Our results demonstrate that the proposed approach is able to learn the ground truth health evolution of milling machines and the learned health indicator is suited for fault detection of railway wheels operated under various operating conditions by outperforming state-of-the-art methods.
In this paper, we propose a fully unsupervised deep learning framework that is able to extract a meaningful and sparse representation of raw HF signals.
In the first step, we identify outliers (including the mislabeled samples) based on the update in the hypothesis space.
A solution to this problem is to perform unsupervised transfer learning (UTL), to transfer complementary data between different units.
It provides interpretability of the results for the domain experts with the identification of the pulses that led to the detection of the PDs.
Essential characteristics of time series, situated outside the time domain, are often difficult to capture with state-of-the-art anomaly detection methods when no transformations have been applied to the time series.
We demonstrate that mixing kervolutional with convolutional layers in the encoder is more sensitive to variations in the input data and is able to detect anomalous time series in a better way.
We demonstrate in this paper that the performance of domain adversarial methods can be vulnerable to an incomplete target label space during training.
In the early life of the system, the collected data is probably not representative of future operating conditions, making it challenging to train a robust model.
Two approaches rely on the data from the unit to be monitored only: the baseline is trained on the early life of the unit.
Thanks to digitization of industrial assets in fleets, the ambitious goal of transferring fault diagnosis models fromone machine to the other has raised great interest.
The performance is first evaluated on a synthetic dataset that encompasses typical characteristics of condition monitoring data.