GB/T 47230-2026Intelligent services - Predictive maintenance - Data definition and interfaces (English PDF)

Foreword

This document complies with the provisions of GB/T 1.1-2020 "Standardization Work Guidelines Part

1.Structure and Drafting Rules of Standardization Documents". Drafting. Please note that some content in this document may involve patents. The issuing organization of this document assumes no responsibility for identifying patents. This document was proposed by the China Machinery Industry Federation. This document is under the jurisdiction of the National Technical Committee on Standardization of Industrial Process Measurement, Control and Automation (SAC/TC124). This document was drafted by: the 10th Research Institute of China Electronics Technology Group Corporation and the Comprehensive Technology and Economic Research Institute of Instrumentation and Meter of the Machinery Industry. Shenyang Aircraft Design Institute of Aviation Industry Corporation of China, Chengdu Moore Global Test Technology Co., Ltd., and CRRC Changchun Railway Vehicles Co., Ltd. Limited Liability Company, Chengdu Tian'ao Measurement and Control Technology Co., Ltd., Hangzhou Beijing University of Aeronautics and Astronautics International Innovation Research Institute (Beijing University of Aeronautics and Astronautics) International Innovation Academy), Chengdu Aircraft Design and Research Institute of Aviation Industry Corporation of China, China Helicopter Design and Research Institute, University of Electronic Science and Technology of China Shanghai Yunshu EasyCalculation Intelligent Technology Co., Ltd., Shenzhen Shuanghe Smart Technology Co., Ltd., Shanghai Jiao Tong University, and Weichai Power Co., Ltd. Limited Liability Company, Shanghai Electric Group Co., Ltd., SKF (China) Co., Ltd., Chongqing University of Posts and Telecommunications, Tsinghua University, Siemens (China) Limited Liability Company, China University of Petroleum (Beijing), Dandong Huatong Measurement & Control Co., Ltd., Dandong Tongbo Electric (Group) Co., Ltd., Beijing Jiaotong University Schaeffler (China) Co., Ltd., CRRC Qingdao Sifang Co., Ltd., Shaanxi Aircraft Industry Co., Ltd., and state-owned Wuhu... Lake Machinery Factory, Southwest Jiaotong University, Sichuan University, Beijing University of Aeronautics and Astronautics, National University of Defense Technology, Northwestern Polytechnical University, Air Force Engineering University Chengdu Aircraft Industry (Group) Co., Ltd., Beijing University of Posts and Telecommunications, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Liaoning University, China Electronics Technology Group Corporation Instrument Science Research Institute Co., Ltd., Zhejiang University, China Unicom Data Intelligence Co., Ltd., Beijing Zhijian Energy Co., Ltd., Beijing Benz Automotive Limited Liability Company, Frequency Exploration Intelligent Technology Jiangsu Co., Ltd., Huadian Engineering Co., Ltd., China Electronics System Engineering Third Construction Co., Ltd. The company, Shanghai Huidu Intelligent Systems Co., Ltd., and Tianjin Xinhai Petroleum Engineering Technology Co., Ltd. The main drafters of this document are. Wen Jia, Wang Chengcheng, Yan Dejin, Wang Kai, Zhao Xiaohu, Liu Dong, Tao Laifa, Li Yajun, Gao Yang, Li Xin, and Liu Guanjun. Li Peng, Zhu Chao, Tang Li, Xia Tangbin, Wang Jianyu, Huang Chenguang, Liu Zhiliang, Li Zhe, Yao Shuo, Liu Hua, Yu Ze, Liu Zhaoxing, Shi Xiumei, Mi Pengyang Wang Linan, Yang Wangfeng, Cai Zhongyi, Yang Shunkun, Zhang Feibin, Huang Qingqing, Zhou Linfei, Wang Jinjiang, Wang Peng, Wang Yanpeng, Wang Biao, Song Peng, Yu Xiang, Liu Chao Huang Chengwenyuan, Liang Tianchen, Miao Qiang, Sun Lin, Xu Hai, Zeng Junhua, Yue Longfei, Wang Min, Wu Zhenyu, Yuan Xiaolin, Song Yan, Wei Kunlun, Guo Liang, Ruan Diwang Zhang Zhenyu, Yang Bingchun, Guo Dongdong, Meng Li, Niu Buzhao, Weng Liang, Yu Yaoxiang, Han Yan, Lü Kehong, Xu Xiaoyu, Wang Yujian, Chen Dalu, Yu Shanglin Zang Hong, Wen Mingbo, Xie Lijuan, Wang Yu, Xin Ge, Xu Yuehua, Liu Qiang, Liang Tianyu.

Predictive maintenance is a new maintenance model. Compared to traditional preventive maintenance and other maintenance methods, it can reduce the risks associated with blind repairs. While reducing high maintenance costs, this approach can effectively reduce downtime caused by equipment failures, thereby improving equipment reliability and safety. This is particularly beneficial for... This is even more urgent and important for China's manufacturing industry during this critical period of digital transformation. Driven by relevant national policies and technologies such as the Industrial Internet, big data, and artificial intelligence, theoretical research and development in the field of predictive maintenance have been progressing rapidly. Enterprise practices are rapidly evolving, and standards systems are constantly being improved. However, there is a lack of predictive maintenance data definitions and interface standards, which is a problem within the industry's predictive maintenance systems. Inconsistent data definitions and inconsistent data interfaces exist during the construction and application of the predictive maintenance system, leading to problems at different levels within the system. Poor interoperability, high integration costs, long system development cycles, and significant difficulties in interconnecting predictive maintenance systems with other information systems greatly hinder the system's effectiveness. This has severely limited the widespread application of predictive maintenance models. This document aims to establish a unified definition and interface standard for predictive maintenance data. Accurate, forming an open and scalable predictive maintenance system application model and ecosystem. Intelligent service predictive maintenance Data Definition and Interface

1 Scope

GB/T 47230-2026 is the Chinese national standard covering the data a predictive maintenance service needs - the condition measurements and their context, the asset and its history, the health indicators and the predictions, with the interfaces between the machine, the platform and the maintenance system. First edition, 17,000 words, in force since 1 September 2026. It was issued on 27 February 2026 and has been in force since 1 September 2026, as a first edition. The document is under the responsibility of the China Machinery Industry Federation. This page is published from the official record of the 2026 edition; the clause text of a standard this recent is not yet in circulation, and the figures, limits and tables it contains are those of the document itself, delivered in full with the English translation.

This document specifies the general rules, data interface, and data definition requirements for predictive maintenance in intelligent services. This document applies to the construction, implementation, and application of predictive maintenance systems, as well as the design and development of related software and hardware.

2 Normative references

The contents of the following documents, through normative references within the text, constitute essential provisions of this document. Dated citations are not included. For references to documents, only the version corresponding to that date applies to this document; for undated references, the latest version (including all amendments) applies. This document.

GB/T 2900.99 Reliability of Electrical Engineering Terminology

GB/T 11457 Terminology for Information Technology Software Engineering

GB/T 40571-2021 General requirements for intelligent service predictive maintenance

3 Terms and Definitions

The terms and definitions defined in GB/T 2900.99, GB/T 11457, and GB/T 40571-2021, as well as the following terms and definitions, apply to this document.

3.1 intelligent service A service that can automatically identify users' explicit and implicit needs and proactively, efficiently, safely, and environmentally friendly meet those needs. [Source: GB/T 40571-2021, 3.3]

3.2 Maintenance, whether continuous or intermittent, is carried out based on observed conditions to monitor, diagnose, or predict the condition of structures, systems, or components. Item indicators. [Source: GB/T 40571-2021, 3.5]

4.Abbreviations The following abbreviations apply to this document.