A Hybrid Deep Learning Model-Based Remaining Useful Life Estimation for Reed Relay With Degradation Pattern Clustering

Gamanayake, Chinthaka; Yan, Qing; Yuen, Chau; Jayasinghe, Lahiru; Dominique-Ea, Tan,; Low, Jenny

doi:10.1109/tii.2022.3210250

Cited by 4 publications

(1 citation statement)

References 40 publications

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“…A production line can be forced to stop with an increasing number of continuously detected faulty products, which can cause * Author to whom any correspondence should be addressed. enormous economic loss [5][6][7]. Therefore, novelty detection of products in intelligent production lines is particularly important.…”

Section: Introductionmentioning

confidence: 99%

Motor online novelty detection scheme based on one-class hyperdisk

Yang,

He,

Shao

et al. 2024

Meas. Sci. Technol.

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Online novelty detection is of great importance in the series production of motors. This study developed an online novelty detection scheme for motors based on a one-class hyperdisk (OCHD) model. In the OCHD approach, the decision boundary is estimated using a hyperdisk (HD), which is derived from the training sample set. The HD model addresses the underestimation issue commonly associated with convex-hull-based methods by providing a more accurate estimation of the class region. Furthermore, an optimal separating hyperplane is constructed at the nearest point on the HD by solving a quadratically constrained quadratic program problem. Statistical features refined by the Laplacian score are employed in the proposed novelty detection scheme. This study introduces an online novelty detection scheme for assessing motor quality in actual series production. The test results from the offline experiment demonstrate the superiority of the OCHD method. Datasets collected at the end of the production line were evaluated using the proposed novelty detection scheme. The inspection results for motor components confirm that the proposed method effectively identifies faulty motors during the series production process.

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Section: Introductionmentioning

confidence: 99%

Motor online novelty detection scheme based on one-class hyperdisk

Yang,

He,

Shao

et al. 2024

Meas. Sci. Technol.

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An Improved Jaccard Coefficient‐Based Clustering Approach with Application to Diagnosis and RUL Estimation

Li,

Tang,

Wang

et al. 2024

IET Signal Processing

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Sample clustering techniques play a crucial role in the data‐driven state evaluation of electromechanical equipment, and selecting an appropriate similarity measurement method for sample sets helps improve the clustering performance. The Jaccard coefficient is a commonly employed indicator of similarity for scalar set‐type samples. In this paper, we propose an incremental clustering algorithm for matrix‐type samples by defining an improved Jaccard coefficient. First, a new binary relation is formulated to derive a relationship matrix between samples. Second, an undirected graph is given by using the relationship matrix, and an improved pruning operation is provided to simplify the graph by eliminating redundant edges. Then, a new relationship matrix is generated according to the modified graph, which enables the calculation of the improved Jaccard coefficient. By using the improved Jaccard coefficient, the improved incremental clustering algorithm updates cluster centers by selecting a particular sample to maximize the sum of similarities between the selected sample and other samples within the same cluster. Finally, the effectiveness of the proposed incremental clustering algorithm is demonstrated in fault diagnosis and remaining useful life estimation application scenarios, respectively. The experimental results indicate that the improved algorithm outperforms traditional clustering methods.

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Application of novel hybrid deep learning architectures combining Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN): construction duration estimates prediction considering preconstruction uncertainties

Demiss,

Elsaigh

2024

Eng. Res. Express

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Construction duration estimation plays a pivotal role in project planning and management, yet it is often fraught with uncertainties that can lead to cost overruns and delays. To address these challenges, this review article proposes three advanced conceptual models leveraging hybrid deep learning architectures that combine Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) while considering construction delivery uncertainties. The first model introduces a Spatio-Temporal Attention CNN-RNN Hybrid Model with Probabilistic Uncertainty Modeling, which integrates attention mechanisms and probabilistic uncertainty modeling to provide accurate and probabilistic estimates of construction duration, offering insights into critical areas of uncertainty. The second model presents a Multi-Modal Graph CNN-RNN Hybrid Model with Bayesian Uncertainty Integration, which harnesses multi-modal data sources and graph representations to offer comprehensive estimates of construction duration while incorporating Bayesian uncertainty measures, facilitating informed decision-making and optimized resource allocation. Lastly, the third model introduces a Hierarchical Spatio-Temporal Transformer CNN-RNN Hybrid Model with Fuzzy Logic Uncertainty Handling, which addresses the inherent vagueness and imprecision in construction duration estimates by incorporating hierarchical spatio-temporal transformer architecture and fuzzy logic uncertainty handling, leading to more nuanced and adaptable project management practices. These advanced models represent significant advancements in addressing construction duration challenges, providing valuable insights and recommendations for future research and industry applications. Moreover, this review article critically examines the application of hybrid deep learning architectures, specifically the combination of CNNs RNNs, in predicting construction duration estimates at the preconstruction stage while considering uncertainties inherent in construction delivery systems.

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A Hybrid Deep Learning Model-Based Remaining Useful Life Estimation for Reed Relay With Degradation Pattern Clustering

Cited by 4 publications

References 40 publications

Motor online novelty detection scheme based on one-class hyperdisk

Motor online novelty detection scheme based on one-class hyperdisk

An Improved Jaccard Coefficient‐Based Clustering Approach with Application to Diagnosis and RUL Estimation

Application of novel hybrid deep learning architectures combining Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN): construction duration estimates prediction considering preconstruction uncertainties

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