Wear is one of the major causes that affect the performance and reliability of tribo-systems. To mitigate its adverse effects, it is necessary to monitor the wear progress so that preventive maintenance can be timely scheduled. To accomplish this objective, in this work, an online visual ferrograph (OLVF) apparatus, which can provide online measurements of wear particle quantities, is used to monitor the wearing of a four-ball tribometer under different lubrication conditions and several popular deep learning algorithms are evaluated for their effectiveness in providing maintenance decisions. The obtained data is converted to the cross-sectional time series (CSTS), for its effectiveness in representing the variation trends of multiple variables, and the data is used as the input to the deep learning algorithms. Experimental results indicate that the CSTS together with the Bi-directional Long Short-Term Memory (Bi-LSTM) architecture, this combined structure outperforms other tested settings in terms of the mean-squared error (MSE). Increased prediction accuracy is observed for tribological pairs with a stochastically changing coefficient of friction.
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