A Parameter-Optimized DBN Using GOA and Its Application in Fault Diagnosis of Gearbox

Gai, Jingbo; Shen, Jun‐Xian; Wang, He; Hu, Y.

doi:10.1155/2020/4294095

Cited by 24 publications

(14 citation statements)

References 14 publications

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“…The temporal and spectral analysis is used for the diagnosis of gear condition. A hybrid deep belief network (DBN) system was utilized by Gai et al [22] for the diagnosis of faulty condition using the optimization based on grasshopper algorithm. Wang et al [23] presented a novel DBN system for the extraction of liabilities in axial piston pump.…”

Section: Literature Reviewmentioning

confidence: 99%

Ensembled mechanical fault recognition system based on deep learning algorithm

Guo¹,

Liu²,

Ding³

et al. 2021

J. vibroeng.

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Primary detection and removal of mechanical fault is vital for the recovery of mechanical and electrical equipment. The conventional mechanical fault recognition modules are not able obtain highly sensitive feature attributes for mechanical fault classification in the absence of prior knowledge. The fault diagnosis via data-driven methods have become a point of expansion with recent development in smart manufacturing and fault recognition techniques using the concept of deep learning. In this work, a combination of feature selection with Artificial Intelligence (AI) algorithm is presented for the mechanical fault recognition to deal with smart machine tools. This article proposes a CNN based fault recognition and classification framework that uses the combination of feature extraction, feature vector decomposition using Empirical Mode Decomposition (EMD) and deep neural network (DNN) for recognising the different fault states of the rotating machinery. The experimental outcomes obtained by the combination of EMD, feature selection module and Convolutional Neural Network (CNN) provides the detailed fault information by selecting the sensitive features from large number of faulty feature attributes. The proposed fault recognition and classification method performs better in terms of all the parameters yielding 99.01 % accuracy with respective cross-entropy loss of 0.325 and time complexity of 18 mins and 31 seconds. The comparative analysis is also done with other mainstream models and other state of the art methods, which reveals that the maximum improvement of 12.29 % is attained in terms of accuracy for the proposed fault recognition method. The presented method is robust in terms of reduction of network size, improvement of mechanical fault recognition, providing classification accuracy along with high fault diagnostic solution.

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Section: Literature Reviewmentioning

confidence: 99%

Ensembled mechanical fault recognition system based on deep learning algorithm

Guo¹,

Liu²,

Ding³

et al. 2021

J. vibroeng.

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show abstract

“…This approach gets 94.6% accuracy in predicting heart diseases. A fault diagnosis model for gearbox fault diagnosis is proposed in study [40]. This model integrates grasshopper optimization algorithm (GOA) with DBN.…”

Section: Related Workmentioning

confidence: 99%

Autonomic Workload Performance Modeling for Large-Scale Databases and Data Warehouses Through Deep Belief Network With Data Augmentation Using Conditional Generative Adversarial Networks

2021

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Databases and warehouses are experiencing workload of different types such as Decision Support System (DSS), Online Transaction Processing (OLTP) and Mixed workloads. Handling variety of workload in autonomic systems is a critical task. After self-configuring the workload, the next challenge is workload performance tuning that motives towards self-predictive systems. Existing studies provide performance modeling solutions on small-scale data repositories of Database Management System (DBMS) and Data Warehouse (DWH) using either classical eager or lazy learning approaches. However, in realworld problems, we normally have to deal with large-scale data repositories. Therefore, there is a need to develop performance models that provide data augmentation to solve large-scale data repositories that are not publicly available. In this study, deep learning approaches have been investigated for performance tuning of large-scale data repositories. We propose a performance prediction model called Optimized GANbased Deep Learning (OGDL) model. For data augmentation, Conditional Generative Adversarial Networks (CGAN) is applied. For autonomic perspective, we incorporated MAPE-K model to manage the workload autonomically. Different deep learning models are applied, and it was observed that Deep Belief Network (DBN) performed better as compared to other deep learning models such as Deep Neural Network (DNN). We performed a number of experiments and from results it is observed that deep learning models performed the best in comparison with classical machine learning and lazy learning and a 6 − 8% increase in accuracy is recorded in our experiments using DBN. The proposed OGDL model performed the best in workload performance predictions in an optimized way.

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“…This technique is very much used in analysing fault diagnosis in multiple rotating components. Based on the novel method furnished by Gai et al., 44 fault diagnosis is carried out in gearboxes using hybrid DBN and grasshopper optimization algorithm. The optimization is performed on minimizing the root mean square error value.…”

Section: In Fault Diagnosismentioning

confidence: 99%

Fault diagnosis of various rotating equipment using machine learning approaches – A review

Manikandan

Duraivelu

2020

Proceedings of the Institution of Mechanical Engineers, Part E:

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Fault diagnosis of various rotating equipment plays a significant role in industries as it guarantees safety, reliability and prevents breakdown and loss of any source of energy. Early identification is a fundamental aspect for diagnosing the faults which saves both time and costs and in fact it avoids perilous conditions. Investigations are being carried out for intelligent fault diagnosis using machine learning approaches. This article analyses various machine learning approaches used for fault diagnosis of rotating equipment. In addition to this, a detailed study of different machine learning strategies which are incorporated on various rotating equipment in the context of fault diagnosis is also carried out. Mainly, the benefits and advance patterns of deep neural network which are applied to multiple components for fault diagnosis are inspected in this study. Finally, different algorithms are proposed to propagate the quality of fault diagnosis and the conceivable research ideas of applying machine learning approaches on various rotating equipment are condensed in this article.

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A Parameter-Optimized DBN Using GOA and Its Application in Fault Diagnosis of Gearbox

Cited by 24 publications

References 14 publications

Ensembled mechanical fault recognition system based on deep learning algorithm

Ensembled mechanical fault recognition system based on deep learning algorithm

Autonomic Workload Performance Modeling for Large-Scale Databases and Data Warehouses Through Deep Belief Network With Data Augmentation Using Conditional Generative Adversarial Networks

Fault diagnosis of various rotating equipment using machine learning approaches – A review

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