Deep Degradation Feature Extraction and RUL Estimation for Switching Power Unit

Guo, Qing; Shi, Jian; Wang, Shaoping; Zhang, Chao

doi:10.1109/phm-qingdao46334.2019.8942852

Cited by 2 publications

(2 citation statements)

References 8 publications

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“…The frequency-domain features were extracted from the signals; a health index was first extracted using the ordered neurons, then the proposed ON-LSTM system model was constructed to produce the RUL prediction value [24]. Data set size was one of the limitations in [25], with further works making emphasis on the need for more data to classify the performance degradation characteristics of SMPS. Several studies have been conducted on various reliability analyzes and RUL estimation of SMPS.…”

Section: Motivation and Literature Reviewmentioning

confidence: 99%

RUL Prediction of Switched Mode Power Supply Using a Kalman Filter Assisted Deep Neural Network

et al. 2021

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Switched-mode power supply (SMPS) has been of vital importance majorly in power management of industrial equipment with much-improved efficiency and reliability. Given the diverse range on loading and operating conditions of SMPS, several anomalies can occur in the device resulting to over-voltage, overloading, erratic atmospheric conditions, etc. Electrical over-stress (EOS) is one of the commonly used causes of failure among power electronic devices. Since there is a limitation for the SMPS in terms of input voltage and current (two methods of controlling an SMPS), the device has been subjected to an accelerated aging test using EOS. This study presents a two-fold approach to evaluate the overall state of health of SMPS using an integration of extended Kalman filter (EKF) and deep neural network. Firstly, the EKF algorithm would assist in fusing fault features to acquire an comprehensive degradation trend. Secondly, the degradation pattern of the SMPS has been monitored for four different electrical loadings, and a bi-directional long short-term memory (BiLSTM) deep neural network is trained for future predictions. The proposed model provides a unique approach and accuracy in SMPS fault indication with the aid of electrical parameters.

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Section: Motivation and Literature Reviewmentioning

confidence: 99%

RUL Prediction of Switched Mode Power Supply Using a Kalman Filter Assisted Deep Neural Network

et al. 2021

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“…Model-based methods [9] usually start from the failure mechanism of the product and obtain the physical model of performance degradation via construction of a relationship between performance degradation amount and stress (high temperature, high pressure, strong vibration, etc.) in order to analyze the health state and RUL of the system [10]. In other words, mathematical representations of interactions between processes and components must be defined, and precise solutions can only be achieved when precise failure behavior degradations are available [11].…”

Section: Introductionmentioning

confidence: 99%

State Estimation and Remaining Useful Life Prediction of PMSTM Based on a Combination of SIR and HSMM

et al. 2022

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The permanent magnet synchronous traction motor (PMSTM) is the core equipment of urban rail transit. If a PMSTM fails, it will cause serious economic losses and casualties. It is essential to estimate the current health state and predict remaining useful life (RUL) for PMSTMs. Directly obtaining the internal representation of a PMSTM is known to be difficult, and PMSTMs have long service lives. In order to address these drawbacks, a combination of SIR and HSMM based state estimation and RUL prediction method is introduced with the multi-parameter fusion health index (MFHI) as the performance indicator. The proposed method’s advantages over the conventional HSMM method were verified through simulation research and examples. The results show that the proposed state estimation method has small error distribution results, and the RUL prediction method can obtain accurate results. The findings of this study demonstrate that the proposed method may serve as a new and effective technique to estimate a PMSTM’s health state and RUL.

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Deep Degradation Feature Extraction and RUL Estimation for Switching Power Unit

Cited by 2 publications

References 8 publications

RUL Prediction of Switched Mode Power Supply Using a Kalman Filter Assisted Deep Neural Network

RUL Prediction of Switched Mode Power Supply Using a Kalman Filter Assisted Deep Neural Network

State Estimation and Remaining Useful Life Prediction of PMSTM Based on a Combination of SIR and HSMM

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