Abstract
Prognostics and Health Management (PHM) plays a key role in Industry 4.0 revolution by providing smart predictive maintenance solutions. Early failure detection and prediction of remaining useful life (RUL) of critical industrial machines/components are the main challenges addressed by PHM methodologies. In literature, model-based and data-driven methods are widely used for RUL estimation. Model-based methods rely on empirical/phenomenological degradation models for RUL prediction using Bayesian formulations. In many cases, the lack of accurate physics-based models emphasizes the need to resort to machine learning based prognostic algorithms. However, data-driven methods require extensive machine failure data incorporating all possible operating conditions along with all possible failure modes pertaining to that particular machine/component, which are seldom available in their entirety. In this work, we propose a three-stage hybrid prognostic algorithm (HyA) combining model-based (Particle Filters-PF) and data-driven (Neural Networks-NN) methods in a unique way. The proposed method aims to overcome the need for accurate degradation modeling or extensive failure data sets. In the first stage, a feedforward neural network is used to formulate lithium-ion battery’s degradation trends and the corresponding NN model parameters are used to define the initial prior distribution of PF algorithm. In the second stage, the PF algorithm optimizes the model parameters and the posterior model parameter distributions are utilized to ‘ warm-start ’ the neural network used for prognosis and the third/final stages focuses on prognosis and RUL estimation using the trained NN model leveraging on the posterior distributions of the PF fine-tuned weights and biases. The proposed method is demonstrated on CALCE and NASA lithium-ion battery capacity degradation datasets. The efficacy of the proposed hybrid algorithm is evaluated using root mean square error (RMSE) values and alpha-lambda prognostic metrics. Also, the impact of the NN architecture on the prediction accuracy and computational load are analyzed.
Highlights
Electronic devices and systems are subjected to thermal, electrical and mechanical stresses on the field and the reliability of these devices is of utmost concern
In this work, a hybrid particle filter based neural network model is proposed for remaining useful life (RUL) prediction of lithium-ion batteries
A neural network model with one hidden layer containing 3 hidden neurons and a sigmoid activation function at the input side and linear activation function in the output side was used as the damage evolution model in the particle filter framework
Summary
Electronic devices and systems are subjected to thermal, electrical and mechanical stresses on the field and the reliability of these devices is of utmost concern. Hybrid prognostic approaches have an upper edge on such new devices as they neither require an accurate degradation model nor a large amount of training data for the purpose of remaining useful life (RUL) estimation. The authors proposed a hybrid prognostic algorithm where the radial bias function (RBF) neural network was used as the degradation model for prognosis of lithium-ion batteries. Since the RBF networks are restricted to one hidden layer and linear output activation functions, the authors further extended their work to adopt a multilayer perceptron (MLP) NN model and tested the approach on NASA and CALCE datasets for estimating the RUL [22]. The PF optimized NN model is trained with available information from the test dataset using a Levenberg Marquardt (LM) algorithm to find the optimal network parameter values which can be used for prediction of the future states and the RUL as well. The trained NN model is used for predicting the battery capacity values on the test dataset
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