A Novel Framework for Real-Time Fault Diagnosis Based on Dynamic Fault Tree Analysis

Abstract

To meet the real-time diagnosis requirements of the complex system, this study proposes a novel framework for real-time fault diagnosis using dynamic fault tree analysis. It pays special attention to meeting two challenges: model development and real-time reasoning. In terms of the challenge of model development, we use a dynamic fault tree model to capture the dynamic behavior of system failure mechanisms and calculate some reliability results by mapping a dynamic fault tree into an equivalent Bayesian Network (BN) in order to avoid the infamous state space explosion problem. In terms of the real-time reasoning challenge, we adopt a logic compilation based inference algorithm, which compiles the BN into an arithmetic circuit and retrieves answers to probabilistic queries by evaluating and differentiating the arithmetic circuit. Furthermore, we incorporate sensors data into fault diagnosis, cope with the sensors reliability and propose the schemes on how to update the Diagnostic Importance Factor (DIF) and the minimal cut sets. Finally, a case study is given to validate the efficiency of this method.

Dynamic Diagnosis Strategy for Redundant Systems Based on Reliability Analysis and Sensors under Epistemic Uncertainty

“…In addition, diagnosis strategies of these methods are only based on DIF and usually caused MCS with a smaller DIF to be diagnosed first [8]. In the work of [9], reliability results were calculated by mapping a dynamic fault tree to a corresponding discrete-time BN (DTBN) and an efficient diagnostic algorithm, proposed based on the DIF of both components and cut sequences, could overcome the above disadvantages. However, DTBN is still an approximate solution for dynamic fault tree and requires huge memory resources to obtain the query variables probability accurately.…”

A Novel Fault Diagnosis Framework Based on Reliability Analysis and Arithmetic Circuit

“…Traditional solution for dynamic fault tree is based on Markov chain (MC) model [10][11], which has the infamous state space explosion problem and cannot solve a larger dynamic fault tree. Therefore, DTBN was proposed to solve the dynamic fault tree in [12,20]. Dynamic logic gates are converted to DTBN and the reliability results are calculated using a standard BN inference algorithm.…”

“…Moreover, it did not incorporate sensors data to update the components' posterior failure probability, which affects the diagnostic efficiency. In the work of [12], reliability results were calculated by mapping a dynamic fault tree to an equivalent discrete-time BN (DTBN) and an efficient diagnostic decision algorithm, proposed based on the DIF of both components and cut sequences, could overcome the above disadvantages. Unfortunately, DTBN is an approximate solution for dynamic fault tree and requires huge memory resources to obtain the query variables probability accurately.…”

Fault Diagnosis Strategy for Complex Systems Based on Multi-Source Heterogeneous Information Under Epistemic Uncertainty

“…In reference [7], DFT is used to model the dynamic fault characteristics, and some importance of components can be calculated and used as a basis for fault diagnosis. Reference [8] introduces a discrete-time Bayesian network to calculate some reliability parameters and update these reliability parameters by fusing sensor information to optimize the diagnosis process in a certain extent. This method can effectively avoid the state space explosion problem.…”