Modeling of Failure Prediction Bayesian Network with Divide-and-Conquer Principle

Zhiqiang Cai,Shudong Sun,Shubin Si,Weitao Si

doi:10.1155/2014/210714

Abstract

For system failure prediction, automatically modeling from historical failure dataset is one of the challenges in practical engineering fields. In this paper, an effective algorithm is proposed to build the failure prediction Bayesian network (FPBN) model with data mining technology. First, the conception of FPBN is introduced to describe the state of components and system and the cause-effect relationships among them. The types of network nodes, the directions of network edges, and the conditional probability distributions (CPDs) of nodes in FPBN are discussed in detail. According to the characteristics of nodes and edges in FPBN, a divide-and-conquer principle based algorithm (FPBN-DC) is introduced to build the best FPBN network structures of different types of nodes separately. Then, the CPDs of nodes in FPBN are calculated by the maximum likelihood estimation method based on the built network. Finally, a simulation study of a helicopter convertor model is carried out to demonstrate the application of FPBN-DC. According to the simulations results, the FPBN-DC algorithm can get better fitness value with the lower number of iterations, which verified its effectiveness and efficiency compared with traditional algorithm.

Highlights

With the developments of information and computer technologies, modern systems have become more complex while the relationships among systems have become more complicated
0.9 actual states of failure detection nodes are inputted, the failure prediction Bayesian network (FPBN) model is operated with conditional probability distributions (CPDs) to estimate the state of failure cause nodes which will determine the state of failure mode node
To verify the effectiveness and efficiency of the proposed FPBN-DC algorithm, the Bayesian network (BN)-IA algorithm, which ignores the assumption of node types and edge directions in FPBN, is introduced to learn the network structure from datasets

Summary

Introduction

With the developments of information and computer technologies, modern systems have become more complex while the relationships among systems have become more complicated. Failure prediction approaches have been divided into three types, including experience-based, condition-based, and model-based methods [2]. Many interesting methods have been proposed for failure prediction, the model-based method has played more important role in engineering fields for its advantages in effectiveness and efficiency. Because system operation data are abundant in quantity and various in characteristics, this paper introduce an expanded BN model to describe the failure prediction process for complex system under uncertainty and proposes a divideand-conquer principle based data mining algorithm to build the corresponding model.

Failure Prediction Bayesian Network

Modeling of FPBN with Divide-andConquer Principle

Simulation Study

Conclusion