PDF Downloads:
888
Open
Access -
Suzen S. Kallungal
Department of Computer Science and Engineering, Adi Shankara Institute of Engineering and Technology (ASIET), Mahatma Gandhi University, State of Kerala (India).
Article Received on :
Article Accepted on :
Article Published :
Automatic fault detection is mainly for applications in the automotive industry. A fault detection system based on multivariate data analysis is needed to increase data reliability and for the purpose of monitoring and controlling of test equipment. The detection scheme has to process different measurements at a time and check them for consistency. An important requirement for the fault detection scheme is that it should be able to automatically adapt itself to new data with high level of accuracy that may not always be achieved manually. The project related to this paper was intended to work on real-time parameters read from high power automotives, especially JCBs used in construction industry. Various parameters including: temperatures; pressures; oil levels; states of the valves are monitored and sent to a server. Results showed that automatic fault detection through neural network system is useful as it saves time, cost and detects faults accurately.
KEYWORDS: Multivariate data analysis; Fault detection; Automotives| Copy the following to cite this article: Kallungal S. S. Automatic Fault Detection in JC Bamford (JCB) Machines in A Construction Industry by the Application of Neural Network System. Orient. J. Comp. Sci. and Technol;4(2) |
| Copy the following to cite this URL: Kallungal S. S. Automatic Fault Detection in JC Bamford (JCB) Machines in A Construction Industry by the Application of Neural Network System. Orient. J. Comp. Sci. and Technol;4(2). Available from: http://www.computerscijournal.org/?p=2492 |
