International Journal of Distributed Sensor Networks
T : Categories of methods for FDD.
Diagnosis method Types Merits Defects
Analytic model
State estimation algorithm
Easy for detection, short
reaction time
(1)Hardformodeling
(2) Incapable of detecting multiple faults
Parameter estimation algorithm
Short detection time, able
to detect multiple faults and
slow or burst malfunctions
(1) Need accurate mathematical model
(2) Unable to diagnose new faults
Parity space algorithm
High accuracy, conciseness,
and reliability
Incapable of dealing with complicated
nonlinear systems
Articial intelligence
Expert system approach algorithm
Easy for adding, removing,
and explaining rules
(1) High development and maintenance fee
(2) Unable to diagnose new faults
(3) Prone to be nodes matching conict and
combination explosion problems
Fault Tree Analysis (FTA) algorithm
Strong intuition, exibility,
and universality
(1) Complicated tree-building procedures
(2) Heavy workload, prone to error
(3) Hard for description of controlling system
Signed Directed Graph (SDG)
algorithm
Strong intuition, able to
reect faults relaying path
and nd out the primary
cause
(1) Unable to tell fake information
(2)emodelistooeasytocompletely
describe the complicated relationship of the
system
Historical data
Neural network diagnosis algorithm
No need for model, high
speed, antinoise
(1)Demandforlargedata
(2) Hard to deal with dynamic characteristic
Principal component analysis
algorithm
Easy to distinguish key
variants, suitable for
large-scale systems
Poor analysis for nonlinear system
Wavelet transform algorithm
High sensibility, strong
anti-interference, low
demand for input signal
quality
Demand for large data
networks and have the characteristic of swi expansion and
strong survivability []. ey improve installing speed and
save the installation fee of the system. When expanding the
monitoring scope, some receiving and transmitting equip-
ment can meet the demand. Countless wireless sensors can
be arranged exibly. e maintenance costs are relatively
low. To some extent, it solves the problem of multisensor
arrangement in wired monitoring.
(3) High Mobility. It can change the monitored positions
exibly according to the monitored demand because WSNs
are not restrained by cables. Whole monitored equipment can
even be moved to another similar piece of equipment easily.
However, WSNs technology has shortages such as easi-
ness to be interfered with, poor data security, limited band-
width, and poor time synchronization. However, WSNs show
great advantages in mechanical equipment SMFD.
Certainly, diverse applications of wireless sensor net-
works (WSNs) in mechanical equipment SMFD have solved
the problems of using cable technology on equipment con-
dition monitoring. It not only is an eective supplement of
wired technologies, but also helps to expand device monitor-
ing range, improve the level of monitoring, and reduce the
monitoring cost. e use of WSNs technology has opened
up a new eld of integrated application of the network
technology, state monitoring, and fault diagnosis. erefore,
it is meaningful to further explore and renovate the methods
of the mechanical equipment SMFD based on WSNs technol-
ogy. We can predict that, in the near future, wireless transmis-
sion technology will be implanted in most of the industrial
instruments and automatic system. Besides, development of
WSNs technology will make great contribution to promoting
revolution of industrial monitoring model [].
e contributions of this paper are listed as follows:
() is paper has collected the existing achievements of
mechanicalequipmentSMFDusingWSNstechnique,
especially in China. e achievements are summa-
rizedintothreecategories:themonitoredparame-
ters, monitored objects, and data fusion methods. It
summarizes the existing problems of each kind in the
researches.
() It predicts the research trend in the eld of mechanical
equipment SMFD based on WSNs. We hope to
provide some ideas for people who are interested in
the research of fault diagnosis based on WSNs.
e rest of paper is organized as follows. In Section ,
related studies of mechanical equipment are presented. We
introduce the foreign research progress and Chinese research
progress of mechanical equipment SMFD based on WSNs, in
Sections and , respectively. We discuss the achievements
and problems in those studies in Section . Section provides