Degradation Prediction of Semiconductor Lasers Using Conditional Variational Autoencoder

Abdelli, Khouloud; Grieser, Helmut; Neumeyr, Christian; Hohenleitner, Robert; Pachnicke, Stephan

doi:10.1109/jlt.2022.3188831

Cited by 6 publications

(2 citation statements)

References 16 publications

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“…As an extension of AE, one can consider the variational AEs [240] and conditional variational AE, where an auxiliary function is used to condition the encoder and the decoder [241]. This approach can be further improved by detaching the encoder from the operating conditions, which is referred to as the semi-conditional variational AE, yielding a more straightforward and less resource-demanding architecture [241].…”

Section: ) Autoencodermentioning

confidence: 99%

Scalability, Explainability and Performance of Data-Driven Algorithms in Predicting the Remaining Useful Life: A Comprehensive Review

et al. 2023

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Early detection of faulty patterns and timely scheduling of maintenance events can minimize risk to the underlying processes and increase a system's lifespan, reliability, and availability. Two main data-driven approaches are used in the literature to determine the Remaining Useful Life (RUL): direct calculation from raw data and indirect analysis by revealing the transitions from one latent state to another and highlighting degradation in a system's Health Indices. The present study discusses the state-of-theart data-driven methods introduced for RUL prediction in predictive maintenance, by looking at their capabilities, scalability, performance, and weaknesses. We will also discuss the challenges faced with the current approaches and the future directions to tackle the current limitations.

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Section: ) Autoencodermentioning

confidence: 99%

Scalability, Explainability and Performance of Data-Driven Algorithms in Predicting the Remaining Useful Life: A Comprehensive Review

et al. 2023

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“…GRU neural networks are designed to get temporal links in the sequences of data. [39] Their internal structure is similar to LSTMs, but it is more simplified with only two gates to control the flow of information within the cells. (Fig.…”

Section: Gated Recurrent Unit (Gru)mentioning

confidence: 99%

Forecasting Electricity Generated in A Rankine Power Station Running in Grid-connected Mode

2023

IJIES

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To ensure proactive and intelligent control of the electrical power generated in industrial units selfproducing energy, the construction of a data-driven model devoted to power prediction is an efficient solution that we have proven in this contribution. The remarkable added value of the paper was articulated around three main pillars: primarily, we have modelled a power station based on Rankine cycle that serves the energy needs of an industrial facility. Secondly, the connection of this plant to the public electric grid has been taken into consideration in modelling. Thirdly, we have used the model to avoid paying unwanted bills thanks to the forecasting of periods when there is an energy importation from grid. Before constructing the model, we have prepared a dataset composed of more than 25 million data points, reflecting the history of 3 years of archived data related to fifteen 15 features, in order to predict the target variable of this study. After finding that GRU and LSTM are among the most successful deep learning algorithms in energy prediction, we decided to use them in our particular application. Afterwards, a comparative study between them was first carried out on accuracy, where encouraging results were obtained on both sides, with respective scores of 98.73% and 99.36% during the training step, followed by 98.51% and 99.11% during the testing phase. Besides, the evaluation in terms of loss functions gave a convergence of the GRU model after 8 epochs, against 11 epochs for the LSTM algorithm. Therefore, we preferred to use LSTM in the definitive model because of its higher accuracy, providing that good computing power is used, so that the processing time could be acceptable by the endusers. Finally, we deployed the model to simulate a real case of forecasting the power drop one week in advance. In this case, we found that if the industrial unit had used the results of the model as a proactive decision support tool, it would have avoided importing 1456.4 MWh for 8 days from the national grid, which was equivalent to a loss of 119,094.8 USD.

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In-Situ early anomaly detection and remaining useful lifetime prediction for high-power white LEDs with distance and entropy-based long short-term memory recurrent neural networks

Wen,

Ibrahim,

Meda

et al. 2024

Expert Systems with Applications

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Degradation Prediction of Semiconductor Lasers Using Conditional Variational Autoencoder

Cited by 6 publications

References 16 publications

Scalability, Explainability and Performance of Data-Driven Algorithms in Predicting the Remaining Useful Life: A Comprehensive Review

Scalability, Explainability and Performance of Data-Driven Algorithms in Predicting the Remaining Useful Life: A Comprehensive Review

Forecasting Electricity Generated in A Rankine Power Station Running in Grid-connected Mode

In-Situ early anomaly detection and remaining useful lifetime prediction for high-power white LEDs with distance and entropy-based long short-term memory recurrent neural networks

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