Railway wheel fault diagnosis using a fuzzy-logic method

A simple method for diagnosis of railway wheel defects using fuzzy-logic is proposed. The method is based on vibration measurements at different train speeds on healthy wheels and wheels with defects known a priori. The measured vibrations are statistically analyzed and confidence intervals for healthy and defective wheels depending on train speed and frequency of analysis are established. To facilitate the implementation of the method a fuzzy-logic is adopted. The fuzzy-logic model stores the obtained experience in a database and performs the decision making on damage extent and consequently the need of preventive maintenance. In view of the obtained results, the advantages and the limitations of the proposed approach as well as suggestions for further improvements are presented and discussed.     

Progress on the application of electrochemiluminescence biosensor based on nanomaterials

In this review, we check out the number of published literature in the field of ECL biosensors trends during nearly a decade, and compare the research status of four different types of biosensors; summarize the application forms of nanomaterials in ECL biosensor. We have an overview of the building patterns and application example of the four main types of biosensors in the paper.     

微量元素锌在治疗Bell麻痹中的作用

为探讨急性早期应用补锌和综合疗法治疗Bell麻痹 ,将 1 5 8例Bell麻痹患者 ,随机分成两组 :(1 )补锌组 (补锌 +综合疗法 ) ;(2 )对照组 (单纯综合疗法 )。对其进行了观察对比治疗 ,同时检测了所有患者治疗前后自身对照发锌值 ,还对所有患者进行了定位诊断、面神经电反应测试、并对面肌功能进行评分。结果表明 ,对定位诊断其病变在D段、面肌功能评分在 1 1分以下、面神经无电反应患者 ,即使给予早期综合治疗 ,效果仍较差 ,而早期给予补锌加综合治疗效果优于对照组 ,其它段内患者治疗次数和治愈天数也均优于对照组。可见微量元素锌在发挥免疫应答、免疫调控中的重要作用     

金属组学在急性心肌梗死早期诊断的应用

为探讨急性心肌梗死(AMI)患者血清中K⁺、Na⁺、Ca²⁺、Fe²⁺、Mg²⁺含量变化,并研究其与心肌梗死患者之间的关系。选取2022年5月至2023年2月收治的AMI患者37例,同时选取健康体检者35例作为对照组。依据入院时或体检时收集的抽血样本进行临床生化分析,比较两组间血清K⁺、Na⁺、Ca²⁺、Fe²⁺、Mg²⁺含量,采用判别方程、主成分分析法(PCA),判断分析哪种金属离子对于心肌梗死的诊断价值大。结果表明,AMI患者的血清中Ca²⁺和Fe²⁺含量低于健康对照组,差异具有统计学意义。基于血钙、铁水平两组具有显著性差异,以它们为基础进行判别分析,获得判别函数式。将血清中K⁺、Na⁺、Ca²⁺、Fe²⁺、Mg²⁺     

Computer-Aided Detection of Quantitative Signatures for Breast Fibroepithelial Tumors Using Label-Free Multi-Photon Imaging

Fibroadenomas (FAs) and phyllodes tumors (PTs) are major benign breast tumors, pathologically classified as fibroepithelial tumors. Although the clinical management of PTs differs from FAs, distinction by core needle biopsy diagnoses is still challenging. Here, a combined technique of label-free imaging with multi-photon microscopy and artificial intelligence was applied to detect quantitative signatures that differentiate fibroepithelial lesions. Multi-photon excited autofluorescence and second harmonic generation (SHG) signals were detected in tissue sections. A pixel-wise semantic segmentation method using a deep learning framework was used to separate epithelial and stromal regions automatically. The epithelial to stromal area ratio and the collagen SHG signal strength were investigated for their ability to distinguish fibroepithelial lesions. An image segmentation analysis with a pixel-wise semantic segmentation framework using a deep convolutional neural network showed the accurate separation of epithelial and stromal regions. A further investigation, to determine if scoring the epithelial to stromal area ratio and the SHG signal strength within the stromal area could be a marker for differentiating fibroepithelial tumors, showed accurate classification. Therefore, molecular and morphological changes, detected through the assistance of computational and label-free multi-photon imaging techniques, enable us to propose quantitative signatures for epithelial and stromal alterations in breast tissues.     

Intelligent Fault Diagnosis of Industrial Robot Based on Multiclass Mahalanobis-Taguchi System for Imbalanced Data

One of the biggest challenges for the fault diagnosis research of industrial robots is that the normal data is far more than the fault data; that is, the data is imbalanced. The traditional diagnosis approaches of industrial robots are more biased toward the majority categories, which makes the diagnosis accuracy of the minority categories decrease. To solve the imbalanced problem, the traditional algorithm is improved by using cost-sensitive learning, single-class learning and other approaches. However, these algorithms also have a series of problems. For instance, it is difficult to estimate the true misclassification cost, overfitting, and long computation time. Therefore, a fault diagnosis approach for industrial robots, based on the Multiclass Mahalanobis-Taguchi system (MMTS), is proposed in this article. It can be classified the categories by measuring the deviation degree from the sample to the reference space, which is more suitable for classifying imbalanced data. The accuracy, G-mean and F-measure are used to verify the effectiveness of the proposed approach on an industrial robot platform. The experimental results show that the proposed approach’s accuracy, F-measure and G-mean improves by an average of 20.74%, 12.85% and 21.68%, compared with the other five traditional approaches when the imbalance ratio is 9. With the increase in the imbalance ratio, the proposed approach has better stability than the traditional algorithms.     

时频分析在旋转机械状态监测及故障诊断中的应用

本文简要介绍了时频分析方法的发展及其在旋转机械状态监测、故障诊断领域中的应用。并以一个实际监测系统为例，对其检测信号应用时频分析的方法进行了数据处理，对用传统傅立叶变换处理的结果与采用时频分析方法处理的结果进行了对比分析．     

故障树模块化在装甲车辆灭火系统故障诊断中的应用

为了实现装甲装备灭火系统故障的快速诊断,提出了一种故障树模块化分析方法;对灭火系统故障树进行深度优先最左遍历,并记录遍历过程,按照遍历顺序对故障树中的每个事件进行标定,并将灭火系统故障树划分为相互独立的模块,依据划分的模块可以通过故障现象对模块内的故障进行排查及修复;实验结果分析表明,该方法可以快速修复模块故障,恢复系统功能,简化了以往对灭火系统所有子事件遍历查错的繁琐过程。该方法同样可以计算故障模块的失效概率,并可以实现故障模块的整体更换,恢复系统性能;证明了故障树模块化方法在灭火系统故障诊断中具有较高的效率,简化了灭火系统诊断流程,在装甲车辆其他系统故障诊断中具有借鉴作用,符合现代作战对于装备保障的需求。     

Raman spectroscopic study on classification of cervical cell specimens

Cervix-cancer is the third most common female cancer worldwide. Papanicolaou (Pap) test, a well-recognized screening tool, is labor intensive, time consuming and prone to subjective interpretations. Optical spectroscopic methods, sensitive to molecular changes are being pursued as potential diagnostics tool. In this study we have explored Raman spectroscopic approach to differentiate exfoliated cell pellets using 94 cervical cell specimens (45-normal and 49-abnormal specimens). Study was carried out by two approaches. In the first approach, spectral data from 37 cell specimens were acquired and analyzed by Principal Component-Linear Discriminant Analysis (PC-LDA), which yielded classification efficiencies of 86% and 84% for normal and abnormal specimens, respectively. Mean and difference spectra suggest presence of blood in abnormal specimen as a major cause of discrimination. However, as tumor is vascular, bleeding was observed during abnormal sample collection. Hence, spectra of abnormal specimens show heme and fibrin features, and this can lead to false interpretations, as bleeding also occur in several non-cancerous conditions. Therefore, remaining 57 specimens were treated with Red Blood Corpuscles (RBC) lysis buffer in order to remove the RBC influence. PC-LDA resulted classification efficiency of about 79% and 78% for normal and abnormal smear, respectively – comparable to Pap test. Thus finding of the study suggests feasibility of Raman spectroscopic classification of normal and cancerous exfoliated cervical cell specimens.     

Mass spectrometry imaging as a tool for surgical decision‐making

Despite significant advances in image‐guided therapy, surgeons are still too often left with uncertainty when deciding to remove tissue. This binary decision between removing and leaving tissue during surgery implies that the surgeon should be able to distinguish tumor from healthy tissue. In neurosurgery, current image‐guidance approaches such as magnetic resonance imaging (MRI) combined with neuronavigation offer a map as to where the tumor should be, but the only definitive method to characterize the tissue at stake is histopathology. Although extremely valuable information is derived from this gold standard approach, it is limited to very few samples during surgery and is not practically used for the delineation of tumor margins. The development and implementation of faster, comprehensive, and complementary approaches for tissue characterization are required to support surgical decision‐making – an incremental and iterative process with tumor removed in multiple and often minute biopsies. The development of atmospheric pressure ionization sources makes it possible to analyze tissue specimens with little to no sample preparation. Here, we highlight the value of desorption electrospray ionization as one of many available approaches for the analysis of surgical tissue. Twelve surgical samples resected from a patient during surgery were analyzed and diagnosed as glioblastoma tumor or necrotic tissue by standard histopathology, and mass spectrometry results were further correlated to histopathology for critical validation of the approach. The use of a robust statistical approach reiterated results from the qualitative detection of potential biomarkers of these tissue types. The correlation of the mass spectrometry and histopathology results to MRI brings significant insight into tumor presentation that could not only serve to guide tumor resection, but that is also worthy of more detailed studies on our understanding of tumor presentation on MRI. Copyright © 2013 John Wiley & Sons, Ltd.