A Railway Running Status Monitoring System based on Energy
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A Railway Running Status Monitoring System based on Energy Harvesting Wireless Sensor Network
Yin Wu, Wenbo Liu
A Railway Running Status Monitoring System based on Energy
Harvesting Wireless Sensor Network
1
Yin Wu, 2Wenbo Liu
*1, First Author and Corresponding Author
College of Automation Engineering, Nanjing University of
Aeronautics and Astronautics, Nanjing, 210016, China, Email: bonnywu2005@yahoo.com.cn
2,
College of Automation Engineering, Nanjing University of Aeronautics and Astronautics,
Nanjing, 210016, China
Abstract
In order to meet the requirement of train running status monitoring in railway environment and
according to the landform characteristics in the rail laneway, this paper introduced a reliable railway
running status monitoring system based on energy harvesting wireless sensor networks (EH-WSN). The
sensor node of the system mainly composed of a magnetostrictive vibration energy harvester, an
energy storage element, and a wireless single chip microcomputer. The sink node is placed in the
railway station with stable power supply to communicate with monitoring server. The design of
hardware and the flow chart of software are given. Through experiments in a railway workshop, we
check the transmission characteristic at 2.4 GHz, and also make sure that while the distance between
two wireless sensor nodes is 60 meters, the system can run successfully and monitor the status of the
track in real-time. This makes a good supplement to the existing railway monitoring system.
Keywords: Energy Harvesting, Wireless Sensor Network, Railway Monitoring, Self-Power
1. Introduction
Nowadays with increasing traffic duties, railway line and train running health monitoring is
becoming a critical issue. More and more remote and distributed sensor networks are being used to
accomplish this task. However, the most limiting factor for these wireless sensor networks (WSN) is
the lack of a long-term, low-maintenance power supply. All such existing monitoring systems based on
WSN still require replacing batteries, which becomes a major time-consuming task that is
uneconomical and unmanageable.
A promising approach to overcome such limitations is to integrate energy harvesting (EH)
techniques with wireless sensor node to form an energy harvesting WSN (EH-WSN), i.e. we could
convert ambient energies such as vibration, temperature, light, etc. into usable electrical energy to
power the electronic devices. In addition, the increased availability of low-power data processing,
storage and transmission, reduce the power consumption of some commercial wireless sensors to the
order of tens of mW . This trend greatly enhances the feasibility of EH for WSN.
Our focus in this paper is on using vibration occurring on railroad tracks as a source for scavenging
power to run the EH-WSN. The state-of-the-art of vibration EH techniques has been recently reviewed
by several papers [1-3] and in the last few years, 30 or more articles have been published based on
these principles and a number of energy harvesting devices have been developed and demonstrated. As
many of these devices have been tested in environments with controllable mechanical excitation
conditions, our goal is to investigate the suitable use of these power scavenging approaches in railroad
environments, therefore the previous studies mentioned provide a springboard for theoretical and
experimental investigation for this application area.
The types of sensors used for monitoring track health and train running status include strain gauges,
pressure and temperature sensors, etc. Most of which require power only on the order of a few mWs
[4], therefore it can be easily implemented in the EH-WSN. In this paper, we propose a novel
distributed railway running status monitoring system based on EH-WSN. The system consists of a
local vibration EH-WSN and a server of railway status monitoring system in user side. Sensor nodes
aim to detect the track parameter values, and then route these to the sink in user side by an energy-
efficient protocol. In the end, server of the railway status monitoring system analyzes the received
parameter values, and reports the analyzed result to the user.
International Journal of Digital Content Technology and its Applications(JDCTA) 560
Volume6,Number17,September 2012
doi:10.4156/jdcta.vol6.issue17.61A Railway Running Status Monitoring System based on Energy Harvesting Wireless Sensor Network
Yin Wu, Wenbo Liu
The rest of this paper is organized as follows: section 2 introduces the framework of the system,
section 3 describes the vibration EH subsystem model, and we present the hardware and software
design of EH-WSN in section 4 and 5. Section 6 evaluates the performance of the proposed system,
and we conclude this paper in Section 7.
2. System profile
The overall system structure is shown in figure 1, as a master-slave structure. The EH-WSN nodes
collect the track parameter values using pressure and temperature sensors installed along the edge of
the railway line. Each node belonging to one side of the rail line should be appropriately separated by a
distance of 20 meters, the length of one rail carriage. All nodes are implemented lined up along the
trains’ running direction, automatically made up the self-organized WSN.
Then, the sink node is mounted in the signal monitoring center of railway station, communicated
with the data monitoring server through CAN bus. The server aggregates all kinds of sensor parameters
and computes the corresponding train running status, thus provides a complementary safeguard to
traffic safety protection.
Figure 1. Architecture of EH-WSN railway monitoring system
3. The magnetostrictive materials (MsM) vibration energy harvesting module
3.1. Model of the MsM power transducer
Smart materials are a primary element in EH, in that it is in the first stage to convert ambient
vibrations to electrical energy. Giant magnetostrictive material (MsM) have a quick mechanical
response, high energy density, wide response frequency bandwidth, its energy density is much higher
than piezoelectric material, and it is probably the most useful material for vibration-to-electric energy
conversion, since it owns a high electromechanical coupling effect and a large coefficient of
magnetostriction at low magnetic field. In this paper we mainly focus on Terfenol-D.
The fundamental basic for design of MsM harvesting module is Villari effect. That is, by applying a
mechanical stress to an MsM, the magnetization along the direction of the applied stress of the material
varies due to the magnetostrictive effect. The flux variation obtained in the material induces a current
in a coil wound on the rod of MsM. Usually the rod is magnetically biased to H 0 , essential to
establish a net magnetization along one direction of the rod axis, and is mechanically pre-stressed to
0 . So when a varying mechanical stress is applied to the MsM, the material permeability alters and
the resulting changes in magnetic flux are converted into an induced voltage in the coil as shown in
figure 2.
561A Railway Running Status Monitoring System based on Energy Harvesting Wireless Sensor Network
Yin Wu, Wenbo Liu
Figure 2. Schematic model of the MsM harvesting module
Figure 2 shows a magnetostrictive rod with length l and cross section S , subjected to a time-
varying mechanical load F (t ) and an electrical load is connected to coil winding wound around
the rod. The induced voltage V according to Faraday's law is:
dB
V NS (1)
dt
Here N and B are the number of coil turns and the magnetic flux density, respectively.
Moreover:
B 0 ( M H ) (2)
Where 0 , M and H are the permeability of free space, magnetic polarization of the
material and the applied magnetic field strength, respectively. It is clear that M depends on H
and applied stress , so we can derive the follow equations due to reference [5]:
t
V (t ) NSf a (t ) cNSe ct [ B0 f a ( )ec d ]
0
F (t )
f a (t ) c[ H 0 d 33
*
]
S (3)
t
B (t ) e ct [ B0 f a ( )ec d ]
0
c Rl / ( N S ) 2
Where B0 B (0) is the initial magnetic flux, R is the external electrical resistance,
*
d33 B / |H const is the parameter of magneto mechanical effect, B / H | const is
the magnetic permeability at a constant stress.
As can be seen from equations (1-3), the induced voltage of energy transducer is proportional
to N , S and the rate of change of B ; the simultaneous rate of change of B is closely related to
the bias magnetic field H 0 , pre-stress 0 and the rate of change of the mechanical load F (t ) , so
we could design the MsM vibration energy harvester based on these key points according to the
parameters of Terfenol-D .
562A Railway Running Status Monitoring System based on Energy Harvesting Wireless Sensor Network
Yin Wu, Wenbo Liu
3.2. Structure of the MsM power transducer
By taking into account all the necessary attentions to the power transfer problem and the
transducer’s working environment, we have designed the detailed structure as follows. By measuring
the bottom width of rail track, a Terfenol-D wafer with length 15 mm and 50 mm in diameter is used as
core element. The wafer is subjected to a mechanical prestress force input, compressing it and forcing
the magnetic dipoles to turn. Notice that this prestress should be as small as possible, so we placed two
metal caps on the top and bottom side of each wafer, just like the “cymbal” [6]. The magnetic bias to
Terfenol-D wafer was also created by using permanent magnets NdFeB, which is placed in the middle
of four square wafers, its magnetic field strength must be varied to suit the MsM’s optimal operating
area [7]. A 50 turn transformer coil is wound around the Terfenol-D wafer. When a railcar passes, the
track deflects and compresses the MsM, thus the change in magnetic field will result in induced electric
current in the coil. The front view of the module and installation is as shown below:
Figure 3. Front view of the module and its installation sketch
4. Framework of an energy harvesting wireless sensor network
4.1. Energy harvesting wireless sensor node
To better understand the performance and functionality of the EH-WSN system, the circuit
architecture of the vibration powered wireless sensor node is presented in figure 4.
Due to the non-continuous property of railway traffic, the MsM energy transducer output is
discontinuous AC power, therefore, the energy collected can not be directly used for electronic load or
battery charging. Additional modules are needed to interface with the harvester and manage the
harvested energy. As proposed in figure 4, the EH-WSN has a complicated architecture including an
EH module, an EH circuit, an energy storage element, a smart regulator and the wireless sensor node
itself.
Figure 4. Circuit architecture of an energy harvesting module and compatible wireless sensor node
563A Railway Running Status Monitoring System based on Energy Harvesting Wireless Sensor Network
Yin Wu, Wenbo Liu
An EH circuit is a power condition circuit for the energy storage device and has at least two
functions. One is rectification, converting AC voltage to DC voltage implemented by a diode bridge.
An essential premise of the rectification is that the amplitude of the AC output voltage from the
harvesting module must be higher than the forward voltage drop of the diodes. The other function is to
do continual matching between the source (MsM energy harvester) and the load (energy storage,
power condition circuit, and sensor node) to achieve optimal power conversion. For instance, a
rechargeable battery must be charged by a voltage greater than its output, and a supercapacitor may be
inoperative if the charging voltage exceeds its rated voltage.
As shown in figure 5, the electric circuit for the MsM harvester consists of two components: a
four stage voltage multiplier circuit as the AC-DC conversion circuit, and a buck-boost converter to
convert the output of voltage multiplier to charge the energy storage (2F supercapacitor). The PWM
driving signal of the converter is controlled by the MCU of the wireless sensor node.
Figure 5. Integrated energy harvesting circuit
The smart regulator resembles a smart valve adjusting the output power from the supercapacitor to
drive the wireless sensor node. Except as voltage regulation, it could self-shutdown when the
supercapacitor cannot achieve the required power consumption.
Due to the huge power consumption of wireless transceiver, the node is working under a wake/sleep
duty cycle. Each transmits its own sensor data to the sink periodically, and a real time clock (RTC) is
used to wake it up. The circuit diagram is shown in figure 6(a).
4.2. Sink with DC power supply
For the sink is installed in the data monitoring center of railway station, it could use a dc power
supply; hence its architecture is much more simplified. It receives wireless nodes’ data and sends
to the server of the monitoring center by CAN bus, it should be in the working state all the time.
The core chip of our sink (same as node) is CC2430-F128, it combines the excellent
performance of the leading CC2420 RF transceiver with an industry-standard enhanced 8051
MCU, 128 KB flash memory, 8 KB RAM and many other powerful features. CC2591 is a cost-
effective and high performance RF integrated balun front end for low-power and low-voltage
2.4-GHz integrated inductors wireless applications. It has seamless interface to CC2430.
MCP2515 is a SPI port CAN controller, direct connected with the CC2430 chip, and MCP2551
is a high-speed CAN bus transceiver to support a run rate of 1 Mb/s connected up to 112 CAN
nodes. The sketch of circuit architecture is shown in figure 6(b).
564A Railway Running Status Monitoring System based on Energy Harvesting Wireless Sensor Network
Yin Wu, Wenbo Liu
Figure 6. a The sketch of node’s circuit diagram b The sketch of sink’s circuit diagram
5. System software
Although the EH-WSN can harvest and reuse the railway environment energy, generally the node
still has a lower processing capability, limited power and storage capacity, so TinyOS embedded
operation system is used here. TinyOS is a specially designed embedded operating system on
component-based mode, by the nesC language, mainly used in WSNs. Depending on different
applications we can easily increase or decrease system functions, and its compiler can avoid the
phenomenon of data competition for the most part, achieve a more rapid response to control operations.
Because of the characteristics of railway track, node power dissipation is unevenly distributed,
resulting in the huge transmission flow and heavy load in those nodes which are close to the sink. So
an energy efficient routing protocol for the EH-WSN is important, here we choose the directed
diffusion routing protocol (DD) as our basic plan: DD is a data centric routing protocol in WSN. It is a
reactive protocol which creates routes based on needs. First sensed data are stored in attribute value
pairs, then when sink request data by sending interests, the interest messages are flooding through the
network and are added to each node’s interest cache. Thus the data that match the interests are sent
towards the sink. Here we improve its path reinforcement scheme with communication cost and energy
harvesting balance. The detailed software design is shown as follows:
5.1. Wireless node module program
The EH railway status monitoring wireless sensor node is mainly responsible for the transduction of
vibration energy, collection of track pressure and temperature data, and transmission of these data
jointly packaged with timestamp to the sink. When there is no data to transmit or receive, the node
would switch to sleep mode. The work flow chart is shown in figure 7.
565A Railway Running Status Monitoring System based on Energy Harvesting Wireless Sensor Network
Yin Wu, Wenbo Liu
Figure 7. EH-wireless sensor node working flow chart
Firstly, the node starts with startup energy retained in the supercapacitor, performs hardware self-
test tasks, completes the initialization of RTC clock, flash data storage, and AD converters, then enter
the cycle of the operation system task management, starts the vibration EH task, keep charging the
supercapacitor, as long as the harvested energy exceeds a threshold, the operating system starts another
network joining task, synchronize network clock until the node successfully joins the network, after all
these have completed the node enter into judgment of two trigger times: one is data acquisition time,
the other is communication time. When the time duration is reached, system will run data acquisition
task and network communication task. Otherwise node will enter sleep task to save power.
5.2. Wireless sink module program
On one hand the sink is responsible for the formation of a WSN, receiving data from each node; on
the other hand, it stores the received data and sends to the monitoring server via CAN bus. As using
direct power supply, it does not need to sleep. Work flow is shown in figure 8.
566A Railway Running Status Monitoring System based on Energy Harvesting Wireless Sensor Network
Yin Wu, Wenbo Liu
Figure 8. Sink working flow chart
The sink starts up with hardware initialization the same as node, performs hardware self-test tasks,
completes the initialization of RTC clock and flash data storage, then sets the data structure of system
message receive buffer, prepare to receive the data coming from each node. After this, the operating
system will start the network building task, record each node in the routing table in the FLASH, next
open the system cycle management and start the CAN communication task, if the node’s data message
is received, the corresponding data will be unpacked and sent to the monitoring sever by CAN task.
6. Experimental studies
In order to identify viable hardware and system designs before field testing, it is necessary to
research the interaction between the train and track deflection, as well as between deflection
and output power of MsM transducer. To accomplish this, first we have adopted an analytical
model of vertical track deflection for simulation purpose. The model used here is referred to as
the Winkler model [8]. The vertical deflection y , for a given point load P , as a function of the
distance from the load x , is given as follows:
P x u 1/4
y ( x) e [cos( x) sin( x)] , where ( ) (4)
2u 4 EI
Here, E is the modulus of elasticity of the rail, I is the second moment of area of the rail,
and u is the track modulus. Using the analytical model of vertical track deflection given by
equation 4, it is possible to integrate knowledge of track loading conditions with the theoretical
models given by equations (1-3) into numerical simulation.
The aim of simulation experiment is to assess the charging ability of the MsM harvester on
the supercapacitor. The output of the pick-up coil is connected to the PCB for energy storage
and regulation. The simulated transient voltages from the pick-up coil along with Winkler
model parameters approximated from data available in the literature [8] is shown in figure 9.
The rail track load input frequency is chosen as 1 kHz and the AC voltage from pick-up coil
reaches 3.1 V, it can easily flow through rectification for charging. Thus we go on with the field
evaluation of the operation performance of supercapacitor, and the experimental voltage history
on it is displayed in figure 10.
567A Railway Running Status Monitoring System based on Energy Harvesting Wireless Sensor Network
Yin Wu, Wenbo Liu
Figure 9. Output AC voltage from pick-up coil Figure 10. Voltage of supercapacitor in charging and
discharging phase
As can be seen from figure 10, it is evident that the MsM vibration energy harvester can
charge the supercapacitor through power condition circuits, and the supercapacitor could also
afford to operate the wireless sensor node by adopting power management technique.
At last, we have implemented and tested a small-scale MsM EH-WSN in a coaching machine
shop of railway station, every node’s data acquisition time interval is 5 second while wireless
communication duration is 10 seconds per minute otherwise the node is sleeping. The nodes
health monitoring screen on the server is shown in figure 11, which shows the ordinary and safe
operation of our railway status monitoring system. The max communication distance between
two nodes under a least acceptable network service quality is 60 meters.
Figure 11. Server screen of nodes health monitoring
7. Conclusion
A prototype of a new class of EH-WSN for railway status monitoring based on magnetomechanical
coupling using MsM has been designed, developed, and tested. The basic concepts are explained and
results of numerical simulations and field tests are presented. The MsM vibration energy harvester is
promising as an alternate scheme to piezoelectric for EH. An electromechanical model is considered to
predict the performance of the harvesting device. Meanwhile, the achievable output performance for a
supercapacitor energy storage device is investigated. The obtained results of experimental study of
energy conversion using Terfenol-D have confirmed the potential of using giant MsM for power
harvesting from vibration.
On the other hand the sensors detect the pressure of the train on the tracks and send the parameters
to the wireless processing nodes in real-time. The deviation of measured data and theoretical values is
at most 5%, the transmission success rate reaches 96%, and all have reached the monitoring standard of
railway signals. These results show agreement between design models and field test results, and
suggest that our system can also be used in other structure health monitoring system as well.
568A Railway Running Status Monitoring System based on Energy Harvesting Wireless Sensor Network
Yin Wu, Wenbo Liu
8. Acknowledgement
This work was financially supported by the National Natural Science Fund of China for young
scholars (51005121).
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