Motivated by the fact that domain experts often have a relatively good understanding on how different fault types affect healthy signals, in the first step of the proposed framework, a synthetic fault dataset is generated by augmenting real vibration samples of healthy bearings.
Using deep learning, we make this architecture fully learnable: both the wavelet bases and the wavelet coefficient denoising are learnable.
However, such a supervision is not always available.
Ranked #2 on Domain Adaptation on SYNTHIA-to-Cityscapes (using extra training data)
Remaining Useful Life (RUL) estimation is the problem of inferring how long a certain industrial asset can be expected to operate within its defined specifications.
In this paper, we implement a Reinforcement Learning-based framework for reliably and efficiently inferring calibration parameters of battery models.
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.
In this paper, we introduce a new reinforcement learning (RL) based neural architecture search (NAS) methodology for effective and efficient generative adversarial network (GAN) architecture search.
Ranked #6 on Image Generation on STL-10
The dynamic, real-time, and accurate inference of model parameters from empirical data is of great importance in many scientific and engineering disciplines that use computational models (such as a digital twin) for the analysis and prediction of complex physical processes.
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.
Deep learning applications have been thriving over the last decade in many different domains, including computer vision and natural language understanding.
We demonstrate in this paper that the performance of domain adversarial methods can be vulnerable to an incomplete target label space during training.
With this work, we propose training a variational autoencoder (VAE) with labeled and unlabeled samples while inducing implicit supervision on the latent representation of the healthy conditions.
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.