[1]胡友为,洪 亮,赵建印,等.基于动态增量箱形图的测试数据异常检测方法[J].机械与电子,2026,44(08):43-50.
　HU Youwei,HONG Liang,ZHAO Jianyin,et al.Dynamic Incremental Boxplot-based Outlier Detection Method for Test Data[J].Machinery & Electronics,2026,44(08):43-50.
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基于动态增量箱形图的测试数据异常检测方法()
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《机械与电子》[ISSN:1001-2257/CN:52-1052/TH]

卷:
44
期数:
2026年08期
页码:
43-50
栏目:
智能检测
出版日期:
2026-08-25

文章信息/Info

Title:
Dynamic Incremental Boxplot-based Outlier Detection Method for Test Data
文章编号:
1001-2257(2026)08-0043-08
作者:
胡友为; 洪 亮; 赵建印; 孙敏奇
海军航空大学,山东 烟台 264001
Author(s):
HU Youwei; HONG Liang; ZHAO Jianyin; SUN Minqi
(Naval Aviation University,Yantai 264001,China)
关键词:
异常检测; 箱形图; 工程阈值; 动态触须; 偏态数据
Keywords:
outlier detection; boxplot; engineering thresholds; dynamic whiskers; skewed data
分类号:
TP274
文献标志码:
A
摘要:
针对大型装设备测试数据存在明确工程阈值、分布随老化呈现偏态、且需增量处理的特点,提出融合工程阈值与动态触须的增量式箱形图异常检测方法。首先,基于工程阈值与健康度概念构建分层分箱体系,实现“故障亚健康健康”状态精细划分;其次,设计基于直方图与累积分布函数(CDF)的增量式四分位数估计算法,实现增量数据的实时处理;再次,采用四分位数偏度度量(QSM)动态感知数据分布形态变化,自适应调整箱形图触须范围,提升偏态数据下的检测稳健性;最后,构建多维诊断规则,将异常分为硬件故障、隐性风险等4类并给出处置策略。以某型飞行器锂电池电压数据为对象进行实验,结果表明,该方法能精准识别超阈值故障、亚健康趋势及弱离群点,且支持实时增量更新,适配装设备全生命周期健康监控。
Abstract:
Test data from large-scale equipment are characterized by explicit engineering thresholds, progressively skewed distribution due to aging,and the demand for incremental processing.To address these characteristics,an incremental boxplot anomaly detection method that integrates engineering thresholds with dynamic whiskers is proposed.Firstly,a hierarchical binning system is constructed based on engineering thresholds and the concept of health degree,enabling a fine-grained division of “fault-subhealth-health”states.Secondly,an incremental quartile estimation algorithm based on histograms and the Cumulative Distribution Function (CDF) is designed to realize real-time processing of incremental data.Thirdly, the Quartile Skewness Measure (QSM) is adopted to dynamically perceive changes in data distribution patterns, adaptively adjusting the whisker range of the boxplot to improve detection robustness under skewed data.Finally,a multi-dimensional diagnostic rules are established to classify anomalies into 4 categories(e.g.,hardware faults,potential risks) and corresponding disposal strategies are provided.Experiments are conducted on voltage data of lithium batteries from a certain type of aircraft.The results show that the proposed method can accurately identify over threshold faults,sub-health trends,and weak outliers,and support real-time incremental updates,making it suitable for full life-cycle health monitoring of large scale equipment.

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备注/Memo

备注/Memo:
收稿日期:2026-02-08
作者简介:胡友为 (1985-),男,河北承德人,硕士研究生,工程师,研究方向为装备综合保障;洪 亮 (1978-),男,安徽淮南人,硕士,副教授,研究方向为装备综合保障、装备管理。
更新日期/Last Update: 2026-08-28