Research On Oil Analysis System Based On Machine Learning Method

As the development of maintenance, a new kind of maintenance method called proactive maintenance came out and became more and more effective during the equipment fault diagnosis. How to define the fault source is the critical problem during the processes of proactive maintenance. On the other hand, the ability of knowledge acquisition to a expert system is very important now, and we always use the machine learning method to achieve the knowledge acquisition automatically. All these problems are discussed in this paper.A proactive maintenance based on machine learning approach is defined at first to solve the problems such as knowledge acquisition automatically and help make decision about the fault source and maintenance.In all kinds of machine learning approach, the conclude method is more suitable than the others to the fault diagnosis expert system. With the ability of incremental learning, it can deal with a new instance without relearning the entire example set. The paper describes an approach that the incremental induction of decision tree is used in the proactive maintenance. On the other hand, how to describe the standard of the fault state of equipment is also not an easy matter. The oil analysis technology, as the primary measure of proactive maintenance, can help make decision to the maintenance all together with the machine learning method.While treating with a new training instance, the existing algorithms almost needed to calculate the entropy of each attribute without consider the importance of each training attribute. Rough Set is now widely used as attribute reduction, so that it can be well used during incremental learning approach of decision tree, without counting each entropy but only the most important attributes'. A new approach is defined in this paper, and through a set of oil samples the approach is demonstrated during the learning process of an oil fault diagnosis system.At last, an example program is developed based on Microsoft SQL Server 2000 and Power Builder to realize the model that had mentioned above.