Application of Pattern Recognition to Failure Detection and Analysis in Non-Digital Systems

2019 ◽  
pp. 155-169
Author(s):  
L. F. Pau
Keyword(s):  
Pattern Recognition ◽  
Failure Detection ◽  
Digital Systems

Gearbox Failure Detection Using Growing Hierarchical Self-Organizing Map

2007 ◽  
Vol 348-349 ◽  
pp. 177-180
Author(s):  
Guang Lan Liao ◽  
Tie Lin Shi ◽  
Zi Rong Tang
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Fault Diagnosis ◽  
Failure Detection ◽  
Operating Conditions ◽  
Hierarchical Architecture ◽  
Self Organizing Map ◽  
Hierarchical Relations ◽  
Unsupervised Neural Network ◽  
Self Organizing

Machine fault diagnosis is essentially an issue of pattern recognition, which heavily depends on suitable unsupervised learning method. The Self-Organizing Map (SOM), a popular unsupervised neural network, has been used for failure detection but with two limitations: needing predefined static architecture and lacking ability for the representation of hierarchical relations in the data. This paper presents a novel study on failure detection of gearbox using the Growing Hierarchical Self-Organizing Map (GHSOM), an artificial neural network model with hierarchical architecture composed of independent growing SOMs. The GHSOM can adapt its architecture during unsupervised training process and provide a global orientation in the individual layers of the hierarchy; hence the original data structure can be described correctly for machine faults diagnosis. Gearbox vibration signals measured under different operating conditions are analyzed using the proposed technique. The results prove that the hierarchical relations in the gearbox failure data can be intuitively represented, and inherent structure can be unfolded. Then gearbox operating conditions including normal, tooth cracked and tooth broken are classified and recognized clearly. The study confirms that GHSOM is very useful and effective for pattern recognition in mechanical fault diagnosis, and provides a good potential for application in practice.

Software pattern recognition applied to nanodiffraction patterns from a STEM instrument

1986 ◽  
Vol 44 ◽  
pp. 694-695
Author(s):  
G.Y. Fan ◽  
J.M. Cowley
Keyword(s):  
Image Processing ◽  
Pattern Recognition ◽  
Computer Software ◽  
Processing System ◽  
Light Level ◽  
Host Computer ◽  
Low Light ◽  
Image Processing System ◽  
Recent Developments ◽  
On Line

In recent developments, the ASU HB5 has been modified so that the timing, positioning, and scanning of the finely focused electron probe can be entirely controlled by a host computer. This made the asynchronized handshake possible between the HB5 STEM and the image processing system which consists of host computer (PDP 11/34), DeAnza image processor (IP 5000) which is interfaced with a low-light level TV camera, array processor (AP 400) and various peripheral devices. This greatly facilitates the pattern recognition technique initiated by Monosmith and Cowley. Software called NANHB5 is under development which, instead of employing a set of photo-diodes to detect strong spots on a TV screen, uses various software techniques including on-line fast Fourier transform (FFT) to recognize patterns of greater complexity, taking advantage of the sophistication of our image processing system and the flexibility of computer software.

Creating a standard method of controlling digital microscopes: the microscope access protocol (map)

1995 ◽  
Vol 53 ◽  
pp. 16-17
Author(s):  
T. A. Dodson ◽  
E. Völkl ◽  
L. F. Allard ◽  
T. A. Nolan
Keyword(s):  
Data Storage ◽  
Storage Systems ◽  
Digital Systems ◽  
Data Networks ◽  
Digital Microscopy ◽  
Command Language ◽  
Access Protocol ◽  
Analog To Digital ◽  
Basic Physics ◽  
Scanning Electron

The process of moving to a fully digital microscopy laboratory requires changes in instrumentation, computing hardware, computing software, data storage systems, and data networks, as well as in the operating procedures of each facility. Moving from analog to digital systems in the microscopy laboratory is similar to the instrumentation projects being undertaken in many scientific labs. A central problem of any of these projects is to create the best combination of hardware and software to effectively control the parameters of data collection and then to actually acquire data from the instrument. This problem is particularly acute for the microscopist who wishes to "digitize" the operation of a transmission or scanning electron microscope. Although the basic physics of each type of instrument and the type of data (images & spectra) generated by each are very similar, each manufacturer approaches automation differently. The communications interfaces vary as well as the command language used to control the instrument.

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