scholarly journals Multilayer Architecture for Fault Diagnosis of Embedded Systems

Author(s):  
Daniel Maas ◽  
Renan Sebem ◽  
André Bittencourt Leal

This work presents a multilayer architecture for fault diagnosis in embedded systems based on formal modeling of Discrete Event Systems (DES). Most works on diagnosis of DES focus in faults of actuators, which are the devices subject to intensive wear in industry. However, embedded systems are commonly subject to cost reduction, which may increase the probability of faults in the electronic hardware. Further, software faults are hard to track and fix, and the common solution is to replace the whole electronic board. We propose a modeling approach which includes the isolation of the source of the fault in the model, regarding three layers of embedded systems: software, hardware, and sensors & actuators. The proposed method is applied to a home appliance refrigerator and after exhaustive practical tests with forced fault occurrences, all faults were diagnosed, precisely identifying the layer and the faulty component. The solution was then incorporated into the product manufactured in industrial scale.

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Zhiwang Zhong ◽  
Tianhua Xu ◽  
Feng Wang ◽  
Tao Tang

In Discrete Event System, such as railway onboard system, overwhelming volume of textual data is recorded in the form of repair verbatim collected during the fault diagnosis process. Efficient text mining of such maintenance data plays an important role in discovering the best-practice repair knowledge from millions of repair verbatims, which help to conduct accurate fault diagnosis and predication. This paper presents a text case-based reasoning framework by cloud computing, which uses the diagnosis ontology for annotating fault features recorded in the repair verbatim. The extracted fault features are further reduced by rough set theory. Finally, the case retrieval is employed to search the best-practice repair actions for fixing faulty parts. By cloud computing, rough set-based attribute reduction and case retrieval are able to scale up the Big Data records and improve the efficiency of fault diagnosis and predication. The effectiveness of the proposed method is validated through a fault diagnosis of train onboard equipment.


2010 ◽  
Author(s):  
A. Bastoni ◽  
P. Boschi ◽  
F. Batino ◽  
C. Di Biagio ◽  
L. Recchia

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