机械设备故障规律及运行趋势预测方法综述

对于机械设备的故障运行问题,技术人员应当深入研究机械设备的故障规律,并研究出运行趋势的预测方法,从传感器的检测时间间隔与使用数量等方面加以深入的研究。本文介绍了机械设备运行状态的故障预测方法,并将机械设备运行状态的故障预测方法总结为三个步骤,分别是数据获取、处理与设备寿命预测,结合这些内容,提出了关于机械设备故障运行的一些方法,旨在为相关技术人员提供参考依据。     

Terrain-Specific: Contemporary Landscape Architecture in Canada

The Canadian landscape has typically captured a global imaginary of a pristine wild, but how might its urban designed landscapes be distinctly understood? Foregrounded by the landscape transformations accelerated by climate change, the book Innate Terrain: Canadian Landscape Architecture, edited by Professor Alissa North from the University of Toronto, highlights landscape architecture projects situated on the unique Canadian terrain. Providing further provocation on Canadian landscape architecture, Innate Terrain seeks to fill the literary gap on contemporary landscape perspectives, distinguishing Canadian landscape architecture from global practice, and particularly, its well-documented American counterpart. Landscape architecture in the Canadian context has evolved and established its own distinct identity, one imbued with national and local sensitivities. Informed by diverse environmental and cultural contexts, Canadian-designed landscapes reflect and refer to the prevailing ecosystems of Canada’s innate terrain. Contrary to the preceding International Style, landscape architecture projects in Canada have adopted the ethos of Critical Regionalism in the second half of the 20th century. Contemporary Canadian practitioners are designing landscapes that are deeply informed by their surrounding geographical context while emphasizing cultural specificity. Central to this cultural specificity, addressed by a new generation of landscape architects, is the increasing recognition of Indigenous Traditional Knowledge within the discipline. Canadian landscape architects have collaborated with First Nations, Inuit, and Métis communities, including the keepers of this knowledge, to develop land management strategies and design landscape interventions.     

Pattern design and optimization of yarn-dyed plaid fabric using isolation niche genetic algorithm and rough set theory

Under the circumstance of perceptual consumption, it is still challenging to grasp consumer's emotions and demands due to the large search space, diversified preferences, and easy fatigue of consumers. To reduce user fatigue and enlarge search space, a novel method was presented to design and optimize the pattern of yarn-dyed plaid fabric using the isolation niche genetic algorithm and rough set theory. Each pattern was encoded as a chromosome based on the real number code. The population was initialized and evolved using INGA to maintain the diversity. The rough set theory was adopted as the fitness function of isolation niche genetic algorithm to extract the consumer's demands. After multiple evolutions, a large set of practical patterns of the yarn-dyed plaid fabric are obtained. Experiments were carried out by 24 testers of different ages and genders. The results prove that the proposed method based on the isolation niche genetic algorithm and rough set theory is feasible and effective, supplying references to the designer.     

Data-driven diagnosis of the high-pressure hydrogen leakage in fuel cell vehicles based on relevance vector machine

The hydrogen pressure inside tanks and its adjacent pipes can reach up to 70 MPa in fuel cell vehicles. This is the weak links of hydrogen leakage. The diagnosis time of mainstream hydrogen leakage diagnosis method based on hydrogen concentration sensors (HCSs) is easily affected by the number and location of installed sensors. In this study, a data-driven diagnosis method is proposed for the high-pressure hydrogen leakage. Fisher discrimination analysis and linear least squares fitting are used for data preprocessing, relevance vector machine is used for pattern recognition. When the total volume of tanks is 82 L and the hydrogen leakage flow rate is larger than 2 g/s, the diagnosis accuracy of the proposed method is higher than 95% and the diagnosis time is constant. When the leakage location is far away from HCSs, the proposed method can the diagnose hydrogen leakage in a shorter time than mainstream method.     

A new signal processing-based islanding detection method using pyramidal algorithm with undecimated wavelet transform for distributed generators of hydrogen energy

Machine learning-based fault detection methods are frequently combined with wavelet transform (WT) to detect an unintentional islanding condition. In contrast to this condition, these methods have long detection and computation time. Thus, selecting a useful signal processing-based approach is required for reliable islanding detection, especially in real-time applications. This paper presents a new modified signal processing-based islanding detection method (IDM) for real-time applications of hydrogen energy-based distributed generators. In the study, a new IDM using a modified pyramidal algorithm approach with an undecimated wavelet transform (UWT) is presented. The proposed method is performed with different grid conditions with the presence of electric noise in real-time. Experimental results show that oscillations in the acquired signal can be reduced by the UWT, and noise sensitivity is lower than other WT-based methods. The non-detection zone is zero and the maximum detection and computational time is also 75 ms at a close power match.     

Study on the influence of segmented fuel cell by grooving method and its application in oxygen starvation diagnosis

The oxygen starvation in fuel cells is an important reason for the deterioration of durability. The segmented fuel cell is a method to study the gas distribution inside the fuel cell. In order to study the influence of the grooving method on segmented fuel cell and its application in oxygen starvation diagnosis, a five-serpentine-channel three-dimensional two-phase simulation model is established by FLUENT. Through steady-state simulation, the effect of grooving method on fuel cell performance is studied. The overall performance (polarization curve) of the fuel cell drops slightly, but the current density distribution on the anode graphite plate changes greatly due to the grooves. The “current concentration” phenomenon is proposed based on the current density distribution. Through dynamic simulation, the oxygen starvation under current load mode and voltage load mode is simulated, and the “starvation coefficient” is defined as an oxygen starvation diagnostic index. In the current load mode, the “starvation coefficient” never exceed 15%, because when the oxygen starvation is severe, the simulation cannot converge or even cannot maintain, which corresponds to the voltage reversal in reality. However, in the voltage load mode, the “starvation coefficient” can reach up to 100%. The conclusions have important guiding significance for the judgment of the internal reaction uniformity of the segmented fuel cell by grooving method and provide a theoretical basis for judging whether a fuel cell is out of oxygen by segmented fuel cell.     

Machine learning algorithm selection for windage alteration fault diagnosis of mine ventilation system

Machine learning algorithms have been widely used in mine fault diagnosis. The correct selection of the suitable algorithms is the key factor that affects the fault diagnosis. However, the impact of machine learning algorithms on the prediction performance of mine fault diagnosis models has not been fully evaluated. In this study, the windage alteration faults (WAFs) diagnosis models, which are based on K-nearest neighbor algorithm (KNN), multi-layer perceptron (MLP), support vector machine (SVM), and decision tree (DT), are constructed. Furthermore, the applicability of these four algorithms in the WAFs diagnosis is explored by a T-type ventilation network simulation experiment and the field empirical application research of Jinchuan No. 2 mine. The accuracy of the fault location diagnosis for the four models in both networks was 100%. In the simulation experiment, the mean absolute percentage error (MAPE) between the predicted values and the real values of the fault volume of the four models was 0.59%, 97.26%, 123.61%, and 8.78%, respectively. The MAPE for the field empirical application was 3.94%, 52.40%, 25.25%, and 7.15%, respectively. The results of the comprehensive evaluation of the fault location and fault volume diagnosis tests showed that the KNN model is the most suitable algorithm for the WAFs diagnosis, whereas the prediction performance of the DT model was the second-best. This study realizes the intelligent diagnosis of WAFs, and provides technical support for the realization of intelligent ventilation.     

基于机器视觉的轮胎表面缺陷检测系统的研究与应用

在传统的轮胎表面缺陷依靠人工检测,存在劳动强度高、受人的主观影响大以及效率低下的问题。针对这一现象,研究了一种基于机器视觉的轮胎表面缺陷3D检测系统。该系统依靠机器视觉系统获取检测轮胎的表面图像,然后创建3D模型、判定缺陷类型,最终实现实时自动预警,为轮胎生产商提供一种自动化检测方案。系统集成了先进的技术、软件和工具,配套的信息管控系统可以对轮胎型号和生产数据进行采集、存储、分析,以便在生产过程中实现更高效、更可靠的质量控制,具有较高的实际应用推广价值。     

液相色谱-串联质谱技术在临床检验中的应用研究进展

液相色谱-串联质谱（LC-MS/MS）技术具有高灵敏度、高特异性、高分辨率和高效率的优点。近年来随着仪器灵敏度的提高，LC-MS/MS在常规临床检验中显示出极大的潜力，并在疾病早期预防和诊断中发挥着不可替代的作用。本文对LC-MS/MS在新生儿疾病筛查、维生素D检测、内分泌激素检测、肽类和蛋白质定量分析等临床检验方面的研究进展进行综述，并讨论了未来面临的挑战。