A Novel Method for Intelligent Single Fault Detection of Bearings Using SAE and Improved D–S Evidence Theory

In order to realize single fault detection (SFD) from the multi-fault coupling bearing data and further research on the multi-fault situation of bearings, this paper proposes a method based on features self-extraction of a Sparse Auto-Encoder (SAE) and results fusion of improved Dempster–Shafer evidence theory (D–S). Multi-fault signal compression features of bearings were extracted by SAE on multiple vibration sensors’ data. Data sets were constructed by the extracted compression features to train the Support Vector Machine (SVM) according to the rule of single fault detection (R-SFD) this paper proposed. Fault detection results were obtained by the improved D–S evidence theory, which was implemented via correcting the 0 factor in the Basic Probability Assignment (BPA) and modifying the evidence weight by Pearson Correlation Coefficient (PCC). Extensive evaluations of the proposed method on the experiment platform datasets showed that the proposed method could realize single fault detection from multi-fault bearings. Fault detection accuracy increases as the output feature dimension of SAE increases; when the feature dimension reached 200, the average detection accuracy of the three sensors for bearing inner, outer, and ball faults achieved 87.36%, 87.86% and 84.46%, respectively. The three types’ fault detection accuracy—reached to 99.12%, 99.33% and 98.46% by the improved Dempster–Shafer evidence theory (IDS) to fuse the sensors’ results—is respectively 0.38%, 2.06% and 0.76% higher than the traditional D–S evidence theory. That indicated the effectiveness of improving the D–S evidence theory by evidence weight calculation of PCC

Aggregation-induced emission fluorogens as biomarkers to assess the viability of microalgae in aquatic ecosystems

Open Access Article. This article is licensed under a Creative Commons Attribution 3.0 Unported Licence.Microalgae can be a valuable indicator for monitoring water pollution due to their sensitivity to the changes induced by pollutants in the environment. In this study, an aggregation-induced emission fluorogen was used as a novel tool to differentiate dead and live microalgae and quantify the link between live algal concentration and fluorogen intensity. Protein in the cell protoplasm is the key component contributing to fluorescence emission in algae

An Ensemble Deep Convolutional Neural Network Model with Improved D-S Evidence Fusion for Bearing Fault Diagnosis

Intelligent machine health monitoring and fault diagnosis are becoming increasingly important for modern manufacturing industries. Current fault diagnosis approaches mostly depend on expert-designed features for building prediction models. In this paper, we proposed IDSCNN, a novel bearing fault diagnosis algorithm based on ensemble deep convolutional neural networks and an improved Dempster–Shafer theory based evidence fusion. The convolutional neural networks take the root mean square (RMS) maps from the FFT (Fast Fourier Transformation) features of the vibration signals from two sensors as inputs. The improved D-S evidence theory is implemented via distance matrix from evidences and modified Gini Index. Extensive evaluations of the IDSCNN on the Case Western Reserve Dataset showed that our IDSCNN algorithm can achieve better fault diagnosis performance than existing machine learning methods by fusing complementary or conflicting evidences from different models and sensors and adapting to different load conditions

An Ensemble Deep Convolutional Neural Network Model with Improved D-S Evidence Fusion for Bearing Fault Diagnosis

Intelligent machine health monitoring and fault diagnosis are becoming increasingly important for modern manufacturing industries. Current fault diagnosis approaches mostly depend on expert-designed features for building prediction models. In this paper, we proposed IDSCNN, a novel bearing fault diagnosis algorithm based on ensemble deep convolutional neural networks and an improved Dempster–Shafer theory based evidence fusion. The convolutional neural networks take the root mean square (RMS) maps from the FFT (Fast Fourier Transformation) features of the vibration signals from two sensors as inputs. The improved D-S evidence theory is implemented via distance matrix from evidences and modified Gini Index. Extensive evaluations of the IDSCNN on the Case Western Reserve Dataset showed that our IDSCNN algorithm can achieve better fault diagnosis performance than existing machine learning methods by fusing complementary or conflicting evidences from different models and sensors and adapting to different load conditions

Physical Information Neural Networks for Solving High-index Differential-algebraic Equation Systems Based on Radau Methods

As is well known, differential algebraic equations (DAEs), which are able to describe dynamic changes and underlying constraints, have been widely applied in engineering fields such as fluid dynamics, multi-body dynamics, mechanical systems and control theory. In practical physical modeling within these domains, the systems often generate high-index DAEs. Classical implicit numerical methods typically result in varying order reduction of numerical accuracy when solving high-index systems.~Recently, the physics-informed neural network (PINN) has gained attention for solving DAE systems. However, it faces challenges like the inability to directly solve high-index systems, lower predictive accuracy, and weaker generalization capabilities. In this paper, we propose a PINN computational framework, combined Radau IIA numerical method with a neural network structure via the attention mechanisms, to directly solve high-index DAEs. Furthermore, we employ a domain decomposition strategy to enhance solution accuracy. We conduct numerical experiments with two classical high-index systems as illustrative examples, investigating how different orders of the Radau IIA method affect the accuracy of neural network solutions. The experimental results demonstrate that the PINN based on a 5th-order Radau IIA method achieves the highest level of system accuracy. Specifically, the absolute errors for all differential variables remains as low as $10^{-6}$, and the absolute errors for algebraic variables is maintained at $10^{-5}$, surpassing the results found in existing literature. Therefore, our method exhibits excellent computational accuracy and strong generalization capabilities, providing a feasible approach for the high-precision solution of larger-scale DAEs with higher indices or challenging high-dimensional partial differential algebraic equation systems

Case report: Desquamating dermatitis, bilateral cerebellar lesions in a late-onset methylmalonic acidemia patient

IntroductionCobalamin C (cblC) deficiency is a rare hereditary disorder affecting intracellular cobalamin metabolism, primarily caused by mutations in MMACHC. This condition is characterized by combined methylmalonic acidemia and hyperhomocysteinemia, displaying a wide range of clinical manifestations involving multiple organs. Owing to its uncommon occurrence and diverse clinical phenotypes, diagnosing cblC deficiency is challenging and often leads to delayed or missed diagnoses.Case descriptionIn this report, we present a case of late-onset cblC deficiency with brown desquamating dermatitis on the buttocks. Magnetic resonance imaging (MRI) of the brain revealed bilateral cerebellar abnormalities. The suspicion of an inherited metabolic disorder was raised by abnormal serum amino acid and acylcarnitine levels, along with increased urine methylmalonic acid and serum homocysteine levels. Whole-exome sequencing helped identify a homozygous variant (c.482G>A) in MMACHC, confirming the diagnosis of cblC deficiency. However, despite receiving treatment with hydroxocobalamin and betaine, the patient did not experience clinical improvement, which may be attributed to the delayed diagnosis as indicated by the declining homocysteine and methylmalonic acid levels.ConclusionCollectively, we emphasize the significance of recognizing the skin lesions and observing serial MRI changes in patients with cblC deficiency. Our case underscores the importance of early diagnosis and timely therapeutic intervention for this severe yet frequently manageable condition

Seroprevalence of Pandemic (H1N1) 2009 in Pregnant Women in China: An Observational Study

BACKGROUND: We investigated the seropositive rates and persistence of antibody against pandemic (H1N1) 2009 virus (pH1N1) in pregnant women and voluntary blood donors after the second wave of the pandemic in Nanjing, China. METHODOLOGY/PRINCIPAL FINDINGS: Serum samples of unvaccinated pregnant women (n = 720) and voluntary blood donors (n = 320) were collected after the second wave of 2009 pandemic in Nanjing. All samples were tested against pH1N1 strain (A/California/7/2009) with hemagglutination inhibition assay. A significant decline in seropositive rates, from above 50% to about 20%, was observed in pregnant women and voluntary blood donors fifteen weeks after the second wave of the pandemic. A quarter of the samples were tested against a seasonal H1N1 strain (A/Brisbane/59/2007). The antibody titers against pH1N1 strain were found to correlate positively with those against seasonal H1N1 strain. The correlation was modest but statistically significant. CONCLUSIONS AND SIGNIFICANCE: The high seropositive rates in both pregnant women and voluntary blood donors suggested that the pH1N1 virus had widely spread in these two populations. Immunity derived from natural infection seemed not to be persistent well

Bidirectional Current WP and CBAR Neural Network Model-Based Bearing Fault Diagnosis

In the era of artificial intelligence, the development of an efficient bearing, fault diagnosis method is of vital importance to ensure smooth production operations and avoid major economic losses. To this end, this paper proposes a bearing fault diagnosis method based on biphasic currents. The method first performs wavelet denoising on the biphasic current signal, then extracts its features by simple vector representation and algebraic operations, and finally, combines the CBAR model of Convolutional Block Attention Module (CBAM) and Residual Network (ResNet) for bearing fault diagnosis. The experimental results show that the highest accuracy rate reaches 100% in both single-point fault and single-point mixed with multiple faults conditions on the open source current bearing fault diagnosis dataset, respectively. Compared with other methods, the method proposed in this paper has the advantage of simple data processing, concise model structure, and high-fault diagnosis accuracy, which provides an effective way for dual-phase current-based bearing fault diagnosis. It is worth emphasizing that based on wavelet denoising, this paper uses the simplest vector representation and algebraic operations to preprocess the signal (WP), making the method more efficient and easy to implement. (Some experiment-related code is posted on the Code Open Source Repository website. https://github.com/LTbig/LT_Bearing_Fault