Journal ArticleDOI
Prediction of Machine Deterioration Using Vibration Based Fault Trends and Recurrent Neural Networks
Peter W. Tse,Derek P. Atherton +1 more
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TLDR
In this article, a new prognostic method is described which has been developed to forecast the rate of machine deterioration using recurrent neural networks, from tests applying the method to the prediction of nonlinear sunspot activities and vibration based fault trends of several industrial machines.Abstract:
High market competition for sales requires companies to reduce the cost of production if they are to maintain their market shares. Since the cost of maintenance contributes a substantial portion of the production cost, companies must budget maintenance effectively. Machine deterioration prognosis can decrease the cost of maintenance by minimizing the loss of production due to machine breakdown and avoiding the overstocking of spare parts. A new prognostic method is described in this paper which has been developed to forecast the rate of machine deterioration using recurrent neural networks. From tests applying the method to the prediction of nonlinear sunspot activities and vibration based fault trends of several industrial machines, the results have shown that the method is promising. It not only evaluates the seriousness of damage caused by faults, but also forecasts the remaining life span of defective components.read more
Citations
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Rotating machinery prognostics: State of the art, challenges and opportunities
TL;DR: In this article, the authors synthesize and place these individual pieces of information in context, while identifying their merits and weaknesses, and discuss the identified challenges, and in doing so, alerts researchers to opportunities for conducting advanced research in the field.
Journal ArticleDOI
A recurrent neural network based health indicator for remaining useful life prediction of bearings
TL;DR: A recurrent neural network based health indicator for RUL prediction of bearings with fairly high monotonicity and correlation values is proposed and it is experimentally demonstrated that the proposed RNN-HI is able to achieve better performance than a self organization map based method.
Journal ArticleDOI
Learning to Monitor Machine Health with Convolutional Bi-Directional LSTM Networks.
TL;DR: A deep neural network structure named Convolutional Bi-directional Long Short-Term Memory networks (CBLSTM) has been designed here to address raw sensory data and is able to outperform several state-of-the-art baseline methods.
Journal ArticleDOI
PCA-based feature selection scheme for machine defect classification
A. Malhi,Robert X. Gao +1 more
TL;DR: The proposed feature selection scheme has shown to provide more accurate defect classification with fewer feature inputs than using all features initially considered relevant, and confirms its utility as an effective tool for machine health assessment.
Journal ArticleDOI
Intelligent Predictive Decision Support System for Condition-Based Maintenance
TL;DR: In this paper, an intelligent predictive decision support system (IPDSS) for condition-based maintenance (CBM) supplements the conventional CBM approach by adding the capability of intelligent conditionbased fault diagnosis and the power of predicting the trend of equipment deterioration.
References
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Journal ArticleDOI
Recurrent neural networks and robust time series prediction
J. Connor,R.D. Martin,Les Atlas +2 more
TL;DR: A robust learning algorithm is proposed and applied to recurrent neural networks, NARMA(p,q), which show advantages over feedforward neural networks for time series with a moving average component and are shown to give better predictions than neural networks trained on unfiltered time series.
Journal ArticleDOI
Threshold Autoregression, Limit Cycles and Cyclical Data
Howell Tong,K. S. Lim +1 more
Journal ArticleDOI
Neural networks in process fault diagnosis
TL;DR: A multilayer perceptron network with a hyperbolic tangent as the nonlinear element seems best suited for the task of fault diagnosis in a realistic heat exchanger-continuous stirred tank reactor system.