Abstract
Today's demands on manufacturing companies, which include high production volume and quality, imply that machine downtime must be reduced as much as possible. However, since mechanical failures in complex industrial plants can never be completely avoided, it is important to support operators in finding the root cause of the failure as quickly as possible in order to restore the machine to a productive state. This can be done using root cause analysis systems. However, current approaches need to process large amounts of data to deliver a result and are difficult to adapt to different machines. This paper solves these problems by combining root cause analysis with alarm flood reduction to reduce the amount of data to be processed. It also presents a semantic model to formulate the relationships between alarm floods and root causes. In addition, a concept that ensures adaptability to different machines is presented.
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