An Online Data-Driven Predictive Maintenance Approach for Railway Switches

被引：0

作者：

Tome, Emanuel Sousa ^{[1
,2
]}

Ribeiro, Rita P. ^{[1
,2
]}

Veloso, Bruno ^{[2
,3
,4
]}

Gama, Joao ^{[2
,3
]}

机构：

[1] Univ Porto, Fac Sci, P-4169007 Porto, Portugal

[2] INESC TEC, P-4200465 Porto, Portugal

[3] Univ Porto, Fac Econ, P-4200464 Porto, Portugal

[4] Univ Portucalense, P-4200072 Porto, Portugal

来源：

MACHINE LEARNING AND PRINCIPLES AND PRACTICE OF KNOWLEDGE DISCOVERY IN DATABASES, ECML PKDD 2022, PT II | 2023年 / 1753卷

关键词：

Predictive maintenance; Remaining useful life; Online learning; Log Data; Railway switches; FAULT-DETECTION METHOD; PROGNOSTICS;

D O I：

10.1007/978-3-031-23633-4_27

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

An online data-driven predictive maintenance approach for railway switches using data logs obtained from the interlocking system of the railway infrastructure is proposed in this paper. The proposed approach is detailed described and consists of a two-phase process: anomaly detection and remaining useful life prediction. The approach is applied to and validated in a real case study, the Metro do Porto, from which seven months of data is available. The approach has been revealed to be satisfactory in detecting anomalies. The results open the possibilities for further studies and validation with a more extensive dataset on the remaining useful life prediction.

引用

页码：410 / 422

页数：13

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