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Due to the megatrend globalization, special machinery is gaining significance for the capital goods sector. Characterized by the fulfillment of individual customer requirements, companies in special machinery have to deal with very specific and technologically complex tasks. Hence, managing information and knowledge becomes vital for a company’s competitive ability, notably when it comes to expert knowledge. The characteristics of special machines leads to iterative processes for problem solving and thereby, increase lead times significantly. The more technologically complex a machine is, the more scattered the expert knowledge, meaning that many different experts need to be consulted before solving a problem. Up to now, in scientific literature, there has been little discussion about the challenges of special machinery and practical solutions regarding an implementation of technical intelligence in a special machinery environment. Therefore, the goal of this paper is to give an example of how an expert system can be applied to special machinery surroundings and thus, increases productivity. A Bayesian network forms the basis of the system as it allows efficient inference algorithms and reasoning under uncertainty, despite its ability to describe complex dependencies. The expert systems capability has been proven in industrial laser manufacturing

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In Germany the growing demand for customized systems and integrated solutions in machinery enhance the importance of special machinery. Within this industry, the commissioning process represents a significant part in the product engineering process and forms the base for reliability and performance during future operation. However, there is little research focusing on this process for special machinery. In particular, there has been little discussion on methods to evaluate alternative test processes or arranging test processes along the commissioning process. Therefore, this paper develops an application-oriented simulation tool that allows an evaluation of test alternatives and an arrangement of test processes during the commissioning process in special machinery. The authors decided to use Bayesian Networks to model the commissioning process as they enable the connectivity of multiple modules and integrate the stochastic dependencies along the processes. In addition the paper reveals two concepts to deal with unknown processes and the lack of data. Applying the simulation tool in a laser system manufacturer reveals that the simulation tool allows an evaluation as well as the identification of risks and need for countermeasures