Development of Ground Special Vehicle PHM with Case-Based Reason Model

Qicai Wu,Haibin Yuan,Haiwen Yuan

doi:10.3390/app11104494

Qicai Wu, Haibin Yuan + Show 1 more

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https://doi.org/10.3390/app11104494

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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.

Highlights

Ground special vehicle is a kind of complex equipment that is used for the purpose of satellite transportation and space launch preparation, which contains hydraulic control subsystem, temperature control subsystem, fixed aiming subsystem, power supply, and distribution subsystem, fuel subsystem, electronic control subsystem, communication subsystem, and so on
In order to comprehensively manage the operation of Ground Special Vehicle (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]
Three methods of EW-case-based reasoning (CBR), coefficient weight allocation CBR (CW-CBR), and Simulated annealing (SA) weight allocation CBR (SA-CBR) are investigated based on the actual historical data set, and experimental application results are shown configuration is convenient; it is, practical for PHM structure

Summary

Introduction

Ground special vehicle is a kind of complex equipment that is used for the purpose of satellite transportation and space launch preparation, which contains hydraulic control subsystem, temperature control subsystem, fixed aiming subsystem, power supply, and distribution subsystem, fuel subsystem, electronic control subsystem, communication subsystem, and so on.In a mission-critical launch, The GSV is required to operate in a reliable and efficient way; at the same time, the incident of system degradations and fault-related abnormality must be reduced or prevented in advance. Consideration of reliability and safety risk for GSV is very important prior to the operation process. For such complex equipment as GSV, effective measures and practical methods must be taken into consideration to ensure high availability of GSV, and having an automated procedure of safety management and health 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]. One of the important tasks involves continuous monitoring with analysis using proven algorithms [5,6,7,8]

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