A variational autoencoder for a semiconductor fault detection model robust to process drift due to incomplete maintenance

Kim, Young‐Ju; Kim, Chang Ouk

doi:10.1007/s10845-021-01810-2

Cited by 15 publications

(5 citation statements)

References 31 publications

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“…The encoder is trained to return a mean and variance given the input, so detecting outliers (or changes in the distribution) is straightforward. These methods are used in multiple scenarios, such as social media sentiment analysis [231,163], detecting drift in videos [199] and fault detection [102].…”

Section: Data Distribution-based Approachmentioning

confidence: 99%

Continual Learning for Predictive Maintenance: Overview and Challenges

Hurtado¹,

Dario²,

Rudy³

et al. 2023

Preprint

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A short review of data-driven solutions for Predictive Maintenance, non-stationary environments, and Continual Learning.• An overview and motivation of the intersection between Predictive Maintenance and Continual Learning.• The current challenge and trends of applying Continual Learning in current Predictive Maintenance benchmarks.• Proposal of a novel construction of benchmarks for Continual Learning, aiming to close the gap between lab and realistic environments.

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Section: Data Distribution-based Approachmentioning

confidence: 99%

Continual Learning for Predictive Maintenance: Overview and Challenges

Hurtado¹,

Dario²,

Rudy³

et al. 2023

Preprint

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“…Costa et al [ 26 ] proposed a semi-supervised recurrent variational autoencoder (RVAE) method to effectively address the diagnosis of atrial fibrillation (AF). Kim et al [ 27 ] presented a fault diagnosis model to robustly process drift by modeling process drift with a variational autoencoder (VAE). Zhang et al [ 28 ] presented a semi-supervised learning framework for bearing fault diagnosis using variational autoencoder (VAE)-based deep generative models.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Reliable Fault Diagnosis of Bearings Using an Optimized Stacked Variational Denoising Auto-Encoder

Yan

She

et al. 2021

Entropy

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Variational auto-encoders (VAE) have recently been successfully applied in the intelligent fault diagnosis of rolling bearings due to its self-learning ability and robustness. However, the hyper-parameters of VAEs depend, to a significant extent, on artificial settings, which is regarded as a common and key problem in existing deep learning models. Additionally, its anti-noise capability may face a decline when VAE is used to analyze bearing vibration data under loud environmental noise. Therefore, in order to improve the anti-noise performance of the VAE model and adaptively select its parameters, this paper proposes an optimized stacked variational denoising autoencoder (OSVDAE) for the reliable fault diagnosis of bearings. Within the proposed method, a robust network, named variational denoising auto-encoder (VDAE), is, first, designed by integrating VAE and a denoising auto-encoder (DAE). Subsequently, a stacked variational denoising auto-encoder (SVDAE) architecture is constructed to extract the robust and discriminative latent fault features via stacking VDAE networks layer on layer, wherein the important parameters of the SVDAE model are automatically determined by employing a novel meta-heuristic intelligent optimizer known as the seagull optimization algorithm (SOA). Finally, the extracted latent features are imported into a softmax classifier to obtain the results of fault recognition in rolling bearings. Experiments are conducted to validate the effectiveness of the proposed method. The results of analysis indicate that the proposed method not only can achieve a high identification accuracy for different bearing health conditions, but also outperforms some representative deep learning methods.

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“…For example, Kim et al utilized a variational AE model to detect semiconductor faults characterized by time‐varying process drift. [ 19 ] In a separate study, Tian et al employed Bayesian inference in conjunction with an AE to estimate and calibrate sensor drift bias caused by high thermal density, thereby achieving optimal control in a data center cooling system. [ 20 ] Moreover, Mirzaei et al utilized sparse AE‐based transfer learning to estimate hydrogen gas concentration and address the data distribution shift problem arising from instrumental variation.…”

Section: Introductionmentioning

confidence: 99%

“…[18] Although some researchers have attempted to mitigate sensor baseline drifting through the use of machine learning approaches, the investigation and validation of solutions specifically tailored for long-term sensor usage drift (over one hour) remain unexplored. Drawing inspiration from the successful application of unsupervised learning methods, such as autoencoder (AE), [19][20][21] in detecting sensor faults, there have been notable instances where unsupervised learning methods have been employed to address the drift detection problem. For example, Kim et al utilized a variational AE model to detect semiconductor faults characterized by time-varying process drift.…”

Section: Introductionmentioning

confidence: 99%

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Drift‐Aware Feature Learning Based on Autoencoder Preprocessing for Soft Sensors

Wang,

Shu,

Alam

et al. 2024

Advanced Intelligent Systems

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In this article, a novel approach is presented for drift‐aware feature learning aimed at calibrating drift biases in soft sensors for long‐term use. The proposed method leverages an autoencoder for data preprocessing to extract expressive signal drift traces features, and incorporates drift characteristics through the latent space representation in a long short‐term memory (LSTM) regression neural network. In the results, it is demonstrated that the proposed approach outperforms other typical recurrent neural networks, such as LSTM, gated recurrent unit, and bidirectional LSTM, with a reduced root mean square error of 60% for the training dataset (≈2.5 h) and 80% for the testing dataset (≈20 h). The proposed approach has the potential to optimize the performance of soft sensors with long‐term drift and reduce the need for frequent recalibration. By compensating for sensor drift using existing prior information and limited time data, the proposed neural network can effectively reduce the complexity and computational burden of the system, without the need for additional settings or hyperparameter fine‐tuning.

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A variational autoencoder for a semiconductor fault detection model robust to process drift due to incomplete maintenance

Cited by 15 publications

References 31 publications

Continual Learning for Predictive Maintenance: Overview and Challenges

Continual Learning for Predictive Maintenance: Overview and Challenges

Reliable Fault Diagnosis of Bearings Using an Optimized Stacked Variational Denoising Auto-Encoder

Drift‐Aware Feature Learning Based on Autoencoder Preprocessing for Soft Sensors

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