Roughening Particle Filter Based Prognosis on Power MOSFETs Using ON-Resistance Variation

Pugalenthi, Karkulali; Raghavan, Nagarajan

doi:10.1109/phm-chongqing.2018.00206

Cited by 5 publications

(3 citation statements)

References 21 publications

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“…Based on Ref. [ 34 ], three different sigma values were used in literature to simulate the jittering effect as shown in Table 1 , and Sigma-2 values were found to be the best-performing ones. The sigma limits were chosen based on the admissible values of measurement noise to be present in the system under consideration.…”

Section: Resultsmentioning

confidence: 99%

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Remaining Useful Life Prediction of Lithium-Ion Batteries Using Neural Networks with Adaptive Bayesian Learning

Pugalenthi

Park

Hussain

et al. 2022

Sensors

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With smart electronic devices delving deeper into our everyday lives, predictive maintenance solutions are gaining more traction in the electronic manufacturing industry. It is imperative for the manufacturers to identify potential failures and predict the system/device’s remaining useful life (RUL). Although data-driven models are commonly used for prognostic applications, they are limited by the necessity of large training datasets and also the optimization algorithms used in such methods run into local minima problems. In order to overcome these drawbacks, we train a Neural Network with Bayesian inference. In this work, we use Neural Networks (NN) as the prediction model and an adaptive Bayesian learning approach to estimate the RUL of electronic devices. The proposed prognostic approach functions in two stages—weight regularization using adaptive Bayesian learning and prognosis using NN. A Bayesian framework (particle filter algorithm) is adopted in the first stage to estimate the network parameters (weights and bias) using the NN prediction model as the state transition function. However, using a higher number of hidden neurons in the NN prediction model leads to particle weight decay in the Bayesian framework. To overcome the weight decay issues, we propose particle roughening as a weight regularization method in the Bayesian framework wherein a small Gaussian jitter is added to the decaying particles. Additionally, weight regularization was also performed by adopting conventional resampling strategies to evaluate the efficiency and robustness of the proposed approach and to reduce optimization problems commonly encountered in NN models. In the second stage, the estimated distributions of network parameters were fed into the NN prediction model to predict the RUL of the device. The lithium-ion battery capacity degradation data (CALCE/NASA) were used to test the proposed method, and RMSE values and execution time were used as metrics to evaluate the performance.

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Section: Resultsmentioning

confidence: 99%

“…Similarly, to address particle impoverishment, we use particle roughening as a weight regularization technique [ 34 ]. Although resampling strategies try to diversify the particle weight distribution, particle roughening, on the other hand, is a compensation technique.…”

Section: Methodsmentioning

confidence: 99%

Remaining Useful Life Prediction of Lithium-Ion Batteries Using Neural Networks with Adaptive Bayesian Learning

Pugalenthi

Park

Hussain

et al. 2022

Sensors

Self Cite

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“…Moreover, in engineering problems, the PF was employed in robot localization for position tracking [22] and hardware usage. In addition to that, resampling was also used to calculate battery endurance [23] . The main idea of resampling is that the particles with small weight will be replaced by new particles in the area around or close to the particles with large weight [24] , resulting in the duplication of significant quantity (particle containing the parameters of interest, frequency in our work) to represent the posterior probability density functions (PDFs) of the investigating parameters.…”

Section: Introductionmentioning

confidence: 99%

A novel adaptive resampling for sequential Bayesian filtering to improve frequency estimation of time-varying signals

Aunsri

Pipatphol

Thikeaw

et al. 2021

Heliyon

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This paper presents a new algorithm for adaptive resampling, called percentile-based resampling (PBR) in a sequential Bayesian filtering, i.e., particle filter (PF) in particular, to improve tracking quality of the frequency trajectories under noisy environments. Since the conventional resampling scheme used in the PF suffers from computational burden, resulting in less efficiency in terms of computation time and complexity as well as the real time applications of the PF. The strategy to remedy this issue is proposed in this work. After state updating, important high particle weights are used to formulate the pre-set percentile in each sequential iteration to create a new set of high quality particles for the next filtering stage. The number of particles after PBR remains the same as the original. To verify the effectiveness of the proposed method, we first evaluated the performance of the method via numerical examples to a complex and highly nonlinear benchmark system. Then, the proposed method was implemented for frequency estimation for two time-varying signals. From the experimental results, via three measurement metrics, our approach delivered better performance than the others. Frequency estimates obtained by our method were excellent as compared to the conventional resampling method when number of particles were identical. In addition, the computation time of the proposed work was faster than those recent adaptive resampling schemes in literature, emphasizing the superior performance to the existing ones.

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2021

Electrical Safety Engineering of Renewable EnergySystems

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Roughening Particle Filter Based Prognosis on Power MOSFETs Using ON-Resistance Variation

Cited by 5 publications

References 21 publications

Remaining Useful Life Prediction of Lithium-Ion Batteries Using Neural Networks with Adaptive Bayesian Learning

Remaining Useful Life Prediction of Lithium-Ion Batteries Using Neural Networks with Adaptive Bayesian Learning

A novel adaptive resampling for sequential Bayesian filtering to improve frequency estimation of time-varying signals

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