Use of time domain features for the neural network based fault diagnosis of a machine tool coolant system

Proceedings of the Institution of Mechanical Engineers. Part I: Journal of Systems and Control Engineering

Volumn 215, Issue 3, 2001, Pages 199-207

  Samanta, B ^a   Al Balushi, K R ^a  

^a SULTAN QABOOS UNIVERSITY   (Oman)

Author keywords

Condition monitoring; Coolant system; Diagnostics; Neural network; Time domain features

Indexed keywords

BACKPROPAGATION; COMPUTER SIMULATION; COOLING SYSTEMS; FAILURE ANALYSIS; FEATURE EXTRACTION; INTELLIGENT CONTROL; LEARNING ALGORITHMS; LEARNING SYSTEMS; LEAST SQUARES APPROXIMATIONS; MACHINE TOOLS; STEP RESPONSE; TIME DOMAIN ANALYSIS;

FAULT DIAGNOSIS; MACHINE TOOL COOLANT SYSTEMS; MEAN SQUARE ERROR; TIME DOMAIN RESPONSE; TRANSIENT RESPONSE;

NEURAL NETWORKS;

EID: 0035736418     PISSN: 09596518     EISSN: None     Source Type: Journal    
DOI: 10.1243/0959651011540987     Document Type: Article

References (14)

1. Diagnostics of a coolant system via neural networks
2. Classification of the rotating machine condition using artificial neural networks
3. Real-time classification of the rotating shaft loading conditions using artificial neural networks
4. Application of periodic time-varying autoregressive models to the detection of bearing faults
5. Monitoring and diagnosis of rolling element bearings using artificial neural networks
6. Bearing condition diagnostics via vibration and acoustics emission measurements
7. Artificial neural network based fault diagnostics of rotating machinery using wavelet transforms as a preprocessor
8. A neural network approach to real-time condition monitoring of induction motors
9. Intelligent detection of drill wear
10. Intelligent classification and measurement of drill wear
11. Representing and diagnosing dynamic process data using neural networks
12. On the nature of fault space classification structure developed by neural networks
13. A neural network approach to fault detection and diagnosis in industrial processes