The Fault Diagnosis of Automotive Airbag Assembly Process Based on Self-Organizing Feature Mapping Network SOM

2012 ◽  
Vol 6 (1) ◽  
pp. 676-678
Author(s):  
Zhang Dejiang ◽  
Zhang Niaona ◽  
Liu Kewei
Keyword(s):  
Fault Diagnosis ◽  
Assembly Process ◽  
Feature Mapping ◽  
Self Organizing

The Fault Diagnosis of Automotive Airbag Assembly Process Based on Self-Organizing Feature Mapping Network SOM

2011 ◽  
Vol 128-129 ◽  
pp. 1101-1104
Author(s):  
De Jiang Zhang ◽  
Niao Na Zhang ◽  
Ke Wei Liu
Keyword(s):  
Fault Diagnosis ◽  
Excitatory Neuron ◽  
Local Optimum ◽  
Assembly Process ◽  
Feature Mapping ◽  
Field Function ◽  
Self Organizing

Automotive airbag assembly process is complex and nonlinear, and one of its characteristics is that the accuracy of making the threshold comparison for fault diagnosis using field multi-sensor measured value is not high,. In this article, adopt self-organizing feature mapping network SOM to realize the fault diagnosis of automotive airbag assembly process, constitute the field function of SOM through wavelet functions, form sub-excitatory neuron to update weights, avoid SOM local optimum, so improve the accuracy of fault diagnosis of automotive airbag assembly process.

Time behavior of topological ordering in self-organizing feature mapping

1997 ◽  
Vol 77 (6) ◽  
pp. 419-427 ◽  
Author(s):  
R. Der ◽  
M. Herrmann ◽  
T. Villmann
Keyword(s):  
Time Behavior ◽  
Feature Mapping ◽  
Self Organizing

Multiple Fault Diagnosis Method in Multistation Assembly Processes Using Orthogonal Diagonalization Analysis

2008 ◽  
Vol 130 (1) ◽  
Author(s):  
Zhenyu Kong ◽  
Dariusz Ceglarek ◽  
Wenzhen Huang
Keyword(s):  
Fault Diagnosis ◽  
Product Information ◽  
Statistical Significance ◽  
Measurement Data ◽  
Space Representation ◽  
Assembly Process ◽  
Dimensional Control ◽  
Multiple Fault ◽  
Multiple Fault Diagnosis ◽  
Multistation Assembly

Dimensional control has a significant impact on overall product quality and performance of large and complex multistation assembly systems. To date, the identification of process-related faults that cause large variations of key product characteristics (KPCs) remains one of the most critical research topics in dimensional control. This paper proposes a new approach for multiple fault diagnosis in a multistation assembly process by integrating multivariate statistical analysis with engineering models. The proposed method is based on the following steps: (i) modeling of fault patterns obtained using state space representation of process and product information that explicitly represents the relationship between process-related error sources denoted by key control characteristics (KCCs) and KPCs, and (ii) orthogonal diagonalization of measurement data using principal component analysis (PCA) to project measurement data onto the axes of an affine space formed by the predetermined fault patterns. Orthogonal diagonalization allows estimating the statistical significance of the root cause of the identified fault. A case study of fault diagnosis for a multistation assembly process illustrates and validates the proposed methodology.

Machine Fault Diagnosis and Prognosis using Self-Organizing Map

2014 ◽  
pp. 129-148
Author(s):  
Kesheng Wang ◽  
Zhenyou Zhang ◽  
Yi Wang
Keyword(s):  
Fault Diagnosis ◽  
Condition Monitoring ◽  
Manufacturing Systems ◽  
Maintenance Scheduling ◽  
Self Organizing Map ◽  
Corrective Maintenance ◽  
Diagnosis And Prognosis ◽  
Machine Fault ◽  
Fault Diagnosis And Prognosis ◽  
Self Organizing

This chapter proposes a Self-Organizing Map (SOM) method for fault diagnosis and prognosis of manufacturing systems, machines, components, and processes. The aim of this work is to optimize the condition monitoring of the health of the system. With this method, manufacturing faults can be classified, and the degradations can be predicted very effectively and clearly. A good maintenance scheduling can then be created, and the number of corrective maintenance actions can be reduced. The results of the experiment show that the SOM method can be used to classify the fault and predict the degradation of machines, components, and processes effectively, clearly, and easily.

scholarly journals An Ensemble Learning Approach for Fault Diagnosis in Self-Organizing Heterogeneous Networks

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 125662-125675 ◽  
Author(s):  
Yueping Wang ◽  
Kun Zhu ◽  
Mengyun Sun ◽  
Yueyu Deng
Keyword(s):  
Fault Diagnosis ◽  
Ensemble Learning ◽  
Heterogeneous Networks ◽  
Learning Approach ◽  
Self Organizing

Sound based induction motor fault diagnosis using Kohonen self-organizing map

2014 ◽  
Vol 46 (1) ◽  
pp. 45-58 ◽  
Author(s):  
Emin Germen ◽  
Murat Başaran ◽  
Mehmet Fidan
Keyword(s):  
Fault Diagnosis ◽  
Induction Motor ◽  
Self Organizing Map ◽  
Self Organizing
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