# Development of Ground Special Vehicle PHM with Case-Based Reason Model
###### Abstract
Unlike other application areas, for GSV, a large amount of historical operational knowledge about problems and solutions were stored during the operation and maintenance cycle, and case-based reasoning (CBR) methods can make better use of this knowledge to provide solutions and maintenance suggestions for the diagnosis and health management of the system [10]. CBR is a kind of reasoning and machine learning method in the field of artificial intelligence (AI). It is especially applicable to the fields with no accurate mathematical models, but with much information of experience and historical knowledge. The basic principle of the CBR method is to solve new problems by reusing solutions of the previous similar problems accumulated in historical experiences [11; 12]. The historical experiences are combination of past operation cases that contain data records, semantic descriptions, evaluation results, and solution references. CBR can imitate analogy human thinking, and learns from experiences, which is in line with the human cognitive process of new things. CBR has the characteristics of easy acquisition of knowledge, simplification of solving process, high quality of the solution, and incremental learning [13]. CBR has developed rapidly in recent years. Several international case-based reasoning conferences have been held in European and other CBR workshops [12; 13]. CBR technology has been applied in various fields, such as fault diagnosis, product design, pattern classification, regression prediction, intelligent control, and has achieved remarkable application success [14; 15; 16; 17; 18; 19]. The basic CBR is composed of 4R procedures, namely, case retrieval, case reuse, case revision, and case retain. In the case of retrieval, the weight allocation of the case attribute determines the similarity between cases, thus affecting the result of case retrieval [20; 21]. Due to the different characteristics of practical fields, the specific case-based reasoning model is not applicable to other application fields; moreover, the reasoning algorithm must not only meet the application model requirements of the actual system, but also be able to make it practical. Weight calculation for case similarity is a key issue of the CBR method. Algorithms for weight allocation must be improved or re-considered according to the case base of a certain field. It is necessary to set weights for the cases in these case libraries through fast and effective algorithms. Simulated annealing (SA) algorithm is a kind of optimization algorithm that converges to the optimal global solution with a probability of 1 [22; 23]. SA has the adaptability to deal with both discrete data and continuous data. SA has the characteristics of universal, easy to achieve, and high quality, and is widely used in engineering fields, such as fault diagnosis, circuit design, vehicle routing,and image processing [23; 24; 25; 26; 27]. It is of high practical value. To make GSV PHM structure more suitable and practical, a CBR model containing a simulated annealing algorithm for optimizing case attribute weights is developed. The model begins with case representation, then, the optimization methods for attribute weights are established for case retrieval. The proposed method is applied to subsystem health evaluation of GSV. Cross-validation results show that the proposed method can reach the accuracy of case-based reasoning of GSV PHM structure. The rest of this paper is organized as follows. In Section 2, the application framework using the CBR model for PHM is briefly introduced, as well as the topics regarding case representation, case retrieval, and case reuse are described. In Section 3, the optimization method of feature attribute weight allocation based on the SA algorithm is presented, which is developed in Java. Section 4 is application validation and design of comparative experiment in some data sets to evaluate the running performance of the application, and the results and analysis are provided as well.