Fault tolerant system based on non-integers order observers: Application in a heat exchanger

This work presents a fault-tolerant (FT) scheme based on the application of non-integer order observers also called fractional observers, the case of study is a double pipe countercurrent heat exchanger (HE). The aim of the FT is to detect sensors faults as soon as possible, and to provide a healthy signal in order to replace the faulty sensor signal by the fractional observer estimation. To develop the FT scheme a bank of high gain fractional order observers (HGFOO) is proposed. The Riemann-Liouville (RL) fractional derivative definition is used to solve each fractional observer. Experimental measures from a HE were used to test the performance of the fractional observers and the control scheme. The results show the robustness of the proposed observers.     

干湿联合冷却塔冷却节水分析软件开发及应用

为实现对干湿联合冷却塔冷却特性、节水特性的综合分析，编制干湿联合冷却塔校核计算流程，基于Visual Studio开发平台，本文开发了干湿联合冷却塔冷却节水特性分析优化软件。通过对某在运干湿联合冷却塔进行冷却节水特性计算，验证了所开发软件计算结果的准确性；在消雾节水计算结果基础上，对标干湿联合消雾冷却塔验收测试规程，生成其成雾频率曲线、耗水量曲线、塔雾指数、冷却特性曲线，综合分析其消雾特性、节水特性及冷却特性，从而确定了干湿联合冷却塔的最佳运行工况。同时运用所开发软件对比分析了某冷却塔设计干段散热面积对其消雾特性、节水特性及冷却特性的耦合影响，并分析了百叶窗开度对干湿联合冷却塔性能的影响，为干湿联合冷却塔的运行优化和设计优化提供了工具性软件支持。     

Chemical process fault diagnosis based on enchanted machine‐learning approach

In the chemical industry, fault diagnosis is a challenging task due to the complexity of chemical equipment. This paper proposes a machine learning‐based approach to achieve the goal of fault diagnosis. First, in order to reduce the impact of redundant features, support vector machine recursive feature elimination (SVMRFE) is used to select important features. The trained probabilistic neural network (PNN) is then used for fault diagnosis. Considering that the diagnostic performance is affected by its hidden layer element smoothing factor (σ), the modified bat algorithm (MBA) is used to optimize the PNN to obtain optimal global parameter values. The MBA adopts a better optimization mechanism than the basic algorithm and achieves excellent global convergence. It can globally optimize the smoothing factor, which effectively improves the fault diagnosis ability of the PNN. During the testing of the Tennessee Eastman (TE) process data set, we evaluate the performance of the proposed model by comparing the F₁‐score and accuracy of the different methods. The charts provided describe the fault diagnostic results and classification for the different models. The results indicate that the MBA has a better optimization ability than other traditional optimization algorithms. At the same time, the combination method proposed in this paper is also superior to others and can significantly improve the accuracy of TE process fault diagnosis.     

Visually deficient working conditions and reduced work performance in office workers: Is it mediated by visual discomfort?

IntroductionThe main purpose of this cross-sectional study was to investigate whether visual discomfort acts as a mediating factor between perceived visual ergonomic working conditions and self-rated visual performance among office workers who carry out administrative tasks and computer-based work at the Swedish Tax Agency.MethodsA questionnaire was sent to 94 office workers addressing: 1) perceived visual quality of the visual display units; 2) prevalence of eye symptoms; and 3) self-rated visual performance. Eighty-six persons (54 women (63%), 31 men (36%), and 1 of unspecified sex) answered the questionnaire. Multiple regression analysis investigated the association between visual ergonomic working conditions and visual performance, both with and without visual discomfort as a mediator.ResultsThe group mean of the Indexed survey questions indicated a reasonably good quality of visual ergonomic working conditions, a relative absence of eye symptoms, and acceptable self-rated visual performance. Results from multiple regression analysis showed a significant association between perceived visual ergonomic working conditions and self-rated visual performance (r² = 0.30, β = 0.327, p < 0.01). When visual discomfort was used as a mediator, the association between perceived visual ergonomic working conditions and self-rated visual performance remained the same (r² = 0.32, β = 0.315, p < 0.01).DiscussionIt was remarkable to discover that self-rated visual performance was independent of visual discomfort. Possible explanations include exposure factors not included in the current study, such as dry air and sensory irritation in the eyes, psychosocial stress, time spent performing near work activities, or time exposed to visually deficient working conditions.Relevance to industryThe strong connection between satisfaction with visual ergonomic working conditions and productivity in this study has implications for workplace profitability and staff satisfaction. If productivity is enhanced by better visual ergonomic working conditions, then managers of workplaces may be able to improve work outcomes by optimizing the physical work environment.     

An improved machine learning technique based on downsized KPCA for Alzheimer's disease classification

Alzheimer's disease (AD), a neurodegenerative disorder, is a very serious illness that cannot be cured, but the early diagnosis allows precautionary measures to be taken. The current used methods to detect Alzheimer's disease are based on tests of cognitive impairment, which does not provide an exact diagnosis before the patient passes a moderate stage of AD. In this article, a novel classifier of brain magnetic resonance images (MRI) based on the new downsized kernel principal component analysis (DKPCA) and multiclass support vector machine (SVM) is proposed. The suggested scheme classifies AD MRIs. First, a multiobjective optimization technique is used to determine the optimal parameter of the kernel function in order to ensure good classification results and to minimize the number of retained principle components simultaneously. The optimal parameter is used to build the optimized DKPCA model. Second, DKPCA is applied to normalized features. Downsized features are then fed to the classifier to output the prediction. To validate the effectiveness of the proposed method, DKPCA was tested using synthetic data to demonstrate its efficiency on dimensionality reduction, then the DKPCA based technique was tested on the OASIS MRI database and the results were satisfactory compared to conventional approaches.     

思维导图在《微机原理及接口技术》教学中的应用

思维导图作为一种可视化图形思维工具，可以有效表征知识体系、层次关系。本文针对《微机原理及接口技术》课程教学现状，介绍了思维导图的特点，分析了思维导图应用于该课程教学中的作用和意义，给出了用 MindManager软件绘制的8086CPU内部结构和输入输出接口的思维导图案例。教学实践证明，思维导图在《微机原理及接口技术》课程教学中可以激发学生学习兴趣，帮助学生建立“整体-局部”思想，促进学生对知识的理解和记忆，提升教学效果。     

Multi-objective iterative optimization algorithm based optimal wavelet filter selection for multi-fault diagnosis of rolling element bearings

Rolling element bearings (REBs) play an essential role in modern machinery and their condition monitoring is significant in predictive maintenance. Due to the harsh operating conditions, multi-fault may co-exist in one bearing and vibration signal always exhibits low signal-to-noise ratio (SNR), which causes difficulties in detecting fault. In the previous studies, maximum correlated kurtosis deconvolution (MCKD) has been validated as an efficient method to extract fault feature in the fault signals. Nonetheless, there are still some challenges when MCKD is applied to fault detection owing to the rigorous requirements of multiple input parameters. To overcome limitation, a multi-objective iterative optimization algorithm (MOIOA) for multi-fault diagnosis is proposed. In this method, correlated kurtosis (CK) is taken as a criterion to select optimal Morlet wavelet filter using the whale optimization algorithm (WOA). Meanwhile, to further eliminate the effect of the inaccurate period on CK, the update process of period is incorporated. After that, the simulated and experimental signals are utilized to testify the validity and superiority of the MOIOA for multiple faults detection by the comparison with MCKD. The results indicate that MOIOA is efficient to extract weak fault features even with heavy noise and harmonic interferences.     

Computer Network Assisted Test of Spoken English

With the development of computer network technology, the means of foreign language teaching have changed. Computer aided spoken English test is a new test method, and there is a great advantage compared with the traditional oral test. In order to further understand the superiority of the computer aided spoken English test, this study took the sophomores of Foreign Language Department in Henan University of Chinese Medicine, China, as the subjects and carried out the traditional interview-type spoken English test and computer-assisted spoken English test. The score system based on Hidden Markov Model (HMM) was used, and then the two tests were carried out. The performance in the two tests was compared, and the attitudes of the participants to the computer assisted Spoken English test were analyzed in the form of questionnaires. The results showed that the computer aided spoken English test could better reflect the true level of the students, and the teachers and students clearly stated that the computer aided spoken English test could relieve tension and reduce the burden of teachers. The research verified the feasibility of the computer-assisted spoken English test, which provides a reference for the promotion of the computer-assisted spoken English test.     

Reliability-based fault analysis models with industrial applications: A systematic literature review

Effective and early fault detection and diagnosis techniques have tremendously enhanced over the years to ensure continuous operations of contemporary complex systems, control cost, and enhance safety in assets-intensive industries, including oil and gas, process, and power generation. The objective of this work is to understand the development of different fault detection and diagnosis methods, their applications, and benefits to the industry. This paper presents a contemporary state-of-the-art systematic literature survey focusing on a comprehensive review of the models for fault detection and their industrial applications. This study uses advanced tools from bibliometric analysis to systematically analyze over 500 peer-reviewed articles on focus areas published since 2010. We first present an exploratory analysis and identify the influential contributions to the field, authors, and countries, among other key indicators.  A network analysis is presented to unveil and visualize the clusters of the distinguishable areas using a co-citation network analysis. Later, a detailed content analysis of the top-100 most-cited papers is carried out to understand the progression of fault detection and artificial intelligence–based algorithms in different industrial applications. The findings of this paper allow us to comprehend the development of reliability-based fault analysis techniques over time, and the use of smart algorithms and their success. This work helps to make a unique contribution toward revealing the future avenues and setting up a prospective research road map for asset-intensive industry, researchers, and policymakers.