Comprehensive Overview on Computational Intelligence Techniques for Machinery Condition Monitoring and Fault Diagnosis
Comprehensive Overview on Computational Intelligence Techniques for Machinery Condition Monitoring and Fault Diagnosis
Wan Zhang 0 1
Min-Ping Jia 0 1
Lin Zhu 0 1
Xiao-An Yan 0 1
0 School of Mechanical Engineering, Southeast University , Nanjing 211189 , China
1 Supported by National Natural Science Foundation of China (Grant No. 51675098)
Computational intelligence is one of the most powerful data processing tools to solve complex nonlinear problems, and thus plays a significant role in intelligent fault diagnosis and prediction. However, only few comprehensive reviews have summarized the ongoing efforts of computational intelligence in machinery condition monitoring and fault diagnosis. The recent research and development of computational intelligence techniques in fault diagnosis, prediction and optimal sensor placement are reviewed. The advantages and limitations of computational intelligence techniques in practical applications are discussed. The characteristics of different algorithms are compared, and application situations of these methods are summarized. Computational intelligence methods need to be further studied in deep understanding algorithm mechanism, improving algorithm efficiency and enhancing engineering application. This review may be considered as a useful guidance for researchers in selecting a suitable method for a specific situation and pointing out potential research directions.
Computational intelligence; Machinery condition monitoring; Fault diagnosis; Neural network; Fuzzy logic; Support vector machine; Evolutionary algorithms
With the rapid development of science and technology in
modern society, the developmental law of machinery and
equipment has become considerably large scale, complex,
and automated. Machinery condition monitoring and fault
diagnosis are critical for modern industrial manufacturing.
Effective condition monitoring enables the early detection
of faults, with the consideration of downtime, maintenance
cost, operation reliability, and production efficiency.
Research on machinery condition monitoring and fault
diagnosis are practically significant [1, 2].
The purposes of machinery condition monitoring and
fault diagnosis are to determine the cause of abnormality
and conduct necessary countermeasures by capturing the
past and present condition data of equipment, such as
vibration, noise, temperature, and lubrication state. A
comprehensive condition monitoring program consists of
three phases, namely, feature extraction, fault diagnosis,
and prediction . Feature extraction and fault diagnosis
are usually used in detecting the abnormal state,
determining the fault location, and predicting the failure extent
. Prognostic techniques relate to the remaining useful
life (RUL) prediction, which is used in planning an
effective maintenance strategy that can improve system
reliability . The realization of the importance of optimal
sensor placement in condition monitoring system and
optimal sensor placement methods are also investigated.
Several methods have been proposed for machinery
condition monitoring and fault diagnosis in the last
decades. These methods can be categorized into model-based,
statistical, and artificial intelligence methods . A
modelbased method is based on the physical characteristics of
monitored machine for the establishment of an explicit
mathematical model. Various model-based diagnostic
methods have been widely used in gearboxes [6–8] and
bearings . However, the model-based method is difficult
to use when the system model is complex, because such
systems are often difficult to describe with a precise
mathematical model. A statistical model assumes that
historical data can be used to represent the future
mechanism of mechanical failure. However, failure mechanism
changes with respect to failure evolution. Therefore,
statistical methods cannot fully represent the wear process,
especially in the case of wear evolution stages .
Artificial intelligence techniques are suitable for addressing the
complex and large-scale nonlinear problems without any
statistical assumptions about the data .
Computational intelligence is a branch of artificial
intelligence and extensively used in scientific research and
engineering practice given the continuous increasing of
computational resources and size decreasing of computing
architectures. Examples of applications are quality control
, robot control , medical and biological , and
environmental . Computational intelligence mimics
nature and human beings by using computer science and
technology; thus, it can also be called intelligent
optimization method. Computational intelligence can be
categorized into three main groups, namely, neural
computation, evolutionary algorithms (EAs), and fuzzy
computing. In the last decades, increased attention has been
given to computational intelligence methods . While
considerable achievements have been acquired, several
new computational intelligence techniques, such as fuzzy
neural network , deep learning network , and
extreme learning machine (ELM), have been proposed to
solve the practical problem .
In existing literature, computational intelligence
techniques have been investigated in the field of wave energy
, financial market , and power quality disturbance
[22, 23]. However, the published review articles about
condition monitoring and fault diagnosis have a limited
scope, by focusing either on fault feature extraction and
classification , or on rotating machinery prediction
techniques [5, 25]. However, few comprehensive reviews
have summarized the ongoing of computational
intelligence. Therefore, this paper attempts to review
computational intelligence techniques and their applications for
fault diagnosis, prognosis, and optimal sensor placement
after 2009. This survey not only reviews the main primary
studies but also discusses characteristics of the methods,
which may be considered as a valuable guide for
researchers in selecting a suitable method for a specific
situation. Finally, some challenges in applying
computational intelligence are discussed to draw some conclusions
from the current research and main work to focus in future
The remainder of this paper is organized as follows.
Section 2 reviews the application of computational
intelligence in fault diagnosis. Section 3 presents the
application of computational intelligence in prognosis. Section 4
investigates the application of computational intelligence
method in optimal sensor placement. Section 5 presents a
discussion on the application situations. Section 6
describes challenges and prospects in this area. Finally,
Section 7 concludes the research.
2 Fault Diagnosis Using Computational Intelligence
Fault diagnosis combining fault mechanism and detection
techniques; it is a subject based on the theory of signal
processing and pattern recognition. Various algorithms
based on computational intelligence for fault diagnosis are
presented in this section. Fig. 1 shows the taxonomy of the
computational intelligence techniques used as classifiers
for machinery fault diagnosis.
2.1 Artificial Neural Network (ANN)
ANN is a special case of neural computation, which is
inspired by the human brain. This neural network is a
mathematical model that can achieve distributed parallel
information processing. ANN can adjust the
interconnections among internal nodes to achieve information
processing of a complex system.
Diagnostic inference can be interpreted as a solution of a
problem based on the specific mapping relationship
between fault symptoms and fault causes. For complex
mechanical systems, the mapping relationship is generally
nonlinear. Therefore, ANN has been widely used in fault
diagnosis because it can effectively approximate various
mapping relations. At present, most of fault classification
Fig. 1 Taxonomy of computational intelligence
methods utilize time-frequency analysis methods as the
early feature extraction, and ANN or its optimized forms
are then employed for fault classification. Fig. 2 shows the
flowchart of fault diagnosis based on ANN. In Ref. ,
wavelet packet transform (WPT) and ANN were integrated
to diagnose fault in internal combustion engine, in which
WPT was used to extract the fault characteristics, and
generalized recurrent neural network (RNN) was proposed
to classify various fault conditions. Lei, et al , proposed
an intelligent diagnosis method based on ensemble
empirical mode decomposition (EEMD) and wavelet
neural network. EEMD was used to extract the characteristics
of time and frequency domains from the sensitive intrinsic
mode functions (IMFs). Wavelet neural network was
adopted to complete the pattern recognition. WPT and
empirical mode decomposition (EMD) were utilized to
preprocess and extract features, and ANN was used to
diagnose early fault in rotating machinery . Cui, et al
, proposed a new backpropagation neural network
(BPNN) based on the coefficient entropy of wavelet packet
decomposition to realize quantitative diagnosis of fault
severity trend of rolling bearings. Saravanan, et al ,
presented a new hybrid method based on discrete wavelet
transform (DWT) and ANN to diagnose various faults of
spur bevel gearbox. Zhao, et al , utilized BPNN and
improved shuffled frog-leaping algorithm (SFLA) to
perform fault classification. The accurate selection of
suitable features that reflect the running status of equipment in
practical application of fault diagnosis is the key point of
research. Therefore, fault feature selection based on ANN
is an important research direction.
Fault diagnosis of mechanical system based on ANN has
some limitations. First, extraction and selection of features
depend largely on the prior knowledge of signal-processing
technique and diagnosis experience, and generalization is
weak. Second, ANN adopts a shallow structure, which also
limits ANN to learn complex nonlinear structures in fault
Fig. 2 Flowchart of fault diagnosis based on ANN
diagnosis . Deep neural network (DNN) is developed
based on deep learning theory, which can enhance the
accuracy of big data classification  and effectively
overcome the preceding shortcomings. Deep learning was
first introduced into the field of fault diagnosis by Tran,
et al , who applied deep belief network (DBN) based
on Teager energy operator to achieve fault diagnosis of
reciprocating compressor valves. A multisensor health
diagnosis method based on the DBN was presented in Ref.
, which classified the sensor signals collected from a
damaged structure. Guo, et al , developed a
hierarchical adaptive deep convolutional neural network for
bearing fault diagnosis. Jia, et al , used DNN for
intelligence fault diagnosis in rotating machinery,
especially in the case when the vibration data were massive.
ELM has been extensively applied and popularized in the
fault diagnosis of mechanical system in recent years. Yang,
et al , proposed a multilayer ELM based on
representational learning for fault diagnosis. The effectiveness of
this method was successfully verified by applying a wind
turbine system. Wei, et al , proposed a method based
on local mean decomposition to identify the different fault
types of gearbox, combining permutation entropy and
ELM. More references on the applications of ELM in
machine fault diagnostics were provided in Refs. [39–42].
2.2 Fuzzy Logic and Neuro-Fuzzy Systems (NFSs)
Fuzzy theory is the process of imitating the way that people
logically think in dealing with fuzzy information, which is
suitable for the qualitative analysis of complex large-scale
systems. The relationship between fault and symptom is
difficult to describe using an accurate mathematical model
due to the complexity of engineering practice. Therefore,
the application based on fuzzy logic theory in fault
diagnostics is closer to human thinking habits and language
expression. Fuzzy logic is an effective pattern recognition
method, which has been successfully used in power ,
transmission line , transportation , and industrial
production . Fuzzy logic mainly imitates human’s
logical thinking and thus has strong capability of
expressing knowledge. ANN imitates the function of human brain
neuron, which has the strong capability of self-learning and
direct processing of data. Adaptive neuro-fuzzy inference
system (ANFIS) comprises both merits of neural network
and fuzzy logic. Zheng, et al , proposed a fault
diagnosis method based on local characteristic-scale
decomposition (LCD) and fuzzy entropy (FuzzyEn) for fault
diagnosis of rolling bearings. A series of intrinsic scale
components (ISCs) was first obtained using LCD, and the
FuzzyEns of the first few ISCs that contained the main
failure information were then extracted. Finally, the
FuzzyEns were used as the input of the ANFIS, which
achieved the accurate classification of bearing fault types.
Zhang, et al , proposed an early fault diagnosis method
based on multiscale entropy and ANFIS. Tran, et al ,
combined ANFIS with decision tree to achieve fault
diagnosis. Result indicated that the proposed method could
effectively diagnose the fault of induction motors. Wu, et al
, combined DWT with ANFIS to identify gear faults.
DWT was used to extract the energy spectrum feature
vector, which was regarded as ANFIS input.
Neural computation is the process of imitating the
physiological structure and information processing of the human
brain, whereas EA is the process of imitating the biological
evolution and group intelligence. EAs provide a new way
to address complex optimization problems, which have the
advantages of simple principle and convenient
implementation, especially in the case of solving large-scale dynamic
The present review of EAs used for fault diagnosis can
be broadly divided into two sections, as shown in Fig. 3.
2.3.1 Fault Feature Extraction Using EAs
The main function of EAs in fault feature extraction is to
optimize signal-processing methods. For example, EAs are
used in optimizing filtering parameters or wavelet basis
function to better extract fault features. At present, genetic
algorithm(GA) and particle swarm optimization(PSO) are
widely used in fault feature extraction. Li, et al ,
presented an adaptive cascaded stochastic resonance method
to detect the weak impulsive features submerged in noise;
the multi-parameters of this method were optimized by GA
synchronously, and results showed that the proposed
method was suitable for extracting the weak impact
features of a gearbox. Lu, et al , applied GA to search the
optimal multi-wavelets from an adaptive multi-wavelet
library. Combination of optimal Morlet wavelet and
autocorrelation analysis was used to extract the early stage fault
of rolling bearings, and GA was employed to optimize the
filtering parameters of the Morlet wavelet . Some
related research was conducted in the author’s laboratory.
Fig. 3 EAs used for fault diagnosis
For example, Yan, et al , employed PSO to optimize
the structural element scale of the combined morphological
hat transformation and improved the accuracy of the
mathematical morphology operator in processing vibration
signals. The experimental results showed that the proposed
method could effectively identify the wear fault on the high
shaft of a wind turbine gearbox. Zhang, et al , utilized
SFLA to optimize the parameters of the Morlet wavelet and
used information entropy as the fitness function. The
optimized Morlet wavelet had superior capability in
extracting the early fault feature of rolling bearings. Yan,
et al  combined the optimal variational mode
decomposition (VMD) and 1.5 envelope spectrum analysis to
separate compound faults. In this current research, GA is
used to select the decomposition parameters of VMD
2.3.2 Fault Classification Using EAs
EAs can simultaneously search multiple regions of the
solution space, and include the computing mechanism of
parallel processing and the characteristics of
self-organization and self-learning, without any other auxiliary
information. EAs are presently applied to fault diagnosis by
combining with other algorithms. EAs are used to optimize
the structural parameters of machine learning algorithm.
Ciancio, et al , discussed the optimization of the
structural parameters of ANN by GA, such as the number
of hidden layers, the activation function of hidden and
output layers, the number of neurons in the hidden layer,
and the training algorithm. Unal, et al , applied GA to
optimize ANN in fault diagnosis of rolling bearings. Shao,
et al , utilized PSO to determine the DBN structure and
employed the optimized DBN in the fault diagnosis of
rolling bearings. More published literature of structural
parameter optimization of ANN or support vector machine
(SVM) based on EAs can be found in Refs. [60–64].
Conversely, feature selection based on EAs combining
with classification method is used for fault diagnosis.
Therefore, a feature selection process is indeed needed
before fault classification. Considerable research has been
conducted on this issue. For example, Sadegh, et al ,
utilized ANN to classify lubrication condition and
employed GA to search for an optimal feature space.
Saxena, et al , used GA to select an optimal feature set,
which was used as the input of ANN for mechanical fault
classification. GA could successfully determine the desired
number of good features in a large search space. The
superiority of the GA-ANN method was manifested in
training accuracy and classification success rate. Ahmed,
et al , integrated GA and ANN to select effective fault
features in reciprocating compressors. Cerrada, et al ,
selected the optimal features of different stages based on
GA and then combined them with ANN for gearbox fault
diagnosis to select the optimal characteristic parameters in
the time, frequency, and time-frequency domains. More
examples of EAs in feature selection are presented in Refs.
Broadly, neural computation includes kernel methods, such
as SVM. However, SVM does not belong to ANN , and
it is a new machine-learning method based on statistical
SVM solves the optimal classification hyperplane by
using the structural risk minimization principle, overcomes
the dimensionality disaster and local minimum problem,
and has a small demand for the samples. Therefore, SVM is
particularly suitable for establishing a fault diagnosis
model. SVM, which was first introduced into the field of
fault diagnosis by Jack and Nandi , was used to achieve
rolling bearing fault classification. SVM has been widely
used in mechanical system fault diagnosis in recent years.
Konar, et al , used wavelet transform and SVM to
detect bearing faults in induction motors. Li, et al ,
proposed a fault diagnosis method based on redundant
second-generation wavelet transform to achieve fault
diagnosis, which was combined with neighborhood rough
set and SVM. Cheng, et al , proposed singular value
decomposition and SVM based on EMD to perform fault
diagnosis of rolling bearings and gear. In this method,
feature vector matrix was the singular value of the sensitive
IMF component decomposed by EMD, which was regarded
as SVM input for intelligent fault diagnosis. Zhang, et al
, proposed a hybrid model based on permutation
entropy, EEMD, and SVM for motor bearing fault
diagnosis. The vibration signal was initially decomposed into a
set of IMF components by EEMD. Then, the permutation
entropy feature vector of the first few IMFs was obtained,
which was regarded as the input of the optimized SVM for
achieving fault-type classification. Some improved SVM
has been proposed, such as ensemble SVM (ESVM) and
fuzzy SVM (FSVM), to solve the problem of multiple fault
classification. The ensemble classifier not only solves the
multi-fault classification problem but also significantly
improves the classification performance compared with the
single SVM [76, 77]. Zheng, et al , proposed composite
multiscale FuzzyEn and ESVM for rolling bearing fault
diagnosis. FSVM was also used to solve
multi-classification problems . Hang, et al , employed EEMD to
extract fault feature vectors, and FSVM was adopted to
solve multi-classification problems in fan fault diagnosis.
Comparison of FSVM classification results with
backpropagation and standard SVM indicated that FSVM had
higher classification accuracy. More recent references of
SVM in fault diagnosis can be found in Refs. [81–88].
Although SVM has made some achievements in the
research of machinery fault diagnosis, some issues need to
be further studied, such as the following: (1) selection of
the appropriate kernel function and its parameters; (2)
selection of the appropriate multi-classification algorithm
to meet the needs of a multi-fault classifier; (3)
improvement of training speed to satisfy the real-time requirement
of fault diagnosis; and (4) combination of other
knowledge-based fault diagnosis methods, such as fuzzy logic
and neural network, with SVM for fault diagnosis.
3 Prognostics Using Computational Intelligence
Prognostic plays a vital role in predicting RUL and
optimizing machine usage in engineering practice .
Effective prognosis can avoid machine downtime that
results in significant losses and reduce the risk of severe
accidents . The effective improvement of the
performance of degradation assessment has been a subject
undergoing intense study in academia and industry.
Prognostic methods are divided into three categories,
namely, physical model-based, data-driven, and hybrid
prediction methods [25, 90]. The methods based on
physical model describe the failure model of the system
by using mathematical theory. The failure models have
such ways as crack growth and peeling growth. However,
describing a more stochastic and complex practical
system model is difficult using these methods. In general,
these models are only suitable for a specific fault type,
and their universality is weak. Data-driven methods are
used to analyze and predict the current and future health
status of the mechanical system by using the condition
monitoring data. These methods are applied to nonlinear
reliability prediction when compared with physical model
methods, which mainly include computational intelligence
methods, statistical methods, and state space methods.
Hybrid prediction methods combine physical model-based
and data-driven methods. These methods can obtain more
accurate and reliable prediction results. However, they are
difficult to implement in practical application because of
their large computation.
A large number of prediction methods have been
proposed in recent years, however, establishing an efficient
prediction method remains a problem that needs to be
addressed. Compared with physical model-based methods,
data-driven methods are a good choice in describing the
complex and nonlinear degradation process. This section
focuses on the application of computational intelligence
approaches for fault prediction.
ANN implements RUL estimation and prediction of future
health conditions by including direct or indirect
observation data, independent from the failure process of a
physical model. Various types of data can be used as the input
of ANN, such as some process variables, monitoring data
(vibration signals), evaluation characteristics (age and stop
time), and some historical features. The output of ANN is
RUL prediction or performance degradation assessment,
which is used for conducting effective maintenance
strategies. ANNs widely used in fault prediction include
BPNN [91–95], radial basis function network (RBFN), and
RNN . Ahmadzadeh, et al , proposed a three-layer
feedforward BPNN for RUL estimation of grinding mill
liners, which considered degeneration and condition
monitoring data as the inputs of ANN, and used RUL as the
output of ANN. Rodriguez, et al , presented ANN (six
input layers, three hidden layers, and one output layer) to
predict and simulate the behavior of life-cycle assessment
in blades of steam turbines. In view of the shortcomings of
traditional incremental training methods in long-term
prediction, Malhi, et al , proposed an RNN based on
competitive learning method to improve the accuracy in
long-term prediction of rolling bearings. Mahamad, et al
, used feedforward neural network and the
LevenbergMarquardt training algorithm to predict the RUL of rolling
bearings. Considering the complexity and nonlinearity of
the pitch system, and the difficulty of describing with
precise mathematical model, Chen, et al , proposed
ANFIS based on a prior knowledge, which was used to
predict wind turbine pitch faults. Existing ANN methods
predict the RUL by using failure history data, but
suspended historical data are rarely utilized. Hong, et al ,
used a self-organizing map, which combined wavelet
packet and EMD for feature extraction, to estimate bearing
performance degradation. Javed, et al , used ELM and
fuzzy clustering to predict the degradation state and the
RUL of complex nonlinear systems. Compared with ANN,
ELM improved the algorithm efficiency by randomly
selecting hidden layer parameters. Zhang, et al ,
proposed a multi-objective DBN ensemble method for
3.2 Fuzzy Logic
The main purpose of introducing fuzzy logic is to
overcome uncertainty and inaccuracy, and fuzzy logic has
obvious superiority in dealing with large time delay, time
variation, and nonlinear processing. This method is based
on fuzzy mathematics theory, including the appropriate
membership and fuzzy rules; then, fuzzy inference is
continued to implement fuzzy prediction. Baban, et al
, acquired the vibration and temperature signals of the
textile machine, evaluated the machine status-based fuzzy
logic, and implemented preventive maintenance. Stetter,
et al , used fuzzy logic to monitor the health status of
the pump system in an engine. Ishibashi, et al ,
combined decision tree, fuzzy logic, and GA for RUL
prediction of aeroengine, and acquired the historical data
from the sensor on the aeroengine. Tian, et al ,
proposed a fuzzy-adaptive unscented Kalman filter to improve
the prediction accuracy of nonlinear processes. Zio, et al
, proposed a method based on fuzzy similarity
analysis to estimate system RUL. This method is subjective to
manually select membership function of fuzzy logic.
Therefore, this method is generally used in conjunction
with fault tree, expert system, and neural network.
Neural network and fuzzy logic are important methods in
computational intelligence. The advantages of neural
network are its parallel processing capability, strong
faulttolerant capability, self-learning, and self-adaptability.
However, the neural network is similar to a black box,
which lacks transparency. Expressive knowledge of
weights in the network is not easy to understand, and ANN
cannot utilize the language knowledge of experts. Fuzzy
logic make the inference process understand easily, which
can use expert knowledge directly, with lower
requirements on the samples. Nevertheless, the disadvantages of
fuzzy logic are slow inference speed and low precision.
Adaptive learning is difficult to implement. Thus, the two
above mentioned methods are integrated to form NFSs,
which have the merits of both methods. NFSs have been
widely used in machinery performance prediction in recent
years. Zhang, et al , used neuro-fuzzy network to
predict tool wear and RUL, considering that tool condition
monitoring is critical to the manufacturing industry.
Gokulachandran, et al , used neuro-fuzzy and support
vector regression to evaluate RUL of cutting tools. The
experimental results showed that the neuro-fuzzy method
could obtain a more accurate prediction. Ali, et al ,
combined fuzzy neural network and Weibull distribution to
predict RUL of rolling bearings. Chen, et al ,
employed ANFIS using a prior-knowledge-based method
to predict wind turbine pitch fault. Zhao, et al , used
neuro-fuzzy method to predict the health condition of
bearings. Compared with the RBFN, the proposed method
is superior with respect to reliability and robustness. Chen,
et al , proposed a method based on NFSs and
Bayesian algorithms to predict the health status of helicopter
gearboxes and bearings. The results showed that the
proposed algorithm was superior to RNN and NFSs in
prediction accuracy. Ramasso, et al , proposed a method
combining NFSs and belief function theory to evaluate the
RUL of a turbofan engine. Wang, et al , proposed an
evolutionary neuro-fuzzy (eNF) predictor on time-varying
dynamic systems. A newly enhanced least squares
estimator was used to train the linear parameters of the eNF
predictor. Experiments showed that the proposed method
could be successfully applied to mechanical condition
monitoring. The existing literature shows that the NFSs are
superior to other nonlinear prediction methods (that is,
RNN and RBFN). Recent development of NFSs based on
fault diagnosis can be found in Refs. [17, 115–122].
Nevertheless, several issues still need to be considered. For
example, the number of nodes in the hidden layer is still
difficult to be determined, and the selection of fuzzy
parameters requires human intervention, all of which affect
the prediction results of NFSs.
The establishment of a suitable model under the limited
monitoring data is the key problem to estimate RUL and an
urgent need for industrial production. SVM is a
machinelearning algorithm based on Vapnik-Chervonenkis theory,
which is used to solve the problems of classification and
prediction when the sample size is small . At present,
SVM research mainly focuses on algorithm solution and
model establishment. The purpose of algorithm solution is
to address a constrained optimization problem by adopting
the appropriate algorithm. The problem of model
establishment includes optimization of model parameters, kernel
function selection, feature vector extraction, and
unbalanced sample. The aforementioned factors directly affect
the model accuracy. Lu, et al , considered the
difficulty of obtaining ideal prediction results under a small
sample size. Thus, they proposed a least squares SVM
(LSSVM) to estimate the degradation trend of slewing
bearings and used PSO to optimize LSSVM parameters.
Compared with the RBFN, the LSSVM model was
demonstrated to be more accurate and effective. Dong, et al
, used principal component analysis (PCA) to fuse the
original features and reduce the dimension. Then, they used
the LSSVM model to predict the bearing degradation
process. Chen, et al , proposed an RUL prediction
method based on relative feature and multivariate SVM
(MSVM). In contrast to univariate SVM, MSVM overcame
the shortcomings of insufficient condition monitoring
information and mined potential, and useful information
from small sample size. Caesarendra, et al , combined
Cox proportional hazard model and SVM for failure
degradation prediction of bearings. Widodo, et al ,
proposed a prediction method based on survival analysis
and SVM. Kaplan-Meier and probability density function
estimators were used to generate survival probability, and
the kurtosis of measured data and survival probability were
used as input and output of the SVM, respectively. The
trained SVM successfully predicted machine failure time.
Tran, et al , combined auto-regressive and moving
average (ARMA) model, Cox proportional hazard model,
and SVM for RUL prediction. Loutas, et al , used
wavelet packet nodal energies and Wiener entropy as the
feature vector, and proposed e-support vector regression
method in predicting the RUL of rolling bearings. He, et al
, proposed a hidden Markov-SVM to predict surface
roughness in hard turning. More published literature of
applying SVM in RUL estimation of mechanical systems
can be found in Refs. [132–137]. SVM has been
successfully developed rapidly in recent years, with an increasing
number of studies focusing on SVM. However, SVM still
has some problems to be solved, such as feature selection
and large-scale training sample problem.
4 Optimal Sensor Placement Using Computational Intelligence
Appropriate measurement strategy should be adopted and
sensor placement should be optimized to obtain sufficient
and effective measurement information, and improve the
diagnostic capability of a machinery system. Practical
experience shows that although sensor redundancy can
effectively reduce the loss of information, the mass of data
transmission will greatly increase the cost of data analysis
and processing, especially in long-distance transmission
network. The optimal sensor placement plays an important
role in structural model updating and structural health
monitoring. The existing literature indicates that sensor
optimization placement has been effectively applied to
various engineering systems, such as bridges [138, 139],
trusses [140–142], beams , plates , gearbox
systems , and manufacturing processes [146, 147].
The optimal sensor placement problem generally consists
of two aspects, as presented in Fig. 4. A multi-objective
combinatorial optimization problem is built due to the
complexity of the actual engineering structure. This problem is an
NP-hard problem, and computational intelligence plays an
important role in solving such a problem, among which GA is
widely applied and studied [148, 149]. Rao, et al , treated
Step1: Determine the objective function
according to the optimal allocation
Step 2: Solve the objective function by
using appropriate optimization method
EAs (GA, PSO, SA, etc)
Fig. 4 Process of solving the optimal sensor placement problem
an optimal sensor placement problem as a combinatorial
optimization problem, which was solved by using a hybrid
PSO, and this method was successfully applied to a slab
bridge. Guo, et al , proposed an improved GA to solve the
optimization objective function and applied this method to
search for the optimal sensor position of the truss structure.
Mahdavi, et al , proposed a wavelet-based GA to search
the optimal sensor location of a 2D steel and pin-jointed truss
structure. Dutta, et al , combined the artificial bee colony
algorithm with the glowworm swarm optimization algorithm
to determine the optimal position of actuators and sensors in
cantilever beams. Pan, et al , solved an optimal sensor
placement problem using adaptive-speed PSO. Ren, et al
, presented a data-mining-guided evolutionary method
to address the optimal sensor location problem in a
multistation assembly process. Based on the finite element
modeling and modal analysis of gearbox, a feature-based approach
for determining the optimal sensor position in a multi-station
assembly project was proposed , and GA was employed
to solve this optimization problem. Lian, et al , proposed
a fitness function of near-neighborhood index, and adopted
improved PSO and clonal selection algorithm to solve the
proposed fitness function. Chow, et al , proposed a
hybrid method integrating GA with entropy-based method to
solve the sensor location problem of transmission towers.
Some research in this field has been performed in the author’s
laboratory. For example, a new improved SFLA was proposed
in Ref.  and was applied to the multi-type sensor network
optimization problem of gearboxes. He, et al , proposed
an adaptive EA based on GA to optimize the sensor placement
for cutting process condition monitoring. In Ref. , an
improved SFLA based on quantitative causal graphs was
applied to address the optimal sensor placement in a
singlestation multistep manufacturing process.
Optimal sensor placement is a key step in the rationality,
accuracy, long term, and economy of structural health
monitoring. Although some great progress has recently
been made in the theory of sensor optimization, it still faces
some problems, such as determining how to use the optimal
sensor placement theory directly as a guide in engineering
practice. Several researchers have focused on improving
the efficiency of the optimization algorithm and reducing
the number of iterations. The fusion of different methods
can form a new hybrid algorithm, which includes the merit
of different methods. The above mentioned aspects should
be given more focus in future research.
In previous section, we have summarized several
computational intelligence methods and their applications in
condition monitoring and fault diagnosis. The application
scope is decided, considering that different computational
intelligence methods have different characteristics. This
section discusses the selection of a suitable method for
special problems instead of choosing randomly.
ANN: On the basis of the literature review, ANN is one
of the most commonly used classifiers in intelligent fault
diagnosis methods, which has the capabilities of high
learning and generalizing performances. The accuracy of
ANN depends heavily on the training sample. In the case of
a limited number of sample size, ANN often shows poor
generalization capability, namely, the over-fitting problem.
Therefore, ANN is usually applied in the case of a
sufficient training sample size.
SVM: By contrast, SVM is based on the principle of
structural risk minimization, with an outstanding
generalization performance. SVM is introduced into machinery
fault diagnosis and prediction according to its high
accuracy and good generalization for a small sample size,
because a sufficient fault sample size is difficult to obtain in
practice. However, SVM comprises a few disadvantages.
For example, SVM is for binary-class classification; thus,
many SVMs need to be combined specifically for a
multiclass classification problem. SVM learning is also time
consuming in dealing with large-scale data. Several
improved methods based on SVM are proposed to
overcome the aforementioned problems. The performance of
such methods is better than that of a single SVM.
DNN: ANN adopts a shallow structure, which limits its
learning of some complex nonlinear structure in fault
diagnosis and prediction. The accuracy of ANN and SVM
also depends largely on feature selection based on the prior
knowledge of signal-processing techniques. The selected
features may be suitable for special problems but not for
other problems. DNN can adaptively mine representative
information from the original data without the need for
prior knowledge because of the depth structure. Therefore,
DNN can be used for fault feature mining and intelligent
fault diagnosis. When the fault feature is difficult to
determine, DNN can be used for fault diagnosis. However,
DNN needs more training time compared with ANN due to
the depth structure. With the development of hardware
technology, DNN can be built more rapidly in the future.
Fuzzy logic: The fuzzy rule base is the key point and
bottleneck in developing fuzzy logic, which is based on
expert knowledge and experience. With the lack of
selflearning and self-adaptability, fuzzy logic is often
combined with other algorithms, such as neural network, fault
tree, and expert system, to achieve fault classification and
EAs: GA and PSO are the most widely used in fault
diagnosis. EAs have been applied to (1) extract a fault
feature by combining with other signal-processing
methods, such as wavelet transform, EMD, and stochastic
resonance; (2) optimize the structural parameters of
classification algorithms (such as ANN, SVM, etc.); (3) select a
suitable feature space combining with machine-learning
methods to achieve fault classification; (4) address the
objective function that is established according to the
optimal allocation criterion in the problem of optimal
6 Challenges and Prospects
With the rapid development of the modern industry, the
processing approaches of condition monitoring and fault
diagnosis would be integrated with two or more intelligent
methods for enhancing the diagnosis performance, given
each computational intelligence method has its own
specialty. For example, NFSs are a combination of neural
network and fuzzy logic, which has the merit of both
methods. Considering the difficulty of determining the
structural parameters of neural network, EAs are used to
optimize the neural network parameters for fault
classification and prediction. The development of computational
intelligence faces the following challenges:
Computational intelligence techniques lack a robust
mathematical foundation. Although neural network
has a relatively perfect theoretical basis, EAs have
not yet perfected mathematical foundation.
Theoretical studies of instance, stability, efficiency, and
convergence remain in the early research stage.
Therefore, computational intelligence can be applied
to condition monitoring and fault diagnosis
appropriately rather than instinctively if researchers have a
deep understanding of the algorithm mechanism.
Further simulated or tested signals are used to verify
the effectiveness of computational intelligence
methods that are difficult to be applied in engineering
practice. Computational intelligence techniques
should be further explored and improved to develop
robust and practical methods for condition
monitoring and fault diagnosis.
Considering that computational intelligence and its
improved methods are based on iterative process,
these algorithms are correspondingly time
consuming. Hence, the development of fast online condition
monitoring and fault diagnosis systems based on
computational intelligence should be the focus in
With the development of industrial big data, the Internet
of Things, and intelligent manufacturing, new technique
based on computational intelligence is an important way to
implement a zero-fault and predicable production system.
The collected data are aggregated to a large data platform,
which can realize real-time monitoring, perform fault
alarm, fault prediction, asset management, intelligent
service, auxiliary research, and development, and meet some
individual requirements, thereby ultimately creating a more
7 Conclusion Remark
Recent developments and applications of
computational intelligence to condition monitoring and fault
diagnosis are reviewed, following the categories of
fault diagnosis, prognosis, and optimal sensor
placement. A comprehensive references are provided
for researchers who are interested in this topic.
A comparative analysis of the characteristics of
computational intelligence methods is performed,
which may be considered as a valuable guide for
researchers and practitioners in selecting a
suitable method for a specific situation.
Based on the survey and summary of recent research
on actual application of mechanical engineering,
challenges and prospects of computational
intelligence in condition monitoring and fault diagnosis
are discussed with emphasis on theoretical
foundation, algorithm efficiency, and engineering practice,
so as to show the potential researches in the further.
Open Access This article is distributed under the terms of the
Creative Commons Attribution 4.0 International License (http://crea
tivecommons.org/licenses/by/4.0/), which permits unrestricted use,
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appropriate credit to the original author(s) and the source, provide a
link to the Creative Commons license, and indicate if changes were
Min-Ping Jia, born in 1960, is currently a full professor at School of
Mechanical Engineering, Southeast University, Nanjing, China. He
received his B.S. and M.S. degrees in mechanical engineering from
Nanjing Institute of Technology (now Southeast University), Nanjing,
China, in 1982 and 1985, respectively, and his PhD degree in
mechanical engineering from Southeast University, Nanjing, China,
in 1991. His research interests include dynamic signal processing,
machine fault diagnosis, and vibration engineering applications.
Lin Zhu, born in 1989, is currently a PhD candidate at School of
Mechanical Engineering, Southeast University, Nanjing, China. He
received his master degree from Guangxi University of Science and
Technology, China, in 2014. His research interests include fatigue life
prediction, fatigue damage detection, structure reliability. E-mail:
Xiao-An Yan, born in 1989, is currently a PhD candidate at School of
Mechanical Engineering, Southeast University, Nanjing, China. He
received his master degree from North China Electric Power
University, China, in 2015. His research interests include signal
processing, intelligent fault diagnosis and pattern recognition. E-mail:
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Wan Zhang, born in 1989, is currently a PhD candidate at School of Mechanical Engineering , Southeast University, Nanjing, China. She received her master degree from Anhui Polytechnic University , China, in 2013 . Her research interests include signal processing, gear and bearing fault diagnosis, sparse signal representation . E-mail: