scholarly journals Investigation of reliability assessement in power electronics circuits using machine learning

2021 ◽  
Vol 12 (1) ◽  
pp. 558
Author(s):  
Soumya Rani Mestha ◽  
Pinto Pius A.J
Keyword(s):  
Machine Learning ◽  
Power Electronics ◽  
New Technologies ◽  
Remaining Useful Life ◽  
Power Electronic ◽  
Electronic Components ◽  
Recent Advances ◽  
Useful Life ◽  
Comprehensive Study

<p>Recent advances in power electronics (PE) and machine learning (ML) have prompted the technologists to adapt these new technologies to improve the reliability of PE systems. During the process, a lot of investigations on the performance and reliability of PE systems is carried out. The intention of this paper is to present a comprehensive study of advances in the field of reliability of PE systems using machine learning. Recent publications in this regard are analysed and findings are tabulated. In addition to this, literatures published in the prediction of remaining useful life (RUL) of power electronic components is discussed with emphasis on its limitations.</p>

scholarly journals A Hybrid Prognostics Deep Learning Model for Remaining Useful Life Prediction

Electronics ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 39
Author(s):  
Zhiyuan Xie ◽  
Shichang Du ◽  
Jun Lv ◽  
Yafei Deng ◽  
Shiyao Jia
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Learning Model ◽  
Recurrent Network ◽  
Remaining Useful Life ◽  
Support Vector ◽  
Second Phase ◽  
Learning Methods ◽  
Useful Life ◽  
Deep Learning Model

Remaining Useful Life (RUL) prediction is significant in indicating the health status of the sophisticated equipment, and it requires historical data because of its complexity. The number and complexity of such environmental parameters as vibration and temperature can cause non-linear states of data, making prediction tremendously difficult. Conventional machine learning models such as support vector machine (SVM), random forest, and back propagation neural network (BPNN), however, have limited capacity to predict accurately. In this paper, a two-phase deep-learning-model attention-convolutional forget-gate recurrent network (AM-ConvFGRNET) for RUL prediction is proposed. The first phase, forget-gate convolutional recurrent network (ConvFGRNET) is proposed based on a one-dimensional analog long short-term memory (LSTM), which removes all the gates except the forget gate and uses chrono-initialized biases. The second phase is the attention mechanism, which ensures the model to extract more specific features for generating an output, compensating the drawbacks of the FGRNET that it is a black box model and improving the interpretability. The performance and effectiveness of AM-ConvFGRNET for RUL prediction is validated by comparing it with other machine learning methods and deep learning methods on the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) dataset and a dataset of ball screw experiment.

Reliability Evaluation of Mid-Voltage DC Distribution Network with Multiple Topologies

2014 ◽  
Vol 666 ◽  
pp. 112-118 ◽  
Author(s):  
Jia Si Zeng ◽  
Yi Bo Gao ◽  
Feng Yang ◽  
Xi Dong Xu ◽  
Peng Qiu ◽  
...  
Keyword(s):  
Power Electronics ◽  
Sequential Monte Carlo ◽  
Reactive Power ◽  
Transmission Loss ◽  
Distribution Network ◽  
Reliability Evaluation ◽  
Power Electronic ◽  
Electronic Components ◽  
Reliability Models ◽  
Dc Distribution

With the development of power electronics, DC distribution network has advantages in power supplying for DC loads, saving transmission loss of reactive power and improving power quality, when compared with traditional AC distribution network. Since DC distribution network has several multiple topologies, lots of power electronic components and DGs, the traditional reliability evaluation methods aren’t applicable any more. Hence, the reliability models of power electronics and DGs are built in this paper, and a hybrid method combining minimum-cut with non-sequential Monte Carlo is presented. Moreover, three topologies of mid-voltage DC distribution network are designed based on IEEE RBTS bus6, by which the feasibility of the method is validated. Results show that two-terminal network is more reliable than radial and looped network.

scholarly journals Remaining Useful Life (RUL) Prediction of Equipment in Production Lines Using Artificial Neural Networks

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 932
Author(s):  
Ziqiu Kang ◽  
Cagatay Catal ◽  
Bedir Tekinerdogan
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Continuous Production ◽  
Case Study Research ◽  
Remaining Useful Life ◽  
Predictive Maintenance ◽  
Production Lines ◽  
Important Concern ◽  
Proposed Model ◽  
Useful Life

Predictive maintenance of production lines is important to early detect possible defects and thus identify and apply the required maintenance activities to avoid possible breakdowns. An important concern in predictive maintenance is the prediction of remaining useful life (RUL), which is an estimate of the number of remaining years that a component in a production line is estimated to be able to function in accordance with its intended purpose before warranting replacement. In this study, we propose a novel machine learning-based approach for automating the prediction of the failure of equipment in continuous production lines. The proposed model applies normalization and principle component analysis during the pre-processing stage, utilizes interpolation, uses grid search for parameter optimization, and is built with multilayer perceptron neural network (MLP) machine learning algorithm. We have evaluated the approach using a case study research to predict the RUL of engines on NASA turbo engine datasets. Experimental results demonstrate that the performance of our proposed model is effective in predicting the RUL of turbo engines and likewise substantially enhances predictive maintenance results.

scholarly journals Remaining useful life estimation of HMPE rope during CBOS testing through machine learning

2021 ◽  
Vol 238 ◽  
pp. 109617
Author(s):  
Shaun Falconer ◽  
Ellen Nordgård-Hansen ◽  
Geir Grasmo
Keyword(s):  
Machine Learning ◽  
Remaining Useful Life ◽  
Life Estimation ◽  
Useful Life
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