<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>With the rapid development of modern industry and science and technology, in recent years the fault diagnosis method based on image processing has become a research hotspot in the field of mechanical fault diagnosis. In this paper, image characteristics are extracted from multiple aspects such as image texture, color, shape, etc. A grayscale symbiotic matrix image feature extraction method is proposed. On this basis, the algorithm for extracting gray symbiotic matrix time-frequency image features is designed. At the same time, the algorithm and parameters of mechanical structure identification are optimized to identify and diagnose mechanical faults. The results show that the grayscale symbiotic matrix time-frequency image feature extraction algorithm is able to accurately diagnose the wear-type faults, overwork-type faults, and short-circuit-type fault behavior of the mechanical equipment. All of them are able to obtain more than 80% accuracy, and all of them are able to reach 99.99% accurate detection of mechanical faults, which proves the effectiveness of the method of this research.</p>
</abstract>

With the rapid development of modern industry and science and technology, in recent years the fault diagnosis method based on image processing has become a research hotspot in the field of mechanical fault diagnosis. In this paper, image characteristics are extracted from multiple aspects such as image texture, color, shape, etc. A grayscale symbiotic matrix image feature extraction method is proposed. On this basis, the algorithm for extracting gray symbiotic matrix time-frequency image features is designed. At the same time, the algorithm and parameters of mechanical structure identification are optimized to identify and diagnose mechanical faults. The results show that the grayscale symbiotic matrix time-frequency image feature extraction algorithm is able to accurately diagnose the wear-type faults, overwork-type faults, and short-circuit-type fault behavior of the mechanical equipment. All of them are able to obtain more than 80% accuracy, and all of them are able to reach 99.99% accurate detection of mechanical faults, which proves the effectiveness of the method of this research.

Research on the Application of Image Feature Extraction in Mechanical Structure Recognition and Fault Diagnosis

College of Mechanical & Electrical Engineering

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

With the rapid development of modern industry and science and technology, in recent years the fault diagnosis method based on image processing has become a...

Research on the Application of Image Feature Extraction in Mechanical Structure Recognition and Fault Diagnosis

Data publikacji: 02 lip 2024

Zakres stron: -

Otrzymano: 04 mar 2024

Przyjęty: 21 maj 2024

DOI: https://doi.org/10.2478/amns-2024-1570

Słowa kluczowe
Image processing, Feature extraction, Fault diagnosis, Grayscale covariance matrix

© 2024 Zhenhua Niu et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Research on the Application of Image Feature Extraction in Mechanical Structure Recognition and Fault Diagnosis

Data publikacji: 02 lip 2024

Zakres stron: -

Otrzymano: 04 mar 2024

Przyjęty: 21 maj 2024

DOI: https://doi.org/10.2478/amns-2024-1570

Słowa kluczoweImage processing, Feature extraction, Fault diagnosis, Grayscale covariance matrix

© 2024 Zhenhua Niu et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Słowa kluczowe
Image processing, Feature extraction, Fault diagnosis, Grayscale covariance matrix