GB/T 41397-2022
Quality control of production process—Fault diagnostics (English Version)
- Standard No.
- GB/T 41397-2022
- Language
- Chinese, Available in English version
- Release Date
- 2022
- Published By
- General Administration of Quality Supervision, Inspection and Quarantine of the People‘s Republic of China
- Latest
- GB/T 41397-2022
- Scope
- This document specifies the equipment fault diagnosis process, diagnostic elements and diagnostic methods for digital workshops in the discrete manufacturing field. This document is applicable to guide the digital workshop in the field of discrete manufacturing to carry out fault diagnosis for the quality control of the production process.
- Introduction
Standard Framework and Technological Evolution
This standard replaces some clauses of GB/T22394 series, introduces the concept of intelligent diagnostic system, and focuses on strengthening the following improvements:
- Integration of mechanism modeling and machine learning dual-dimensional analysis path
- Establishment of a hierarchical diagnostic knowledge graph structure
- Improve specialized processing solutions for typical equipment such as CNC machine tools
Comparison of core technical indicators
Diagnostic method Data requirements Confidence level Application scenarios Analytical model method Historical data ≥ 5 years 82-95% Rotating machinery system Fuzzy expert system Maintenance cases ≥ 200 76-89% Hydraulic control device Hybrid modeling method Data middle platform support 91-97% Smart assembly line
Implementation of critical path
Construction of abnormal response module
Integrated sensor network needs to collect class='instrument'>vibration spectrum parameters meet the ISO10816-III vibration tolerance limit requirements and avoid false diagnosis in force coupling scenarios (see Appendix C Case 3 for details).
Expert experience network fusion
According to the recommendations in clause 6.3, a credibility factor library is built. It is recommended to adopt a 1+N hierarchical configuration mode: the main knowledge base is connected to the PLM system log, and the sub-knowledge base stores the maintenance base point experience data of specific machine tools.
Equipment Adaptation Guide
Four stages of demand planning:- If the historical failure rate of the main equipment is greater than 3 times, online diagnosis must be deployed
- For VMC850 machining center, it is recommended to control the pattern matching deviation within ±3μm
- When the alarm frequency of the MRPⅡ system is greater than 2 times/shift, start FTA traceability
- It is recommended to retain ≥20% computing redundancy for the decision support system
GB/T 41397-2022 Referenced Document
- GB/T 22394.1-2015 Machine condition monitoring and diagnostic data interpretation and diagnostic techniques - Part 1: General
- GB/T 37942-2019 Quality control of production process—Condition monitoring of equipment
- GB/T 7826-2012 Analysis techniques for system reliability.Procedure for failure mode and effects analysis(FMEA)
GB/T 41397-2022 history
- 2022 GB/T 41397-2022 Quality control of production process—Fault diagnostics