Effective machine maintenance policy is a critical element of a smooth running manufacturing system. This paper evaluates a multiple-recipe predictive maintenance problem modeled using a M/G/1 queueing system. A numerical study is performed on an optimal predictive maintenance policy. A simulated job-based maintenance policy is used as a baseline for the optimal policy. We investigate the effects of varying degradation rates, holding costs, preventive maintenance times, and preventive maintenance costs. We also examine a two-recipe problem. / text

The term resource is used to refer to a machine, tool-group, piece of equipment or personnel. Optimization models for resource maintenance are obtained in conjunction with other production related decisions like production planning, production scheduling, resource allocation and job inspection. Emphasis is laid on integrating the above inter-dependent decisions into a unified optimization framework. This is accomplished for large stationary resources, small non-stationary resources with high breaking rate and for resources that form a part of a network. Owing to large problem size and high uncertainty, the optimal decisions are determined by formulating and solving the above problems as Markov decision processes (MDPs). Approximate dynamic programming based algorithms are used for solving the large optimization problems at hand. The performance of resulting near optimal policies is compared with that of traditional formulations in all cases. The latter treat the resource maintenance decisions independent of other manufacturing related decisions. In certain formulations, the resource state is not completely observable. This results in a partially observable MDP (POMDP). An alternative algorithm for the solution of POMDP is developed, where several mixed integer linear programs (MILPs) are solved during each ADP iteration. This helps obtain better quality solutions for the POMDPs with very large or continuous action spaces in an efficient manner.

In this Thesis, we first deploy Gittins index theory to establish the indexability of inter-alia general families of restless bandits that arise in problems of stochastic scheduling with switching penalties and machine maintenance. We also give formulae for the resulting indices. Numerical investigations testify the strong performance of the index heuristics.The second class of problems concerns two families of Markov decision problems. The spinning plates problem concerns the optimal management of a portfolio of assets whose yields grow with investment but otherwise decline. In the model of asset exploitation called the squad system, the yield from an asset declines when it is utilised but will recover when the asset is at rest. Simply stated conditions are given which guarantee general indexability of the problem together with necessary and sufficient conditions for strict indexability. The index heuristics, which emerge from the analysis, are assessed numerically and found to perform strongly.

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The diploma thesis deals with the technical diagnostics of machines with a closer focus on vibrational diagnostics. In the thesis is elaborated a design of a diagnostic system with measurement methodology, measurement procedure and evaluation of measured data on the machine produced by Siemens.

The diploma thesis is focused on the determination of the life cycle state of the production machine. The thesis is divided into several chapters. First, the life cycle of the machine is defined, and the phrase used here is given. Subsequently, the work is devoted to maintenance, repairs and modernization of the production machine. Then there is a demonstration of Schaeffler Skalica's corporate structure and individual methods that determine the state of the machine's life cycle. In the next chapter, the machine is described and then the analysis is made for the current state of the machine. Then the analyses created are evaluated.

<p dir="ltr">This study evaluated a technology-based intervention aimed at reducing injuries among young and new farmers and ranchers (YNFR) by enhancing access to critical machinery safety information. To address high injury rates linked to agricultural machinery, this project developed an educational program integrating Quick Response (QR) codes directly on equipment to teach critical maintenance and safety concepts. These codes allowed YNFRs to instantly access tailored instructional content, including video modules and maintenance checklists, via mobile devices. The content was derived from Purdue University’s "Gearing Up for Safety" curriculum and emphasized competencies such as pre-operation inspections, hazard awareness, and essential maintenance procedures.</p><p dir="ltr">With guidance from an advisory group comprising agricultural educators, small-scale farmers, and safety experts, the project prioritized addressing common injury causes such as rollovers, entanglements, and falls. Analysis of more than 200 incident reports informed the development of QR-linked instructional materials tailored to the unique safety challenges of the YNFR demographic. This system provided accessible, reliable, and practical safety information through instructional videos, enabling users to engage with critical content directly at the point of need.</p><p dir="ltr">Findings indicated that QR-based delivery facilitated user access to reliable, practical instruction. Participants were able to learn safer equipment handling and maintenance practices. Pre- and post-quiz results demonstrated a measurable increase in knowledge. Suggested future research could quantify the long-term impact of this approach on YNFR safety performance and behavioral change.</p>