The requirements of increasing the productivity and reducing the operation costs in modern factories has forced the managers to perform maintenance effectively. This may be indicated by the rapid adoption of predictive planned maintenance from conventional scheduled maintenance so that the time of shutting down the machines becomes minimum. In this paper, a hybrid neural networks approach based on the combination of recurrent backpropagation (REP) neural networks and fuzzy adaptive resonance theory (ART) classifier is introduced. The main advantage of this approach is that the prediction is based on multiple parameters with on-line adaptation. To illustrate the effectiveness of this approach, the problem of predicting the operation conditions of a series of similar compressors has been studied. By extracting features from the vibration signals in both time and frequency domain, the REP neural networks can be trained to forecast the future values of various vibration features. These future values will then input into the fuzzy ART neural networks in order to classify the operating conditions of the compressors and diagnose the existing of faults. Therefore, the operator will be acknowledged in advanced whether a maintenance is necessary. The results have proved that the proposed method can track the trend of features continuously ana predict the conditions of machines in good accuracy.
[1]
Stephen Grossberg,et al.
Art 2: Self-Organization Of Stable Category Recognition Codes For Analog Input Patterns
,
1988,
Other Conferences.
[2]
Les E. Atlas,et al.
Recurrent neural networks and robust time series prediction
,
1994,
IEEE Trans. Neural Networks.
[3]
Jens Trampe Broch,et al.
Mechanical Vibration and Shock Measurements
,
1980
.
[4]
Abhay B. Bulsari,et al.
A recurrent network for modeling noisy temporal sequences
,
1995,
Neurocomputing.
[5]
D. E. Brown,et al.
A polynomial network for predicting temperature distributions
,
1994,
IEEE Trans. Neural Networks.
[6]
F. S. Wong,et al.
Time series forecasting using backpropagation neural networks
,
1991,
Neurocomputing.
[7]
Abhay B. Bulsari,et al.
A Partially Recurrent Connectionist Model
,
1992,
ECAI.
[8]
Pineda,et al.
Generalization of back-propagation to recurrent neural networks.
,
1987,
Physical review letters.
[9]
David E. Rumelhart,et al.
Predicting the Future: a Connectionist Approach
,
1990,
Int. J. Neural Syst..
[10]
Stephen Grossberg,et al.
The ART of adaptive pattern recognition by a self-organizing neural network
,
1988,
Computer.