Smart filter aided domain adversarial neural network: An unsupervised domain adaptation method for fault diagnosis in noisy industrial scenarios

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.

Slab Track Condition Monitoring Based on Learned Sparse Features from Acoustic and Acceleration Signals

The implementation of concrete slab track solutions has been recently increasing particularly for high-speed lines. While it is typically associated with low periodic maintenance, there is a significant need to detect the state of slab tracks in an efficient way. Data-driven detection methods are promising. However, collecting large amounts of labeled data is particularly challenging since abnormal states are rare for such safety-critical infrastructure. To imitate different healthy and unhealthy states of slab tracks, this study uses three types of slab track supporting conditions in a railway test line. Acceleration sensors (contact) and acoustic sensors (contactless), are installed next to the three types of slab track to collect the acceleration and acoustic signals as a train passes by with different speeds. We use a deep learning framework based on the recently proposed Denoising Sparse Wavelet Network (DeSpaWN) to automatically learn meaningful and sparse representations of raw high-frequency signals. A comparative study is conducted among the feature learning / extraction methods, and between acceleration signals and acoustic signals, by evaluating the detection effectiveness using a multi-class support vector machine. It is found that the classification accuracy using acceleration signals can reach almost 100%, irrespective which feature learning / extraction method is adopted. Due to the more severe noise interference in acoustic signals, the performance of using acoustic signals is worse than of using acceleration signals. However, it can be significantly improved by leaning meaningful features with DeSpaWN.