Hierarchical cognize framework for the multi-fault diagnosis of the interconnected system based on domain knowledge and data fusion

The CNC system is a complex mechatronics system, which make it difficult to diagnose fault. Expert system for fault diagnosis that utilizes domain knowledge and the profiles of experts to fix the problem of the complex system has become an important issue. A hybrid expert fault system combining the rule-base reasoning (RBR) with case-based reasoning (CBR) for CNC system is proposed. The combination can trouble-shoot rapidly, improve the CNC system reliability and maintainability. The hybrid system is implemented by using QT and SQLITE database. The experiment result of the system shows that the system diagnosis efficiently and accurately.

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Bearing Fault Diagnosis of Switched Reluctance Motor in Electric Vehicle Powertrain via Multi-sensor Data Fusion

IEEE Transactions on Industrial Informatics ◽

10.1109/tii.2021.3095086 ◽

2021 ◽

pp. 1-1

Author(s):

Xiaoxian Wang ◽

Siliang Lu ◽

Kang Chen ◽

Qunjing Wang ◽

Shiwu Zhang

Keyword(s):

Fault Diagnosis ◽

Data Fusion ◽

Electric Vehicle ◽

Switched Reluctance Motor ◽

Sensor Data ◽

Switched Reluctance ◽

Bearing Fault ◽

Bearing Fault Diagnosis ◽

Multi Sensor Data Fusion ◽

Reluctance Motor

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Fault Diagnosis of New Certain Mine Sweeping Plough’s Electrical Control System Based on Data Fusion

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.713-715.539 ◽

2015 ◽

Vol 713-715 ◽

pp. 539-543

Author(s):

Yong Zhao ◽

Xiao Qiang Yang ◽

Yin Hua Xu ◽

Jian Bin Li

Keyword(s):

Neural Network ◽

Control System ◽

Fault Diagnosis ◽

Data Fusion ◽

Complex Structure ◽

Evidence Theory ◽

System Level ◽

Sensor Failure ◽

Advanced Technique ◽

Electrical Control

The fault diagnosis of electrical control system of certain type mine sweeping vehicle is difficult due to its complex structure and advanced technique. So in the multi-sensor failure diagnosis process, as a result of various reasons, such as the existence of measurement noise, diagnosis knowledge incomplete and so on, it makes the fault diagnosis uncertainty and affects the reliability and the accuracy of the diagnosis result. This article according to the analysis of electrical control system's fault characteristic of the mine sweeping plough’s, proposes a technique based on data fusion fault diagnosis method. The diagnosis process is divided into the sub system and the system-level, the subsystem uses the BP neural network to classify the fault mode, the system-level uses the D-S evidence theory carries on the comprehensive decision judgment for the whole system's fault. Application shows if some sub-neural network diagnosis has error, using D-S evidence theory fusion can effectively improve the accuracy of diagnosis.

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