Advanced forecasting and classification technique for condition monitoring of rotating machinery

Authors:
Ilya Mokhov;Alexey Minin
Affiliations:
Saint-Petersburg State University, Applied Mathematics and Control Processes;Saint-Petersburg State University, Applied Computational Physics
Venue:
IDEAL'07 Proceedings of the 8th international conference on Intelligent data engineering and automated learning
Year:
2007

Citing 3
Cited 0

Towards long-term prediction

Physica D
The application of neural networks to vibrational diagnostics for multiple fault conditions

Computer Standards & Interfaces - Intelligent data acquisition and advanced computing systems
Training feedforward networks with the Marquardt algorithm

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

Abstract. Prediction and classification of particular faults in rotating machinery, based on a given set of measurements, could significantly reduce the overall costs of maintenance and repair. Usually the vibration signal is sampled with a very high frequency due to its nature, thus it is quite difficult to do considerably long forecasting based on the methods, which are suitable for e.g. financial time series (where the sampling frequency is smaller). In this paper new forecasting and classification technique for particular vibration signal characteristics is proposed. Suggested approach allows creating a part of control system responsible for early fault detection, which could be used for preventive maintenance of industrial equipment. Presented approach can be extended to high frequency financial data for the prediction of "faults" on the market.