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Report for InterviewReport for Interview

Dr Hanxin Chen(Research Associate)

Department of Intelligent Control and System EngineeringUniversity of Sheffield

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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

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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

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New PEC sensing module

sampleDefects

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).

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Frequency response analysis of time domain model determined using raw input signal

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Time domain modeling and frequency domain feature extraction

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Case 2: Ultrasonic data analysis for sizes and location of crack detection

output signals

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Time domain modeling and frequency domain feature extraction

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PCA is used to analyze normalized frequency domain features

Case 3: Radio frequency identification (RFID) data analysis for corrosion detection

a) 1 month uncoated,

b) b) 1 month coated rust patch

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Frequency domain feature index for six corrosion coated samples

Coated sample corrosion time (month)

Coated corrosion sample detection

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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

Case 1: Design and fabrication of the copper/steel samples with original surface and artificial subsurface defects simulated by flat bottom holes (FBH)

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Steel

Copper

Ø1.6 mm Ø2.4 mm

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original surface

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Copper

Ø0.8 mm

Case 2: the micro-structural analysis of the “Defect”

Copper

Steel

Pulse-echo waveform

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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

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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

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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.

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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.

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Research Projects and funding in China(1.6m RMB): (1) National Natural Science Foundation of China (Grant No.61273176), “Multi-source dynamic feature extraction and recognition for the mechanical nonlinear multi-fault mode and adaptive diagnosis”, 800,000REB, 2012,1 – 2015,12. (2)Program for New Century Excellent Talents in University by the Ministry of Education of China, "Health management and fault diagnosis system of complex engineering system", 500,000RMB, 2011,1-2013,12. (3) Natural Science Foundation of Hubei Province of China, "Earlier fault diagnosis of fluid power equipment in Chemical process", 20,000RMB, 2008,7-2010,6. (4) The Education Department of Hubei Province outstanding youth talent project, "

Ananysis of the weak fault signal in the earlier fault diagnosis of fluid power system", 20,000RMB, 2009,1-2010,6.

(5) Scientific Research Foundation for the Returned Overseas Chinese Scholars, State Education Ministry,"Ultrasonic detection of the oil pipeline", 30,000RMB, 2009.

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(6) Key grant of Educational Commission of Hubei Province of China, ""Fault diagnosis of Complex mechanical system under multi-variety condition based on the nonparameter statistical theory and optimization", 80,000RMB, 2010,1-2011,12.

(7) Key project of Wuchan Science and Technology Bureau, "Automatic target and assessment system of Weld by ultrasonic image analysis" , 200,000RMB, 2010,1-2012,12.

(8) Key teaching project by Education Department of Hubei Province of China, "Research on the education of the modern engineer by Frence-China co-supervise project", 8000RMB, 2011,1,2011,12.

Assessment system of weld by phased array

test-multi 2000

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(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

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

ComputerGearbox

sensor

analyzer

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Ultrasonic experimental system

Bi-slide

Omniscan

vertical

Rotational motor

sensor

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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.

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III. Ongoing research projects

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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

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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.

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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.

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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

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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.

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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

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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.

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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.