=Paper=
{{Paper
|id=None
|storemode=property
|title=Sensing Spaces: Light-Weight Monitoring of Industrial Facilities
|pdfUrl=https://ceur-ws.org/Vol-678/BMI10-05.pdf
|volume=Vol-678
}}
==Sensing Spaces: Light-Weight Monitoring of Industrial Facilities==
https://ceur-ws.org/Vol-678/BMI10-05.pdf
BMI'10 53 Karlsruhe, September 21th, 2010
Sensing Spaces: Light-Weight Monitoring
of Industrial Facilities1
Yong DING a , Friedemann LAUE b , Hedda R. SCHMIDTKE a , and Michael BEIGL a
a
Karlsruhe Institute of Technology (KIT) TecO, Germany,
E-mail: {yong.ding, hedda.schmidtke, michael.beigl}@kit.edu
b
Technische Universität Braunschweig, Germany,
E-mail: flaue@ibr.cs.tu-bs.de
Abstract. Defects in complex industrial facilities such as engines, power plants, or
energy infrastructure are difficult to diagnose, not only because of the complexity
of the machinery, but also because of its spatial structure. Large energy infrastruc-
ture, for instance, stretches over vast areas, so that human inspection and diagno-
sis would take several months. Wireless Sensor Networks (WSN) provide a cheap
and easy way for online monitoring of environments where human inspection is
impossible.
We present a light-weight distributed sensing approach for localizing defects of
facilities in industrial monitoring. In contrast to current usage of WSNs in indus-
trial monitoring, we not only collect data on the machine, but also directly process
it where it is generated. We illustrate how reasoning can be used for localizing de-
fects on a long conveyor belt. We demonstrate, with an algorithm for finding the
maximum vibration measurements among a set of sensor nodes, how local knowl-
edge exchange between neighboring nodes can establish globally valid knowledge
on the WSN as a whole. Results from an experiment with an implementation on
WSN hardware illustrate that our approach is practically feasible.
Keywords. Distributed Sensing, Localizing Defects, Industrial Monitoring
Introduction
Defects in complex industrial facilities such as engines, power plants, or energy infras-
tructure are difficult to diagnose, not only because of the complexity of the machinery,
but also because of its spatial structure. Engines are constructed so as to occupy as small a
volume as possible, so that many parts are usually not accessible from the outside and di-
agnosis itself involves disassembly of the machine. The space occupied by power plants,
in contrast, has areas that are not viable for a human inspector under operation, so that,
likewise, diagnosis might require shutdown. Energy infrastructure, in turn, is spread out
widely, so that, again, it cannot be inspected directly. Wireless Sensor Networks (WSN)
provide a way for monitoring industrial operation where human inspection is impossible
1 The research reported in this paper has been supported by the German Research Foundation (DFG) in the
project SenseCast: Context Prediction for Optimization of Network Parameters in Wireless Sensor Networks
(BE4319/1).
BMI'10 54 Karlsruhe, September 21th, 2010
or costly and installing standard measurement and network hardware would be infeasible
because of financial, local, or technical restrictions.
Many monitoring applications use a centralized architecture, e.g. the application for
monitoring the structural health of bridges in [3]. In a centralized architecture, the sensor
nodes are only used to collect sensor data. The raw data are sent to a central server where
they are processed. The network is only used to connect the sensor nodes with the central
server. However, decentralized architectures have two main advantages over centralized
architectures:
• Lower energy consumption and communication overhead. In the centralized ar-
chitecture, the sensor nodes send unprocessed data: relevant and irrelevant infor-
mation can first be separated by the central server. In a decentralized architec-
ture, nodes collaborate and communicate mainly locally. Only information that is
globally relevant is communicated globally through the network.
• Robustness. Failure of a node leads to local problems, which can be detected
and handled locally. In a centralized architecture, any failure of the central server
renders the whole system unusable.
In this paper, we illustrate a decentralized architecture for the scenario of conveyor belt
monitoring in open-cast mining. We propose an approach to locally process raw data into
meaningful events using only value comparison operations, simple reasoning, and local
communication.
In the following sections, we introduce the overall scenario (Sect. 1) and its require-
ments (Sect. 2). We then discuss the underlying idea of distributed sensing and reason-
ing and how it can be realized on WSN (Sect. 3). Finally, we present results from two
experiments using an implementation on sensor node hardware (Sect. 4).
1. Scenario: Conveyor Belt Systems in Open-Cast Mining
In open-cast mining the extraction of coal, gravel and sand without tunneling into the
earth, is performed by combination of different production units with downstream trans-
port [6]. The extraction form can be basically divided into continuous and discontinu-
ous. German open-cast mining has accomplished two device combinations for continu-
ous extraction, such as combination of bucket-wheel excavator and spreader. Continu-
ous extraction requires continuous evacuation following as well. Therefore, continuous
transport in open-cast mining is only possible via belt conveyor systems that are adapted
to the production capacity of the mining device. These conveyor belt systems span large
areas, which are expensive to monitor: Figure 1 shows an overview of the German ope-
cast mining Garzweiler2 .
Due to the large dimensions in open-cast mining, the conveyor belt system consists
of a large number of conveyor belts of different lengths and conveyor power stations at
the interfaces. Figure 2 shows a schematic plan of an example conveyor belt system: coal
is transported from the bucket-wheel excavator, distributed by shuttle heads and finally
collected through spreaders to a coal bunker. Arrows in the plan stand on the one hand
for the positions of conveyor power stations, and on the other hand show the transport
direction. Each power station is equipped with three-phase asynchronous motors [5] and
2 http://de.wikipedia.org/wiki/Tagebau_Garzweiler
BMI'10 55 Karlsruhe, September 21th, 2010
Figure 1. Panorama from the open pit Garzweiler.
Figure 2. Schematic plan of conveyor belt system.
brakes (electronic or hydraulic). Through the Siemens SPC (stored program control) that
is installed in the power station, the conveyor belt is controlled automatically.
The rubber belt moves forward through a series of supporting stands that are inher-
ent parts of scaffolding, see Figure 3. Some types of collisions cannot be avoided in un-
monitored, automatic operation, such as collisions caused by lateral off-set of scaffold-
ing or by belt wear. If they remain undetected for a longer duration, such failures can
lead to system collapse. When an operator detects such problems, he can repair or, in the
emergency that a system collapse is imminent, manually stop the conveyor system with
a safety device, the pull cord switch. A monitoring device that could determine, by status
BMI'10 56 Karlsruhe, September 21th, 2010
Figure 3. Cross section of the conveyor belt facility.
observation of conveyor belts, when a maintenance action should be taken, could prevent
collisions that are a result of belt wear in time, so that high availability of the conveyor
system could be guaranteed.
2. Requirements for Online Monitoring of Conveyor Belt Systems
During usual transport operation the conveyor belt is always in vibration, the scaffold-
ing and garlands vibrate as well. This vibration exhibits characteristic patterns of fre-
quency range and magnitudes under specified marginal conditions. Different materials
and weather conditions (rain, strong sunshine) have significant influence on wear or dam-
age of the rubber conveyor belt. The consequence of this can be a change in the vibration
pattern. Because the worn out or damaged belt part moves forward, the vibration of the
scaffolding and garlands is also affected as the damaged part passes by. Consequently,
sudden changes in vibration patterns can be used to detect, localize, and track defects.
Vibration sensors attached to the scaffolding, e.g. every five meters, could be used
to monitor vibration patterns (see Figure 4). By monitoring the spatial movement of the
anomalous vibration we can then locate, in which 5 meters the belt’s defect is to be
be found. From the increase of magnitude of vibration we can then deduce the degree
of wear of the rubber belt. Consequently, actuators can be activated to start one of the
following actions:
• For little wear, the maintenance schedule should be adjusted.
• For excessive wear, an alert should be signaled acoustically and optically.
• For complete damage, the pull cord switch should be operated immediately.
In this scenario, monitoring thus is not only used to detect and localize defects, but also
to trigger appropriate actions, in order to avoid collisions and ensure operational avail-
ability.
As each conveyor belt is a few kilometers long, a wireless sensor network is cheaper
than a cable-based solution for sensor data transmission. The sensor nodes deployed
BMI'10 57 Karlsruhe, September 21th, 2010
Figure 4. Top down view of the conveyor belt.
in the scenario should be as simple as possible, since a large area has to be covered
and powerful sensor nodes would be financially infeasible. Typically devices used in
these settings are small sensor nodes with only about 1-128k of RAM (used for volatile
data), about 5-256k of ROM (used for program and constant data) and 1-100 MIPS of
computing power. For monitoring vibration, acceleration sensors or even special low-cost
vibration sensors can be used. This is a very ambitious setting for monitoring systems
and requires us to minimize resource consumption especially regarding RAM and ROM.
The types of algorithms that can be executed on such a platform are highly restricted.
Current solutions for monitoring industrial facilities therefore usually have a centralized
architecture: sensor nodes send measurement raw data to a central server, which then
performs all computations necessary for monitoring. As mentioned above, problems of
the centralized architecture are: communication overhead for continuous sending of raw
data and lack of robustness, as failures of the central server render the whole system
unusable. Therefore decentralized architectures have been proposed. With a decentral-
ized architecture, monitoring is realized locally on the sensor nodes. An example is the
light-weight Prolog implementation proposed in [7]. However, energy restrictions in our
scenario make a more light-weight approach to distributed monitoring necessary, which
can only be realized by combining the computational power of several sensor nodes.
3. Distributed Sensing
In our scenario, energy consumption of sensor nodes, and robustness are critical,. Com-
munication overhead must be compensated through energy harvesting from the contin-
uous motion of the belt. We present first results on parts of a qualitative reasoning task,
that can be performed using only local value comparison and local communication be-
tween nodes. We discuss how a global property, namely that a node has a maximal value
for some measurement dimension, can be computed. For the rest of the paper we discuss
the following simplified scenario. We consider a row of sensor nodes along a conveyor
belt. A typical problem described above is conveyor belt wear. In our simplified example,
nodes measure vibration on the scaffolding and go for several minutes into an alert state
when they detect that a certain threshold of vibration has been exceeded. The nodes then
track the maximal vibration, by successively computing it, every 10 seconds. A node
that believes it has the maximum at a given time lights a red LED, so that a maintenance
BMI'10 58 Karlsruhe, September 21th, 2010
P (y, z) y=z iP (y, z) converse relation
P (x, y) P (x, z) P (x, z) any P (x, y) iP (y, x)
x=y P (x, z) x=z iP (x, z) x=y y=x
iP (x, y) any iP (x, z) iP (x, z) iP (x, y) P (y, x)
Table 1. Composition table and converse relations for a partial order P , =, and its inverse iP . The entry any
denotes the set of all three relations, that is, a situation, in which no information can be inferred.
worker can find the position in the conveyor belt that has the defect before it becomes
critical and maybe even repair it without stopping the machine.
Although the computation of the maximum in a node itself is not a challenging
computational task, it serves as an example to illustrate how local reasoning steps with
an interesting class of relations can establish global knowledge.
3.1. Partial Orderings of Sensor Values
Partial order reasoning can be used for reasoning about sensor values. It provides mainly
transitivity inference, a type of inference that can be realized efficiently and is also partic-
ularly useful: examples of partial orders are the part-of relation underlying mereotopo-
logical reasoning [4], the sub-concept relation of description logics, and the logical con-
sequence relation of itself. Also, any linear ordering relation, including sensor value
comparison, is a special type of partial order.
A partial order P is a relation that has the properties of antisymmetry (1) and tran-
sitivity (2). It is called strict, if it is asymmetric (3). The converse relation of P is called
iP (4).
∀x, y : P (x, y) ∧ P (y, x) → x = y (1)
∀x, y, z : P (x, y) ∧ P (y, z) → P (x, z) (2)
s s
∀x, y : P (x, y) → ¬P (y, x) (3)
∀x, y : iP (x, y) ↔ P (y, x) (4)
Table ?? illustrates the simplicity of transitivity reasoning with a partial order: inference
yields either a unique, that is certain, result or no information (entries any). This makes
it an ideal candidate for implementation on a sensor network.
When we directly compare two sensor measurement values ma and mb , e.g. fre-
quencies 15Hz and 60Hz, we can exactly determine whether ma > mb , ma = mb , or
ma < mb . This property is characteristic for linear orders:
∀x, y : Plin (x, y) ∨ Plin (y, x) (5)
In sensor networks however, we usually reason about a large number of values of which
only local, relative comparison information is known: nodes can easily exchange infor-
mation locally with their neighbors, but sharing information globally is costly. Thus, the
linearity constraint, being a disjunctive constraint, is difficult to employ for practical rea-
soning. We should therefore only employ the partial order properties and limit transmis-
sion of global information as far as possible.
BMI'10 59 Karlsruhe, September 21th, 2010
3.2. Distributed Sensing through Local Communication
Algorithm 1 illustrates, for the example of the computation of the maximum, how infer-
ence on a sensor network can be realized through communication between neighboring
nodes. The algorithm serves two purposes: most specifically, it shows that a network of
very simple sensor nodes can be used to monitor alarm conditions; more generally, the
algorithm exemplifies how partial order reasoning can be realized on WSNs.
Sensor nodes exchange messages of a fixed format. The message reflects that the
sender y is ‘larger’ with respect to some partial order R than some other (possibly re-
mote) node z. The receiving node x uses the message together with its own sensory in-
put to evaluate whether it is larger than the sender: if yes, x sends this information to
all neighboring nodes; if it is not larger, it knows it cannot be the maximum and sends
no message. Due to the different behaviors in the two cases, information flows ‘upward’
along the gradient of the measurement signal. The recipient x only sends a message if its
sensor value is larger than that of the sender y.
Algorithm 1 At a node with id x given sensor value vx and a message msg sent from
another node y, determine whether x is ‘larger’ (with respect to a relation R) than y, and
update other nodes. Reasoning is performed with numerical comparison using a default
interpretation for the relation R (`). The transitivity inference of Table 1 is achieved
through the sending operation. That is, the network as a whole performs the inference,
whereas the individual nodes only perform comparison operations.
Require: The node x is the ID of the node performing the computation. The variable
y denotes a (possibly remote) node. The threshold vt determines whether a measured
value indicates that an event occurred.
while true do
vx ← measurement() . retrieving measurement
if KB ` R(vx , vt ) and KB = ∅ then . if vx is ‘larger’ w.r.t. R than vt
LED on . going into alarm state
send({v = x, R(x, x)}) . updating the neighborhood about event
timer.start() . going into alarm state
end if
if message received then
msg ← {vy = y, R(y, z)} ← received() . using fixed format messages
if msg ` R(vx , vy ) then . evaluating
send({vx = x, R(x, y)}) . updating the neighborhood
else if msg ` R(vy , vx ) then . evaluating
LED off . the source of the alarm is not here
end if
end if
if timer.done() then
KB ← ∅ . ending alarm state
end if
end while
As a distributed algorithm on the network, the algorithm computes the maximum
value measured and lights the LED on the node that measured it. From a local perspective
BMI'10 60 Karlsruhe, September 21th, 2010
of a single node, the algorithm informs the node about its relation to possibly distant
other nodes and allows it to detect whether it is closest to some measurable event. The
goal for each node is to determine whether it is the node closest to the defect, that is, the
node with the most vibration. As long as it believes it is the node closest to the defect, it
keeps the warning LED on. If it is updated that another node measured a higher value it
turns its LED off. If another node measured a lower value, but sent it out, nodes in the
immediate environment have to be updated that there is a larger value.
The transmitted information can become invalid as sensory input changes. As long
as sensory information remains constant however, the information transmitted through
the algorithm establishes a global hierarchy of nodes based on their sensor values. In our
experimental evaluation below, we achieved this by keeping the measurement function
constant for a certain time after an event occurred.
4. Experimental Implementation
We implemented the algorithm on a JN5139-based sensor node platform3 with the low-
cost mirco-vibration ballswitch sensor described in [2]. To illustrate our scenario we
performed two experiments. The first experiment was conducted at a realistic scale but
with simulated sensor data for showing the general setting of sending and reception of
messages in the scenario. The second experiment was set up at a smaller scale but with
actually measured sensor data, illustrating how the algorithm works on actual sensor
data. We separated the two experiments, as it is difficult to generate vibrations in a larger
scale. However, both experiments were performed using the same distributed sensing
algorithm.
In the first experiment, we placed sensor nodes along a hallway at a distance of
about 5 meters as in the open-cast mining scenario: nodes were initialized so as to always
measure a fixed value, as shown in Figure 5. They were laid out along the gradient of
this simulated measurement value. To start the experiment, we sent a start-signal to every
node and after 2-3 seconds the LEDs on every node were switched off except for the
node with the maximum.
The second experiment (see Figure 6) focused on the sensory aspect. We placed
the sensor nodes on a table and generated a vibration signal by knocking once on the
table, so that the communication between neighboring nodes would be triggered. Due to
the short distances between the nodes, the computation of the maximum was completed
faster than in the first experiment. After about one second, the node number four with
its still switched on LED indicated that it detected the most vibration after the knock on
the table. We could also notice the process of exchanging messages between the nodes
and the area of high vibration values getting smaller and smaller. At first, node number
two, six and seven switched off their LEDs because they received information about a
vibration value which was higher than their own measurement. The transmitter of these
messages could, for instance, have been node number two, four or five, since they had
less distance to the knock. The same happened with the other nodes except for number
4 which never received a higher value than its own. So the maximum and the origin of
the vibration was found. The results illustrate that our algorithm for distributed sensing
3 http://www.jennic.com/products/wireless_microcontrollers/jn5139
BMI'10 61 Karlsruhe, September 21th, 2010
Figure 5. Experiment 1: sensor nodes are laid out along a hallway at approximately 5m distance.
short sent received LED
address mes- mes- on time
sages sages (ms)
1 1 53 585
2 1 96 87
3 13 31 272
4 7 6 ∞
5 2 13 804
6 5 39 220
7 6 87 123
short sent received LED
address mes- mes- on time
sages sages (ms)
1 4 13 1170
2 4 82 87
3 8 48 544
4 8 7 ∞
5 3 15 704
6 1 87 220
7 1 118 89
(a) (b)
Figure 6. Experiment 2: design (a) and timings for two runs (b). The red arrow in (a) indicates the location of
the knocking event and the numbers indicate the short-addresses of the sensor nodes, as listed in (b).
BMI'10 62 Karlsruhe, September 21th, 2010
is able to detect the origin of an occurring event using sensor values of several nodes. It
worked effectively in both the small and wider range layout.
5. Conclusions and Future Works
We presented a distributed sensing system usable for monitoring industrial facilities and
described an example scenario of conveyor belt monitoring in open-cast mining. The
approach features robustness of the whole system, lower communication overhead and
light-weight implementation. Information is transmitted along the gradients of a mea-
surable vibration event, and actuators can be triggered by the nodes that are closest to
an event without any communication with a back-end server. This guarantees high net-
work reliability, as failure of a single node can be compensated by its neighbors. Two
experiments with an implementation were presented to illustrate actual feasibility in the
scenario.
The computation of the node registering the maximal vibration is based on the par-
ticularly simple inference mechanisms underlying partial order reasoning. The presented
study suggests that this type of reasoning is light-weight enough to be practically feasible
as a reasoning mechanism for WSNs and versatile enough to apply to a broad range of
interesting problems. In future works, we will develop a more expressive communication
protocol instead of the fixed message format. The presented work is a first step towards
light-weight reasoning facilities for context-awareness on sensor node hardware [1].
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