Entwicklung und Validierung von Anomaliedetektionsverfahren für Weichen

Das Überwachungssystem POSS® von Strukton Rail (SR) erfasst kontinuierlich den Stromverbrauch von Weichenantrieben, um deren Zustand zu überwachen. Basierend auf dieser Datengrundlage werden Wartungsmaßnahmen geplant. Dieser Beitrag berichtet über die gemeinsamen Forschungsaktivitäten von DLR und SR zur Entwicklung und Verbesserung von Algorithmen zur automatisierten Fehlererkennung. Ziel ist es, Anomalien und Fehler der Weichen zu erkennen, Warnmeldungen zu generieren und diese in ein umfassendes Zustandsüberwachungssystem zu integrieren. Hierzu werden in enger Kooperation Verfahren entwickelt, die das Expertenwissen der Wartungsanalytiker berücksichtigen. Die Verfahren, ihre prototypische Implementierung im SR Kontrollzentrum und erste Ergebnisse der Validierung werden nachfolgend vorgestellt

Expert system based fault diagnosis for railway point machines

To meet the increasing demands for availability at reasonable cost, operators and maintainers of railway point machines are constantly looking for innovative techniques for switch condition monitoring and prediction. This includes automated fault root cause diagnosis based on measurement data (such as motor current curves) and other information. However, large, comprehensive sets of labeled data suitable for standard machine learning are not yet available. Existing data-driven approaches focus only on the differentiation of a few major fault categories at the level of the measurement data (i.e. the "fault symptoms"). There is great potential in hybrid models that use expert knowledge in combination with multiple sources of information to automatically identify failure causes at a much more detailed level. This paper discusses a Bayesian network diagnostic model for determining the root causes of faults in point machines, based on expert knowledge and few labeled data examples from the Netherlands. Human-interpretable current curve features and other information sources (e.g. past maintenance actions) are used as evidence. The result of the model is a ranking of the most likely failure causes with associated probabilities in terms of fuzzy multi-label classification, which is directly aimed at providing decision support to maintenance engineers. The validity and limitations of the model are demonstrated by a scenario-based evaluation and a brief analysis using information theoretic measures. We present the information sources used, the detailed development process and the analysis methodology. This article is intended to be a guide to developing similar models for various complex technical assets

Anomaly Detection and Forecasting Methods Applied to Point Machine Monitoring Data for Prevention of Switch Failures

Railway switches are a crucial asset since they enable trains to change tracks without stopping. Switch failures can compromise a larger part of the railway infrastructure, which can have a negative impact on reputation and revenues. Switches are a costly asset due to frequent inspections, maintenance and renewal of components. Therefore knowing current and future asset condi-tion can be helpful in optimizing switch maintenance to prevent complete failure. The goal of the research presented here is to exploit switch condition monitoring and weather data to identify switch failures on an early stage. Approaches for detection of anomalous switch behavior and prediction of failures are developed. To validate the anomaly detection results obtained by applying the Isolation Forest algorithm, two different annotated data sets are considered. It is found that the anomaly detection approach performs well when applied to a switch, which is characterized by narrow feature distributions within temperature bins. Moreover first results from an Autoregressive Integrated Moving Average model for failure evolution prediction are presented

Towards the automation of anomaly detection and integrated fault identification for railway switches in a real operational environment

We report on the latest developments regarding automatic anomaly detection and fault diagnosis for railway switches achieved in the context of the EU Shift2Rail project In2Smart2. This paper presents improvements regarding the interpretability of the output of a data-driven anomaly detection routine, thereby increasing its usability for maintenance engineers. Warning messages are generated by a new module in case certain critical engine failure types are detected or additional information on detected anomalies is available. Further developments in the diagnostic model brought it to a state in which it is ready for a first plausibility check under supervision of maintenance experts. Taken together, these developments are an important step towards the integration of anomaly detection and diagnosis into a comprehensive condition monitoring system. The advances have recently been or are projected to be implemented in an upgrade of the workflow running in near real-time at the maintenance control center of Strukton Rail, in the Netherlands. This paper gives an overview of the workflow which includes the new warning module, the extended anomaly detection pipeline, and how the diagnostic model is to be conceptually embedded, i.e. its interactions with the other modules. Moreover, methodological improvements, e.g., in data preprocessing for feature extraction and warning generation, are also described