Multimodal degradation prognostics based on switching kalman filter ensemble

Pin Lim, Chi Keong Goh, Kay Chen Tan, Partha Dutta

Research output: Journal article publicationJournal articleAcademic researchpeer-review

57 Citations (Scopus)


For accurate prognostics, users have to determine the current health of the system and predict future degradation pattern of the system. An increasingly popular approach toward tackling prognostic problems involves the use of switching models to represent various degradation phases, which the system undergoes. Such approaches have the advantage of determining the exact degradation phase of the system and being able to handle nonlinear degradation models through piecewise linear approximation. However, limitations of such existing methods include, limited applicability due to the discretization of predicted remaining useful life, insufficient robustness due to the use of single models and others. This paper circumvents these limitations by proposing a hybrid of ensemble methods with switching methods. The proposed method first implements a switching Kalman filter (SKF) to classify between various linear degradation phases, then predict the future propagation of fault dimension using appropriate Kalman filters for each phase. This proposed method achieves both continuous and discrete prediction values representing the remaining life and degradation phase of the system, respectively. The proposed framework is shown via a case study on benchmark simulated aeroengine data sets. The evaluation of the proposed framework shows that the proposed method achieves better accuracy and robustness against noise compared with other methods reported in the literature. The results also indicate the effectiveness of the SKF in detecting the switching point between various degradation modes.

Original languageEnglish
Article number7358146
Pages (from-to)136-148
Number of pages13
JournalIEEE Transactions on Neural Networks and Learning Systems
Issue number1
Publication statusPublished - Jan 2017
Externally publishedYes


  • Aerospace
  • commercial modular aero-propulsion system simulation (C-MAPSS)
  • diagnostics
  • multimodal degradation
  • neural networks
  • prognostics
  • switching Kalman filter (SKF) ensemble

ASJC Scopus subject areas

  • Software
  • Computer Science Applications
  • Computer Networks and Communications
  • Artificial Intelligence


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