Date of Graduation
Doctor of Philosophy in Engineering (PhD)
Second Committee Member
Third Committee Member
Fourth Committee Member
Failure Analysis, Fault Adaption, Industrial Engineering, Mechanical Engineering, Mission Planning, Workload Allocation
This research proposes novel fault adaptive workload allocation (FAWA) strategies for the health management of complex manufacturing systems. The primary goal of these strategies is to minimize maintenance costs and maximize production by strategically controlling when and where failures occur through condition-based workload allocation.
For complex systems that are capable of performing tasks a variety of different ways, such as an industrial robot arm that can move between locations using different joint angle configurations and path trajectories, each option, i.e. mission plan, will result in different degradation rates and life-expectancies. Consequently, this can make it difficult to predict when a machine will require maintenance, as it will depend not only on the type and quality of the machine, but the actual tasks and mission plans it is performing. Furthermore, effective maintenance planning becomes increasingly challenging when dealing with complex systems, such as manufacturing production lines, that have multiple machines all performing different tasks, as the different degradation rates of each task will likely cause sporadic failures, leading to excessive work stoppages and lost production.
In response, this work proposes novel strategies for optimizing maintenance schedules through fault adaptive workload allocation (FAWA). This work will show how we can alternate between multiple mission plans and task assignments to control degradation across multiple components, guiding failures to occur at optimal times and locations. We will present two unique strategies for degradation control. The first strategy attempts to synchronize maintenance by utilizing multiple mission plans and task assignments, such that the healthiest components do the most work, whenever possible, in order to compensate for the more degraded components. This promotes balanced degradation and synchronized failures across all components, allowing the number of work stoppages to be minimized. The second strategy involves desynchronizing maintenance by alternating between mission plans and task assignments where the healthiest components do either the most work or the least work in order to maintain an optimal difference between component degradation rates, such that overlapping failures are minimized. In this work, FAWA is applied to several case studies involving two types of manufacturing systems: industrial robot arms and 3D printers.
DeStefano, C. B. (2019). Fault Adaptive Workload Allocation for Complex Manufacturing Systems. Graduate Theses and Dissertations Retrieved from https://scholarworks.uark.edu/etd/3151
Artificial Intelligence and Robotics Commons, Manufacturing Commons, Operational Research Commons