Paper Info
Title
Chaotic extension neural network theory-based XXY stage collision fault detection using a single accelerometer sensor.
Abstract
The collision fault detection of a XXY stage is proposed for the first time in this paper. The stage characteristic signals are extracted and imported into the master and slave chaos error systems by signal filtering from the vibratory magnitude of the stage. The trajectory diagram is made from the chaos synchronization dynamic error signals E-1 and E-2. The distance between characteristic positive and negative centers of gravity, as well as the maximum and minimum distances of trajectory diagram, are captured as the characteristics of fault recognition by observing the variation in various signal trajectory diagrams. The matter-element model of normal status and collision status is built by an extension neural network. The correlation grade of various fault statuses of the XXY stage was calculated for diagnosis. The dSPACE is used for real-time analysis of stage fault status with an accelerometer sensor. Three stage fault statuses are detected in this study, including normal status, Y collision fault and X collision fault. It is shown that the scheme can have at least 75% diagnosis rate for collision faults of the XXY stage. As a result, the fault diagnosis system can be implemented using just one sensor, and consequently the hardware cost is significantly reduced.
Year
DOI
Venue
2014
10.3390/s141121549
SENSORS
Keywords
Field
DocType
master and slave chaos error systems,extension neural network,XXY stage,dSPACE
Synchronization,Accelerometer,Fault detection and isolation,Simulation,Filter (signal processing),Algorithm,Electronic engineering,Collision,Engineering,Chaotic,Trajectory,Center of gravity
Journal
Volume
Issue
ISSN
14
11
1424-8220
Citations 
PageRank 
References 
0
0.34
8
Authors
4
Name
Order
Citations
PageRank
Chin-Tsung Hsieh1224.71
Her-Terng Yau272.26
Shang-Yi Wu300.34
Huo-Cheng Lin400.34