Keynotes

#1 PHM - A Historical Perspective and Projection of Future Challenges

Abstract:

The management of product life cycles and the development of maintenance strategies for electronics and mechanical systems have been the subject of research over the past century. The discipline of prognostics and health management (PHM) is currently one of the hottest topics in the reliability engineering global community, and has evolved into a focal point for such topics as condition-based maintenance, proportional hazards modeling and product lifecycle and residual useful life management. Recent advances in the areas of data science and artificial intelligence have fueled further interest in developing advanced methods using big data and internet of things (IoT) technologies.

In this presentation we look at some key historical milestones in the development of the PHM discipline and propose areas of future interest and challenges seen through the lens of IEEE and the IEEE Reliability Society and its numerous initiatives and scientific publications that have helped shape this discipline over the past 75 years.

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#2 The mechanism and control of nanoscale friction behavior on graphene surface

Abstract:

The technology of superlubricity not only can reduce friction by several orders of magnitude, but also can decrease wear and friction noise, and thereby increase the reliability of mechanical equipment. Two-dimensional (2D) layered materials, represented by graphite/graphene, are excellent solid lubricants. Utilizing these materials to create lubricant coatings is considered as an important method for achieving superlubricity in practical engineering. 2D layered material lubricant coatings are composed of numerous thin flakes. Therefore, the coating surface consists of basal planes and step edges with completely different physical and chemical properties. To understand the friction behaviors of different regions, this study investigated the friction mechanism and control methods on both graphene basal planes and step edges with atomic force microscopy. By revealing the friction mechanisms of graphene basal planes and step edges, along with the friction reduction methods, this work is of instructive significance for 2D layered material lubricant coating design and fabrication.

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#3 Securing the Future of Surgical Robotics

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This presentation introduces the integration of systems engineering principles into the field of surgical robotics, aiming to excel in this competitive and dynamic market. It presents the overall systems engineering principles and practices, explores the potential benefits and challenges of applying these methodologies at various phases of the product development. The talk highlights the importance of balancing the complex interactions between various components, stakeholders and processes involved in surgical robotics, and to maximize the overall system performance, safety, reliability.

This presentation discloses valuable insight from the industry’s experience in overcoming headwinds and secure the success of robotics development. It also points out opportunities for future research to explore.

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#4 Is “smartness” sustainable? - Open issues on the Smart Sustainable Manufacturing Paradigm

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Information is the cornerstone of the society to come and the same is for manufacturing, which has been since a long time a fundamental of the national development and prosperity.

Information is the way we interpret the world surrounding us; it may have different forms according to our perception: data, procedures, experience and even wisdom. Independently of its form, reality is made of information at the present time to the limit where information can model the reality or be the reality itself: virtualisation is just one trivial example of such a statement.

In this light, the major issue of sustainable manufacturing activities is the management of useful information, which may determine the success of this vision. To give an example, the way we choose data for measuring a phenomenon may strongly change the perception of its nature and influence. Thus sustainability, which is an emerging paradigm in manufacturing, is now leading the most of the scientific efforts in defining the assessment of sustainability and the collection of significant measures of transition toward actions that satisfies the economic, environmental, social and technological targets.

Smartness paradigm in manufacturing, on the other hand, is deeply tied to the information management and use, provided the Digital Twins as well as Cyber Physical Systems are mostly based on data get from sensing systems and on their elaboration to predict the evolution of systems simulated.

Open issues are still on the ground about the interconnections of these two paradigms: can smartness completely support sustainability or there are other advanced drivers to be considered to reach sustainability in manufacturing? Is smartness the cornerstone of sustainable manufacturing or it has even implicit drawbacks in this direction? Relevant issues are emerging up to now: to cite the most critical, AI advancements are threatening workers and their jobs, pushing the humans to higher levels of expertise and flexibility.

Speculation on these issues has already started but still there is room for discussions that can be of great interest to scientists to deepen the perception of the sustainability dimensions and reflect on the future direction of our I5.0 society.

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#5 The principles of the prognostic system in healthcare

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Personalized healthcare moves at the pace of AI and IoT technologies. Wearable sensors have enabled the discovery of prognostic multidimensional markers 24x7 and democratized access to health conditions, risk prediction, and intervention. AI and VR-powered therapy for children with Autism Spectrum Disorder (ASD) is making innovative breakthroughs. The prognostic system in healthcare empowers individuals to take proactive measures toward better health and better life. Personalized assessment, adaptive treatment, and actionable insights are the pillars. Furthermore, synthesizing time series data enables public research and data augmentation at large. The benefits and risks should be carefully balanced.

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#6 Data Stream Processing at the Edge

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With the rapid integration of massive amounts of data and proliferation of new devices (e.g., smart mobile devices, drones, Internet-of-Things (IoT), etc.), today’s network system infrastructures are being stretched to their limits. Although edge computing brings the computation closer to both delay-sensitive services and interested users, the challenges restricting the cloud model still remain. In addition, as the plethora of data generated across connected devices continues to vastly increase, the need to query the “edge” so as to derive in-time analytic insights is more evident than ever. In this talk, I will present three novel adaptive monitoring frameworks and a fog computing emulation framework. First, I will present the AdaM framework, which dynamically adjusts the monitoring intensity and the amount of data disseminated through the network based on a runtime estimation model capturing the current data evolution and variability. By accomplishing this, energy consumption and data volume are reduced, allowing IoT devices to preserve battery and ease processing at data consuming services, while still preserving accuracy. Then, I will present the ADMin framework. Rather than transmitting the entire stream, ADMin favors sending updates for its estimation model from which values can be inferred, triggering dissemination only when shifts in the stream evolution are detected. This is achieved by efficiently adapting the rate at which IoT devices disseminate monitoring streams based on run-time knowledge of the stream evolution, variability and seasonal behavior. The third framework that I will present is called ATMoN and focuses on dynamic networks. Specifically, ATMoN framework dynamically adjusts the temporal granularity graph metrics which are computed based on runtime knowledge captured by a low-cost probabilistic learning model approximating both the metric stream evolution and the runtime volatility of the graph topology. This computationally offloads graph processing engines and eases the communication overhead in edge computing networks where the wealth of data dissemination is plentiful. Next, I will present Fogify, an emulator easing the modeling, deployment and large-scale experimentation of fog and edge testbeds. Fogify provides a toolset to: model complex fog topologies comprised of heterogeneous resources, network capabilities and QoS criteria; deploy the modelled configuration and services using popular containerized descriptions to a cloud or local environment; experiment, measure and evaluate the deployment by injecting faults and adapting the configuration at runtime to test different “what-if” scenarios that reveal the limitations of a service before introduced to the public.

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#7 Intelligent Maintenance Based on Flux Leakage Detection for Special Equipment

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Special equipment refers to professional equipment with high potential risks to life safety. Therefore, safety is always the primary consideration in the design and usage processes of special equipment. Magnetic flux leakage detection is one of the important inspection methods for special equipment in industrial settings. It is widely used in infrastructure, military equipment, and engineering machinery such as railway tracks, oil and gas pipelines, pressure vessels, aircraft carrier arresting cables, and elevators, effectively ensuring the safe operation and usage of special equipment.

This report will introduce the physical principles and key technologies of magnetic flux leakage detection, as well as its application in intelligent maintenance of special equipment. Moreover, given the complex signal characteristics such as time-varying and non-stationary signals encountered in magnetic flux leakage detection, the report will share the latest research progress of the speaker’s research team in areas such as magnetic flux leakage signal processing and design of specialized magnetic flux leakage sensors.

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#8 Importance measures and system reliability optimization

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Importance measures, which are the basis and premise of system reliability optimization, are the most useful tool to find the shortness component in the complex systems. The functions of importance measures include: ① Reduce the solution complexity of system reliability optimization models; ② Find the shortness of the systems and help the preliminary decision making; ③ Improve the solving algorithm efficiency of complex system reliability optimization. Importance measures have been introduced as one of main function modules of SAS-JMP, Reliasoft, ITEM for practical engineering applications. This speech will introduce the developing history of importance measures firstly. Then, the latest innovation methods of importance measures will be discussed. Some new solving algorithms based on importance measures for system reliability optimization models are also showed thirdly. Finally, a few examples of the application for importance measures are presented.

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#9 Motor Electric Signature Analysis for Planetary Gearbox Fault Diagnosis

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Planetary gearboxes have high gear ratio in compact structure. In industrial applications, they are often driven by electric motors, and are widely used in the drivetrain of various equipment. Planetary gearbox fault diagnosis is important for equipment operation safety, but is highly challenging because of the complex kinematics and dynamics. Compared with traditional vibration-based methods, motor electric signals can be measured anywhere along power cables, so that is free of sensor placement issue. Moreover, motor electric signals have lower complexity than vibrations, owing to the rotational symmetry of electric motor–planetary gearbox drivetrain. Therefore, motor electric signature analysis provides a promising approach to planetary gearbox fault diagnosis. In this speech, our recent research on motor electric signal analytical modelling for explicit revelation of planetary gearbox fault symptoms will be introduced. Then, amplitude and frequency demodulation, and time-frequency analysis methods for fault feature extraction under both constant and nonstationary conditions will be elaborated. Finally, some future works will be discussed.

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#10 Dynamic modeling of gear transmissions for PHM

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Fault detection, diagnosis, prognosis and health management are important to ensure the safety running of gear transmissions. Dynamic modeling and analysis are extensively adopted as effective tools for PHM, due to the advantages of helping researchers to obtain the physical interpretation of complex interactions, and providing sufficient signals to train the fault diagnosis and prognosis algorithms. This presentation introduces a multi-level modeling method to establish element-component-system models of complex gear transmission structure. The spur gear, bevel gear, planetary gear, gear coupled rotors, and gear transmission with the box are modeled. Calculation methods of mesh stiffness under different faults for these gear types are developed to seed faults in the dynamic models. The vibration behaviors and fault features are analyzed. The modeling method and results might be helpful aiding researchers and engineers in the PHM studies and applications.

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#11 Knowledge transfer-driven emerging fault detection in industrial scenarios

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Intelligent maintenance technique provides a strong support to the intelligent upgrading of mechanical equipment, as well as guarantees the safe and smooth operation of mechanical systems. During the continuous operational process of machine, critical components are frequently susceptible to emerging faults, which poses a serious threat to the production safety. Knowledge transfer is a process of sharing knowledge and expertise from one context to another. In the field of mechanical fault diagnosis, knowledge transfer primarily focuses on mitigating data distribution discrepancy among different operational conditions to enhance the generalization performance of diagnostic model. It is of crucial significance in industrial scenarios to effectively achieve knowledge transfer in varying operational conditions and enable intelligent detection of emerging faults. This presentation highlights various case studies of emerging fault in industrial scenarios, including single emerging fault detection, multiple emerging faults detection and continuous detection of emerging faults in streaming data. Finally, challenges and future trends of the emerging fault detection will be discussed.

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#12 A Research on Condition Monitoring, Fault Prognostics and Intelligent Operation and Maintenance for elevators

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With the rapid growth of elevator ownership, many faults occur in elevators, which become a big threat to the safety and reliability of the elevators. Due to the difficulty of monitoring mechanical components of an elevator, and the failure mechanism is unclear, severe elevator accidents such as the car hitting the roof of the well, the car dropping to the bottom of the well, accidental movement of the car causing shearing accidents still occur frequently. In 2020, the State Market Administration issued a document to promote the "on-demand maintenance" of elevators, and the number of old elevators is increasing, there is a lack of the safety assessment technology for elevators, and also there is a lack of theoretical basis for "replacement" or "overhaul". It is urgent to study the mechanism of component performance degradation, clarify the failure law, carry out condition monitoring, fault diagnosis, fault prediction, and on-demand maintenance.

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#13 The security and reliability of Mobile Communication with RF Fingerprint Technology

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With the rapid deployment of 5GNR, the mobile communication network has been greatly developed and expanded, and the security problems hidden in it have become more and more significant. Compared with traditional security technology, radio frequency fingerprint identification technology can provide reliable physical layer security protection, and has strong application value in the future 5G, even 6G.This report will introduce the security issues in 5G system, as well as the background and development process of RF fingerprint technology, share the relevant achievements of the research group in solving the security problems of wireless communication physical layer by using RF fingerprint, and look forward to the future development trend of RF fingerprint technology.

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