Quantum assimilation based state-of-health assessment and remaining useful life estimation for electronic systems

Cui, Yiqian; Shi, Junyou; Wang, Zili

doi:10.1109/tie.2015.2500199

Cited by 29 publications

(16 citation statements)

References 37 publications

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“…14 for connecting the offline models and the online models, which is a key step for practical applications. If a specific AI method lacks an adaptive capability, its application is limited since one prerequisite is that the training data and the test data should be generated under similar situations (e.g., external environments and operational modes) and share a high-level similarity [95]. It is challenging for power electronics since operational settings of the insitu system (i.e., the test data) are quite different from that of the training dataset, which is generally obtained with accelerated testing experiments.…”

Section: Remaining Useful Life Predictionmentioning

confidence: 99%

An Overview of Artificial Intelligence Applications for Power Electronics

Zhao

Blaabjerg

Wang

2021

IEEE Trans. Power Electron.

510

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This paper gives an overview of the Artificial Intelligence (AI) applications for power electronic systems. The three distinctive life-cycle phases, design, control, and maintenance are correlated with one or more tasks to be addressed by AI, including optimization, classification, regression, and data structure exploration. The applications of four categories of AI are discussed, which are expert system, fuzzy logic, metaheuristic method, and machine learning. More than 500 publications have been reviewed to identify the common understandings, practical implementation challenges, and research opportunities in the application of AI for power electronics. This paper is accompanied by an Excel file listing the relevant publications for statistical analytics.

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Section: Remaining Useful Life Predictionmentioning

confidence: 99%

An Overview of Artificial Intelligence Applications for Power Electronics

Zhao

Blaabjerg

Wang

2021

IEEE Trans. Power Electron.

510

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show abstract

“…14 for connecting the offline models and the online models, which is a key step for practical applications. If a specific AI method lacks an adaptive capability, its application is limited since one prerequisite is that the training data and the test data should be generated under similar situations (e.g, external environments and operational modes) and share a high-level similarity [94]. It is challenging for power electronics since operational settings of the insitu system (i.e., the test data) are quite different from that of the training dataset, which is generally obtained with accelerated testing experiments.…”

Section: Number Of Cyclesmentioning

confidence: 99%

An overview of artificial intelligence applications for power electronics

Blaabjerg¹,

Wang²

2020

Preprint

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<div>This is a preprint version of the manuscript submitted to IEEE on June 4, 2020.</div><div><br></div><div>This paper gives an overview of the Artificial Intelligence (AI) applications for power electronic systems. The three distinctive life-cycle phases, design, control, and maintenance are correlated with one or more tasks to be addressed by AI, including optimization, classification, regression, and data structure exploration. The applications of four categories of AI are discussed, which are expert system, fuzzy logic, metaheuristic method, and machine learning. More than 500 publications have been reviewed to identify the common understandings, practical implementation challenges, and research opportunities in the application of AI for power electronics.<br></div>

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“…Commonly used frequency-domain vibration signal detection methods include fast Fourier transforms, power spectra, and filtering. However, each of these methods has flaws and loses some characteristics of nonlinear vibrations, meaning that the monitoring frequency band has a decisive impact on the analysis results [38][39][40][41]. e timefrequency analysis method can accurately describe the time and frequency characteristics of fault signals and has more advantages.…”

Section: Frequency-domain Analysismentioning

confidence: 99%

Predicting Remaining Useful Life Based on Hilbert–Huang Entropy with Degradation Model

Zheng

2019

Journal of Electrical and Computer Engineering

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Prognostics health management (PHM) of rotating machinery has become an important process for increasing reliability and reducing machine malfunctions in industry. Bearings are one of the most important equipment parts and are also one of the most common failure points. To assess the degradation of a machine, this paper presents a bearing remaining useful life (RUL) prediction method. The method relies on a novel health indicator and a linear degradation model to predict bearing RUL. The health indicator is extracted by using Hilbert–Huang entropy to process horizontal vibration signals obtained from bearings. We present a linear degradation model to estimate RUL using this health indicator. In the training phase, the degradation detection threshold and the failure threshold of this model are estimated by the distribution of 600 bootstrapped samples. These bootstrapped samples are taken from the six training sets. In the test phase, the health indicator and the model are used to estimate the bearing’s current health state and predict its RUL. This method is suitable for the degradation of bearings. The experimental results show that this method can effectively monitor bearing degradation and predict its RUL.

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Quantum assimilation based state-of-health assessment and remaining useful life estimation for electronic systems

Cited by 29 publications

References 37 publications

An Overview of Artificial Intelligence Applications for Power Electronics

An Overview of Artificial Intelligence Applications for Power Electronics

An overview of artificial intelligence applications for power electronics

Predicting Remaining Useful Life Based on Hilbert–Huang Entropy with Degradation Model

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