensors (Brillouin-scattering-based distributed FOS). FOS are embedded in newly constructed civil structures, including bridges, buildings, and dams to yield information about strain ( static and dynamic), temperature, defects (delamination, cracks, and corrosion ) and concentration of chloride ions. On existing structures, FOSsare generally surface mounted. The data collected by FOSs is used to evaluate the safety of both the new-built structures and repaired structures, and diagnose the location and degree of damage.
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A. Data-centric protocols Due to the sheer number of nodes deployed, it is not feasible to assign global identifiers to each node in many applications of sensor networks. It is hard to select a specific set of sensor nodes to be queried due to lack of global identification and random deployment of sensor. Therefore, from every sensor node, the data is usually transmittedwithin the deployment region with significant redundancy. Since this is very inefficient in terms of energy consumption, routing protocols have been considered that select a set of sensor nodes and utilize data aggregation during the relaying of data. This consideration is known as data-centric routing. In data-centric routing, the sink sends queries to certain regionsand waits for data from the sensors located in the selected regions. Attribute-based naming is necessary to specify the properties of data, since the data is being requested through queries. In the first data-centric protocol SPIN [14],data negotiation between nodes is considered in order to eliminate redundant data and save energy. Later, a breakthrough Directed Diffusion [18] data-centric routing has been developed. Based on Directed Diffusion [17–18], many similar concepts and protocolshave been proposed [16,15,19,20]. B. Flooding and gossiping To relay data insensor networks without the need for any routing algorithms and topology maintenance, there are two classical mechanisms: : Flooding and gossiping [21]. Inflooding, each sensor receives a data packet, broadcasts it to
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all of its neighbors and this processcontinues until the packet arrives at the destination or the maximum number of hops for the packetis reached. Gossiping is a slightly enhanced version of flooding, where thereceiving node sends the packet to a randomly selected neighbor, and that neighbor picks another randomneighbor to forward the packet to and so on. C. Sensor protocols for information via negotiation SPIN technique was amongst the early works to pursue adatacentric routing mechanism [15]. SPIN uses high-level descriptors or meta-data. A dataadvertisement mechanism is used to exchange the data among the sensorsbefore thetransmission which is the key feature of SPIN. After receiving new data, each node advertises it to its neighbors and interested neighbors. D. Directed Diffusion In the data-centric routingresearch of sensor networks, Directed Diffusion [18,19] is an important milestone. The idea is to diffuse data through sensor nodes by using anaming scheme for the data. E. Energy-aware routing To increase the lifetime of the network, Shah and Rabaey [19] proposed the occasional use of set of sub-optimal paths. Depending on the energy consumption, these paths are chosen by means of a probability function. The approach is concerned with network survivability as the main metric. Energy-aware routingapproach argues that using the minimum energy path all the time will deplete the energy of nodes on that path. The assumption of the protocol that each node is addressable through a class-based addressing which includes thelocation and types of the nodes. F. Rumor routing Directed Diffusion has another variation ‘Rumor routing’ [16]. It is used in contextswhere geographic routing criteria are not applicable. Generally, in the entire network, Directed Diffusion floods the query when there is no geographic criterion to diffuse tasks. The use of flooding is unnecessary in some cases where only a little amount of data is requested from the nodes. When the number of queries is large and the number of events is small, an alternative approach floods the events. G. Gradient-based routing A slightly changed version of Directed Diffusion, called Gradient-based routing (GBR) was proposed by Schurgers et al. [17]. Here each node can discover the minimum number of hops to the sink, which is called height of the node. The gradient on the link is considered as the difference between a node'sheight and that of its neighbor on that link. A packet is forwarded on alink with the largest gradient. H. CADR Constrained anisotropic diffusion routing (CADR) is ageneral form of Directed Diffusion [18]protocol. Two techniques are
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proposed:information driven sensor querying(IDSQ) and constrained anisotropic diffusion routing. The idea is to query sensorsand route data in a networkin order to maximize the information gain while minimizing the latency and bandwidth.This is achieved by activating only the sensors that are close to a particular eventand dynamically adjusting data routes. I. COUGAR The network as a huge distributed database system in datacentric protocol has beenproposed [14]. The main idea is to use declarative queries in order to abstract query processingfrom the network layer functions such as selection of relevant sensors etc. and utilize in-networkdata aggregation to save energy. The abstraction is supported through a new query layer betweenthe network and application layers. J. ACQUIRE Active Queryforwarding in sensor networks (ACQUIRE) [20] is a fairly new data-centric mechanism for querying sensor networks.The approach views the sensornetwork as a distributed database and is well-suited for complex queries which consist of severalsub queries. TABLE 1 COMPARISON OF PROTOCOLS AND FEATURES Routing Protocol
DataCentric
Hierarch Location ical based
QoS
Networ Data k flow aggregatio n
SPIN
Directed Diffusion
Rumor routing
Shah and Rabey
GBR
CADR
COUGAR
ACQUIRE
LEACH TEEN and APTEEN
PEGASIS
MECN and SMECN GAF GEAR
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related to the WSN. Traditionally, wired system is used for collecting sensor data periodically, but the SHM system has several disadvantages. The main issues in the use of WSN in SHM are the scalability, accuracy, reliability and data precision.
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REFERENCES
Fe et al.
VI. ISSUE RELATED TO WSN Depending on the application scenario and specific structure, issues related to WSN for monitoring structural health systems may impose different requirements. The following issue are the base of the building structure. A. Quality of Data Data is the essential evidence. Quality of data is more important because it carries structural health information. Any missing data is an error result of the analysis. Other parameter related to signal processing must be accurately specified during signal synchronization. To continue error free analysis, lossless data transmission is required and packet/symbol/bit error must be avoided. B. Reliability and Scalability It seems that wireless communication could be unreliable because is uses a share transmission media and information error is also calculated on probability base. Increases in the transmission node in the network lead collision and packet loss. Unknown errors and lack of reliability may also occur while analyzing the results. One of the most important issues is to cover the large geographical civil infrastructure. Scalability of the WSN will provide adjustment flexibility with infrastructure for monitoring structural health by adding new transmission node in the network with higher precision of damage detection. The sensor coverage area defines the complexity of the scalability to cover the whole service area. C. Real-time Response and Lifetime of the Overall The measurement of the overall system should be a real time response. Efficient design of the fault management solution of the wireless sensor network is an another important challenge based on real time environment. Every system is defined by real time response. The faster real time system response may provide more accurate data and such system helps in correct decision for better result. The lifetime of the overall monitoring system should be increased to reduce the overall system cost. Limited maintenance and power efficiency are also the important parameters. CONCLUSION This paper presents a review of recent research and development activities in SHM of civil structures and discusses several techniques that evaluate structural damage and issue
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[1] Dawson, Brian . "Vibration condition monitoring techniques for rotating machinery". The Shock and Vibration Digest 8 (12): 3. 1976. [2] Yanev, B. 2003 Structural health monitoring as a bridge management tool. In Proc. SHMII-1, Structural Health Monitoring and Intelligent Infrastructures, vol. 1 , pp. 87–98,2003. [3] Jeary, A. P. & Ellis, B. R. 1981 Vibration tests on structures at varied amplitudes. In Proc. ASCE EMD specialty conference-dynamic response of structures, Atlanta, Georgia, pp. 281–294. [4] Smith L.M 1996 In-service monitoring of nuclear-safety-related structures. . 74, 210–211. [5] Okundi, E., Aylott, P. J. & Hassenein, A. M. 2003 Structural health monitoring of underground railways. In Proc. SHMII-1, structural health monitoring and intelligent infrastructures, vol. 2 (ed. Z. Wu & M. Abe), pp. 1039–1046. Swets&Zeitlinger. [6] B. Culshaw and J. Dakin, Eds.," Optical Fiber Sensors. Applications, Analysis, and Future Trends, vol. 4, Artech House, London, UK, 1996. [7] C. I. Merzbacher, A. D. Kersey, and E. J. Friebele, “Fiber optic sensors in concrete structures: a review,” Smart Materials and Structures, vol. 5, no. 2, pp. 196–208, 1996. View at Scopus [8] F. Ansari, “State-of-the-art in the applications of fiber-optic sensors to cementitious composites,” Cement and Concrete Composites, vol. 19, no. 1, pp. 3–19, 1997. [9] C. K. Y. Leung, “Fiber optic sensors in concrete: the future?” NDT and E International, vol. 34, , pp. 85–94, 2001. [10] H. Kwun and K. A. Bartels, “Magnetostrictive sensor technology and its applications,” Ultrasonics, vol. 36, pp. 171–178, 1998. [11] D. A. Khazem, H. Kwun, S. Y. Kim, and C. Dynes, “Long-range inspection of suspender ropes in suspension bridges using the magnetostrictive sensor technology,” in Proceedings of the 3rd International Workshop on Structural Health Monitoring: The Demands and Challenges, , pp. 384–392, 2001. [12] IoanRaicu , “Routing Algorithms for Wireless Sensor Networks” Department of Computer Science Wayne State University Detroit, MI 48202 [13] A. Manjeshwar, D.P. Agrawal, TEEN: a protocol for enhanced efficiency in wireless sensor networks, in: Pro-ceedings of the 1st International Workshop on Parallel and Distributed Computing Issues in Wireless Networks and Mobile Computing, San Francisco, CA, April 2001. [14] Y. Yao, J. Gehrke, "The cougar approach to in-network query processing in sensor networks", SIGMOD Record, vol. 31 ,pp. 9-18, 2002. [15] W. Heinzelman, J. Kulik, H. Balakrishnan," Adaptive protocols for information dissemination in wireless sensor networks",5th Annual ACM/IEEE International Conference on Mobile Computing and Networking , pp.174-185, 1999. [16] D. Braginsky, D. Estrin," Rumor routing algorithm for sensor networks",First Workshop on Sensor Networks and Applications (WSNA), ,pp. 22-31, 2002. [17] C. Schurgers, M.B. Srivastava, "Energy efficient routing in wireless sensor networks", in: The MILCOM Proceedings on Communications for Network-Centric Operations:Creating the Information Force, vol.1 , pp. 357 361,2001. [18] M. Chu, H. Haussecker, F. Zhao, "Scalable information-driven sensor querying and routing for ad hoc heteroge-neous sensor networks", The International Journal of High Performance Computing Applications,vol.16,pp. 293–313,2002. [19] R. Shah, J. Rabaey, "Energy aware routing for low energy ad hoc sensor networks", IEEE Wireless Communications and Networking Conference(WCNC), vol. 1, pp. 350 - 355, 2002. [20] N. Sadagopan et al.," The ACQUIRE mechanism for efficient querying in sensor networks", First International Workshop on Sensor Network Protocol and Applications, pp. 149 - 155 , 2003. [21] S. Hedetniemi, A. Liestman," A survey of gossiping and broadcasting in communication networks", Networks, vol 18, pp. 319–34,1988.
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