A classification method to detect faults in a rotating machinery based on kernelled support tensor machine and multilinear principal component analysis

Chen, Hu; He, Shuilong; Wang, Yanxue

doi:10.1007/s10489-020-02011-9

Cited by 44 publications

(9 citation statements)

References 24 publications

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“…On the one hand, it can consume an excessive amount of human resources. On the other hand, the speed and efficiency of emergency response can hardly meet the requirements, and it is difficult to satisfy the needs in the practical situation [1][2][3].…”

Section: Introductionmentioning

confidence: 99%

Thermal Fault Detection and Diagnosis of Electrical Equipment Based on the Infrared Image Segmentation Algorithm

2021

Advances in Multimedia

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With the advancement of social economy, electricity has gradually entered thousands of households and become a commonly used energy source. However, it cannot be ignored that electricity is dangerous in itself and should be used rationally and effectively. The fault detection of power equipment has become a top priority because they are essential tools for storing, transmitting, and transferring electric power. Based on infrared imaging technology, the principle of infrared imaging technology is introduced in this paper, and effective diagnosis methods are analyzed and summarized in detail. The effectiveness of the proposed infrared image segmentation algorithm is verified through the practical application of the infrared image segmentation algorithm in the detection of interior and exterior faults of electrical equipment.

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Section: Introductionmentioning

confidence: 99%

Thermal Fault Detection and Diagnosis of Electrical Equipment Based on the Infrared Image Segmentation Algorithm

2021

Advances in Multimedia

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“…For instance, in a system that predicts if a patient will die in a given surgery, an output such as high-risk, medium-risk, and low-risk is more informative than a binary output. Finally, future work will focus on the use of the method when the data are in tensor form [14,15].…”

Section: Discussionmentioning

confidence: 99%

General Performance Score for classification problems

et al. 2022

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Several performance metrics are currently available to evaluate the performance of Machine Learning (ML) models in classification problems. ML models are usually assessed using a single measure because it facilitates the comparison between several models. However, there is no silver bullet since each performance metric emphasizes a different aspect of the classification. Thus, the choice depends on the particular requirements and characteristics of the problem. An additional problem arises in multi-class classification problems, since most of the well-known metrics are only directly applicable to binary classification problems. In this paper, we propose the General Performance Score (GPS), a methodological approach to build performance metrics for binary and multi-class classification problems. The basic idea behind GPS is to combine a set of individual metrics, penalising low values in any of them. Thus, users can combine several performance metrics that are relevant in the particular problem based on their preferences obtaining a conservative combination. Different GPS-based performance metrics are compared with alternatives in classification problems using real and simulated datasets. The metrics built using the proposed method improve the stability and explainability of the usual performance metrics. Finally, the GPS brings benefits in both new research lines and practical usage, where performance metrics tailored for each particular problem are considered.

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“…Since sparse learning could make the data have better interpretation after dimensionality reduc-tion [20], one of the future studies also includes considering a sparse model. In the end, applying our algorithm to track fault detection is worth studying [39,40].…”

Section: Discussionmentioning

confidence: 99%

Two-dimensional Bhattacharyya bound linear discriminant analysis with its applications

Guo

Bai

et al. 2021

Appl Intell

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A classification method to detect faults in a rotating machinery based on kernelled support tensor machine and multilinear principal component analysis

Cited by 44 publications

References 24 publications

Thermal Fault Detection and Diagnosis of Electrical Equipment Based on the Infrared Image Segmentation Algorithm

Thermal Fault Detection and Diagnosis of Electrical Equipment Based on the Infrared Image Segmentation Algorithm

General Performance Score for classification problems

Two-dimensional Bhattacharyya bound linear discriminant analysis with its applications

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