研究目的
To predict the remaining useful life (RUL) of T/R modules in large phased array radar systems using a method based on index similarity to improve maintenance support and equipment effectiveness.
研究成果
The proposed method effectively predicts the RUL of T/R modules by extracting key indexes with association rules and using similarity-based prediction with entropy weighting. It shows higher accuracy compared to conventional methods, as validated by example analysis, and provides a practical approach for maintenance planning in military equipment. Future work should address non-slow decline processes and further optimize parameters.
研究不足
The method assumes a slow decline process for state degradation; it may not be effective for non-slow decline processes. The accuracy depends on the quality and quantity of historical data, and the optimization of parameters like the weight factor α.
1：Experimental Design and Method Selection:
The method involves extracting key degradation indexes using association rules, calculating similarity between service and reference samples, and predicting RUL based on weighted indexes.
2：Sample Selection and Data Sources:
Uses historical fault data from T/R modules, including 413 fault samples from 2015, with specific fault modes (transmitter channel fault, receiver channel fault, simultaneous fault). Data is acquired from radar equipment BIT and field test equipment.
3：List of Experimental Equipment and Materials:
T/R modules, Build-In Test Equipment (BITE), field test equipment (specific models not mentioned).
4：Experimental Procedures and Operational Workflow:
Extract key indexes (e.g., output power, transmitter channel gain) using association rules with support and confidence thresholds; calculate similarity using Euclidean distance with a weight factor; predict RUL by combining similar samples and weighting indexes.
5：Data Analysis Methods:
Use entropy weight method for index weighting; calculate Average Prediction Error (APE) and Overall Prediction Error (OPE) for accuracy evaluation; optimize weight factor α using cross-validation.
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