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Computational adaptive multivariable degradation model for improving the remaining useful life prediction in industrial systems

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Abstract

Accuracy in predicting the remaining useful life (RUL) of industrial systems is crucial for maintenance tasks. Obtaining models that improve the RUL prediction and that are increasingly adjusting to the reality of the process is an open research problem. This paper proposes an adaptive method for predicting RUL based on modeling the behavior of multiple variables during degradation. The information from each model is weighted to predict the RUL of the system, improving the prediction results significantly. The proposed method is applied to NASA Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) dataset benchmark, showing promising results. Finally, a comparison is made with current prediction techniques present in the scientific literature where it is evidenced that the proposed model has better results.

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Abbreviations

AE:

Autoencoder

BLSTM:

Bidirectional long short-term memory neural network

C-MAPSS:

Commercial modular aero-propulsion system simulation

DNN:

Deep neural network

EOL:

End of life

RUL:

Remaining useful life

SVDD:

Support vector data description

\(\alpha _{i,t}\) :

Weight of RUL prediction error of system i at time t

\(\mathrm{EOL}_{j,i,t}^*\) :

Predicted EOL with variable j of system i at time t

\(\mathrm{ET}_j\) :

EOL threshold of variable j

\(f_j \) :

Degradation model of variable j

N :

Number of systems

\(\mathbf {p}\) :

vector of parameters of f

\(p_k\) :

parameters k of f

\(\mathbf {p}_L, \mathbf {p}_U\) :

Lower and upper bounds of parameters \( \mathbf {p} \)

\(R_s\) :

Constrained region of the parameter space

RMSE:

Root mean squared error

\(\mathrm{RUL}_{i,t}\) :

True RUL of system i at time t

\(\mathrm{RUL}_{j,i,t}^*\) :

Predicted RUL with variable j of system i at time t

\(\mathrm{RUL}_{i,t}^*\) :

Predicted RUL of system i at time t

\( t_d \) :

Detection time of the degradation process

T :

Number of observations

TWEB:

Timeliness weighted error bias

V :

Number of variables

\(w_j\) :

Weight of variable j

\(y_j\) :

variable j

\( \zeta \) :

Late prediction based penalization function

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Acknowledgements

The authors Adriana Villalón-Falcón, Alberto Prieto-Moreno y Orestes Llanes-Santiago acknowledge the financial support provided by the National Research Program Automatica, Robotics and Artificial Intelligence (ARIA) of the Ministry of Science, Technology and Environment (CITMA) from Cuba and Universidad Tecnológica de La Habana José Antonio Echeverría, CUJAE.

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Authors and Affiliations

  1. Universidad Tecnológica de La Habana José Antonio Echeverría, CUJAE, Calle 114 No. 11901, Marianao, La Habana, Cuba

    Adriana Villalón-Falcón, Alberto Prieto-Moreno & Orestes Llanes-Santiago

  2. Vanderbit University, Nashville, TN, 37209, USA

    Marcos Quiñones-Grueiro

Authors
  1. Adriana Villalón-Falcón
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  2. Alberto Prieto-Moreno
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  3. Marcos Quiñones-Grueiro
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  4. Orestes Llanes-Santiago
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Correspondence to Orestes Llanes-Santiago.

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Communicated by Antonio José Silva Neto.

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Villalón-Falcón, A., Prieto-Moreno, A., Quiñones-Grueiro, M. et al. Computational adaptive multivariable degradation model for improving the remaining useful life prediction in industrial systems. Comp. Appl. Math. 41, 48 (2022). https://doi.org/10.1007/s40314-021-01752-8

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