Residual Life Prediction for Induction Furnace by Sequential Encoder with s-Convolutional LSTM

Choi, Yulim; Kwun, Hyeonho; Kim, Do-Hee; Lee, Eun‐Ju; Bae, Hyerim

doi:10.3390/pr9071121

Cited by 7 publications

(2 citation statements)

References 31 publications

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“…Apart from explainability, two data-driven studies were conducted by Yolim Choi et al [28,29] to predict the RUL of an IF refractory. They prove that multilayer perceptron (MLP) performs better than recurrent neural networks (RNNs) and LSTM methods in predicting RUL.…”

Section: Related Workmentioning

confidence: 99%

Explainable Artificial Intelligence Approach for Diagnosing Faults in an Induction Furnace

Moosavi,

Razavi-Far,

Palade

et al. 2024

Electronics

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For over a century, induction furnaces have been used in the core of foundries for metal melting and heating. They provide high melting/heating rates with optimal efficiency. The occurrence of faults not only imposes safety risks but also reduces productivity due to unscheduled shutdowns. The problem of diagnosing faults in induction furnaces has not yet been studied, and this work is the first to propose a data-driven framework for diagnosing faults in this application. This paper presents a deep neural network framework for diagnosing electrical faults by measuring real-time electrical parameters at the supply side. Experimental and sensory measurements are collected from multiple energy analyzer devices installed in the foundry. Next, a semi-supervised learning approach, known as the local outlier factor, has been used to discriminate normal and faulty samples from each other and label the data samples. Then, a deep neural network is trained with the collected labeled samples. The performance of the developed model is compared with several state-of-the-art techniques in terms of various performance metrics. The results demonstrate the superior performance of the selected deep neural network model over other classifiers, with an average F-measure of 0.9187. Due to the black box nature of the constructed neural network, the model predictions are interpreted by Shapley additive explanations and local interpretable model-agnostic explanations. The interpretability analysis reveals that classified faults are closely linked to variations in odd voltage/current harmonics of order 3, 11, 13, and 17, highlighting the critical impact of these parameters on the model’s prediction.

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Section: Related Workmentioning

confidence: 99%

Explainable Artificial Intelligence Approach for Diagnosing Faults in an Induction Furnace

Moosavi,

Razavi-Far,

Palade

et al. 2024

Electronics

View full text Add to dashboard Cite

show abstract

“…In the former, melting is induced by combustion, whereas in the latter, it is induced by electricity. Electric furnaces were developed in the 19th century and are divided into electric-arc furnaces, in which an electrical current runs through electrodes inside the furnace, and induction furnaces, which use induction heating [ 2 ]. An electric-arc furnace uses the energy from the arc between the electrodes, whereas in an induction furnace, melting is caused by the heat produced by inducing a high-frequency current.…”

Section: Introductionmentioning

confidence: 99%

Generation of Time-Series Working Patterns for Manufacturing High-Quality Products through Auxiliary Classifier Generative Adversarial Network

Bazarbaev¹,

Chuluunsaikhan

et al. 2021

Sensors

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Product quality is a major concern in manufacturing. In the metal processing industry, low-quality products must be remanufactured, which requires additional labor, money, and time. Therefore, user-controllable variables for machines and raw material compositions are key factors for ensuring product quality. In this study, we propose a method for generating the time-series working patterns of the control variables for metal-melting induction furnaces and continuous casting machines, thus improving product quality by aiding machine operators. We used an auxiliary classifier generative adversarial network (AC-GAN) model to generate time-series working patterns of two processes depending on product type and additional material data. To check accuracy, the difference between the generated time-series data of the model and the ground truth data was calculated. Specifically, the proposed model results were compared with those of other deep learning models: multilayer perceptron (MLP), convolutional neural network (CNN), long short-term memory (LSTM), and gated recurrent unit (GRU). It was demonstrated that the proposed model outperformed the other deep learning models. Moreover, the proposed method generated different time-series data for different inputs, whereas the other deep learning models generated the same time-series data.

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Study on Carbon Deposition and Coil Breakdown Mechanism of Medium Frequency Coreless Induction Furnace

Cong

Wang

et al. 2023

Inter Metalcast

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Residual Life Prediction for Induction Furnace by Sequential Encoder with s-Convolutional LSTM

Cited by 7 publications

References 31 publications

Explainable Artificial Intelligence Approach for Diagnosing Faults in an Induction Furnace

Explainable Artificial Intelligence Approach for Diagnosing Faults in an Induction Furnace

Generation of Time-Series Working Patterns for Manufacturing High-Quality Products through Auxiliary Classifier Generative Adversarial Network

Study on Carbon Deposition and Coil Breakdown Mechanism of Medium Frequency Coreless Induction Furnace

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