Classification and regression models of audio and vibration signals for machine state monitoring in precision machining systems

We present a data-driven method for monitoring machine status in manufacturing processes. Audio and vibration data from precision machining are used for inference in two operating scenarios: (a) variable machine health states (anomaly detection); and (b) settings of machine operation (state estimation). Audio and vibration signals are first processed through Fast Fourier Transform and Principal Component Analysis to extract transformed and informative features. These features are then used in the training of classification and regression models for machine state monitoring. Specifically, three classifiers (K-nearest neighbors, convolutional neural networks and support vector machines) and two regressors (support vector regression and neural network regression) were explored, in terms of their accuracy in machine state prediction. It is shown that the audio and vibration signals are sufficiently rich in information about the machine that 100% state classification accuracy could be accomplished. Data fusion was also explored, showing overall superior accuracy of data-driven regression models.     

Identification of digital technologies and digitalisation trends in the mining industry

Digitalisation in mining refers to the use of computerised or digital devices or systems and digitised data that are to reduce costs, improve business productivity, and transform mining practices. However, it remains increasingly difficult for mining companies to decide which digital technologies are most relevant to their needs and individual mines. This paper provides an overview of digital technologies currently relevant to mining companies as presented and discussed by mining journals, the media and insight reports of leading consultancy agencies. Relevant technologies were systematically identified using text-mining techniques, and network analyses established the relations between significant technologies. Results demonstrated that currently 107 different digital technologies are pursued in the mining sector. Also, an analysis of the actual implementation of digital technologies in 158 active surface and underground mines reveals a limited uptake of digital technologies in general and that the uptake increases with the run-of-mine production. Large-scale mining operations appear to select and apply digital technologies suitable to their needs, whereas operations with lower production rates do not implement the currently available digital technologies to the same extent. These minor producers may require other digital transformation solutions tailored to their capabilities and needs and applicable to their scale of operations.     

An intelligent web-page classifier with fair feature-subset selection

The explosion of on-line information has given rise to many manually constructed topic hierarchies (such as Yahoo!!). But with the current growth rate in the amount of information, manual classification in topic hierarchies results in an immense information bottleneck. Therefore, developing an automatic classifier is an urgent need. However, classifiers suffer from enormous dimensionality, since the dimensionality is determined by the number of distinct keywords in a document corpus. More seriously, most classifiers are either working slowly or they are constructed subjectively without any learning ability. In this paper, we address these problems with a fair feature-subset selection (FFSS) algorithm and an adaptive fuzzy learning network (AFLN) for classification. The FFSS algorithm is used to reduce the enormous dimensionality. It not only gives fair treatment to each category but also has ability to identify useful features, including both positive and negative features. On the other hand, the AFLN provides extremely fast learning ability to model the uncertain behavior for classification so as to correct the fuzzy matrix automatically. Experimental results show that both FFSS algorithm and the AFLN lead to a significant improvement in document classification, compared to alternative approaches.     

用C51系列单片机设计物体分级设备的测量光幕

首先介绍了光幕测量高度的原理 ,给出了高度测量光幕的一种实现方法 ,分析了由该方法设计的系统结构和主要性能。从而彻底解决了相邻通路间的干扰 ,提高了测量精度。     

机械CAD课程教学改革探讨

文章分析了机械CAD技术的特点以及国内CAD教学的现状,提出了机械CAD教学改革的方法,并从教学体系、教学方法、考试方式等方面进行了探讨.     

活塞综合检测仪的研制

介绍了活塞综合检测仪的功能、组成、检测过程、测量原理与方法和主要技术性能指标。该仪器是国内研制的第一台智能型活塞综合检测仪。实际应用表明,这是一台高精度、高稳定性和高效率的检测仪器。     

单片机控制泄漏电流测试仪

以8031单片机为核心的泄漏电流测试仪的硬件结构和软件设计。     

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当前我国管道天然气计量系以体积表示，就其计量器具而言，差压式流量计、容积式流量计和速变式流量计都有，但大流量计量大多仍采用标准孔板差压式流量计，其流量计量标准招待我国SY／T6143－1996《天然气流量的标准孔板计量方法》。文章介绍目前常用的三种流量计的优缺点比较；天然气标准孔板差压式流量计算机自动计量系统的检定，其中包括：标准孔板节流装置，静压、差压变送器、温度传感器、数据采集系统等组件的检定；以及计量器具的正确使用和维护。最后，文章指出目前流量计量的发展主要是二次仪表的发展，籍助应用计算机大大提高流量法的测量范围和准确度，但对用户来说，应选择性／价比合适的仪表，并要考虑所选用仪表要便于检定、维护，同时，计量检定部门则应跟上发展，为广大用户提供技术支持。     

数控加工的基本坐标系和对刀问题

数控加工中的对刀问题影响机械加工的各个方面。这里从介绍数控机床的基本坐标系出发，重点阐述了对刀的基本原理和方法以及常见数控机床对刀问题的处理。     

热物理性质测试技术研究现状和发展趋势

本文在对热物理性质研究在热能工程、材料科学、信息科学、航天工程、环境工程、生物科学、微电子技术和计量学等众多科技领域中的重要性进行探讨的基础上，评述了热物理性质测试技术的研究现状和发展趋势。鉴于薄膜材料在微电子器件、集成电路和微电子机械系统等领域中日益广泛的应用，本文还综述了亚微米－纳米尺度薄膜材料热导率和热扩散率的测试新技术。