  The application of unsupervised domain adaptation (UDA)-based fault diagnosis
methods has shown significant efficacy in industrial settings, facilitating the
transfer of operational experience and fault signatures between different
operating conditions, different units of a fleet or between simulated and real
data. However, in real industrial scenarios, unknown levels and types of noise
can amplify the difficulty of domain alignment, thus severely affecting the
diagnostic performance of deep learning models. To address this issue, we
propose an UDA method called Smart Filter-Aided Domain Adversarial Neural
Network (SFDANN) for fault diagnosis in noisy industrial scenarios. The
proposed methodology comprises two steps. In the first step, we develop a smart
filter that dynamically enforces similarity between the source and target
domain data in the time-frequency domain. This is achieved by combining a
learnable wavelet packet transform network (LWPT) and a traditional wavelet
packet transform module. In the second step, we input the data reconstructed by
the smart filter into a domain adversarial neural network (DANN). To learn
domain-invariant and discriminative features, the learnable modules of SFDANN
are trained in a unified manner with three objectives: time-frequency feature
proximity, domain alignment, and fault classification. We validate the
effectiveness of the proposed SFDANN method based on two fault diagnosis cases:
one involving fault diagnosis of bearings in noisy environments and another
involving fault diagnosis of slab tracks in a train-track-bridge coupling
vibration system, where the transfer task involves transferring from numerical
simulations to field measurements. Results show that compared to other
representative state of the art UDA methods, SFDANN exhibits superior
performance and remarkable stability.
