A Hybrid Generalization Network for Intelligent Fault Diagnosis of Rotating Machinery Under Unseen Working Conditions

Han, Te; Li, Yan‐Fu; Qian, Min

doi:10.1109/tim.2021.3088489

Cited by 122 publications

(50 citation statements)

References 42 publications

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“…For instance, a model trained with data collected from one machinery could be applied to another machinery. Especially for the purpose of improving the ability to generalize on a different domain (e.g., conditions of machinery), advanced deep neural network architectures that are capable of learning robust feature representations in unseen domains, such as [99], should be further studied. While the proposed early fusion approach could technically distinguish different kinds of faults (e.g., flow marks, bubbles), they are not distinguished in this case study, because MES of the car parts company in Ulsan used in this case study does not consider different kinds of faults.…”

Section: Discussionmentioning

confidence: 99%

A Multimodal Deep Learning-Based Fault Detection Model for a Plastic Injection Molding Process

Kim

Choi

et al. 2021

IEEE Access

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The authors of this work propose a deep learning-based fault detection model that can be implemented in the field of plastic injection molding. Compared to conventional approaches to fault detection in this domain, recent deep learning approaches prove useful for on-site problems involving complex underlying dynamics with a large number of variables. In addition, the advent of advanced sensors that generate data types in multiple modalities prompts the need for multimodal learning with deep neural networks to detect faults. This process is able to facilitate information from various modalities in an endto-end learning fashion. The proposed deep learning-based approach opts for an early fusion scheme, in which the low-level feature representations of modalities are combined. A case study involving real-world data, obtained from a car parts company and related to a car window side molding process, validates that the proposed model outperforms late fusion methods and conventional models in solving the problem. INDEX TERMS Machine learning, deep learning, multimodal learning, early fusion, industrial AI, plastic injection moldingThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.

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Section: Discussionmentioning

confidence: 99%

A Multimodal Deep Learning-Based Fault Detection Model for a Plastic Injection Molding Process

Kim

Choi

et al. 2021

IEEE Access

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“…Recent advances in the field of artificial intelligence have demonstrated their success in different fields of interest such as the environment [1], climate change [2], agriculture [3], industry [4] and health [5][6][7], among others. In particular, the application of modelling and the development of machine learning algorithms have been emerging in recent times and have been responsible for these multiple applications of interest for data-driven decision support and profit maximisation.…”

Section: Introduction 1contextmentioning

confidence: 99%

“…In addition to these advantages, deep learning techniques have the ability to adapt to different domains more easily [4], for example through transfer learning by using pre-trained deep neural networks.…”

Section: Introduction 1contextmentioning

confidence: 99%

A Clinical Decision Web to Predict ICU Admission or Death for Patients Hospitalised with COVID-19 Using Machine Learning Algorithms

Aznar-Gimeno

Esteban

Labata-Lezaun

et al. 2021

IJERPH

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The purpose of the study was to build a predictive model for estimating the risk of ICU admission or mortality among patients hospitalized with COVID-19 and provide a user-friendly tool to assist clinicians in the decision-making process. The study cohort comprised 3623 patients with confirmed COVID-19 who were hospitalized in the SALUD hospital network of Aragon (Spain), which includes 23 hospitals, between February 2020 and January 2021, a period that includes several pandemic waves. Up to 165 variables were analysed, including demographics, comorbidity, chronic drugs, vital signs, and laboratory data. To build the predictive models, different techniques and machine learning (ML) algorithms were explored: multilayer perceptron, random forest, and extreme gradient boosting (XGBoost). A reduction dimensionality procedure was used to minimize the features to 20, ensuring feasible use of the tool in practice. Our model was validated both internally and externally. We also assessed its calibration and provide an analysis of the optimal cut-off points depending on the metric to be optimized. The best performing algorithm was XGBoost. The final model achieved good discrimination for the external validation set (AUC = 0.821, 95% CI 0.787–0.854) and accurate calibration (slope = 1, intercept = −0.12). A cut-off of 0.4 provides a sensitivity and specificity of 0.71 and 0.78, respectively. In conclusion, we built a risk prediction model from a large amount of data from several pandemic waves, which had good calibration and discrimination ability. We also created a user-friendly web application that can aid rapid decision-making in clinical practice.

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“…Basically, the success of state-of-theart data-driven predictive maintenance frameworks rely greatly on the effectiveness of the DL-based diagnostic and/or prognostic algorithm at its core. This presents ample opportunities for developing (and improving) high-performing generalized models for accurate failure diagnostics and prognostics, even for unseen equipment working conditions [3]. Particularly for the remaining useful life estimation of equipment/components, several articles have recorded various successes in the use of DL methods regardless of the types and architecture of the artificial neural network (ANN) employed [4].…”

Section: Introductionmentioning

confidence: 99%

A CEEMDAN-Assisted Deep Learning Model for the RUL Estimation of Solenoid Pumps

Akpudo¹,

Hur²

2021

Electronics

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This paper develops a data-driven remaining useful life prediction model for solenoid pumps. The model extracts high-level features using stacked autoencoders from decomposed pressure signals (using complementary ensemble empirical mode decomposition with adaptive noise (CEEMDAN) algorithm). These high-level features are then received by a recurrent neural network-gated recurrent units (GRUs) for the RUL estimation. The case study presented demonstrates the robustness of the proposed RUL estimation model with extensive empirical validations. Results support the validity of using the CEEMDAN for non-stationary signal decomposition and the accuracy, ease-of-use, and superiority of the proposed DL-based model for solenoid pump failure prognostics.

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A Hybrid Generalization Network for Intelligent Fault Diagnosis of Rotating Machinery Under Unseen Working Conditions

Cited by 122 publications

References 42 publications

A Multimodal Deep Learning-Based Fault Detection Model for a Plastic Injection Molding Process

A Multimodal Deep Learning-Based Fault Detection Model for a Plastic Injection Molding Process

A Clinical Decision Web to Predict ICU Admission or Death for Patients Hospitalised with COVID-19 Using Machine Learning Algorithms

A CEEMDAN-Assisted Deep Learning Model for the RUL Estimation of Solenoid Pumps

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