Wireless Sensor Network Simulation for Fault Detection in Industrial

Processes

Rui Pinto

, Rosaldo J. F. Rossetti

1,2

and Gil Gonc¸alves

Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias s/n, 4200-465 Porto, Portugal

Artiﬁcial Intelligence and Computer Science Lab, University of Porto,

Rua Dr. Roberto Frias s/n, 4200-465 Porto, Portugal

Keywords:

Castalia, Sensor Diagnosis and Validation, Industrial Wireless Sensor Networks.

Abstract:

Sensor data is extremely important to monitor machines at the shop-ﬂoor level and its environmental surround-

ing conditions for condition-based monitoring, machine diagnosis and process adaptation to new requirements.

Based on the described scope, self-diagnostics and self-organizing capabilities are core functionalities of any

Industrial Wireless Sensor Network (IWSN). In the present work, a simulated case study was developed with

the main intent of validating techniques implemented for sensor data diagnosis of error detection and equip-

ment failure. The scenarios explored try to mimic some common situations of a manufacturing environment

when dealing with WSNs, where a piece of sensor equipment suddenly stops working or an unpredictable

change in the environment leads to faulty data readings. This paper introduces Castalia and describes how

it was used to simulate a direct application of an Optical Metrology System on an industrial Resistance Spot

Welding process, which is composed of a camera and several luminosity sensors. More speciﬁcally, a sensor

data validation module was proposed, implemented and used to extend Castalia functionalities.

1 INTRODUCTION

Nowadays, several European initiatives towards smart

manufacturing systems and Industry 4.0 are a cur-

rent subject of research, aiming at the improvement

of the European Industry competitiveness regarding

other leading markets. This can be achieved by turn-

ing shop-ﬂoor production methods more ﬂexible and

efﬁcient facing the constant changes in production,

which is driven by consumer demands. Since con-

sumers’ preferences are becoming more customized

and competitiveness between companies is extremely

high, manufacturers must quickly adapt their product

to new trends. Usually, the company pioneer on hav-

ing a new product available on the market have the

upper hand over the competition. When a mass pro-

duction model is used, maintaining a large envelope

of products or enlarging it with more product vari-

ants is extremely difﬁcult and entails a great effort and

cost due to the inﬂexibility of mass production man-

ufacturing systems. To support the mass customiza-

tion paradigm at the shop-ﬂoor level, the production

systems should be capable of rapidly reconﬁguring as

product demand varies, as well as does machine pa-

rameter calibration during all production stages. The

time spent to calibrate and to adjust the machine pro-

cess parameters when a new product variation should

be performed, or after a maintenance phase, can be re-

duced if the machines’ parameter adjustment is done

automatically instead of the usual trial-and-error ap-

proach, by performing destructive tests to evaluate

the process quality. Monitoring machine’s execution

is extremely important and, to do so, sensors are de-

ployed on the shop-ﬂoor to infer surrounding environ-

mental conditions and adapt the machine execution

accordingly.

An Optical Metrology System (OMS) is a system

where optical measurements are used for several in-

dustrial applications, e.g. for robot guidance when

optical metrology sensors are mounted on a robot

arm. In this case, a OMS is used on a Resistance Spot

Welding (RSW) process, to infer if the metal parts to

be weld are well positioned. In order to insure the

correct setting up and easy ramping up of the system,

in site factory sensor positioning adjustment and im-

age setup is a signiﬁcant procedure, which involves

conﬁguration of camera parameters, such as exposure

and gain. With help of luminosity sensors by mea-

suring the luminosity conditions in site, the camera

parameters can be adjusted on the ﬂy, according to

Pinto, R., Rossetti, R. and Gonçalves, G.

Wireless Sensor Network Simulation for Fault Detection in Industrial Processes.

DOI: 10.5220/0006011003330338

In Proceedings of the 6th International Conference on Simulation and Modeling Methodologies, Technologies and Applications (SIMULTECH 2016), pages 333-338

ISBN: 978-989-758-199-1

333

the context. This work focus on the simulation of an

OMS applied to a RSW process using Castalia. The

goal was to understand the behaviour of the luminos-

ity sensors, specially when they are malfunctioning.

A sensor data detection module was implemented in

Castalia and used on a simulated version of the de-

scribed environment.

The paper is organized in four more different sec-

tions. Section 2 details the approaches regarding

IWSN, the main methods for sensor data validation

and categorize WSN simulators, specially Castalia.

Section 3 identiﬁes the case study used to develop the

simulation scenario. Section 4 depicts all the results

obtained, whereas in Section 5 ﬁnal remarks about the

overall work exposed in the paper and further steps to

be followed are presented.

2 INDUSTRIAL WIRELESS

SENSOR NETWORKS

In the past few years WSNs have become more in-

teresting to be explored in and applied to several do-

mains. Their use was motivated mainly due to lat-

est advances in wireless communications as well as

in more reliable, robust and long-lasting hardware.

These factors have a great impact on the feasibility

of installation, when it is difﬁcult to use wired so-

lutions, either by harsh location or high number of

sensor nodes used and also because of the easy main-

tenance and cabling reduced costs. As main advan-

tages, Chen et al. (Chen et al., 2015) pinpoints the

large coverage area, ubiquitous information, fast com-

munication via RF and self-organisation throughout

the direct communication between entities. A Smart

Sensor Platform (Ramamurthy et al., 2007) was de-

veloped, which applies the plug and play concept by

means of hardware interface, payload, communica-

tion between sensors and actuators, and ultimately

allows for software update using ’over-the-air’ pro-

gramming. Cao et al. (Cao et al., 2008) developed a

distributed approach to put closer sensors and actua-

tors in a collaborative environment using WSNs and,

Chen et al. (Chen et al., 2015), push this approach

forward considering the same methodology, but tak-

ing into account all the industrial domain restrictions.

Despite the huge potential of WSNs, these so-

lutions still have several issues and future research

challenges. Data collected from WSNs is prone

to be faulty due to internal and external inﬂuences,

such as environmental effects, hardware malfunc-

tions, software problems, energy constrains, net-

work issues, security threats, among others, as shown

elsewhere (Tolle et al., 2005), (Barrenetxea et al.,

2008), (Ramanathan et al., 2006) and (Szewczyk

et al., 2004). IWSNs are used to monitor real-

time production equipment in the factory and the

surrounding environmental conditions, acting accord-

ingly when changes are observed. According to Neu-

mann (Neumann, 2007), there are important restric-

tions in industrial applications to be met, such as real-

timeliness, functional safety, security, energy efﬁ-

ciency, and Quality of Service (QoS). So, IWSNs face

some challenges, such as safety-critical functions, se-

curity and privacy of collected data, availability to

avoid complete production stop when failures occur,

latency/retransmission of messages, support for ac-

tuators (by using the same sensor controller), efﬁ-

cient integration with existing automation infrastruc-

tures, scalability, coexistence and wireless communi-

cation interference avoidance and energy harvesting

(

Akerberg et al., 2011).

2.1 Sensor Data Validation

To maintain QoS, one should be able to detect and

deal with compromised sensor nodes during opera-

tion. To determine whether a sensor node is mal-

functioning, validation methods are applied to sen-

sor data, aiming to ﬁnd devious sensor readings from

normal ones. There is no such an ideal validation

method, because they are very dependent on sensor

measurement conditions and the overall environment

context (Freitas et al., 2010), (Branisavljevi

c et al.,

2011), (Bertrand-Krajewski et al., 2003), (Vasconce-

los et al., 2012), and (Liu et al., 2013). Usually, sev-

eral methods are applied successively to the sensor

data, because each method is suitable to detect par-

ticular types of faulty data. Sensor data validation

methods are typically divided into online and ofﬂine

methods, where online methods are efﬁcient, simpler

to implement and demand less data processing, and

ofﬂine techniques, such as Bayesian Networks, Ar-

tiﬁcial Neural Networks and Regression Techniques,

are more robust and complex techniques, which are

suitable to process data not in real-time.

The most common WSNs’ data fault types are:

Out-of-range faults - values out of predeﬁned mini-

mum and maximum boundaries; Struck-at-fault se-

quence of values that have little or no variation for a

large period of time; Outliers misplaced data sam-

ples that represent a sensor misbehaviour, which can

be caused by sensor malfunctioning or changes in sur-

rounding environment; Stop Communication gaps in

the dataset caused by an absence of sensor informa-

tion on the gateway, due to power outage or commu-

nication problems; Devious Data when the behaviour

of a certain sensor is devious from the majority, and it

SIMULTECH 2016 - 6th International Conference on Simulation and Modeling Methodologies, Technologies and Applications

334

persists for a large period of time. A graphical exam-

ple of these faults’ behaviour (on a temperature sen-

sor) is presented in Figure 1.

According to Sharma et al. (Sharma et al.,

2010), Ravichandran and Arulappan (Ravichandran

and Arulappan, 2013), the most common techniques

used to perform online detection of the described sen-

sor data faults are Min/Max Detection, Flat Line De-

tection, Modiﬁed Z-Score, No value Detection and

Spatial Correlation. The Min/Max Detection ﬁnds

out-of-range faults using upper and lower limits that

are deﬁned by process limitations. On the other hand,

the Modiﬁed Z-Score ﬁnds outliers faults which, de-

spite being inside the process limitations, are values

that are unusual when compared to the sensor data

history. The Flat Line Detection ﬁnds struck-at-faults,

which are characterized by little or no variation in

data and the No value Detection detects situations

when the data stops being communicated. Finally, the

Spatial Correlation is the technique used to compare

datasets of several sensors nodes physically located

near to one another, and if one of them presents a de-

vious dataset in comparison to its neighbours, then the

corresponding sensor node is probably faulty.

2.2 WSN Simulation

A WSN simulation consists in using simulators

speciﬁcally designed to imitate WSNs behaviour.

Traditionally, the three main methodologies for

analysing the performance of WSNs are physical

measurements, which consists in setting up all the

physical nodes and collecting sensor data, analytical

methods, and computer simulation. Since deploying

a WSNs requires a huge effort, due to constrains re-

garding the cost associated with hardware and the fact

that analytical models are not effective, because of the

complexity of the models for WSNs regarding energy

limitation, decentralized collaboration and fault toler-

ance, simulation is the only feasible approach to the

quantitative analysis of sensor networks, getting ac-

cess to ﬁne grained results easier than with real world

experiments (Dwivedi et al., 2010). Because there

are so many simulators with many different charac-

teristics, Eriksson (Eriksson, 2009) proposes to cat-

egorize WSNs as Generic Network Simulators - fo-

cus more on network aspects, such as radio protocols,

network stacks and channel distortions; Code Level

Simulators - focus more on the simulation of the sen-

sor nodes, such as deployable code and functional-

ity logic; Firmware Level Simulators - focus more on

emulating the sensor node, such as microprocessor,

radio chip and other peripherals.

Song (Song et al., 2011) implemented a fault de-

tection mechanism to analyse the stability and relia-

bility of data transmission in a ZigBee network, which

was simulated on the NS-2 simulator. Results focus

on the data loss rate in the communication between

sensor nodes. Dai (Dai et al., 2011) also proposed

a fault detection mechanism, but focused more on

the delays introduced by the wireless communication

networks, where the control system was simulated in

MATLAB and the network-induced delays were sim-

ulated in OMNeT++. Both Song and Dai focused

more on faults with root cause on network aspects. On

the other hand, Zhang (Zhang et al., 2009) and Szc-

zodrak (Szczodrak et al., 2008) used Castalia to im-

plement a fault detection algorithm based on temporal

and spatial correlations of sensor data from neighbour

sensor nodes, classifying each node as good or faulty.

2.2.1 Castalia

Castalia (Boulis, 2007) was the simulator chosen

for this work. Figure 2 presents Castalia’s architec-

ture, where each module accepts messages from other

modules or itself and, according to the message, it ex-

ecutes a given code. The nodes do not connect to

each other directly, but through the Wireless Chan-

nel module, which estimate the average path loss be-

tween two nodes using the Lognormal Shadowing.

The nodes are also linked through Physical Process

modules that they monitor, which ”feed” the sensors

with data. For every physical process there is one

module which holds the ”truth” on the quantity of the

physical process that is representing. The nodes sam-

ple the physical process in space and time to get their

sensor readings.

The Physical Process modules are based on an ar-

bitrary number of point sources whose ’inﬂuence’ is

diffused over space and they can change their posi-

tion and their value over time. The effect of multiple

sources in a certain point is additive. Calculating the

value of the physical process at a certain location and

at a certain time is represented in Equation (1), where

V (p, t) denotes the value of the physical process at

point p and at time t, V

(t) denotes the value of the i

source at time t, d

(p, t) denotes the distance of point

p from the i

source at time t, K and a are parame-

ters that determine how the value from a source is dif-

fused. N(0, θ) is a zero-mean Gaussian random vari-

able with standard deviation θ. The ”ground truth”

offered by the physical process is distorted by the in-

accuracies of the sensing devices, implemented by the

Sensor Manager.

V (p, t) =

∑

(t)

(Kd

(p, t) + 1)

+ N(0, θ) (1)

Regarding the internal structure of a node, the Appli-

Wireless Sensor Network Simulation for Fault Detection in Industrial Processes

335

Figure 1: Sensor Data Faults Characterization.

Figure 2: Model Structure of Castalia.

cation module is the one that speciﬁes the algorithm

that receives sensor data and acts accordingly. The

MAC and Routing modules deﬁne several communi-

cation protocols, which can be used by the nodes. The

Mobility module deﬁnes the nodes position and mo-

bility pattern. The Sensor Manager deﬁnes the sens-

ing devices present in each node, by introducing a

distortion of the ground truth offered by the physical

process. The Resource Manager deﬁnes the energy

consumed by the different components of the node.

3 METHODOLOGY

The simulation scenario tried to mimic a OMS,

which is composed by an IWSN, where all nodes are

equipped with luminosity sensors and a central node

is equipped with a camera. This OMS is used to anal-

yse the quality of a RSW process, namely if both parts

to be weld are correctly placed and, in the end of the

welding cycle, evaluates the quality of the weld. This

analysis consists in capturing and processing a camera

image captured, which must have a minimum quality,

otherwise, image analysis will be impossible. In order

to avoid excessive darkness/clarity in the image, the

exposure index of the camera must be calibrated ac-

cording to the luminosity conditions in the surround-

ing environment. This process consists in controlling

the lightness and darkness of the image, which can be

done by the camera light meter. Since the camera light

meter may have a lower accuracy and, it measures

only one single point of luminosity per sample, sev-

eral luminosity sensors are used to infer the luminos-

ity conditions around the welding area and improve

the control of the camera exposure index.

In this case, a camera is placed right on top of the

two electrodes that will weld the parts together. In or-

der to evaluate the luminosity conditions, nine lumi-

nosity sensors were placed around the electrodes. All

the sensor data is processed on a gateway node, result-

ing on an average of the nine luminosity points. The

simulation of this scenario will allow to implement

and test a sensor fault detection module before being

deployed in a real environment. Figure 3 represents

a real application of this scenario and the schema of

the virtual world to be simulated. There are eight sen-

sor nodes (represented by the green circles), each one

equipped with a luminosity sensor that outputs val-

ues between 0 and 100%. At the center of the world

are placed the luminosity source and sensor node 0.

Even numbered sensor nodes are placed at 20 cm dis-

tance around the center, so their output should be sim-

ilar. The same is true for odd numbered sensor nodes,

which are placed at 50 cm distance and, in conse-

quence, their output should be smaller than the even

numbered sensor nodes.

Regarding the simulation scenario, only one lumi-

nosity source was considered (placed at the center of

the world), which had default parametrization values,

namely K equals 0.25, a equals 1.0 and θ equals 0.2.

The output of the source was deﬁned at a theoretical

value of 65%. According to the simulated world im-

SIMULTECH 2016 - 6th International Conference on Simulation and Modeling Methodologies, Technologies and Applications

336

Figure 3: OMS used on a RSW process: a) real prototype;

b) simulation.

plemented, sensor node 0 outputs higher luminosity

values (around 65%), because it is the one closest to

the luminosity source. Then, even numbered sensor

nodes output values around 55% and odd numbered

sensor nodes output values around 30%. For the com-

munication module, the radio’s parameters used are

the ones proposed in the IEEE 802.15 Task Group 6

documents, the routing protocol is the Multipath Ring

Routing and the MAC protocol is the IEEE 802.15.4.

4 TESTS AND PRELIMINARY

RESULTS

Some tests were performed in order to simulate devi-

ations in sensor readings. The sensor manager’s de-

vice noise property was changed accordingly, which

is by default as low as possible (but larger than 0).

When this value increases, the difference of sensor

output values compared to normal outputs also in-

creases. Bigger deviations resulted on out-of-range

and outlier faults. On the other hand, a device noise

equals to 0 corresponds to a null variation, resulting

on struck-at-fault samples. Figure 4 plots the percent-

age of simulated errors in the dataset of sensor node

0 and the efﬁciency of the techniques for error detec-

tion, calculated by determining the false positives and

false negatives.

The considered dataset had 18% of out-of-range

faults, 36% of outliers and spikes and 14% of struck-

at-faults. The remaining 32% of the dataset is normal

data. The Min/Max Detection pinpointed all out-of-

range faults, presenting zero false positive and nega-

tive. The Flat Line Detection technique missed one

out of 14 stuck-at-faults present in the data set, pre-

senting 1 false positive and zero false negatives. The

Modiﬁed Z-Score detected only 12 out of 18 outliers

present in the sensor data set, having 6 false positives

and zero false negatives. The Modiﬁed Z-Score tech-

niques efﬁciency depends greatly on the data set his-

tory, namely the rate between the number of normal

samples and the number of outliers, and the differ-

Figure 4: Efﬁciency of techniques for sensor data error de-

tection.

ence between the normal and outlier sample values.

In order to improve the efﬁciency of this method, the

number of normal samples must be much larger than

the number of outliers.

5 CONCLUSIONS AND FUTURE

WORK

The present work simulates an OMS applied to an in-

dustrial RSW process, which analyses the position of

materials to be welded by means of a camera and sev-

eral luminosity sensors. The simulation implements

an IWSN whose nodes have a luminosity sensor. In

Castalia, sensors are ”fed” by a luminosity physical

process and run a sensor data validation module to

detect failing sensors. Such a sensor data validation

module implements Min/Max Detection, Flat Line

Detection and Modiﬁed Z-Score, which are used to

detect out-of-range faults, struck-at-faults, and out-

liers and spikes, respectively. All techniques per-

formed as expected, even though the Modiﬁed Z-

Score accuracy demonstrated to depend greatly on the

history of dataset samples, as well as on the mean dif-

ference between normal and outlier samples as dis-

cussed in the result analysis.

Regarding future steps, the sensor data validation

module can be improved by implementing the Spatial

Correlation method, considering complex scenarios

with different location luminosity sources and detect-

ing the presence and location of a sensor fault. This

complex feature implies the use of online sensor fu-

sion techniques of several heterogeneous sensor data

or ofﬂine complex methods, such as machine learn-

ing techniques. Such an approach to using multiple

data types and sources is the essence of a multi-agent

methodology (Passos et al., 2011) and architecture

(Rossetti et al., 2007) currently under development,

also with applications to the industrial domain (Braga

et al., 2008).

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337

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