Bidirectional LSTM-Based Soft Sensor for Rotor Displacement Trajectory Estimation

Abstract

Constant rotor system monitoring enables timely control and maintenance actions that decrease the likelihood of severe malfunctions and end product quality deficits. Soft sensors represent a promising branch of solutions enhancing rotor system monitoring. A soft sensor can substitute a malfunctioning physical sensor and provide estimates of a quantity that is difficult to measure. This research demonstrates a soft sensor based on bidirectional long short-term memory (LSTM), and a training procedure for rotor system monitoring at high sampling frequency and varied operating conditions. This study adopts a large rotor and bearing vibration dataset. The soft sensor accurately estimates lateral displacement trajectories of the rotor from the bearing reaction forces over a large range of constant rotating speeds and constant support stiffnesses. The mean absolute error (MAE) of the LSTM-based soft sensor is 0.0063 mm over the test trajectories in the complete operating condition space. The soft sensor performance is shown to decrease significantly to a MAE of 0.0442 mm, if the training dataset is limited in the rotating speed range.

Keywords

• Long short-term memory (LSTM)
• recurrent neural network (RNN)
• rotor system
• soft sensor
• vibration