Predicting the quality of a machined workpiece with a variational autoencoder approach

Proteau, Antoine; Tahan, Antoine; Zemouri, Ryad; Thomas, Marc

doi:10.1007/s10845-021-01822-y

Cited by 15 publications

(3 citation statements)

References 62 publications

(87 reference statements)

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“…In this context, the variable y is a result of the categorical onehot-encoding transformation, while ŷ corresponds to the output of the classifier. Typically, β KL = 10 −3 and β CL = 10 −1 are the usual values used to obtained balanced components [27].…”

Section: A Vae Architecture and Training Methodologymentioning

confidence: 99%

Fault Detection Based on Vibration Measurements and Variational Autoencoder-Desirability Function

Ibrahim,

Zemouri,

Tahan

et al. 2024

IEEE Open J. Ind. Applicat.

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In the field of electrical machines maintenance, accurate and timely diagnosis plays a crucial role in ensuring reliability and efficiency. Variational Autoencoder (VAE) techniques have emerged as a promising tool for fault classification due to their robustness in handling complex data. However, the inherent non-deterministic aspect of the VAE creates a significant challenge as it leads to varying cluster locations for identical health states across different machines. This variability complicates the creation of a standardized applicable diagnostic tool and challenges for the implementation of effective real-time health monitoring and prognostics. Addressing this issue, a novel approach is proposed wherein a desirability function-based term is integrated into the cost function of the VAE. The enhancement achieved by this approach arises from the standardization of classification, guaranteeing that analogous faults are assigned to identical geolocations within a 2D user-friendly space. This method's efficacy is validated through two separate case studies: one analyzing vibration data from two diverse designs of large existing hydrogenerators, and the other utilizing vibration data sourced from an open-access dataset focused on bearing fault. The findings of both studies show that the model can cluster 97% of similar faults into preset zones, compared to 40% when the Desirability term is excluded.

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Section: A Vae Architecture and Training Methodologymentioning

confidence: 99%

Fault Detection Based on Vibration Measurements and Variational Autoencoder-Desirability Function

Ibrahim,

Zemouri,

Tahan

et al. 2024

IEEE Open J. Ind. Applicat.

Self Cite

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“…Benardos and Vosniakos (2003) review both approaches in the context of machining processes to describe the surface roughness as a function of different process variables. Data-driven quality prediction models for process optimization have recently been applied to a machining process in Proteau et al (2021), to textile drapping processes in Pfrommer et al (2018), and to a laser cutting process in Chaki et al (2015), just to name a few. Weichert et al (2019) show that ML models used for optimization of production processes are often trained with relatively small datasets.…”

Section: Related Workmentioning

confidence: 99%

Capturing and incorporating expert knowledge into machine learning models for quality prediction in manufacturing

et al. 2022

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Increasing digitalization enables the use of machine learning (ML) methods for analyzing and optimizing manufacturing processes. A main application of ML is the construction of quality prediction models, which can be used, among other things, for documentation purposes, as assistance systems for process operators, or for adaptive process control. The quality of such ML models typically strongly depends on the amount and the quality of data used for training. In manufacturing, the size of available datasets before start of production (SOP) is often limited. In contrast to data, expert knowledge commonly is available in manufacturing. Therefore, this study introduces a general methodology for building quality prediction models with ML methods on small datasets by integrating shape expert knowledge, that is, prior knowledge about the shape of the input–output relationship to be learned. The proposed methodology is applied to a brushing process with 125 data points for predicting the surface roughness as a function of five process variables. As opposed to conventional ML methods for small datasets, the proposed methodology produces prediction models that strictly comply with all the expert knowledge specified by the involved process specialists. In particular, the direct involvement of process experts in the training of the models leads to a very clear interpretation and, by extension, to a high acceptance of the models. While working out the shape knowledge requires some iterations in general, another clear merit of the proposed methodology is that, in contrast to most conventional ML, it involves no time-consuming and often heuristic hyperparameter tuning or model selection step.

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“…Such as, Zhang et al [33] proposed a conditional variational generative adversarial network model to solve the problem of insufficient fault samples and realize the fault diagnosis of the wind turbine. Proteau et al [34] proposed a new measurement method based on VAE and the concept of Euclidean distance, which is used to predict the quality measurement values of processed workpieces during the monitoring process. Unfortunately, when VAE is adopted to conduct a complex nonlinear classification task, more weight parameters will be required in the feature learning process of VAE, this easily leading to increase model complexity and even overfitting.…”

Section: Introductionmentioning

confidence: 99%

Attention mechanism-guided residual convolution variational autoencoder for bearing fault diagnosis under noisy environments

Yan,

Lu,

Liu

et al. 2023

Meas. Sci. Technol.

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Due to rolling bearings usually operate under fluctuating working conditions in practical engineering, the raw vibration signals generated by bearing faults have nonlinear and non-stationary characteristics. Additionally, there is a lot of noise interference in the collected bearing vibration signal, which indicates that it is difficult to extract bearing fault information and obtain a satisfactory diagnosis accuracy via using traditional method. Deep learning provides a shining road to address this issue. Nevertheless, traditional deep network model has the shortcomings of poor generalization performance and weak robustness in the feature learning. To improve fault recognition accuracy and obtain a favorable anti-noise robustness, this paper proposes a novel bearing fault diagnosis approach based on attention mechanism-guided residual convolutional variational autoencoder (AM-RCVAE). Firstly, the improved residual module is constructed to overcome the convergence difficulty problem caused by network degradation and promote the model generalization performance by replacing the batch normalization (BN) layer in the traditional residual module with the adaptive BN layer. Subsequently, by incorporating the convolutional block attention module and the improved residual module into convolutional variational autoencoder, a deep network model termed as AM-RCVAE is presented to automatically learn fault features from the original data and perform fault diagnosis tasks. The effectiveness of the proposed approach is verified via two experimental cases. Moreover, the recognition accuracy and diagnostic performance of the proposed approach have been certain improved compared with several representative methods.

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Predicting the quality of a machined workpiece with a variational autoencoder approach

Cited by 15 publications

References 62 publications

Fault Detection Based on Vibration Measurements and Variational Autoencoder-Desirability Function

Fault Detection Based on Vibration Measurements and Variational Autoencoder-Desirability Function

Capturing and incorporating expert knowledge into machine learning models for quality prediction in manufacturing

Attention mechanism-guided residual convolution variational autoencoder for bearing fault diagnosis under noisy environments

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