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

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

Filter-informed Spectral Graph Wavelet Networks for Multiscale Feature Extraction and Intelligent Fault Diagnosis

Intelligent fault diagnosis has been increasingly improved with the evolution of deep learning (DL) approaches. Recently, the emerging graph neural networks (GNNs) have also been introduced in the field of fault diagnosis with the goal to make better use of the inductive bias of the interdependencies between the different sensor measurements. However, there are some limitations with these GNN-based fault diagnosis methods. First, they lack the ability to realize multiscale feature extraction due to the fixed receptive field of GNNs. Secondly, they eventually encounter the over-smoothing problem with increase of model depth. Lastly, the extracted features of these GNNs are hard to understand owing to the black-box nature of GNNs. To address these issues, a filter-informed spectral graph wavelet network (SGWN) is proposed in this paper. In SGWN, the spectral graph wavelet convolutional (SGWConv) layer is established upon the spectral graph wavelet transform, which can decompose a graph signal into scaling function coefficients and spectral graph wavelet coefficients. With the help of SGWConv, SGWN is able to prevent the over-smoothing problem caused by long-range low-pass filtering, by simultaneously extracting low-pass and band-pass features. Furthermore, to speed up the computation of SGWN, the scaling kernel function and graph wavelet kernel function in SGWConv are approximated by the Chebyshev polynomials. The effectiveness of the proposed SGWN is evaluated on the collected solenoid valve dataset and aero-engine intershaft bearing dataset. The experimental results show that SGWN can outperform the comparative methods in both diagnostic accuracy and the ability to prevent over-smoothing. Moreover, its extracted features are also interpretable with domain knowledge

Role of DNA Methylation and Adenosine in Ketogenic Diet for Pharmacoresistant Epilepsy: Focus on Epileptogenesis and Associated Comorbidities

Epilepsy is a neurological disorder characterized by a long term propensity to produce unprovoked seizures and by the associated comorbidities including neurological, cognitive, psychiatric, and impairment the quality of life. Despite the clinic availability of several novel antiepileptic drugs (AEDs) with different mechanisms of action, more than one-third of patients with epilepsy suffer with pharmacoresistant epilepsy. Until now, no AEDs have been proven to confer the efficacy in alteration of disease progression or inhibition of the development of epilepsy. The ketogenic diet, the high-fat, low-carbohydrate composition is an alternative metabolic therapy for epilepsy, especially for children with drug-resistant epilepsy. Recently clinical and experimental results demonstrate its efficacy in ameliorating both seizures and comorbidities associated with epilepsy, such as cognitive/psychiatric concerns for the patients with refractory epilepsy. Of importance, ketogenic diet demonstrates to be a promising disease-modifying or partial antiepileptogenesis therapy for epilepsy. The mechanisms of action of ketogenic diet in epilepsy have been revealed recently, such as epigenetic mechanism for increase the adenosine level in the brain and inhibition of DNA methylation. In the present review, we will focus on the mechanisms of ketogenic diet therapies underlying adenosine system in the prevention of epileptogenesis and disease modification. In addition, we will review the role of ketogenic diet therapy in comorbidities associated epilepsy and the underlying mechanisms of adenosine