DSU-LSTM-Based Trend Prediction Method for Lubricating Oil

Abstract

Oil monitoring plays an important role in early maintenance of mechanical equipment on account of the fact that lubricating oil contains a large amount of wear information. However, due to extreme industrial environment and long-term service, the data history and the sample size of lubricating oil are very limited. Therefore, to address problems due to a lack of oil samples, this paper proposes a new prediction strategy that fuses the domain shifts with uncertainty (DSU) method and long short-term memory (LSTM) method. The proposed DSU-LSTM model combines the advantages of the DSU model, such as increasing data diversity and uncertainty, reducing the impact of independent or identical domains on neural network training, and mitigating domain changes between different oil data histories, with the advantages of LSTM in predicting time series, thereby improving prediction capability. To validate the proposed method, a case study with real lubricating oil data is conducted, and comparisons are given by calculating the root-mean-square error (RMSE), mean absolute error (MAE), and mean relative error (MRE) with LSTM, support vector machine (SVM), and DSU-SVM models. The results illustrate the effectiveness of the proposed DSU-LSTM method for lubricating oil, and the robustness of the prediction model can be improved as well.

Keywords

• domain shifts with uncertainty (DSU)
• long short-term memory (LSTM)
• lubricating oil
• trend prediction