Fault Diagnosis of Manufacturing Systems

2014 ◽  
pp. 599-599
Keyword(s):  
Fault Diagnosis ◽  
Manufacturing Systems

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.

Integrated Condition Monitoring and Fault Diagnosis for Modern Manufacturing Systems

CIRP Annals ◽  
2000 ◽  
Vol 49 (1) ◽  
pp. 387-390 ◽  
Author(s):  
Z.D. Zhou ◽  
Y.P. Chen ◽  
J.Y.H. Fuh ◽  
A.Y.C. Nee
Keyword(s):  
Fault Diagnosis ◽  
Condition Monitoring ◽  
Manufacturing Systems ◽  
Modern Manufacturing

scholarly journals Product Lifecycle Management as Data Repository for Manufacturing Problem Solving

Materials ◽  
2018 ◽  
Vol 11 (8) ◽  
pp. 1469 ◽  
Author(s):  
Alvaro Camarillo ◽  
José Ríos ◽  
Klaus-Dieter Althoff
Keyword(s):  
Fault Diagnosis ◽  
Problem Solving ◽  
Manufacturing Systems ◽  
Product Lifecycle Management ◽  
Shop Floor ◽  
Case Based Reasoning ◽  
Product Lifecycle ◽  
Considerable Difficulty ◽  
Lifecycle Management ◽  
Manufacturing Problems

Fault diagnosis presents a considerable difficulty to human operators in supervisory control of manufacturing systems. Implementing Internet of Things (IoT) technologies in existing manufacturing facilities implies an investment, since it requires upgrading them with sensors, connectivity capabilities, and IoT software platforms. Aligned with the technological vision of Industry 4.0 and based on currently existing information databases in the industry, this work proposes a lower-investment alternative solution for fault diagnosis and problem solving. This paper presents the details of the information and communication models of an application prototype oriented to production. It aims at assisting shop-floor actors during a Manufacturing Problem Solving (MPS) process. It captures and shares knowledge, taking existing Process Failure Mode and Effect Analysis (PFMEA) documents as an initial source of information related to potential manufacturing problems. It uses a Product Lifecycle Management (PLM) system as source of manufacturing context information related to the problems under investigation and integrates Case-Based Reasoning (CBR) technology to provide information about similar manufacturing problems.

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