353 research outputs found
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
A Novel Unsupervised Graph Wavelet Autoencoder for Mechanical System Fault Detection
Reliable fault detection is an essential requirement for safe and efficient
operation of complex mechanical systems in various industrial applications.
Despite the abundance of existing approaches and the maturity of the fault
detection research field, the interdependencies between condition monitoring
data have often been overlooked. Recently, graph neural networks have been
proposed as a solution for learning the interdependencies among data, and the
graph autoencoder (GAE) architecture, similar to standard autoencoders, has
gained widespread use in fault detection. However, both the GAE and the graph
variational autoencoder (GVAE) have fixed receptive fields, limiting their
ability to extract multiscale features and model performance. To overcome these
limitations, we propose two graph neural network models: the graph wavelet
autoencoder (GWAE), and the graph wavelet variational autoencoder (GWVAE). GWAE
consists mainly of the spectral graph wavelet convolutional (SGWConv) encoder
and a feature decoder, while GWVAE is the variational form of GWAE. The
developed SGWConv is built upon the spectral graph wavelet transform which can
realize multiscale feature extraction by decomposing the graph signal into one
scaling function coefficient and several spectral graph wavelet coefficients.
To achieve unsupervised mechanical system fault detection, we transform the
collected system signals into PathGraph by considering the neighboring
relationships of each data sample. Fault detection is then achieved by
evaluating the reconstruction errors of normal and abnormal samples. We carried
out experiments on two condition monitoring datasets collected from fuel
control systems and one acoustic monitoring dataset from a valve. The results
show that the proposed methods improve the performance by around 3%~4% compared
to the comparison methods
Channel Capacity and Bounds In Mixed Gaussian-Impulsive Noise
Communication systems suffer from the mixed noise consisting of both
non-Gaussian impulsive noise (IN) and white Gaussian noise (WGN) in many
practical applications. However, there is little literature about the channel
capacity under mixed noise. In this paper, we prove the existence of the
capacity under p-th moment constraint and show that there are only finite mass
points in the capacity-achieving distribution. Moreover, we provide lower and
upper capacity bounds with closed forms. It is shown that the lower bounds can
degenerate to the well-known Shannon formula under special scenarios. In
addition, the capacity for specific modulations and the corresponding lower
bounds are discussed. Numerical results reveal that the capacity decreases when
the impulsiveness of the mixed noise becomes dominant and the obtained capacity
bounds are shown to be very tight
WaveletKernelNet: An Interpretable Deep Neural Network for Industrial Intelligent Diagnosis
Convolutional neural network (CNN), with ability of feature learning and
nonlinear mapping, has demonstrated its effectiveness in prognostics and health
management (PHM). However, explanation on the physical meaning of a CNN
architecture has rarely been studied. In this paper, a novel wavelet driven
deep neural network termed as WaveletKernelNet (WKN) is presented, where a
continuous wavelet convolutional (CWConv) layer is designed to replace the
first convolutional layer of the standard CNN. This enables the first CWConv
layer to discover more meaningful filters. Furthermore, only the scale
parameter and translation parameter are directly learned from raw data at this
CWConv layer. This provides a very effective way to obtain a customized filter
bank, specifically tuned for extracting defect-related impact component
embedded in the vibration signal. In addition, three experimental verification
using data from laboratory environment are carried out to verify effectiveness
of the proposed method for mechanical fault diagnosis. The results show the
importance of the designed CWConv layer and the output of CWConv layer is
interpretable. Besides, it is found that WKN has fewer parameters, higher fault
classification accuracy and faster convergence speed than standard CNN
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