The Application Of Data Mining In Ship Propulsion Systems For Fault Diagnosis

Data mining is a new emerging technology with the development of artificial intelligence and database technology in recent years. In essence, data mining technology efficiently and automatically discover information from the collection of large amounts of data, which is previously unknown implicitly and is potentially valuable for decisions. An important feature of these traditional data mining techniques is that it is in the need of a large enough sample data, which must be large enough to be able to obtain valuable information. However, even if there is a large enough sample data, it does not necessarily have access to valuable knowledge in some cases. If the amount of sample data is not or has defects, the overall data on the law is complex but there is a strong regularity of local data and other such cases. It is difficult to obtain valuable information of traditional data mining techniques.The essence of gray data mining is to make the advantage of the role of gray system theory in the field of data mining, and it makes the discovery of knowledge more effective and credible. According to the theory of data mining and the characteristics of gray, the basic idea of gray data mining is to use the concepts and techniques of gray theory, combined with data mining system forming, automated knowledge to discovery techniques and to construct "small samples, poor information" knowledge of the system automatically found in information systems. The gray correlation analysis method was applied to marine diesel propulsion systems for fault diagnosis, and diagnostic methods to overcome the general diagnostic method requiring a large number of samples, slow convergence, the relative difficulty of solving global extreme disadvantage.This article mainly apply sophisticated data mining techniques combined with gray theory to build a gray data mining architecture. It mainly regards the diesel engine as a gray system, the fact to carry out fault diagnosis system is to use known information to determine the unknown information, which uses state of the diesel engine to determine a comprehensive evaluation and analysis. First, the gray system modeling, Gray Relational Analysis and gray model prediction; Second, extract the appropriate signal from the state of the characteristic parameters, composition can reflect the normal and a variety of diesel engine fault state characteristics of the benchmark model; finally, calculate the correlation between the inspection mode (to be diagnostic status) and the benchmark model, in accordance with the principle of maximum correlation to determine what kind of benchmark for inspection status and the status associated with the largest for state identification, and determine the most likely failures.This experiment shows that the gray correlation analysis method in diesel engine fault diagnosis is a very effective and reliable, and the diagnosis is a very effective method of fault diagnosis.