arXiv:1509.06065v1 [cs.DC] 20 Sep 2015
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Dependable Structural Health Monitoring
Using Wireless Sensor Networks
Md Zakirul Alam Bhuiyan, Member, IEEE, Guojun Wang, Member, IEEE, Jie
Wu, Fellow, IEEE, and Jiannong Cao, Fellow, IEEE,
Abstract
As an alternative to current wired-based networks, wireless sensor networks (WSNs) are becoming
an increasingly compelling platform for engineering structural health monitoring (SHM) due to relatively
low-cost, easy installation, and so forth. However, there is still an unaddressed challenge: the application-
specific dependability in terms of sensor fault detection and tolerance. The dependability is also affected
by a reduction on the quality of monitoring when mitigating WSN constrains (e.g., limited energy, narrow
bandwidth). We address these by designing a dependable distributed WSN framework for SHM (called
DependSHM) and then examining its ability to cope with sensor faults and constraints. We find evidence
that faulty sensors can corrupt results of a health event (e.g., damage) in a structural system without
being detected. More specifically, we bring attention to an undiscovered yet interesting fact, i.e., the
real measured signals introduced by one or more faulty sensors may cause an undamaged location to be
identified as damaged (false positive) or a damaged location as undamaged (false negative) diagnosis.
This can be caused by faults in sensor bonding, precision degradation, amplification gain, bias, drift,
noise, and so forth. In DependSHM, we present a distributed automated algorithm to detect such types
M. Z. A. Bhuiyan is with the School of Information Science and Engineering, Central South University, Changsha, China,
410083, and the Department of Computer and Information Sciences, Temple University, Philadelphia, PA 19122. E-mail:
zakirulalam@gmail.com.
G. Wang is with the School of Information Science and Engineering, Central South University, Changsha, China, 410083.
E-mail: csgjwang@gmail.com (Corresponding Author).
J. Wu is with the Department of Computer and Information Sciences, Temple University, Philadelphia, PA 19122. E-mail:
jiewu@temple.edu.
J. Cao and X. Liu are with the Department of Computing, The Hong Kong Polytechnic University, Hong Kong. E-mail:
{csjcao,csxfliu}@comp.polyu.edu.hk.
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of faults, and we offer an online signal reconstruction algorithm to recover from the wrong diagnosis.
Through comprehensive simulations and a WSN prototype system implementation, we evaluate the
effectiveness of DependSHM.
Index Terms
Wireless sensor networks, structural health monitoring, dependability, fault detection, fault-tolerance,
energy-efficiency.
I. INTRODUCTION
Wireless sensor networks (WSNs) consist of a number of sensor nodes that can collaborate
with each other to perform monitoring tasks. WSNs have been widely deployed on the ground,
vehicles, structures, and the like for enabling various applications, e.g., target detection, scientific
observation, safety-related, and traffic monitoring [1], [2], [3], [4], [5], [6], [7], [8]. A WSN
typically consists of a large number of resource-limited sensor nodes working in a self-organizing
and distributed manner. Sensor nodes Applications of WSNs include military sensing, wildlife
tracking, traffic surveillance, health care, environment monitoring. Recent work has explored
that WSNs can be a compelling platform for engineering structural health monitoring (SHM),
due to relatively low-cost, easy installation, and so forth [9], [10], [11], [12], [13]. In a typical
SHM system, the interest is in monitoring possible changes (e.g., damage, crack, corrosion) on
physical structures (e.g., aerospace vehicles, buildings, bridges, nuclear plants, etc.) and providing
an “alert” at an early stage to reduce safety-risk. This prevails throughout the aerospace, civil,
structural, or mechanical (ACSM) engineering communities.
Both ACSM and computer science (CS) communities have already addressed numerous chal-
lenges/requirements, including data acquisition, compression, aggregation, damage detection,
distributed computing. However, there is still an unaddressed challenge: the application-specific
dependability, which is the ability of a WSN providing application-specific meaningful monitor-
ing results under sensor faults. Particularly, such a system should be able to detect the sensor data
faults online and take recovery actions immediately to avoid meaningless monitoring operations.
In fact, dependability is highly desired in a WSN-based SHM, as an “alert” about a structural
event conveys a serious concern with public safety and economic losses.
On the one hand, SHM algorithms in wired sensor networks used by ACSM are generally
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centralized/global-based [11], [14], [15], [16], in which they may not need to seriously consider
data collection quality and synchronization errors, etc. This is because they may not often handle
data losses or mismatch, as there are no issues like poor wireless connectivity, narrow bandwidth,
and energy constraints. The dependability is affected by a reduction on the quality of data when
mitigating the constraints. Moreover, once data from the WSN is collected at a centralized base
station (BS), it becomes complex to scrutinize all the collected data (including faulty signals).
On the other hand, significant efforts have been made for specific fault types in WSNs
[17], [18]. Some prominent schemes, namely, decision fusion (or 0/1 decision), threshold-based
decision, heartbeat reception have been suggested for fault-tolerant phenomenon (such as an
event) detection problems [17], [19], [20], [21], [22]. These often use simplified data and few
measurements to adequately detect certain faults. However, they are not able to function properly
in an SHM system, since SHM algorithms use totally different methods to detect a damage event.
For example, the algorithms need raw measured signals rather than the decision fusion, and the
analysis of signals (vibration, strain, damping, etc.) that requires a substantial knowledge from
ACSM domains (e.g., finite element model updating, Eigen matrix, mode shape properties) [23],
[13], [10], [24]. We have evidence from experimental settings that when there is a change in
structural health properties (as shown Fig. 1a), 0/1 decision schemes tell sensor 5 is faulty, but
they cannot tell what happen (faulty signals or damage event) around sensors 4 and 6. Regarding
all these issues above, a question might be posed: is it possible to have a dependable SHM system
using WSNs?
The answer is positive. In this paper, we design a dependable and distributed WSN frame-
work for SHM (called DependSHM) that jointly considers ACSM and CS requirements. In
DependSHM, we propose an algorithm to detect sensor faults efficiently under the constraints
of the WSN. Dependability in WSNs suffers from various types of faults, including, transceiver
failure, link errors, security attacks (e.g., collusion), etc [18], [25]. Numerous efforts are being
published every day in handling these fault types. Instead, we are interested in some types of
sensor faults that are common but difficult to identify: sensor debonding (when a sensor partially
or completely debonds from the host structure), faulty signals, faults in offset, bias, precision
degradation, and the amplification gain factor of signals, noise faults, node missing or failure.
Most of the sensor data faults fall within these fault models and they directly interrupt a
WSN system from detecting damage. Sensors with some of these faults seem to work properly,
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Fig. 1. Investigation of the dependability performance of different schemes in structural health monitoring (SHM).
to communicate to neighbors, to exchange heartbeats, but they return incorrect readings or
decisions. Under any of the fault occurrences in a practical SHM, we discover a fact that goes to
SHM system dependability: both faulty and non-faulty sensors can generate abnormal signals or
decisions (i.e., remarkable changes in the measured signals). The difficult part is that sensor data,
the only available information, will be affected by both structural damage and sensor faults. We
further discover an interesting fact that such a possibility can cause an undamaged location to
be identified as damaged (false positive) or a damaged location can be given undamaged (false
negative) diagnosis. When we transform these false positive and negative rates into a structural
health event detection ability as the performance of system dependability (as shown in Fig. 1b),
we find that those decision based and current SHM schemes do not perform well.
We use a new general measurement, mutual information independence (MII), between two
signals u and v from two different sensors for evaluating results in the absence of the ground
truth. We think that mutual statistical information can be used as an indicator to decide on a
sensor fault detection in conjunction with damage detection. We attempt to reconstruct faulty
sensor signals using Kalman filter techniques so that if there is damage, it can be recovered after
the reconstruction. This does not require any costly actions, including sensor grouping, faulty
sensor avoiding, masking, isolating, or replacing.
Our major contributions in this paper are as follows:
• We study a WSN-based SHM system dependability problem and design DependSHM to
address the problem. This task is by no means easy, as it requires multi-domain knowledge
and is associated with optimizing WSN resource constraints.
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• We propose a non-faulty data collection algorithm, by which we utilize an online faulty
sensor detection algorithm based on the function of MII. Although we focus on sensor
faulty signals in DependSHM, MII does not rely on a particular fault type.
• In DependSHM, we present a recovery algorithm to reconstruct faulty sensor signals based
on the Kalman filter technique. The recovery is directly applicable to any kind of spatially
and temporally correlated signals that are caused by numerous sensor faults in a WSN-based
SHM system.
• We evaluate DependSHM via simulations using real data sets, adopted from a SHM system
deployed on the GNTVT structure [26]. We implement a prototype system developed by
the TinyOS [27] running on the Imote2, and verify it on a test structure. The results show
that a careful use of recovery from faulty signals in DependSHM is effective and can lead
to a dependable WSN-based SHM system.
This paper is organized as follows. Section 2 reviews related Work. Section 3 provides system
models and problem formulation. Section 4 presents the DependSHM framework. The faulty
sensor detection algorithm is in Section 5. Faulty sensor signal reconstruction is detailed in
Section 6. Performance evaluation is outlined in Section 7. Section 8 concludes this paper.
II. RELATED WORK
Dependability in WSN-based SHM. WSNs have been widely suggested and validated in
experimentation for SHM system by both the ACSM and CS communities in recent years [10],
[11], [13], [14], [15], [24], [9], [28]. Existing schemes already have sufficient contributions to
ACSM and CS requirements [2], [9], [10], [11], [12], [13], [14], [15], [16], [24], [29], [30], [31],
[32], [32], but they suffer from the dependability problem.
On the one hand, generally data can be corrupted at four stages, namely acquisition, processing
and local decisions, wireless transmission, and the final analysis at the BS. Among them, the
most important stage is the acquisition stage that can ensure the quality of sensor readings in
WSNs at the beginning. The quality is also affected when application-specific requirements are
considered, including high-resolution data, raw data, non-faulty data, dependable and real-time
decision-making to analyze actual structural health conditions. These additional requirements are
traditionally guaranteed by using wired networks. To make WSNs effective alternatives to wired
network system instruments, a first step in this direction is SHM system dependability in terms
