Fault diagnosis for industrial robots based on a combined approach of manifold learning, treelet transform and Naive Bayes

Wu, You; Fu, Zhuang; Fei, Jian

doi:10.1063/1.5118000

Cited by 21 publications

(16 citation statements)

References 31 publications

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“…In the data processing stage, we use the Naive Bayes algorithm to analyze the data collected by the IoT system [26]. e Naive Bayes classifier is based on the assumption of conditional independence and is widely used in actual production [27][28][29]. Substituting the independent assumption of attribute conditions, the Naive Bayes classifier integrates the relevant information provided by the data, prior knowledge, and unobserved variables and has higher accuracy.…”

Section: Application Of Naive Bayes In the Management Of Public Health Environment In Government Agenciesmentioning

confidence: 99%

Application of Internet of Things and Naive Bayes in Public Health Environmental Management of Government Institutions in China

Zhang

2021

Journal of Healthcare Engineering

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As the builder and maintainer of the city appearance and environment, the city appearance and environmental service industry provides urban residents with a beautiful, livable, and comfortable living environment and is also responsible for providing city appearance and environmental public health services for the economic and social functions carried by the global cities task. Based on this problem, this study has carried out the work of government agencies’ public health environmental management and influencing factors based on the Internet of Things and edge computing. This study analyzes the influencing factors of public health environmental management of government agencies based on traditional research and conducts data analysis through the Naive Bayes algorithm. On this basis, the study combines the Internet of Things technology to construct system architecture for public health environmental management. The proposed management system is used to manage government agencies, which meets security, privacy and efficiency, and data security requirements. Experimental results prove that after algorithm optimization and architecture design, the evaluation level of the public health environment is significantly improved.

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Section: Application Of Naive Bayes In the Management Of Public Health Environment In Government Agenciesmentioning

confidence: 99%

Application of Internet of Things and Naive Bayes in Public Health Environmental Management of Government Institutions in China

Zhang

2021

Journal of Healthcare Engineering

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“…Suppose that x ∈ ℝ p×1 with its label y is a generative classification model learning the joint distribution expressed as Eq. (15).…”

Section: Imbalance Problem Formulationmentioning

confidence: 99%

“…However, these two major forms of learning possess their strength and limitations. For instance, among the widely used supervised algorithms for fault classification like Artificial Neural Networks (ANNs) [6][7][8], Support Vector Machine (SVM) [2,9,10], Linear Discriminant Analysis (LDA) [11][12][13] and Bayes classifiers [3,14,15] are considered superior in producing labels, but assumes that the objects classified are drawn from an independent and identical distribution, and as such does not consider their interdependencies [16].…”

Section: Introductionmentioning

confidence: 99%

A novel hybrid predictive maintenance model based on clustering, smote and multi-layer perceptron neural network optimised with grey wolf algorithm

et al. 2021

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Considering the complexities and challenges in the classification of multiclass and imbalanced fault conditions, this study explores the systematic combination of unsupervised and supervised learning by hybridising clustering (CLUST) and optimised multi-layer perceptron neural network with grey wolf algorithm (GWO-MLP). The hybrid technique was meticulously examined on a historical hydraulic system dataset by first, extracting and selecting the most significant statistical time-domain features. The selected features were then grouped into distinct clusters allowing for reduced computational complexity through a comparative study of four different and frequently used categories of unsupervised clustering algorithms in fault classification. The Synthetic Minority Over Sampling Technique (SMOTE) was then employed to balance the classes of the training samples from the various clusters which then served as inputs for training the supervised GWO-MLP. To validate the proposed hybrid technique (CLUST-SMOTE-GWO-MLP), it was compared with its distinct modifications (variants). The superiority of CLUST-SMOTE-GWO-MLP is demonstrated by outperforming all the distinct modifications in terms of test accuracy and seven other statistical performance evaluation metrics (error rate, sensitivity, specificity, precision, F score, Mathews Correlation Coefficient and geometric mean). The overall analysis indicates that the proposed CLUST-SMOTE-GWO-MLP is efficient and can be used to classify multiclass and imbalanced fault conditions. Article Highlights The issue of multiclass and imbalanced class outputs is addressed for improving predictive maintenance. A multiclass fault classifier based on clustering and optimised multi-layer perceptron with grey wolf is proposed. The robustness and feasibility of the proposed technique is validated on a complex hydraulic system dataset.

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“…Y.K.Gu [10] et al proposed fault diagnosis method of rolling bearing using principal component analysis and support vector machine. Y.Wu [11] et al proposed fault diagnosis for industrial robots based on a combined approach of manifold learning, treelet transform and Naive Bayes. Z.Zhuang [12] et al proposed fault Detection of High-Speed Train Wheelset Bearing Based on Impulse-Envelope Manifold.…”

Section: Introductionmentioning

confidence: 99%

CvT Fault Diagnosis Method of Manifold Sensitive Modal Matrix Under Variable Speed

DeChen¹,

Li²,

Huang³

2021

Preprint

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Rotating machinery (RM) is one of the most common mechanical equipment in engineering applications and has a broad and vital role. Rotating machinery includes gearboxes, bearing motors, generators, etc. In industrial production, the important position of rotating machinery and its variable speed and complex working conditions lead to unstable vibration characteristics, which have become a research hotspot in mechanical fault diagnosis. Aiming at the multi-classification problem of rotating machinery with variable speed and complex working conditions, this paper proposes a fault diagnosis method based on the construction of improved sensitive mode matrix (ISMM), isometric mapping (ISOMAP) and Convolution-Vision Transformer network (CvT) structure. After overlapping and sampling the variable speed signals, a high-dimensional ISMM is constructed, and the ISMM is mapped into the manifold space through ISOMAP manifold learning. This method can extract the fault transient characteristics of the variable speed signal, and the experiment proves that it can solve the problem that the conventional method cannot effectively extract the characteristics of the variable speed data. CvT combines the advantages of self-attention mechanism and convolution in CNN, so the CvT network structure is used for feature extraction and fault recognition and classification. The CvT network structure takes into account both global feature extraction and local feature extraction, which greatly reduces the number of training iterations and the size of the network model. Two data sets (the HFXZ-I planetary gearbox variable speed data set in the laboratory and the bearing variable speed public data set of the University of Ottawa in Canada) are used to experimentally verify the proposed fault diagnosis model. Experimental results show that the proposed fault diagnosis model has good recognition accuracy and robustness.

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Fault diagnosis for industrial robots based on a combined approach of manifold learning, treelet transform and Naive Bayes

Cited by 21 publications

References 31 publications

Application of Internet of Things and Naive Bayes in Public Health Environmental Management of Government Institutions in China

Application of Internet of Things and Naive Bayes in Public Health Environmental Management of Government Institutions in China

A novel hybrid predictive maintenance model based on clustering, smote and multi-layer perceptron neural network optimised with grey wolf algorithm

CvT Fault Diagnosis Method of Manifold Sensitive Modal Matrix Under Variable Speed

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