Abstract
In the fault diagnosis of the flywheel system, the input information of the system is uncertain. This uncertainty is mainly caused by the interference of environmental factors and the limited cognitive ability of experts. The BRB (belief rule base) shows a good ability for dealing with problems of information uncertainty and small sample data. However, the initialization of the BRB relies on expert knowledge, and it is difficult to obtain the accurate knowledge of flywheel faults when constructing BRB models. Therefore, this paper proposes a new BRB model, called the FFBRB (fuzzy fault tree analysis and belief rule base), which can effectively solve the problems existing in the BRB. The FFBRB uses the Bayesian network as a bridge, uses an FFTA (fuzzy fault tree analysis) mechanism to build the BRB’s expert knowledge, uses ER (evidential reasoning) as its reasoning tool, and uses P-CMA-ES (projection covariance matrix adaptation evolutionary strategies) as its optimization model algorithm. The feasibility and superiority of the proposed method are verified by an example of a flywheel friction torque fault tree.
Highlights
The flywheel [1] system is a key actuator for spacecraft attitude control, which is widely used in the aerospace field
Xinchang Zhang et al [3] developed a set of methods for inputting correct premises, and based on consistency test results, presented a fault diagnosis model based on finite state machines, which could locate and diagnose some faults
The FFTA mechanism is used to expand the BRB knowledge base and solve the problem of constructing an expert knowledge base of complex flywheel system; (2) A new flywheel fault diagnosis model based on BRB is proposed
Summary
The flywheel [1] system is a key actuator for spacecraft attitude control, which is widely used in the aerospace field. The FFTA mechanism is used to expand the BRB knowledge base and solve the problem of constructing an expert knowledge base of complex flywheel system; (2) A new flywheel fault diagnosis model based on BRB is proposed This model can obtain relatively accurate data even with a small number of samples and has higher applicability. The function to solve this problem is denoted as CovBridge(∗) and is the set of parameters in this process, the process can be described by the following expression: BRB(BeliefRule, input/output) = CovBridge (FFTA(LogicGate, event), ) (1). The BRB belongs to the second type of method, which can expand the model information input through expert knowledge, so as to realize model training under small samples
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