Abstract
According to the characteristics of fault diagnosis for pumping station, such as the complex structure, multiple mappings, and numerous uncertainties, a new approach combining T-S fuzzy gate fault tree and Bayesian network (BN) is proposed. On the one hand, traditional fault tree method needs the logical relationship between events and probability value of events and can only represent the events with two states. T-S fuzzy gate fault tree method can solve these disadvantages but still has weaknesses in complex reasoning and only one-way reasoning. On the other hand, the BN is suitable for fault diagnosis of pumping station because of its powerful ability to deal with uncertain information. However, it is difficult to determine the structure and conditional probability tables of the BN. Therefore, the proposed method integrates the advantages of the two methods. Finally, the feasibility of the method is verified through a fault diagnosis model of the rotor in the pumping unit, the accuracy of the method is verified by comparing with the methods based on traditional Bayesian network and BP neural network, respectively, when the historical data is sufficient, and the results are more superior to the above two when the historical data is insufficient.
Highlights
With the operation of the first phase of the South-to-North Water Diversion Project, the reliability and ability to achieve preset functions of the pumping stations and units at every level will affect the effectiveness of the whole project, while the faults of each pumping station may cause major issues about engineering safety, significant economic losses, and serious social impact if expanded further
In paper [7], Bayesian network is firstly applied to the fault diagnosis of the hydrogenerator by constructing a simple fault diagnosis system for the hydrogenerator set, SmartHydro, which uses vibration of different frequency as the fault features to realize the diagnosis of several major faults caused by factors that are mechanical, hydraulic, electromagnetic, etc
It can be seen from the figure that when the historical data is complete, the results of the method proposed in this paper are similar to that of traditional fault diagnosis method based on Bayesian network (BN), so the effectiveness of the method proposed in this paper is demonstrated
Summary
With the operation of the first phase of the South-to-North Water Diversion Project, the reliability and ability to achieve preset functions of the pumping stations and units at every level will affect the effectiveness of the whole project, while the faults of each pumping station may cause major issues about engineering safety, significant economic losses, and serious social impact if expanded further. In paper [7], Bayesian network is firstly applied to the fault diagnosis of the hydrogenerator by constructing a simple fault diagnosis system for the hydrogenerator set, SmartHydro, which uses vibration of different frequency as the fault features to realize the diagnosis of several major faults caused by factors that are mechanical, hydraulic, electromagnetic, etc This method makes full use of the advantage of Bayesian network to solve the problem of uncertain fault diagnosis in pumping unit. This paper combines T-S fuzzy gate fault tree method and Bayesian network method [10] that can convert the fuzzy gate rule of T-S fuzzy gate fault tree into the conditional probability table of Bayesian network and make full use of the efficient parallel two-way reasoning ability of Bayesian network to realize the uncertain fault diagnosis of pumping station. The effectiveness and superiority of the proposed approach are illustrated by taking the rotor which is one of the most important and most prone to fault in the pump unit
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