Fault Classification of the Error Detection Logic in the Blade Resilient Template

2016 ◽  
Author(s):  
Felipe A. Kuentzer ◽  
Alexandre M. Amory
Keyword(s):  
Error Detection ◽  
Fault Classification

Fault Classification of Hydroelectric Generating Unit Based on Improved Evidence Theory

2014 ◽  
Vol 7 (1) ◽  
pp. 78-83 ◽  
Author(s):  
Jiatang Cheng ◽  
Li Ai ◽  
Zhimei Duan ◽  
Yan Xiong
Keyword(s):  
Fault Diagnosis ◽  
Information Fusion ◽  
Evidence Theory ◽  
Fault Classification ◽  
Fusion Method ◽  
Inconsistent Result ◽  
High Conflict

Aiming at the problem of the conventional vibration fault diagnosis technology with inconsistent result of a hydroelectric generating unit, an information fusion method was proposed based on the improved evidence theory. In this algorithm, the original evidence was amended by the credibility factor, and then the synthesis rule of standard evidence theory was utilized to carry out information fusion. The results show that the proposed method can obtain any definitive conclusion even if there is high conflict evidence in the synthesis evidence process, and may avoid the divergent phenomenon when the consistent evidence is fused, and is suitable for the fault classification of hydroelectric generating unit.

A Study on Fault Classification of Solenoid Pumps based on Multi-Layer Perceptron

2021 ◽  
Vol 21 (1) ◽  
pp. 12-19
Author(s):  
Suju Kim ◽  
Akpudo Ugochukwu Ejike ◽  
Jangwook Hur
Keyword(s):  
Fault Classification ◽  
Multi Layer Perceptron

scholarly journals Mechanical Fault Classification of an Induction Motor using Texture Analysis

2013 ◽  
Vol 18 (12) ◽  
pp. 11-19 ◽  
Author(s):  
Won-Chul Jang ◽  
Yong-Hoon Park ◽  
Myeong-Su Kang ◽  
Jong-Myon Kim
Keyword(s):  
Induction Motor ◽  
Texture Analysis ◽  
Fault Classification

Shapelets-Based Research on Fault Classification of Rotating Machinery

2019 ◽  
Author(s):  
Ling Zheng ◽  
Biying Chen ◽  
Fei Chen ◽  
Wenhao Xue ◽  
Pengtian Guo ◽  
...  
Keyword(s):  
Rotating Machinery ◽  
Fault Classification

scholarly journals Machine Fault Classification Based on Local Discriminant Bases and Locality Preserving Projections

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Qingbo He ◽  
Xiaoxi Ding ◽  
Yuanyuan Pan
Keyword(s):  
Feature Extraction ◽  
Wavelet Packet ◽  
Fault Classification ◽  
Feature Extraction Method ◽  
Locality Preserving Projections ◽  
Vibration Data ◽  
Pattern Structure ◽  
Locality Preserving ◽  
Machine Fault

Machine fault classification is an important task for intelligent identification of the health patterns for a mechanical system being monitored. Effective feature extraction of vibration data is very critical to reliable classification of machine faults with different types and severities. In this paper, a new method is proposed to acquire the sensitive features through a combination of local discriminant bases (LDB) and locality preserving projections (LPP). In the method, the LDB is employed to select the optimal wavelet packet (WP) nodes that exhibit high discrimination from a redundant WP library of wavelet packet transform (WPT). Considering that the obtained discriminatory features on these selected nodes characterize the class pattern in different sensitivity, the LPP is then applied to address mining inherent class pattern feature embedded in the raw features. The proposed feature extraction method combines the merits of LDB and LPP and extracts the inherent pattern structure embedded in the discriminatory feature values of samples in different classes. Therefore, the proposed feature not only considers the discriminatory features themselves but also considers the dynamic sensitive class pattern structure. The effectiveness of the proposed feature is verified by case studies on vibration data-based classification of bearing fault types and severities.

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