@ Technology & Innovation Centre, University of Strathclyde Space Robotics Symposium 2015 An Integrated Framework for Intelligent Reliability Design and Prognostic Health Management of Space Robotic System Prof. Zhonglai Wang University of Electronic Science and Technology of China Dr. Yi Chen Glasgow Caledonian University Dr. Erfu Yang University of Strathclyde @ Technology & Innovation Centre, University of Strathclyde 2015-10-29 1/15 1

OUTLINE Motivation Integrated Framework Case Study Future Work 1 2 3 4 2/15

● Degradation ● Fracture Time-variant failure modes Motivation ● Vacuum ● Microgravity ● Temperature ● Debris ● Friction ● Shocks ● Degradation ● Fracture Time-variant failure modes Harsh Space Environment Improve designed reliability Reliability-based design optimization (RBDO) Prognostic health management(PHM) Integrated framework of RBDO and PHM Improve operating reliability Improve design and operating reliability 3/15

Integrated Framework (iRPHM) Methodologies and Applications of iRPHM for Space Robotic Systems Failure Mechanism analysis Time-variant reliability indexes PHM framework and analysis iRPHM framework design Case study Dynamic analysis method under coupled uncertain field; Failure mechanism analysis method; PHM framework Construction; analysis; Time-variant reliability analysis method; Time-variant model validation technique; iRPHM mathematic model; iRPHM design; Application; Methodology validation. Validation and Improvement of Proposed Methodologies 4/15

1

Failure mechanism analysis Coupled space conditions analysis Dynamic analysis Failure mechanism analysis ► Vacuum ► Microgravity ► Variant temperature ► Micrometeoroid ► Space debris ● Dynamic behaviour ● Failure mechanism State of the Art: MORE literatures on single input but LESS on multiple inputs. Novelties: Add new ports by improving the bond graph for more inputs; evolution procedure of failure mechanism. 5/15

Time-variant reliability analysis Space environment Strength degradation Mechanism motion Time-variant Correlated failure modes State of the Art: Time-invariant correlation MORE, time-variant LESS. Novelties: Time-variant Copula function for improving computational accuracy. 6/15

Time-variant reliability model validation Monitoring data Field data Experimental data Time-variant State of the Art: Time-invariant and sample invariant MORE, consider time and sample LESS. Novelties: Time-variant B-distance validation index covers time and samples. 7/15

PHM framework and analysis State of the Art: The construction of PHM framework at the early stage; MORE data-driven prognosis, LESS failure mechanism driven prognosis. Novelties: From the aspect of lifecycle, study the PHM framework; Dynamic prognosis based on failure mechanism. 8/15

iRPHM design Mathematic model construction and design 9/15

Computation Intelligence (CI)-aided design for iRPHM CI Aided Design (CIAD) for iRPHM CI integrated solver Y. Chen, SwarmWolf - The Artificial Wolf Pack Algorithm (AWPA), http://www.mathworks.com/matlabcentral/fileexchange/xxx http://1drv.ms/1sRfFgZ 10/15

Case study Random variables 4 design variables and 21 constraints 11/15

Considering strength degradation Case study Considering strength degradation 12/15

Concurrent design optimization Case study Concurrent design optimization 13/15

Case study 14/15

Future Works IRPHM software platform development and testing System integration and validation Application explorations for more space robotic systems, e.g.: robotic space tether space exoskeleton Orbital-servicing manipulators Planetary rovers,etc

Q&A Thanks! 15/15