Deep Belief Networks Ensemble with Multi-objective Optimization for Failure Diagnosis

Zhang, Chong; Sun, J.; Tan, Kay Chen

doi:10.1109/smc.2015.19

Cited by 36 publications

(16 citation statements)

References 17 publications

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“…Two main conclusions were reached by comparisons with previous work: first, the proposed model can use domain adaptation while strengthening the representative information of the original data to achieve high classification accuracy in the target domain; second, several strategies were addressed to investigate the optimal hyperparameters of the model. Zhang et al [34] investigated the diagnosis and prognosis of learning methods and used multi-objective optimization to tackle failure diagnosis. Liao et al [35] used an enhanced restricted Boltzmann machine to deal with prognosis and health assessment, and Zhang et al [36] used DBNs to solve remaining useful life estimation in prognostics.…”

Section: Deep Neural Network (Dnns)mentioning

confidence: 99%

“…In addition, we have provided an explanatory tree diagram of this study (see Figure 1), and the organized literature is listed in Table 1. DL (51): RNN [4][5][6][7][8][9][10][11][12][13][14], CNN [15][16][17][18][19][20][21][22][23][24][25][26][27][28], DNN [29][30][31][32][33], RBM [34][35][36], others [1][2][3][37][38][39][40][41][42][43][44][45][46][47][48][49]…”

Section: Brief Introductionmentioning

confidence: 99%

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A Review of Artificial Intelligence Algorithms Used for Smart Machine Tools

2018

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This paper offers a review of the artificial intelligence (AI) algorithms and applications presently being used for smart machine tools. These AI methods can be classified as learning algorithms (deep, meta-, unsupervised, supervised, and reinforcement learning) for diagnosis and detection of faults in mechanical components and AI technique applications in smart machine tools including intelligent manufacturing, cyber-physical systems, mechanical components prognosis, and smart sensors. A diagram of the architecture of AI schemes used for smart machine tools has been included. The respective strengths and weaknesses of the methods, as well as the challenges and future trends in AI schemes, are discussed. In the future, we will propose several AI approaches to tackle mechanical components as well as addressing different AI algorithms to deal with smart machine tools and the acquisition of accurate results.Keywords: artificial intelligence; smart machine tools; learning algorithms; intelligent manufacturing; fault diagnosis and prognosis Brief IntroductionWe believe that a new epoch of the "Industrial Internet of Things (IIoT) plus artificial intelligence (AI)", characterized by big machinery data, data-driven techniques, ubiquitous networks, mass innovation, automatic intelligence, cross-border integration, and shared services, has arrived [1-3]. The fast development and combination of new AI and energy technologies, for materials, bioscience, the Internet, and new-generation information exchange, is a fundamental part of this new epoch. This will, in turn, permit game-changing transformation of models, ecosystems, and means in the light of their application to national security, well-being, and the economy. The main objective is a review and summary of recent achievement in data-based techniques, especially for complicated industrial applications, offering reference for further study from both an academic and practical point of view. Yin et al.[1] describes a brief evolutionary overview of data-based techniques over the last two decades. Recent development of modern industrial applications is presented mainly from the perspectives of monitoring and control. Their methodology, based on process measurements and model-data integrated techniques, will be introduced in the next study. Jeschke et al. [2] developed the core system science needed to enable the development of complex IIoT/manufacturing cyber-physical systems (CPS). Moreover, readers can learn the current state of IIoT and the concept of cybermanufacturing from this book. In 2014, Lund et al. [3] described the central issues contributing to, and characterizing, the worldwide and regional growth of the IoT. Besides, researchers can utilize the trend analysis of IoT their region markets in the future.There are many AI algorithms for machine health monitoring and other machine tool applications: The second-order recurrent neural networks (RNN) method for the learning and extraction of finite

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Section: Deep Neural Network (Dnns)mentioning

confidence: 99%

Section: Brief Introductionmentioning

confidence: 99%

A Review of Artificial Intelligence Algorithms Used for Smart Machine Tools

2018

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“…C LASS imbalance with disproportionate number of class instances commonly affects the quality of learning algorithms. Multifarious imbalanced data problems exist in numerous real-world applications, such as fault diagnosis [1], recommendation systems, fraud detection [2], risk management [3], tool condition monitoring [4], [5], [6] and medical diagnosis [7], brain computer interface (BCI) [8], [9], data visualization [10], etc. As a result of the equal misclassification costs or balanced class distribution assumption, the traditional learning algorithms are prone to the majority class when dealing with complicated classification problems that have skewed class distribution.…”

Section: Introductionmentioning

confidence: 99%

A Cost-Sensitive Deep Belief Network for Imbalanced Classification

Zhang

Tan

et al. 2019

IEEE Trans. Neural Netw. Learning Syst.

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228

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Imbalanced data with a skewed class distribution are common in many real-world applications. Deep Belief Network (DBN) is a machine learning technique that is effective in classification tasks. However, conventional DBN does not work well for imbalanced data classification because it assumes equal costs for each class. To deal with this problem, cost-sensitive approaches assign different misclassification costs for different classes without disrupting the true data sample distributions. However, due to lack of prior knowledge, the misclassification costs are usually unknown and hard to choose in practice. Moreover, it has not been well studied as to how cost-sensitive learning could improve DBN performance on imbalanced data problems. This paper proposes an evolutionary cost-sensitive deep belief network (ECS-DBN) for imbalanced classification. ECS-DBN uses adaptive differential evolution to optimize the misclassification costs based on the training data that presents an effective approach to incorporating the evaluation measure (i.e., G-mean) into the objective function. We first optimize the misclassification costs, and then apply them to DBN. Adaptive differential evolution optimization is implemented as the optimization algorithm that automatically updates its corresponding parameters without the need of prior domain knowledge. The experiments have shown that the proposed approach consistently outperforms the state of the art on both benchmark data sets and real-world data set for fault diagnosis in tool condition monitoring.

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“…A number of researches neglected the change of working conditions, which assumed that the distribution of training data and testing data is the same. Zhang et al [4] designed a deep belief network and verified the effectiveness of the proposed method through the turbofan engine degradation dataset. Lei et al [5] established the two learning stages: one is using unsupervised networks to extract features and the other is using softmax regression to classify the health conditions.…”

Section: Introductionmentioning

confidence: 99%

A New Deep Convolutional Domain Adaptation Network for Bearing Fault Diagnosis under Different Working Conditions

Ren

Zhou

2020

Shock and Vibration

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Effective fault diagnosis methods can ensure the safe and reliable operation of the machines. In recent years, deep learning technology has been applied to diagnose various mechanical equipment faults. However, in real industries, the data distribution under different working conditions is often different, which leads to serious degradation of diagnostic performance. In order to solve the issue, this study proposes a new deep convolutional domain adaptation network (DCDAN) method for bearing fault diagnosis. This method implements cross-domain fault diagnosis by using the labeled source domain data and the unlabeled target domain data as training data. In DCDAN, firstly, a convolutional neural network is applied to extract features of source domain data and target domain data. Then, the domain distribution discrepancy is reduced through minimizing probability distribution distance of multiple kernel maximum mean discrepancies (MK-MMD) and maximizing the domain recognition error of domain classifier. Finally, the source domain classification error is minimized. Extensive experiments on two rolling bearing datasets verify that the proposed method can implement accurate cross-domain fault diagnosis under different working conditions. The study may provide a promising tool for bearing fault diagnosis under different working conditions.

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Deep Belief Networks Ensemble with Multi-objective Optimization for Failure Diagnosis

Cited by 36 publications

References 17 publications

A Review of Artificial Intelligence Algorithms Used for Smart Machine Tools

A Review of Artificial Intelligence Algorithms Used for Smart Machine Tools

A Cost-Sensitive Deep Belief Network for Imbalanced Classification

A New Deep Convolutional Domain Adaptation Network for Bearing Fault Diagnosis under Different Working Conditions

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