Supporting group maintenance through prognostics-enhanced dynamic dependability prediction

Condition-based maintenance strategies adapt maintenance planning through the integration of online condition monitoring of assets. The accuracy and cost-effectiveness of these strategies can be improved by integrating prognostics predictions and grouping maintenance actions respectively. In complex industrial systems, however, effective condition-based maintenance is intricate. Such systems are comprised of repairable assets which can fail in different ways, with various effects, and typically governed by dynamics which include time-dependent and conditional events. In this context, system reliability prediction is complex and effective maintenance planning is virtually impossible prior to system deployment and hard even in the case of condition-based maintenance. Addressing these issues, this paper presents an online system maintenance method that takes into account the system dynamics. The method employs an online predictive diagnosis algorithm to distinguish between critical and non-critical assets. A prognostics-updated method for predicting the system health is then employed to yield well-informed, more accurate, condition-based suggestions for the maintenance of critical assets and for the group-based reactive repair of non-critical assets. The cost-effectiveness of the approach is discussed in a case study from the power industry

A Reconfigurable Computing Solution to the Parameterized Vertex Cover Problem

Active research has been done in the past two decades in the field of computational intractability. This thesis explores parallel implementations on a RC (reconfigurable computing) platform for FPT (fixed-parameter tractable) algorithms. Reconfigurable hardware implementations of algorithms for solving NP-Complete problems have been of great interest for research in the past few years. However, most of the research that has been done target exact algorithms for solving problems of this nature. Although such implementations have generated good results, it should be kept in mind that the input sizes were small. Moreover, most of these implementations are instance-specific in nature making it mandatory to generate a different circuit for every new problem instance. In this work, we present an efficient and scalable algorithm that breaks out of the conventional instance-specific approach towards a more general parameterized approach to solve such problems. We present approaches based on the theory of fixed-parameter tractability. The prototype problem used as a case study here is the classic vertex cover problem. The hardware implementation has demonstrated speedups of the order of 100x over the software version of the vertex cover problem

An extendible reconfigurable robot based on hot melt adhesives

The ability to physically enlarge one’s own body structures plays an important role in robustness and adaptability of biological systems. It is, however, a significant challenge for robotic systems to autonomously extend their bodies. To address this challenge, this paper presents an approach using Hot Melt Adhesives (HMAs) to assemble and integrate extensions into the robotic body. HMAs are thermoplastics with temperature dependent adhesiveness and bonding strength. We exploit this property of HMAs to connect passive external objects to the robot’s own body structures, and investigate the characteristics of the approach. In a set of elementary configurations, we analyze to which extent a robot can self-reconfigure using the proposed method. We found that the extension limit depends on the mechanical properties of the extension, and the reconfiguration algorithm. A five-axis robot manipulator equipped with specialized HMA handling devices is employed to demonstrate these findings in four experiments. It is shown that the robot can construct and integrate extensions into its own body, which allow it to solve tasks that it could not achieve in its initial configuration.This work was supported by the Swiss National Science Foundation Professorship Grant No. PP00P2123387/1, and the ETH Zurich Research Grant ETH-23-10-3.This is the author accepted manuscript. The final version is available from Springer via http://dx.doi.org/10.1007/s10514-015-9428-