Bidirectional LSTM-Based Soft Sensor for Rotor Displacement Trajectory Estimation

Jesse Miettinen*, Tuomas Tiainen, Risto Viitala, Kari Hiekkanen, Raine Viitala

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

2 Citations (Scopus)
80 Downloads (Pure)


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.
Original languageEnglish
Article number9654210
Pages (from-to)167556-167569
Number of pages14
JournalIEEE Access
Publication statusPublished - 16 Dec 2021
MoE publication typeA1 Journal article-refereed


  • Soft sensors
  • Rotors
  • Vibrations
  • Monitoring
  • Logic gates
  • Trajectory
  • Training


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