Design and development of automatic fault diagnosis system for rotating parts of mining machinery based on artificial intelligence technology

Hui Song; Gerile Gerile; Shuang Cai

doi:10.1117/12.2671871

16 March 2023 Design and development of automatic fault diagnosis system for rotating parts of mining machinery based on artificial intelligence technology

Hui Song, Gerile Gerile, Shuang Cai

Author Affiliations +

Proceedings Volume 12593, Second Guangdong-Hong Kong-Macao Greater Bay Area Artificial Intelligence and Big Data Forum (AIBDF 2022); 125931N (2023) https://doi.org/10.1117/12.2671871
Event: 2nd Guangdong-Hong Kong-Macao Greater Bay Area Artificial Intelligence and Big Data Forum (AIBDF 2022), 2022, Guangzhou, China

Abstract

Based on the BP neural network in the category of artificial intelligence technology, this paper combined with the conventional expert system diagnosis and discrimination method, and completed the construction of automatic fault diagnosis system for rotating parts of mining machinery in ASP.NET environment. Taking the common rotating machinery in mining machinery and equipment as the research object, aiming at the fault characteristics of mining machinery and the difficulties faced by maintenance, such as high difficulty, high cost and high risk factor, the system provides a new comprehensive application solution for the fault diagnosis of rotating machinery with the help of the application advantages of various information technologies. Through data feature extraction, automatic diagnosis, manual diagnosis, data management and other modules in the system, the whole life cycle management of mining machinery and equipment, early warning and treatment of faults, historical data query and other functions are realized. It not only improves the level of health management of mining machinery and equipment, but also establishes a solid guarantee for the safe and stable production of enterprises, and further makes a positive and beneficial attempt for the construction of smart mines in China.

Citation Download Citation

Hui Song, Gerile Gerile, and Shuang Cai "Design and development of automatic fault diagnosis system for rotating parts of mining machinery based on artificial intelligence technology", Proc. SPIE 12593, Second Guangdong-Hong Kong-Macao Greater Bay Area Artificial Intelligence and Big Data Forum (AIBDF 2022), 125931N (16 March 2023); https://doi.org/10.1117/12.2671871

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KEYWORDS

Mining

Design and modelling

Artificial neural networks

Standards development

Feature extraction

Artificial intelligence

Signal processing

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