Contrastive Feature Learning for Fault Detection and Diagnostics in Railway Applications

by   Katharina Rombach, et al.

A railway is a complex system comprising multiple infrastructure and rolling stock assets. To operate the system safely, reliably, and efficiently, the condition many components needs to be monitored. To automate this process, data-driven fault detection and diagnostics models can be employed. In practice, however, the performance of data-driven models can be compromised if the training dataset is not representative of all possible future conditions. We propose to approach this problem by learning a feature representation that is, on the one hand, invariant to operating or environmental factors but, on the other hand, sensitive to changes in the asset's health condition. We evaluate how contrastive learning can be employed on supervised and unsupervised fault detection and diagnostics tasks given real condition monitoring datasets within a railway system - one image dataset from infrastructure assets and one time-series dataset from rolling stock assets. First, we evaluate the performance of supervised contrastive feature learning on a railway sleeper defect classification task given a labeled image dataset. Second, we evaluate the performance of unsupervised contrastive feature learning without access to faulty samples on an anomaly detection task given a railway wheel dataset. Here, we test the hypothesis of whether a feature encoder's sensitivity to degradation is also sensitive to novel fault patterns in the data. Our results demonstrate that contrastive feature learning improves the performance on the supervised classification task regarding sleepers compared to a state-of-the-art method. Moreover, on the anomaly detection task concerning the railway wheels, the detection of shelling defects is improved compared to state-of-the-art methods.


page 5

page 6


Feature Learning for Fault Detection in High-Dimensional Condition-Monitoring Signals

Complex industrial systems are continuously monitored by a large number ...

Transfer Learning from an Auxiliary Discriminative Task for Unsupervised Anomaly Detection

Unsupervised anomaly detection from high dimensional data like mobility ...

DCdetector: Dual Attention Contrastive Representation Learning for Time Series Anomaly Detection

Time series anomaly detection is critical for a wide range of applicatio...

Discriminative Feature Learning Framework with Gradient Preference for Anomaly Detection

Unsupervised representation learning has been extensively employed in an...

Hard Sample Mining Enabled Contrastive Feature Learning for Wind Turbine Pitch System Fault Diagnosis

The efficient utilization of wind power by wind turbines relies on the a...

A Multi-State Diagnosis and Prognosis Framework with Feature Learning for Tool Condition Monitoring

In this paper, a multi-state diagnosis and prognosis (MDP) framework is ...

Fully Unsupervised Feature Alignment for Critical System Health Monitoring with Varied Operating Conditions

The failure of a complex and safety critical industrial asset can have e...

Please sign up or login with your details

Forgot password? Click here to reset