International Symposium on Dynamics, Monitoring and Diagnostics  & Journal of Dynamics, Monitoring and Diagnostics Launch Event

1.Symposium Theme 

We are greatly honored to announce that the online International Symposium on Dynamics, Monitoring and Diagnostics & Journal of Dynamics, Monitoring and Diagnostics Launch Event will be jointly held on 21-22 April 2022 in Chongqing, P. R. China by Chongqing College of Architecture and Technology, Chongqing University of Technology and Chongqing (Sino German) Future Factory Research Institute.

The Symposium aims to enhance the intelligence of condition monitoring for engineering systems and will conduct in-depth thematic dialogues to build a high-level cooperation and communication platform for international mechanical fault diagnosis and condition monitoring, and promote the intelligentialize and informatization development of advanced machinery manufacturing. At the same time, online activities such as the JDMD Launch Event and the first global Editorial Board Meeting of JDMD will be held. We welcome the specialists and scholars in the related fields to join the Symposium!

2. Symposium Topics 

Fault Diagnosis and Health Management; Feature Extraction, Fault Detection and Severity Assessment; Fault Dynamics Simulation and Failure Mechanism Analysis; New Technology for Testing and Sensing; New Techniques of Signal Processing for Condition Monitoring

3.  Join the Zoom Meeting


Room ID:914 7031 1113    Password:******(will be shared on April 20)

4. Program and Guests

 Jinji Gao

Gao Jinji is a specialist in equipment diagnosis engineering, Academician of the Chinese Academy of Engineering, and a Professor of Beijing University of Chemical Technology (BUCT). He received a Ph.D. degree in Engineering from Tsinghua University in 1993. He served as Deputy Chief Engineer of PetroChina Liaoyang Petrochemical Company from 1988 to 1999. He has been Professor of BUCT since 2000. He was conferred on the title of National Young and Middle-aged Expert with Outstanding Contributions, and Chief Scientist of both "973" Project of the MOST (Ministry of Science and Technology) and "973" Project of the Ministry of National Defense. Now Professor Gao is Deputy Director of Special Equipment Professional Committee of Work Safety Committee of the State Council, and Director of Technical Expert Committee of China Academy of Industrial Internet.

Professor Gao and his research team have taken the lead in the development and application of equipment diagnosis technology in China, and have developed the network monitoring and diagnosis system of machine pump clusters. This system has carried out remote real-time monitoring and diagnosis of over 2,000 key machine units in more than 100 enterprises and avoided many major accidents successfully. Professor Gao obtains a number of scientific and technological achievements in artificial self-recovery and power machinery health monitoring. He has published hundreds of journal papers and two books (Machine Fault Diagnosis-cure and Self-recovery, etc.). In 2016, Professor Gao received the Lifetime Achievement Award of the World Engineering Asset Management Association.In 1999, Professor Gao was elected to Academician of Chinese Academy of Engineering.

--Title  Turbine Machinery Intelligent Fault Source Tracing Diagnosis and Self-recovery Regulation


  1. Development of networked and intelligent monitoring and diagnosis of turbine machinery used in the petrochemical industry both home and abroad;
  2. The discussion of dynamic analysis as the basis of source tracing diagnosis, illustrated by the "critical load" of the integral gear-driven centrifugal compressor and the "six-dimensional alignment" of the multi-rotor shaft system;
  3. Fusion of big data analysis and artificial intelligence based on the industrial Internet to help fast and accurate source tracing diagnosis;
  4. Case analysis of the experiment research and engineering application related to artificial fault self-recovery to enable assisted recovery and autonomous health of turbine machinery.

Jing Lin

Professor Jing Lin, Ph.D. in Engineering, is Dean of School of Reliability and Systems Engineering, Beihang University. He received his PhD degree in Mechanical Manufacturing and Automation from Xi'an Jiaotong University in May 1999. He is the leader of the Innovative Research Group of the National Natural Science Foundation of China, a recipient of the National Science Fund for Distinguished Young Scholars, Distinguished Visiting Professor of Changjiang Scholars Program initiated by the Ministry of Education, a leading talent of National High-level Talents Special Support Program, and a state-level candidate of the Millions of Talents Project. He mainly engages in dynamic testing of mechanical equipment, fault diagnosis, industrial big data technology and other aspects of research work. In recent years, he has chaired 6 major special projects of National Science and Technology, and published more than 80 SCI papers, which have been cited by SCI for more than 2000 times. Theory of Wavelet Entropy Detection and Adaptive Extraction of the Transient Information for Early Mechanical Fault, a scientific research project supervised by Professor Jing Lin, won Second Prize of National Natural Science in 2013. Its research results have been applied to the equipment fault diagnosis of energy and power, petrochemical industry, equipment manufacturing, rail transportation and other fields. Directly adopted and tracked by scholars in Europe, America, Japan and other countries, the research results have also been applied in various fields like architecture, biology, electric industry, and ocean research.  

--Title  Machinery Informatics: A New Direction for the Engineering Science


A promising and efficient way to improve the performance of the machinery is to continuously integrate new technologies into the total life cycle. With the in-depth development of the integration of industrialization and informatization, how to deeply integrate modern information technology with machinery is the key to improving the level of intelligentization of mechanical products.

The subject of machinery information proposed in the paper is used to investigate all the dynamic information of the machinery throughout the life cycle, which integrates the disciplines of information technology, mechanics, manufacturing and other related disciplines so as to provide much more detail and abundant information about the performance and it related parameters. The concept and content of the machinery information is also introduced, some examples are given to illustrate how to solve the practical problems based on this theory.

Wade Smith

Wade Smith is a Research Fellow at the University of New South Wales in Sydney, Australia. Dr Smith studied for his Bachelor (2005) and PhD (2009) degrees in Mechanical Engineering at the University of Technology, Sydney. The topic of his PhD was the dynamic modelling and analysis of hydraulic suspension systems for automotive applications. After a two-year stint at Australia’s National Measurement Institute, in late 2011 Dr Smith joined Professor Bob Randall’s machine condition monitoring group at UNSW Sydney as a post-doctoral fellow. Since then, Dr Smith’s research has focused mainly on vibration-based machine diagnostics and prognostics, in particular of gears and bearings. Recent research projects include: measurement and diagnosis of gear wear, transmission-error-based gear diagnostics, operational modal analysis of machines, planetary gearbox diagnostics, development of digital twins for transmission systems, and high-speed bearing diagnostics. Dr Smith is a member of the International Society for Condition Monitoring and is an Associate Editor of the Journal of Dynamics, Monitoring and Diagnostics. He has authored and co-authored more than 100 journal and conference publications and has supervised several PhD and Master’s projects to completion.

--Title  Gear Wear: Measurement, Diagnosis and Prognosis


Geared transmission systems inevitably experience wear in their service lives. In its broadest definition, wear includes any material removal process, of which the most relevant for gears are abrasive wear, based on the contact and breakage of asperities in sliding contact, and fatigue pitting, based on repetitive loading.While techniques for basic vibration-based gear wear detection have existed for decades, there has been limited research on the quantification of gear wear and its detailed diagnosis. And as with most areas of condition monitoring, prognosis of wear remains a major challenge, but one with enormous potential economic benefits.

In recent years, however, there have been several key advances in the measurement, diagnosis and prognosis of wear. This talk will discuss some of these advances, with a focus on recent activities undertaken by the Tribology and Machine Condition Monitoring group at the University of New South Wales in Sydney, Australia. The advances include developments in measurement approaches, advanced signal processing techniques using cyclostationary signal analysis, and the deployment of digital twins for wear prediction. The talk will also canvass possible future directions in gear wear and gear diagnostics more broadly.

Jerome Antoni

Jerome Antoni received the M.S. degree in Mechanical Engineering from the University of Technology of Compiegne, Compiegne, France, in 1995, and the Ph.D. degree in Signal Processing from the Grenoble Institute of Technology, Grenoble, France, in 2000. He currently holds a full professor position at the University of Lyon, Lyon, France. His current research addresses the development of signal processing methods in mechanical applications. This includes vibration-based condition monitoring and the resolution of inverse problems in acoustics and vibration. Dr. Antoni served as Handling Editor for the International Journal of Condition Monitoring, the International Journal of Rotating Machinery, and the Diagnostika. He is currently with the Editorial Board of the Mechanical Systems and Signal Processing and Applied Sciences.

--Title  Choosing the Good Signal Model for Vibration-based Condition Monitoring


Vibration-based condition monitoring heavily relies on signal processing, for designing methods of detection, identification, and characterization of faults, or for pre-processing signals before they are fed to machine learning classifiers. A plethora of ad hoc methods have been proposed to achieve these tasks, sustained by an impressive diversity of heuristics and algorithmic variants, and leading to everlasting round-robin benchmarks. Although the quest for universal methods is hopeless, the point made by this presentation is that “optimal” indicators and signal processing methods can be designed, provided that they are guaranteed to capture the whole amount of diagnostic information contained in a “good signal mode”. This presentation will first give an overview of the signal models most commonly used in vibration-based condition monitoring, with an emphasis on those that can capture the intermittent and repetitive nature of machine faults. It will next discuss their extensions for coping with adverse configurations, such as impulsive noise or non-constant regime. In particular, we will introduce Conditionally Angle-Time Cyclostationary processes (CAT), which stand as a the most advanced version of a series of attempts to describe the properties of vibration signals produced by rotating machines in the most general setting. The presentation will eventually discuss how “optima” indicators can be designed within this framework and it will illustrate their use for signal processing tasks such as detection and deconvolution.

Stephan Heyns

Stephan Heyns is professor in the Centre for Asset Integrity Management in the Department of Mechanical and Aeronautical Engineering at the University of Pretoria in Pretoria, South Africa. He was awarded his PhD in mechanical engineering from the University of Pretoria in 1988.

His personal research interests focus on rotating machinery diagnostics and prognostics with a special emphasis on gearbox, bearing and turbomachinery applications. He is interested in the application of signal processing as well as machine learning approaches and recently specifically in the complementary use of these approaches.

He is an accredited researcher with the National Research Foundation in South Africa, a registered professional engineer in South Africa, as well as a fellow of the South African Academy of Engineering, a fellow of the Royal Aeronautical Society, a fellow of the International Society of Engineering Asset Management and honorary fellow of the South African Institution of Mechanical Engineers.

--Title  Enhancement of Vibration Monitoring Under Noisy Non-stationary Conditions


Vibration monitoring of rotating machinery under noisy non-stationary operating conditions remains difficult because of factors such as amplitude and frequency modulation and impulsive noise that impede the application of conventional condition indicators.

To improve the performance of these monitoring techniques, various techniques that rely on traditional signal processing can be used. These range from the enhancement of weak damage components in the vibration signals, identifying informative frequency bands and to the use of new synchronous statistics for gearbox fault diagnostics.

Learning-based methods provide a complementary perspective on the signal processing problem, by potentially addressing some of the shortcomings of traditional condition monitoring methods.

This paper highlights some of these concepts to enhance vibration monitoring under non-stationary conditions from signal processing and learning-based perspectives.

Huajiang Ouyang

Huajiang Ouyang received BEng and MEng in Engineering Mechanics in 1982 and 1985, respectively, and PhD in Structural Engineering in 1989, at Dalian University of Technology, China. He is a full Professor at the School of Engineering at the University of Liverpool and the Head of the Dynamics and Control Group.

Dr Ouyang is a Fellow of Institute of Physics and a Fellow of Higher Education Academy. He was a Royal Academy of Engineering and Leverhulme Trust Senior Research Fellow in 2009-2010. He is also a Changjiang Chair Professor in China. He is an editor of Journal of Sound and Vibration, Mechanical Systems and Signal Processing, and European Editor of International Journal of Vehicle Nosie and Vibration; and on the editorial boards of Chinese Journal of Computational Mechanics, Chinese Journal of Mechanical Engineering, Journal of Dynamics, Monitoring and Diagnostics. He has published 280+ journal papers and 110+ conference papers. His papers have received over 7600 citations (H-index of 46).

His main search areas are structural dynamics and control, and structural identification. He is particularly interested in friction-induce vibration, moving-load dynamics, inverse structural modifications, and vibration-based energy harvesting.

--Title  Frequency and Mode Assignment via Structural Modifications -- Basic Theory and Examples


Vibration induced by an excitation in a few single frequencies or in a narrow frequency band can be mitigated effectively by shifting the natural frequencies in question away from those of the excitation. A node may be created on a structure where vibration at a frequency is suppressed. These can be done via inverse structural modifications whereby the mass and stiffness required to shift the natural frequencies and/or assign modes (including nodes) are determined. This methodology is powerful and useful in practical applications. This talk describes the motivations of structural modifications, introduces the basic theory behind and present real examples of structural modifications.