循环平稳信号处理在机械设备故障诊断中的应用综述

循环平稳信号处理学科的引入，丰富了机械设备故障诊断的内容。本文总结了循环平稳信号处理在机械设备信号特征提取和故障诊断领域的研究概况，分析了该方法目前所存在的问题，最后简要指出了循环平稳信号处理在机械设备故障诊断中应用的发展方向。     

基于神经网络的智能诊断

人工智能与诊断理论的结合形成了智能诊断,早期发展的模拟人脑思维推理的、基于知识的专家系统以串行运行的格式进入设备诊断领域,形成了基于知识的诊断推理专家系统,国内外已有许多成熟的商品化软件系统。近几年新发展起来的人工智能的一个分支－－人工神经网络模仿人脑物理结构以其强大的并行运算和联想能力非常适合于设备诊断中状态识别,本单位研制的通用型神经网络智能诊断系统,已达到商品化水平,并已在生产线上运行。     

基于小波滤波与循环平稳度分析的滚动轴承早期故障诊断方法

由于噪声的严重干扰，在滚动轴承早期故障时，仅仅利用二阶循环平稳分析方法往往较难成功提取出信号中潜在的有用周期信息。因此将小波滤波和循环平稳度分析方法相结合，进行滚动轴承的早期故障诊断。采用最小熵方法对小波滤波器参数进行优化。实验结果证明了该方法的有效性。     

Metaheuristics with Deep Learning Empowered Biomedical Atherosclerosis Disease Diagnosis and Classification

Atherosclerosis diagnosis is an inarticulate and complicated cognitive process. Researches on medical diagnosis necessitate maximum accuracy and performance to make optimal clinical decisions. Since the medical diagnostic outcomes need to be prompt and accurate, the recently developed artificial intelligence (AI) and deep learning (DL) models have received considerable attention among research communities. This study develops a novel Metaheuristics with Deep Learning Empowered Biomedical Atherosclerosis Disease Diagnosis and Classification (MDL-BADDC) model. The proposed MDL-BADDC technique encompasses several stages of operations such as pre-processing, feature selection, classification, and parameter tuning. Besides, the proposed MDL-BADDC technique designs a novel Quasi-Oppositional Barnacles Mating Optimizer (QOBMO) based feature selection technique. Moreover, the deep stacked autoencoder (DSAE) based classification model is designed for the detection and classification of atherosclerosis disease. Furthermore, the krill herd algorithm (KHA) based parameter tuning technique is applied to properly adjust the parameter values. In order to showcase the enhanced classification performance of the MDL-BADDC technique, a wide range of simulations take place on three benchmarks biomedical datasets. The comparative result analysis reported the better performance of the MDL-BADDC technique over the compared methods.     

现代故障诊断技术研究现状与趋势

目的为了能更有效、准确地对复杂设备进行状态监测和故障诊断。方法综述近年故障诊断技术中重要方法的基本原理、特点、局限性和研究现状。在大量文献的基础上,基于计算机技术、信号处理技术、人工智能技术和互联网技术讨论现代故障诊断技术的发展趋势。结果故障诊断技术主要研究机器或机组运行状态的变化在诊断信息中的反映,分为基于模型、基于信号和基于人工智能等3类。结论随着基础学科和前沿学科的不断发展和交叉渗透,故障诊断技术也在不断创新,未来的发展趋势主要集中于将不同人工智能技术以某种方式结合、集成或融合以及开放式远程协作诊断技术。     

石墨烯类材料在疾病诊断治疗方面的研究进展

石墨烯是处于蜂窝状晶体点阵上的碳原子以sp2杂化链接形成的单原子层二维晶体,因独特的结构及优异的性能而备受关注,并在复合材料、纳米电子器件、能量储存和转换、液晶显示等众多领域具有重要的应用前景.主要介绍石墨烯类材料在疾病诊断和治疗方面的应用研究,包括生物分子检测、生物成像等诊断检测和药物载体、光动力治疗等肿瘤治疗方面的应用,并展望了其在生物医学领域的发展前景.     

Enhancing Parkinson’s Disease Diagnosis Accuracy Through Speech Signal Algorithm Modeling

Parkinson’s disease (PD), one of whose symptoms is dysphonia, is a prevalent neurodegenerative disease. The use of outdated diagnosis techniques, which yield inaccurate and unreliable results, continues to represent an obstacle in early-stage detection and diagnosis for clinical professionals in the medical field. To solve this issue, the study proposes using machine learning and deep learning models to analyze processed speech signals of patients’ voice recordings. Datasets of these processed speech signals were obtained and experimented on by random forest and logistic regression classifiers. Results were highly successful, with 90% accuracy produced by the random forest classifier and 81.5% by the logistic regression classifier. Furthermore, a deep neural network was implemented to investigate if such variation in method could add to the findings. It proved to be effective, as the neural network yielded an accuracy of nearly 92%. Such results suggest that it is possible to accurately diagnose early-stage PD through merely testing patients’ voices. This research calls for a revolutionary diagnostic approach in decision support systems, and is the first step in a market-wide implementation of healthcare software dedicated to the aid of clinicians in early diagnosis of PD.     

A New Multi-Agent Feature Wrapper Machine Learning Approach for Heart Disease Diagnosis

Heart disease (HD) is a serious widespread life-threatening disease. The heart of patients with HD fails to pump sufficient amounts of blood to the entire body. Diagnosing the occurrence of HD early and efficiently may prevent the manifestation of the debilitating effects of this disease and aid in its effective treatment. Classical methods for diagnosing HD are sometimes unreliable and insufficient in analyzing the related symptoms. As an alternative, noninvasive medical procedures based on machine learning (ML) methods provide reliable HD diagnosis and efficient prediction of HD conditions. However, the existing models of automated ML-based HD diagnostic methods cannot satisfy clinical evaluation criteria because of their inability to recognize anomalies in extracted symptoms represented as classification features from patients with HD. In this study, we propose an automated heart disease diagnosis (AHDD) system that integrates a binary convolutional neural network (CNN) with a new multi-agent feature wrapper (MAFW) model. The MAFW model consists of four software agents that operate a genetic algorithm (GA), a support vector machine (SVM), and Naïve Bayes (NB). The agents instruct the GA to perform a global search on HD features and adjust the weights of SVM and BN during initial classification. A final tuning to CNN is then performed to ensure that the best set of features are included in HD identification. The CNN consists of five layers that categorize patients as healthy or with HD according to the analysis of optimized HD features. We evaluate the classification performance of the proposed AHDD system via 12 common ML techniques and conventional CNN models by using a cross-validation technique and by assessing six evaluation criteria. The AHDD system achieves the highest accuracy of 90.1%, whereas the other ML and conventional CNN models attain only 72.3%–83.8% accuracy on average. Therefore, the AHDD system proposed herein has the highest capability to identify patients with HD. This system can be used by medical practitioners to diagnose HD efficiently.     

面向柔性制造环境的智能集成诊断系统

介绍了一个面向柔性制造环境的智能集成诊断系统的结构与功能，并研究了相关的诊断信息集成，诊断模型和诊断推理等关键技术，最后给出了研究的结论。     

Artificial Intelligence Based Sentiment Analysis for Health Crisis Management in Smart Cities

Smart city promotes the unification of conventional urban infrastructure and information technology (IT) to improve the quality of living and sustainable urban services in the city. To accomplish this, smart cities necessitate collaboration among the public as well as private sectors to install IT platforms to collect and examine massive quantities of data. At the same time, it is essential to design effective artificial intelligence (AI) based tools to handle healthcare crisis situations in smart cities. To offer proficient services to people during healthcare crisis time, the authorities need to look closer towards them. Sentiment analysis (SA) in social networking can provide valuable information regarding public opinion towards government actions. With this motivation, this paper presents a new AI based SA tool for healthcare crisis management (AISA-HCM) in smart cities. The AISA-HCM technique aims to determine the emotions of the people during the healthcare crisis time, such as COVID-19. The proposed AISA-HCM technique involves distinct operations such as pre-processing, feature extraction, and classification. Besides, brain storm optimization (BSO) with deep belief network (DBN), called BSO-DBN model is employed for feature extraction. Moreover, beetle antenna search with extreme learning machine (BAS-ELM) method was utilized for classifying the sentiments as to various classes. The use of BSO and BAS algorithms helps to effectively modify the parameters involved in the DBN and ELM models respectively. The performance validation of the AISA-HCM technique takes place using Twitter data and the outcomes are examined with respect to various measures. The experimental outcomes highlighted the enhanced performance of the AISA-HCM technique over the recent state of art SA approaches with the maximum precision of 0.89, recall of 0.88, F-measure of 0.89, and accuracy of 0.94.     

Novel Computer-Aided Diagnosis System for the Early Detection of Alzheimer’s Disease

Aging is a natural process that leads to debility, disease, and dependency. Alzheimer’s disease (AD) causes degeneration of the brain cells leading to cognitive decline and memory loss, as well as dependence on others to fulfill basic daily needs. AD is the major cause of dementia. Computer-aided diagnosis (CADx) tools aid medical practitioners in accurately identifying diseases such as AD in patients. This study aimed to develop a CADx tool for the early detection of AD using the Intelligent Water Drop (IWD) algorithm and the Random Forest (RF) classifier. The IWD algorithm an efficient feature selection method, was used to identify the most deterministic features of AD in the dataset. RF is an ensemble method that leverages multiple weak learners to classify a patient’s disease as either demented (DN) or cognitively normal (CN). The proposed tool also classifies patients as mild cognitive impairment (MCI) or CN. The dataset on which the performance of the proposed CADx was evaluated was sourced from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). The RF ensemble method achieves 100% accuracy in identifying DN patients from CN patients. The classification accuracy for classifying patients as MCI or CN is 92%. This study emphasizes the significance of pre-processing prior to classification to improve the classification results of the proposed CADx tool.