基于深度学习的传感器优化布置方法

针对传感器优化布置(optimal sensor placement,简称OSP)问题,提出了一种新的使用深度神经网络的解决方案,并以简化的桥梁形状的桁架结构中的振动测试传感器优化为例进行了验证。首先,选择一种传统的传感器优化布置方法,对自动化生成的大量不同的桁架结构分别进行传感器优化布置计算,将所得优化布置结果在进行数据预处理后构建出深度学习方法所需要的训练集与验证集;其次,使用Python语言和深度学习框架TensorFlow设计实现与本研究问题适配的深度神经网络模型并训练;然后,随机生成了新的桁架结构参数;最后,将深度神经网络输出的传感器布置结果和传统方法的计算结果进行了比较,验证了本研究方法的有效性以及在速度上、可移植性与可扩展性方面的性能优势。     

A neural network based technique for vibration characterization using Gaussian laser beams

This paper presents a neural network technique combined with an optical measurement system for the characterization of mechanical vibrations in industrial machinery. In the proposed system, the Gaussian beam of a laser source illuminates on an array of photodetectors. If either the laser source or the photodetector array is coupled with a vibrating system, then the optical powers detected by the photodetectors will vary accordingly, and are expected to reflect the magnitude and frequency of the X–Y planar vibrations of the monitored system. The time-varying optical powers are input to an artificial neural network-based vibration monitoring system which maps the power distributions to the X–Y position of the laser beam center. An experimental setup of the system is built and used for training and testing purposes. The obtained experimental results demonstrate the adequacy of combining optical techniques with neural networks to estimate the vibration frequency and magnitude. Estimated frequencies were within 1% of the actual ones, and the estimated magnitudes were within 29% of the actual magnitudes when using a chirp signal in the training phase. The magnitude estimation percentage error was further reduced below 12% when the neural network was trained with a decaying chirp signal.     

中控机房的改造思路

由于我台倒班方式，造成了技术管理分散，不利于人才的培养和电台健康的发展。为进一步提高电台安全播出管理水平，推进电台的自动化建设，充分利用现代化手段，对中控机房进行了改造。新模式的成功实施，能极大地提高我台安全传输发射水平。     

基于云概率密度分布估计的大坝监测数据分析

大坝运行监测易受自然环境和监测条件影响，存在时间和空间上的变异性，监测数据具有不确定性。以云理论的随机性和不确定性分析方法为基础，并与空间数据辐射思想相结合，建立了云滴概率密度分布估计模型，然后导出云概率密度分布函数，依据样本监测数据推求母体空间数据的分布特征，并设计了基于逆向云算法云变换的计算程序。分析陆浑水库1979~1999年测压管监测数据和位移变形数据的云概率密度分布特征和云数字特征，得出了20 a来大坝的数据分布特征和运行状态。监测数据分析结果表明，云概率密度分布估计不仅能有效合理地分析大坝的运行状态，而且能够依据云数字特征来判断监测状态和监测环境的异常变化。      

Structural health monitoring system based on multi-agent coordination and fusion for large structure

In practical applications of structural health monitoring technology, a large number of distributed sensors are usually adopted to monitor the big dimension structures and different kinds of damage. The monitored structures are usually divided into different sub-structures and monitored by different sensor sets. Under this situation, how to manage the distributed sensor set and fuse different methods to obtain a fast and accurate evaluation result is an important problem to be addressed deeply. In the paper, a multi-agent fusion and coordination system is presented to deal with the damage identification for the strain distribution and joint failure in the large structure. Firstly, the monitoring system is adopted to distributedly monitor two kinds of damages, and it self-judges whether the static load happens in the monitored sub-region, and focuses on the static load on the sub-region boundary to obtain the sensor network information with blackboard model. Then, the improved contract net protocol is used to dynamically distribute the damage evaluation module for monitoring two kinds of damage uninterruptedly. Lastly, a reliable assessment for the whole structure is given by combing various heterogeneous classifiers strengths with voting-based fusion. The proposed multi-agent system is illustrated through a large aerospace aluminum plate structure experiment. The result shows that the method can significantly improve the monitoring performance for the large-scale structure.     

Investigating software testing and maintenance reports: Case study

ContextAlthough many papers have been published on software development and defect prediction techniques, problem reports in real projects quite often differ from those described in the literature. Hence, there is still a need for deeper exploration of case studies from industry.ObjectiveThe aim of this study is to present the impact of fine-grained problem reports on improving evaluation of testing and maintenance processes. It is targeted at projects involving several releases and complex schemes of problem handling. This is based on our experience gained while monitoring several commercial projects.MethodExtracting certain features from detailed problem reports, we derive various measures and present analysis models which characterize and visualize the effectiveness of testing and problem resolution processes. The considered reports describe types of problems (e.g. defects), their locations in project versions and software modules, ways of their resolution, etc. The performed analysis is related to eleven projects developed in the same company. This study is an exploratory research with some explanatory features. Moreover, having identified some drawbacks, we present extensions of problem reports and their analysis which have been verified in another industrial case study project.ResultsFine-grained (accurate) problem handling reports provide a wider scope of possible measures to assess the relevant development processes. This is helpful in controlling single projects (local perspective) as well as in managing these processes in the whole company (global perspective).ConclusionDetailed problem handling reports extend the space and quality of statistical analysis, they provide significant enhancement in evaluation and refinement of software development processes as well as in reliability prediction.     

基于机器人的石化监测系统的设计与实现

在石化行业中，管道、罐体、塔体等介质的监测一直是课题研究热点。基于机器人的石化监测系统采用ARM处理器控制智能移动机器人，深入到人员不便进入的介质中，全方位、多角度监测介质的内部情况，准确地监测重要指标的数据，并能及时对图像、指标等数据进行处理。监测人员可在远离现场的地方查看现场情况，监测灵活、便捷。现场测试准确性高，有很高的推广价值。     

基于DCS的换热站管网监控系统设计与实现

针对华能青岛热电新建小区供暖管网的工艺流程和实际需要，以Opto 22为开发平台，设计开发了基于DCS的换热站管网监控系统。该系统以新建小区换热站为实际监控对象，可以实现对换热站供暖管网各监控测点的数据采集和过程控制，实现对流量、温度和压力的数据显示，人机界面操作和对整个工艺流程的控制管理。系统采用经典的DCS设计方法，实现集中监控、分散控制。相比于其他PLC监控平台，系统具有目标用户针对性强，在保证系统安全可靠运行的情况下具有操作简单易学易用的特点,设计内容完全符合华能青岛热电新建小区的要求。     

Operational time-series data modeling via LSTM network integrating principal component analysis based on human experience

Today’s information technologies involve increasingly intelligent systems, which come at the cost of increasingly complex equipment. Modern monitoring systems collect multi-measuring-point and long-term data which make equipment health prediction a “big data” problem. It is difficult to extract information from such condition monitoring data to accurately estimate or predict health statuses. Deep learning is a powerful tool for big data processing that is widely utilized in image and speech recognition applications, and can also provide effective predictions in industrial processes. This paper proposes the Long Short-term Memory Integrating Principal Component Analysis based on Human Experience (HEPCA-LSTM), which uses operational time-series data for equipment health prognostics. Principal component analysis based on human experience is first conducted to extract condition parameters from the condition monitoring system. The long short-term memory (LSTM) framework is then constructed to predict the target status. Finally, a dynamic update of the prediction model with incoming data is performed at a certain interval to prevent any model misalignment caused by the drifting of relevant variables. The proposed model is validated on a practical case and found to outperform other prediction methods. It utilizes a powerful deep learning analysis method, the LSTM, to fully process big condition monitoring series data; it effectively extracts the features involved with human experience and takes dynamic updates into consideration.     

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.