A Novel Framework for Online Remaining Useful Life Prediction of an Industrial Slurry Pump

Abstract

An efficient Remaining Useful Life (RUL) prediction method is one of the most important features of a condition-based maintenance system. A running machine’s RUL prognosis in its real-time is a challenging task, especially when there is no historic failure data available for that particular machine. In this paper, an online RUL of an in-operation industrial slurry pump having no historical failure data has been predicted. At first, the available raw vibration datasets were filtered out for valid datasets. The obtained valid datasets were utilized for constructing the Health Degradation Trends (HDTs) using principal component analysis and a moving average method. Then, a novel procedure for automatically selecting the HDT’s data points for initiating the iteration process of prediction was formulated. Afterward, a hybrid deep LSTM model embedded with a smart learning rate mechanism was developed for estimating the online RUL using the selected points of HDTs. The online RUL prediction results produced by the developed model were quite satisfactory when they were compared with other online RUL prediction methods.

Keywords

• Remaining Useful Life
• prediction
• vibration data
• LSTM modelling
• learning rate