BSPST: Fault Classification Algorithm for Deformation Monitoring Instruments

doi:10.3778/j.issn.1002-8331.2305-0467

Abstract

Abstract: A time series classification algorithm (best qualify Shapelet Transform (BSPST)) based on the maximum zone molecular sequence (Shapelet) transformation is proposed to address the problem that it is difficult to accurately classify fault categories when faults occur in existing deformation monitoring instruments. In order to improve the quality of Shapelet, a set of high-quality candidates Shapelet is retained by using Bloom filter and similarity matching to construct the classification model. BSPST uses Bloom filter to filter out the duplicate SAX words in the same category, and then evaluates the duplication of SAX words by the words marked in the bitmap, so as to remove the similar SAX words in the category. Finally, the processed symbolic aggregation approximation (SAX) words are transformed into high quality Shapelet. The data are transformed by Shapelet transformation technique. Finally, an integrated classifier is adopted for classification. Seven seismic equipment failure datasets are created based on the seismic deformation instrumentation failure data, and 44 datasets selected from the University of East Anglia and University of California Riverside (UEA&UCR) time series classification repository are combined with representative state-of-the-art classification tools. The results show that the BSPST algorithm has been fully experimentally validated with a representative state-of-the-art method. The results show that the BSPST algorithm is in the top position in terms of classification accuracy and classification speed. The problem of fault classification in deformation monitoring instruments is effectively solved.

Key words: fault diagnosis, time series classification, Shapelet, deformation instrument

摘要： 针对现有形变监测仪器发生故障时故障类别难以准确分类的问题，提出了一种基于最大区分子序列（Shapelet）转换的时间序列分类算法（best qualify Shapelet Transform，BSPST）。为了提升Shapelet质量，利用布隆过滤器和相似度匹配保留一组高质量的候选Shapelet来构建分类模型，BSPST利用布隆过滤器筛选出同类别中重复的符号聚合近似（symbolic aggregation approximation，SAX）单词。随后通过位图中标记的单词来评价SAX单词的重复度，以此去除类别中相似的SAX单词。最终将处理后的符号聚合近似单词转化为高质量的Shapelet。通过Shapelet转换技术，对数据进行转换。最后采取集成分类器进行分类。根据地震形变仪器故障数据建立了7个地震设备故障数据集，并结合东安格利亚大学和加州大学河滨分校时间序列分类仓库中选取的44个数据集和具代表性的最先进的方法进行了充分的实验验证。结果表明，BQST算法在分类精度、分类速度上稳居前列，有效解决了形变监测仪器的故障分类问题。

关键词: 故障诊断, 时间序列分类, 最大区分子序列, 形变仪器

WU Xiaoying, DENG Hongxia, HU Yuliang, LI Ying, MU Huimin. BSPST: Fault Classification Algorithm for Deformation Monitoring Instruments[J]. Computer Engineering and Applications, 2024, 60(18): 306-315.

吴晓赢, 邓红霞, 胡玉良, 李颖, 穆慧敏. BSPST：形变监测仪器故障分类算法[J]. 计算机工程与应用, 2024, 60(18): 306-315.

References

[1] 胡亚轩, Akito Araya, 汪翠枝, 等. 地震地形变观测系统的组成发展及应用[J]. 地震科学进展, 2020, 50(10): 23-29.
HU Y X, AKITO A, WANG C Z, et al. The composition, development and application of crustal deformation observation system in earthquake monitoring and prediction[J]. Progress in Earthquake Sciences, 2020, 50(10): 23-29.
[2] 陈吉锋, 陈军辉, 应昶, 等. 无人职守地震台站远程监控系统的设计与实现[J].地震研究, 2012, 35(3): 429-433.
CHEN J F, CHEN J H, YING C, et al. Design and implementation of remote monitoring system for unattended seismic stations[J]. Journal of Seismological Research, 2012, 35(3): 429-433.
[3] 李松华, 瞿旻, 陆德铭, 等. 江苏省测震台站故障特征分析[J]. 国际地震动态, 2019(6): 20-26.
LI S H, QU M, LU D M, et al. Fault characteristics analysis of seismic stations in Jiangsu province[J]. Recent Developments in World Seismology, 2019(6): 20-26.
[4] BAGNALL A, ?LINES J, ?BOSTROM A, et al. The great time series classification bake off: a review and experimental evaluation of recent algorithmic advances[J]. Data Mining and Knowledge Discovery, 2017, 31(3): 606-660.
[5] YUAN J D, LIN Q H, ZHANG W, et al. Locally slope-based dynamic time warping for time series classification[C]//Proceedings of the 28th ACM International Conference on Information and Knowledge Management, 2019: 1713-1722.
[6] LINES J, TAYLOR S, BAGNALL A.Time series classification with HIVE-COTE: the hierarchical vote collective of transformation-based ensembles[J]. ACM Transactions on Knowledge Discovery from Data, 2018, 12(5): 1-35.
[7] RAHUL J, SORA M, SHARMA L D, et al. An improved cardiac arrhythmia classification using an RR interval-based approach[J]. Biocybernetics and Biomedical Engineering, 2021, 41(2): 656-666.
[8] ARUL M, KAREEM A. Data anomaly detection for structural health monitoring of bridges using shapelet transform[J]. Smart Structures and Systems, 2022, 29(1): 93-103.
[9] LI G, CHOI B, XU J, et al. Efficient shapelet discovery for time series classification[J]. IEEE Transactions on Knowledge and Data Engineering, 2022, 34（3）:1149-1163.
[10] YE L, KEOGH E. Time series shapelets: a novel technique that allows accurate, interpretable and fast classification[J]. Data Mining and Knowledge Discovery, 2011, 22(1/2): 149-182.
[11] ZHANG Q, WU J, ZHANG P, et al. Salient subsequence learning for time series clustering[J] . IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019, 41(9):2193-2207.
[12] LI G, YAN W, WU Z. Discovering shapelets with key points in time series classification[J]. Expert Systems with Applications, 2019, 132:76-86.
[13] JI C, ZHAO C, PAN L, et al. A just-in-time shapelet selection service for online time series classification[J]. Computer Networks, 2019, 157(5): 89-98.
[14] KARLSSON I, PAPAPETROU P, BOSTROM H. Generalized random shapelet forests[J]. Data Mining and Knowledge Discovery, 2016, 30(5): 1053-1085.
[15] CHEN J H, WAN Y, WANG Y Y, et al. Learning-based shapelets discovery by feature selection for time series classification[J]. Applied?Intelligence, 2022, 52(8): 9460-9475.
[16] SHU W B, YAO Y Q, LYU S F, et al. Short isometric shapelet transform for binary time series classification[J]. Knowledge and Information Systems, 2021, 63(8): 2023-2051.
[17] BAGNALL A, LINES J, VICKERS W, et al. The UEA & UCR time series classification repository[EB/OL]. (2022).http://www.timeseriesclassification.com.
[18] JI C, ZHAO C, LIU S, et al. A fast shapelet selection algorithm for time series classification[J]. Computer Networks, 2019, 148: 231-240.
[19] 陆慧娟, 刘亚卿, 孟亚琼, 等. 面向基因数据分类的核主成分分析旋转森林算法[J]. 计算机科学与探索, 2017, 11(10): 1570-1578.
LU H J, LIU Y Q, MENG Y Q, et al. Classifier algorithm of genetic data based on kernel principal component analysis and rotation forest[J]. Journal of Frontiers of Computer Science and Technology, 2017, 11(10): 1570-1578.
[20] 朱紫纯, 吕盛坪, 廖鑫婷, 等.基于时间加权改进的LDTW算法[J].计算机应用研究, 2022, 39(4):998-1002.
ZHU Z C, LYU S P, LIAO X T, et al. Improved LDTW algorithm based on time-weighting[J]. Application Research of Computer, 2022, 39(4):998-1002.
[21] GRABOCKA J, WISTUBA M, SCHMIDT-THIEME L. Fast classification of univariate and multivariate time series through shapelet discovery[J]. Knowledge and Information Systems, 2016, 49(2): 429-454.
[22] FANG Z C, WANG P, WANG W. Efficient learning interpretable shapelets for accurate time series classification[C]//Proceedings of the 2018 IEEE 34th International Conference on Data Engineering, 2018.