Fleet-based transfer learning for anomaly detection in industrial systems

doi:10.1016/j.ymssp.2025.113725

Fleet-based transfer learning for anomaly detection in industrial systems

2025-12-08

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DOI：10.1016/j.ymssp.2025.113725

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Abstract

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This work considers the problem of developing a data-driven anomaly detection method in the common situation in which a large dataset is available only for few systems of a fleet and the data therein are unlabeled, i.e., the equipment health state (normal/anomalous) is unknown. An innovative two-stage unsupervised Transfer Learning (TL) framework is developed. In the first stage, a Long Short-Term Memory Encoder–Decoder (LSTM-ED) is pre-trained to reconstruct the values expected in normal condition of a specific (“source”) system of the fleet using a dataset of signal measurements collected during a long period of time. Since industrial systems are typically in normal condition during most of their operational time, this pre-trained model is expected to reproduce their behavior in normal condition. In the second stage, the network architecture and parameters of the pre-trained model are used to initialize the dedicated LSTM-ED-based signal reconstruction model for another (“target”) system of the same fleet, for which only a limited amount of data collected during a short period of time is available. Data of the Aramis Data Challenge and of traction systems of a fleet of trains have been used to validate the proposed anomaly detection method in two different case studies. The obtained results show the superior performance of the proposed method in comparison to other state-of-the-art methods.

Keywords:

Fleets of systems

Anomaly detection

Unlabeled data

Unsupervised transfer learning

LSTM

Encoder–Decoder

Traction systems

Fleet-based transfer learning for anomaly detection in industrial systems

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