1. 1
Report for InterviewReport for Interview
Dr Hanxin Chen
(Research Associate)
Department of Intelligent Control and System Engineering
University of Sheffield

2. 2
I. Educational Background
Ph.D (2005) in School of Mechanical and Aerospace
Engineering, Nanyang Technological University, Singapore
Master(2000) in Department of Measurement Technology and
Instrument, Huazhong University of Science and Technology
in China
Bachelor(1992) in Department of Mechanical Engineering
• Wuhan Polytechnic University in China

3. 3
1. Research Associate (12/2012 – 12/2015)
Department of Intelligent Control and System
Engineering, University of Sheffield, UK
Research project: “Novel Sensing Networks for
Intelligent monitoring ”
 bring together sensor technologies, non-destructive
evaluation (NDE), structural health monitoring
(SHM), wireless sensor networks for environmental
monitoring, intelligent nonlinear system identification
and analysis for structural feature extraction and
classification, robotics and distributed software
structure and decision support from three universities
(Newcastle, Sheffield and York).
II. Research Experience

4. 0 2 4 6 8 10
0
50
100
150
200
250
300
0 2 4 6 8 10
0
50
100
150
200
250
0mm defect
2mm defect
4mm defect
6mm defect
8mm defect
10mm defect
12mm defect
14mm defect
16mm defect
New PEC sensing
module
sample
Defects
Case 1: PEC data analysis for crack detection
 Propose intelligent nonlinear system identification model:
 based on NARMAX (Nonlinear Auto-Regressive Moving
Average with eXogenous Inputs
 and NOFRFs (Nonlinear Output Frequency Response
Functions).

5. 0 2 4 6 8 10 12
0
50
100
150
200
250
300
0mm defect
2mm defect
4mm defect
6mm defect
8mm defect
10mm defect
12mm defect
14mm defect
16mm defect
Frequency response analysis of time domain model determined using raw input signal
0 2 4 6 8 10
0
50
100
150
200
250
0mm defect
2mm defect
4mm defect
6mm defect
8mm defect
10mm defect
12mm defect
14mm defect
16mm defect
Time domain modeling and frequency domain feature extraction
0 2 4 6 8 10
1100
1150
1200
1250
Index
Feature
extraction

6. 6
Case 2: Ultrasonic data analysis for sizes and
location of crack detection
output signals
0 200 400 600 800 1000
-0.03
-0.02
-0.01
0
0.01
0.02
0.03
input signal
0 200 400 600 800 1000
-0.03
-0.02
-0.01
0
0.01
0.02
0.03
Output signal
0mm
0.3mm
0.5mm
1.0mm
1.5mm
input signal

7. Time domain modeling and frequency domain feature extraction
0 1 2 3 4 5 6
1.4
1.6
1.8
2
2.2
2.4
2.6
2.8
AmplitudeofG1at0.8MHz
Defect size increase
-4 -2 0 2 4 6
-2
-1
0
1
2
3
1st PCA component
2ndPCAcomponent
PCA analysis precdition for G1 at 0.2MHz-3.5MHz for D200 and D210
0mm
0.3mm
0.5mm
1.0mm
1.5mm
 PCA is used
to analyze
normalized
frequency
domain features

8. Case 3: Radio frequency identification (RFID) data
analysis for corrosion detection
a) 1 month uncoated,
b) b) 1 month coated rust patch
0 1000 2000 3000 4000 5000 6000 7000 8000 9000
0
0.2
0.4
0.6
0.8
1
1.2
1.4
Input signal
0 2000 4000 6000 8000 10000
3.5
4
4.5
5
5.5
Output signal

9.  Frequency domain
feature index for six
corrosion coated
samples
Coated sample corrosion time (month)
Coated corrosion sample detection
0 2 4 6 8 10 12
0.85
0.9
0.95
1
1.05
1.1
1.15
1.2
AmplitudeofG1at3125Hz
 PCA analysis for
frequency domain
feature index of six
coated corrosion
samples
-0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6
-0.2
-0.1
0
0.1
0.2
0.3
1st PCA component
2ndPCAcomponent
NC
1month
3month
6month
10month
12month

10. 10
2. Research Scientist (05/2012 – 12/2012)
Institute for diagnostic imaging research, School
of Physics, University of Windsor, Canada
Research project: “Higher resolution NDT method for
nuclear waste container”
 A typical joint consists of steel or aluminum sheets with
thicknesses in the range of 0.7–2 mm. During the
manufacturing process, adhesives or sealants are
typically applied between these sheets prior to the
formation of complex joints by means of spot welds.
 the nominal thickness of this adhesive layer should be
approximately 0.1 – 0.5 mm.
 uncured adhesives tend to accumulate in locations
where the gap between adherents is increased

11.  Case 1: Design and fabrication of the copper/steel samples with original
surface and artificial subsurface defects simulated by flat bottom holes (FBH)
3 mm
25 mm
original surface
Steel
Copper
Ø1.6 mm Ø2.4 mm
3 mm
25 mm
original surface
Steel
Copper
Ø0.8 mm
Case 2: the micro-structural analysis of the “Defect”
Copper
Steel
Pulse-echo waveform

12. 12
Acoustic microscopy C-scan Matrix array C-scan
Cross section of the defect area. Cluster of small inclusions is observed.
Question: how to
detect the micro-
structural defect by
ultrasonic phased
array technology

13. 13
3. Professor (04/2008 – 04/2012)
School of Mechanical and electrical engineering,
Wuhan Institute of Technology, China
Teaching Courses in English for undergraduate
and master students:
 Intelligent fault diagnosis and prognosis for
engineering systems
 Process equipment and control theory
Supervising Master students:
 12 chinese master students
 8 exchanged master students from three institutes
in France (Internship):
 Ecole Nationale d'Ingénieurs de Metz (ENIM)
 Ecole Nationale d'Ingénieurs de Valde Loire (ENIV)
 Ecole Nationale d'Ingénieurs de Montréal

14. 14
Guest Professor (05/2010-06/2010)
 Ecole Nationale d'Ingénieurs de Metz (ENIM) ， France
 Research project: “Fault diagnosis of large-scale
engineering system under the absence of the data”
 Cooperation for supervising the exchanged PhD and
Master students
Research interests:
 Phased array ultrasonic technology for weld
detection
 Condition monitoring and fault diagnosis of
mechanical system including fluid power system,
gearbox, slurry pump etc.

15. 15
I am paper reviewer for more than twenty journals such
as Ultrasonics, Mechanical system and signal
processing, Journal of sound and vibration,
International journal of fluid power, International
journal of mechanical engineering, measurement,
Sensor and Actuator etc.
I am member of several international conference
committee.
I am member of several professional society such as
China Mechanical Engineering Society.

16. 16
Research Projects and funding in China(
 (1) National Natural Science Foundation of China (Grant No.61
 (2)Program for New Century Excellent Talents in University by
 (3) Natural Science Foundation of Hubei Province of China, "E
 (4) The Education Department of Hubei Province outstanding y
Ananysis
of the weak fault signal in the earlier fault diagnosis of fluid pow
 (5) Scientific Research Foundation for the Returned Overseas
Ministry,"Ultrasonic
detection of the oil pipeline", 30,000RMB, 2009.

17. 17
(6
) Key grant of Educational Commission of Hubei Province of
nonparameter
statistical theory and optimization", 80,000RMB, 2010,1-2011
(7) Key project of Wuchan
Science and Technology Bureau, "Automatic target and assessm
by ultrasonic image analysis" , 200,000RMB, 2010,1-2012,12.
(8) Key teaching project by Education Department of Hubei Province
Frence-China co-supervise project", 8000RMB, 2011,1,2011,12.
Assessment system of
weld by phased array
test-multi 2000

18. 18
(a) (b) (c)
(d)
Fig: Fault diagnosis system of slurry pump in oil sand process (Sycrude and University of
Alberta) (a) Slurry pump; (b) pipeline and control system; (c) Multi-channel signal
acquisition system; (d) vane defect

19. 4. Post-doc fellow (03/2006 – 03/2008)
Department of Mechanical Engineering, University of
Alberta, Canada
• Research projects: (1)“An advanced quantitative fault
diagnosis system on pipeline by ultrasonic signal”;
(2)“Fault diagnosis of gearbox”
Fig: fault diagnosis of gearbox
Computer
Gearbox
sensor
analyzer

20. 20
 Ultrasonic experimental system
Bi-slide
Omnisca
n
vertical
Rotational
motor
sensor

21. 21
5. Ph.D(07/2001 – 12/2005)
School of Mechanical and Aerospace Engineering,
Nanyang Technological University, Singapore
Dissertation: “Vibration mechanism analysis and fault diagnosis of water
hydraulic system”
Publish ten journal
papers in Mechanical
systems and signal
processing, Mechanism
and machinery theory,
International journal of
fluid power, Journal of
sound and vibration etc.

22. 22
III. Ongoing research projects

23. 23
Motivation:
The variables in the industrial process is characteristic
of nonlinear, non-Gaussian and multi-scale, which
generates non-stationary excitement for the machine.
So the 2-D time-frequency analysis of faulty feature
signal is indistinct, uncertainty and absent.
Single signal source is difficult to extract the features in
the mechanical fault diagnosis during the nonlinear
industrial process.
Proposal 1: Multi-source dynamic feature
extraction and recognition for the nonlinear multi-
fault model and adaptive diagnosis
Proposal 1: Multi-source dynamic feature
extraction and recognition for the nonlinear multi-
fault model and adaptive diagnosis

24. 24
Method:
Not only extracts the time-frequency feature from
the single signal source, but also ensure the
corresponding optimal relations among the nonlinear
running variables, multi-fault modes and faulty
features from the multiple signal sources during the
three-dimensional signal-frequency-space model.
Propose the reconstruction model of three-
dimensional signal-frequency-space faulty features
from multiple signal sources.
Analyze the nonlinear mechanism of the dynamic
faulty features from the multiple source.

25. 25
Significance: Study the novel information fusion method
from multiple source including working condition variable,
vibration signal etc, which is beneficial to:
Reduce the complex effect of system running mode
on the precision of fault diagnosis.
Fuse the changing running variables into the
advanced signal processing method.
Present the interacting principle between fault model
and running variables of system.
The algorithm and theory of the multi-dimensional
dynamic feature extraction, recognition and decision
making, as well as the methodology of the adaptive
diagnosis is beneficial for the application of mechanical
fault diagnosis during the industrial process.

26. 26
Motivation:
At incipient fault stage, the mechanical system works under
normal condition. The mechanical defect structure is excited
by the outside force and system dynamic response is weak
and intrinsic, which is buried by the higher noise.
Currently, the method based on two-dimensional time-
frequency analysis is not capable of pursuing and locating the
fault source.
Proposal 2: Incipient weak fault signal active excitation
enhancement and three-dimensional measurement of
mechanical structural system
Proposal 2: Incipient weak fault signal active excitation
enhancement and three-dimensional measurement of
mechanical structural system

27. 27
Method:
 Establish the system identification model for the nonlinear
vibro-acoustic emission of structural defect under excitation
force, and to improve the enhancement sensitivity of the
defect feature signal.
Develop the three-dimensional enhanced feature extraction
algorithm based on the inversion algorithm theory and elastic
parameter of material.
Significance:
 Study the weak fault feature enhancement method by
active excitement.
Reduce effect of the higher noise and strong disturbance of
system on precise feature extraction of weak fault signal.
Solve the contradiction between fault-tolerance running
condition and mechanical incipient fault diagnosis.

28. 28
Motivation:
Conventional ultrasonic technique can not identify the
micro-defect in material.
 By the beginning of 1990s, phased array technology was
incorporated as a new NDE method. The majority of the
applications were related to nuclear pressure vessels
(Nozzles), large forging shafts and low-pressure turbine
components
Proposal 3: Higher resolution Phased Array
Technology for Mechanical Structural Micro-defect
Detection
Proposal 3: Higher resolution Phased Array
Technology for Mechanical Structural Micro-defect
Detection

29. 29
 Cross section of the defect area at lower
magnification.
The size of the observed cluster of small inclusions is
about 1.5 mm X 1 mm
 The current
Phased array
technique is
difficult to
visualize the
micro-defect
inside material.

30. 30
Method:
To propose novel higher resolution ultrasonic Phased Array
theory and method for structural micro-defect detection.
To develop novel nonlinear system identification theory to
improve the detection resolution of phased array technique
Significance:
 The proposed method and theory is the new generation
NDT, which resolution is much higher than current NDT
technique.
The novel higher resolution phased array technique is
capable of assessing the micro-structural defect of material in
nuclear waste container, large-scale structure etc that
conventional UT can not detect.