Qualitative Model-Based Decision Tree Generation for Diagnosis in Real World Industrial Application

Computer diagnosis systems grounded on hand-crafted decision trees are wide-spread in industrial practice. Since the complexity of technical systems increases and innovation cycles get shorter, the need for systematic decision tree generation and maintenance arises. In this paper, the MAD system is introduced which generates decision trees based on a new method for qualitative electrical circuit analysis. Different resources such as design data and expert design know-how as well as diagnosis knowledge can easily be integrated into decision tree generation. Since a decision tree can be generated automatically based on a device model, the cost for providing, modifying, and maintaining diagnosis equipment can be drastically reduced and quality management of diagnosis equipment can be facilitated. Furthermore, the cost of decision-tree-based fault identification can be reduced because model-generated decision trees can be optimized. We have successfully evaluated the MAD system in cooperation with the german forklift manufacturer STILL GmbH Hamburg.