GB/T 43555-2023 in English
VALIDIntelligent service—Predictive maintenance—Algorithm evaluation method
- Issued on:2023-12-28
- Implemented on:2024-07-01
- File Format:PDF
- Delivery:Via email within 5 business days
$495.00
《GB/T 43555-2023智能服务 预测性维护 算法测评方法》由TC124（全国工业过程测量控制和自动化标准化技术委员会）归口，主管部门为中国机械工业联合会。
Introduction
Analysis of the core content of the standard
GB/T 43555-2023 has built an algorithm evaluation system covering the entire process of predictive maintenance, focusing on standardizing three types of core algorithms:
- Condition monitoring algorithm: Determine the abnormal state of equipment through thresholds
- Fault diagnosis algorithm: Identify fault types based on expert systems or machine learning
- Life prediction algorithm: Calculate the remaining useful life (RUL)
Algorithm evaluation index system
| Algorithm type | Core indicator | Calculation formula | Qualification standard |
|---|---|---|---|
| Condition monitoring | Discrimination accuracy | $A_{cm}=\frac{C_s}{S}\times100\%$ | >80% |
| Fault diagnosis | Macro average accuracy | $P_{rma}=\frac{1}{n}\sum_{i=1}^n\frac{TP_i}{TP_i+FP_i}$ | >70% |
| Life prediction | Prediction accuracy | $Ac(r,r_*)=\frac{1}{N}\sum_{i=1}^N e^{-\frac{|e(t_i)|}{r(t_i)}}$ | >60% |
Key Technology Evolution
Signal Processing AlgorithmAdded time-frequency distribution quality indicators (Appendix A):
- Time-frequency clustering (CM): measures the degree of energy concentration
- Time-frequency similarity ($c_{tf}$): evaluates the match with the benchmark signal
Prediction AlgorithmIntroduces an asymmetric scoring mechanism (Article 8.3.6):
- Late prediction penalty factor α∈[10,15]
- Early prediction penalty factor β∈[7,12]
Implementation Suggestions
- Data preparation phase: Ensure that the test samples cover the entire life cycle data (Article E.2.4)
- Algorithm selection: Prioritize prediction algorithms with SPE score ≤$\frac{S_{SPE}(0.9)+S_{SPE}(1.1)}{2}$
- System integration: The database needs to support more than 1000 concurrent queries (Article E.1)
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