# Development of Ground Special Vehicle PHM with Case-Based Reason Model
###### Abstract  
The case-based reasoning (CBR) method can effectively predict the future health condition of the system based on past and present operating data records, so it can be applied to the prognostic and health management (PHM) framework, which is a type of data-driven problem-solving. The establishment of a CBR model for practical application of the Ground Special Vehicle (GSV) PHM framework is in great demand. Since many CBR algorithms are too complicated in weight optimization methods, and are difficult to establish effective knowledge and reasoning models for engineering practice, an application development using a CBR model that includes case representation, case retrieval, case reuse, and simulated annealing algorithm is introduced in this paper. The purpose is to solve the problem of normal/abnormal determination and the degree of health performance prediction. Based on the proposed CBR model, optimization methods for attribute weights are described. State classification accuracy rate and root mean square error are adopted to setup objective functions. According to the reasoning steps, attribute weights are trained and put into case retrieval; after that, different rules of case reuse are established for these two kinds of problems. To validate the model performance of the application, a cross-validation test is carried on a historical data set. Comparative analysis of even weight allocation CBR (EW-CBR) method, correlation coefficient weight allocation CBR (CW-CBR) method, and SA weight allocation CBR (SA-CBR) method is carried out. Cross-validation results show that the proposed method can reach better results compared with the EW-CBR model and CW-CBR model. The developed PHM framework is applied to practical usage for over three years, and the proposed CBR model is an effective approach toward the best PHM framework solutions in practical applications. c 2021 11 May 2021 14 May 2021  
maintenance is very important. In order to comprehensively manage the operation of GSV, researchers prefer to adopt the Prognostic and Health Management (PHM) structure to ensure safety and reliability, and to provide solutions based on health management [1; 2; 3; 4]. Based on diagnostics/prognostic information, available resources, and operational demand, PHM is capable of making appropriate decisions about maintenance actions in its actual life-cycle conditions. One of the important tasks involves continuous monitoring with analysis using proven algorithms [5; 6; 7; 8]. When it comes to GSV, the development of data collecting and processing technologies is introduced, a large number of multiple on-board sensors are deployed to perform condition monitoring and health management of GSV, at the same time, a large amount of data about the operation conditions of the GSV are recorded. In addition to data from historical operation record, historical experiences from expert's domain knowledge are also maintained and stored. In this way, GSV stores a large amount of historical operational knowledge about problems and solutions during the operation and maintenance cycle, this information constitutes a case library for reasoning. How to make better use of the knowledge as a reference to offer feasible solutions is the main topic. Based on the inference of a large amount of knowledge retrieval and analysis, a quantitative or qualitative conclusion about the health of the system can be derived. In some practical cases, a rule-based reasoning model can be appropriate for knowledge representation in PHM structure [9]. However, in this model, knowledge acquisition and expression are based on expert experience and have a certain degree of subjectivity. Therefore, it is difficult to express and acquire knowledge.