Research of Fuzzy Comprehensive Evaluation Model for Displacement Efficiency of Cementing

The existing hydraulic pressure control fault diagnosis system is effective on fault detection, but the fault isolation capability is bad. In order to improve the capability of the fault isolation, the artificial neural network (ANN) is used in the fault diagnosis system. Aimed at the representative diagnosis of the hydraulic pressure control system, the three layers feedback network is adopted, the basic theory of conjugate gradient BP neural network is explained in detail, and the key techniques are introduced. Five types of typical faults of hydraulic pressure control system can be distinguished easily by it, the faults diagnosis efficiency is higher 30% than ever and the fault diagnosis capability is better 80% than before.

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Section: A Establishment Of Knowledge Databasementioning

confidence: 99%

Application of ANN in Hydraulic Pressure Control Fault Diagnosis System

Zhang¹,

Ding²

2013

Proceedings of the 2nd International Conference on Computer Science and Electronics Engineering (ICCSEE 2013)

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“…Lots of useful diagnosis maintenance experience and fault cases are accumulated in the fault diagnosis and maintenance of the airborne electron-device. They can be got together and packed up as rules or special fault cases, and then the rule database is established [6][7]. In order to improve the process speed of the system, the multidimensional relation table is used to organize and memory the fault diagnosis rules.…”

Section: Key Techniquesmentioning

confidence: 99%

“…The complex relationship between symptoms and faults is incarnated in the whole network which is composed of many samples set. So the three feedback neural network which has relatively mature technology is used as the topology structure of faults diagnosis network [7].…”

Section: Application Of Ann In Hydraulic Pressure Control Systemmentioning

confidence: 99%