漏损管道无损检测的仿真分析

地下供水管道漏损的准确定位问题一直是困扰供水系统和市政建设的难题,它不仅造成水资源的损失,而且对基础设施的破坏较大。目前,提出的探测管道漏损的方法有流量差法、压力差法、化学法和应力波法等,用这些方法来准确定位漏损位置时误差较大,然而声学方法却能有效检测漏损并确定漏损位置。该文利用仿真方法,从声学的角度对此进行了有益的尝试和合理的分析,并利用虚拟仪器技术软件Labview对漏损点的声信号进行仿真分析,对准确定位管道的泄漏位置有很大的指导意义。     

基于多声学传感器融合的管道泄漏检测方法研究

针对单一类型传感器检测管道泄漏的局限性,本文提出了一种基于多声学传感器融合的管道泄漏检测方法.它将振动传感器和声音传感器相结合以获取泄漏点声波信号,通过对这两种声波信号提取某种特征量进行相应的融合计算推断出管道泄漏是否发生.实验结果表明,该种方法能有效地提高管道泄漏检测的准确性.     

基于声压传感器的供热管道泄漏检测

为实现对供热管道泄漏的检测,研究了基于声压传感器的供热管道泄漏检测方法。首先利用PCB公司的声压传感器和NI cDAQ搭建了实验平台,并在现场进行了泄漏检测实验;研究了长输大口径供热管道泄漏声压脉动信号特征,并对db小波、sym小波、haar小波处理含噪声管道声压信号的去噪能力进行比较。虽然在处理实验室小规模管道模型泄漏信号时db小波和sym小波取得了较好的效果,但它们无法有效滤除大口径长输供热管道泄漏信号中的噪声信号。通过对比发现,haar小波去噪方式能较好地应用于长输大口径供热管道的信号滤波中。因此,基于音波法的泄漏检测方法适用于长输大口径供热管道的泄漏检测。提出的方法对于长输大口径供热管道泄漏检测有较好的效果,在工业生产中将取得一定的经济效益。     

Model falsification diagnosis and sensor placement for leak detection in pressurized pipe networks

Pressurized pipe networks used for fresh-water distribution can take advantage of recent advances in sensing technologies and data-interpretation to evaluate their performance. In this paper, a leak-detection and a sensor placement methodology are proposed based on leak-scenario falsification. The approach includes modeling and measurement uncertainties during the leak detection process. The performance of the methodology proposed is tested on a full-scale water distribution network using simulated data. Findings indicate that when monitoring the flow velocity for 14 pipes over the entire network (295 pipes) leaks are circumscribed within a few potential locations. The case-study shows that a good detectability is expected for leaks of 50 L/min or more. A study of measurement configurations shows that smaller leak levels could also be detected if additional pipes are instrumented.     

基于声发射传感器与ChiMerge粗糙集的埋地水管泄漏检测

针对水管泄漏检测,为了从不完整的信息中得出满意的诊断结果,提高泄漏识别率,提出了一种基于声发射传感器与ChiMerge粗糙集的埋地水管泄漏识别方法.这种方法首先用声学传感器采集数据,然后用EMD(经验模态分解方法)和能量特征提取相结合的方法建立决策表,通过ChiMerge算法将决策表离散化,最后通过粗糙集的约简来识别水管泄漏.实验表明,该方法是有效的.     

压电式加速度计在供水管道泄漏定位中的应用

供水管道发生泄漏时,泄漏噪声在管道内的水中传播,由于流固耦合作用引起管壁的径向振动。选择合适的压电式加速度计,并正确地安装在管道上,可以检测到微弱的管壁振动。将2个压电式加速度计放在漏点两侧的管道暴露点上,用相关分析的方法得到泄漏噪声到达2个传感器的时间差,根据时间延迟、泄漏噪声传播速度和2个传感器距离之间的关系估计泄漏的位置,相对误差在5%以内,与人工听漏相比,提高了定位的效率。     

Wireless Acoustic Sensor Networks and Edge Computing for Rapid Acoustic Monitoring

Passive acoustic monitoring is emerging as a promising solution to the urgent, global need for new biodiversity assessment methods. The ecological relevance of the soundscape is increasingly recognised, and the affordability of robust hardware for remote audio recording is stimulating international interest in the potential for acoustic methods for biodiversity monitoring. The scale of the data involved requires automated methods, however, the development of acoustic sensor networks capable of sampling the soundscape across time and space and relaying the data to an accessible storage location remains a significant technical challenge, with power management at its core. Recording and transmitting large quantities of audio data is power intensive, hampering long-term deployment in remote, off-grid locations of key ecological interest. Rather than transmitting heavy audio data, in this paper, we propose a low-cost and energy efficient wireless acoustic sensor network integrated with edge computing structure for remote acoustic monitoring and in situ analysis. Recording and computation of acoustic indices are carried out directly on edge devices built from low noise primo condenser microphones and Teensy microcontrollers, using internal FFT hardware support. Resultant indices are transmitted over a ZigBee-based wireless mesh network to a destination server. Benchmark tests of audio quality, indices computation and power consumption demonstrate acoustic equivalence and significant power savings over current solutions.      

Optimal pressure sensor placement and assessment for leak location using a relaxed isolation index: Application to the Barcelona water network

Water distribution networks are large complex systems affected by leaks, which often entail high costs and may severely jeopardise the overall water distribution performance. Successful leak location is paramount in order to minimize the impact of these leaks when occurring. Sensor placement is a key issue in the leak location process, since the overall performance and success of this process highly depends on the choice of the sensors gathering data from the network. Common problems when isolating leaks in large scale highly gridded real water distribution networks include leak mislabelling and the obtention of large number of possible leak locations. This is due to similarity of leak effect in the measurements, which may be caused by topological issues and led to incomplete coverage of the whole network. The sensor placement strategy may minimize these undesired effects by setting the sensor placement optimisation problem with the appropriate assumptions (e.g. geographically cluster alike leak behaviors) and by taking into account real aspects of the practical application, such as the acceptable leak location distance. In this paper, a sensor placement methodology considering these aspects and a general sensor distribution assessment method for leak diagnosis in water distribution systems is presented and exemplified with a small illustrative case study. Finally, the proposed method is applied to two real District Metered Areas (DMAs) located within the Barcelona water distribution network.     

油气井出砂监测系统超声信号采集及调理

针对现有的油气井出砂监测系统数据采集的噪声过多、效率较低等问题,设计了油气井出砂监测系统超声信号调理集采集方法。首先,砂粒撞击管壁的超声信号通过传感器采集;然后,采集后的信号经过驱动放大后先通过一个带通滤波器,确保进入ADC的信号频率为砂粒撞击主要存在的频率段;最后,信号进入ADC后再进行数字滤波,进一步滤除处于砂粒撞击频段的噪声。实验结果表明,经过二级滤噪后,系统采集信号的信噪比提高,且系统的工作效率显著提高。     

Leakage diagnosis with a contamination mitigation control framework using a graph theory based model

This work proposes an operational management approach for water distribution networks (WDNs) that can detect and localize leakages while also mitigating contamination resulting from these leaks. The primary emphasis of this work is the development of a contamination mitigation control scheme. A leak typically leads to a drop in network pressure that increases the risk of contamination. A leakage localization algorithm is responsible for detecting and localizing the leakage in the WDN. When a leak is detected in the network the contamination mitigation control is activated. The flow and pressure settings of the pumps are regulated by the contamination mitigation control in an optimal manner to minimize the risk of contamination. The entire framework is tested on the Smart Water Infrastructure Laboratory situated at Aalborg University, Denmark and a large-scale benchmark water network, which is part of a city network, L-town.     

On-line robust identification of tool-wear via multi-sensor neural-network fusion

Real-time identification and monitoring of tool-wear in shop-floor environments is essential for the optimization of machining processes and the implementation of automated manufacturing systems. This paper analyzes the signals from an acoustic emission sensor and a power sensor during machining processes, and extracts a set of feature parameters that characterize the tool-wear conditions. In order to realize real-time and robust tool-wear monitoring for different cutting conditions, a sensor-integration strategy that combines the information obtained from multiple sensors (acoustic emission sensor and power sensor) with machining parameters is proposed. A neural network based on an improved backpropagation algorithm is developed, and a prototype scheme for the real-time identification of tool-wear is implemented. Experiments under different conditions have proved that a higher rate of tool-wear identification can be achieved by using the sensor integration model with a neural network. The results also indicate that neural networks provide a very effective method of implementing sensor integration for the on-line monitoring of tool abnormalities.     

基于有界模型检测的C/C 程序内存泄漏检测

C/C 语言中的动态内存管理机制自由且灵活,但动态内存的使用容易引入内存泄漏,导致系统性能降低甚至系统崩溃。为了更加有效的检测内存泄漏,提出了一个基于有界模型检测技术的C/C 程序内存泄漏检测方案MLD-CBMC。该方案以C/C 程序文件为输入,利用有界模型检测技术,对程序进行展开处理,加入内存泄漏性质,并利用可满足性模理论(SMT)对程序约束和性质组成的验证条件编码,使用SMT求解器对验证条件求解,将检测内存泄漏问题转换为求解可满足性问题,实现C/C 程序内存泄漏的检测。通过实验验证了方案的有效性,并与其他有界模型检测工具进行对比实验,实验证明方案对内存泄漏的检测能力更强。     

50 years of acoustic signal processing for detection: coping with the digital revolution

We describe a rather personal history of the transition from analog to digital signal processing for acoustic signals. This history provides an example of the impact of the huge advances in digital technology on those doing research and development in acoustic signal processing for detection that must parallel the impact in other scientific fields. Collaborating with BBN scientists from other disciplines, the authors have been challenged to find the best technical solutions to a given problem. Examples are monitoring airport noise and detecting sound from submarines in the oceans.     

无线4G管网监测系统在新疆某工程中的应用

为了解决长距离供水管道运行状况及企业用水量巡查困难、实现对长距离供水管道运行状况及企业用水量实时监测,设计了以微功耗遥测终端、流量计、压力传感器作为现地控制单元,以操作员站、服务器、工程师站等作为信息管理及中央控制层的无线4G管网监测系统。该监测系统通过4G网络技术连接现地控制单元和中央控制层,实现了对管网的压力、流量、企业用水量的实时监测。介绍了系统组成、系统功能、通信网络、电源。实际应用表明,该系统在实现管网监测过程中的可靠性、准确性。该系统充分发挥了微功耗终端、4G网络的优势,使供水管网监测更经济、可靠、简单易行,为解决长距离、监测点多且分散的管网监测提供了参考。     

Robust acoustic bird recognition for habitat monitoring with wireless sensor networks

The key solution to study birds in their natural habitat is the continuous survey using wireless sensors networks (WSN). The final objective of this study is to conceive a system for monitoring threatened bird species using audio sensor nodes. The principal feature for their recognition is their sound. The main limitations encountered with this process are environmental noise and energy consumption in sensor nodes. Over the years, a variety of birdsong classification methods has been introduced, but very few have focused to find an adequate one for WSN. In this paper, a tonal region detector (TRD) using sigmoid function is proposed. This approach for noise power estimation offers flexibility, since the slope and the mean of the sigmoid function can be adapted autonomously for a better trade-off between noise overvaluation and undervaluation. Once the tonal regions in the noisy bird sound are detected, the features gammatone teager energy cepstral coefficients (GTECC) post-processed by quantile-based cepstral normalization were extracted from the above signals for classification using deep neural network classifier. Experimental results for the identification of 36 bird species from Tonga lake (northeast of Algeria) demonstrate that the proposed TRD–GTECC feature is highly effective and performs satisfactorily compared to popular front-ends considered in this study. Moreover, recognition performance, noise immunity and energy consumption are considerably improved after tonal region detection, indicating that it is a very suitable approach for the acoustic bird recognition in complex environments with wireless sensor nodes.     

EPA协议应用服务的安全性研究

通过对EPA应用服务进行安全性分析,针对EPA服务的访问控制、欺骗、拒绝服务3种漏洞,提出服务响应报文与EPA访问控制漏洞特征相匹配的漏洞检测方式以及拒绝服务攻击、欺骗网络模拟攻击的安全测试方法。在搭建的EPA网络测试环境中,通过设计的安全测试方法验证漏洞的存在,并提出EPA服务防御策略。     

A multi-agent architecture for diagnosing simultaneous faults along water canals

Water is intensively used in mankind activities, in particular in agriculture. Water is commonly conveyed for agriculture purposes through water canal networks which are large-scale spatially distributed systems crossing extensive regions. In the presence of leaks, unauthorized water withdrawals, water depth sensor faults or gate faults, the quality of service can be severely compromised. A system able to diagnose which type of fault is present at a given time is of vital importance to access the current state of the water canal and proceed to restore its nominal condition. This paper proposes a multi-agent architecture to simultaneously detect, isolate and estimate lateral outflows (e.g., leaks or water withdrawals) and hardware faults (e.g., a gate obstruction or a downstream water depth sensor fault) in water canal networks. First, the main canal network is broken down into several subsystems composed of a single canal pool with the corresponding gate. Then, an agent is assigned to each subsystem aiming at its fault diagnosis. The approach is based on the generation and evaluation of residuals obtained from the comparison of model-based output signals with real data. Application to an experimental water canal bears out the proposed architecture as a valuable tool for monitoring and supervising general water canals.     

Implementation of an energy-efficient scheduling scheme based on pipeline flux leak monitoring networks

Flow against pipeline leakage and the pipe network sudden burst pipe to pipeline leakage flow for the application objects,an energy-efficient real-time scheduling scheme is designed extensively used in pipeline leak monitoring. The proposed scheme can adaptively adjust the network rate in real-time and reduce the cell loss rate,so that it can efficiently avoid the traffic congestion. The recent evolution of wireless sensor networks has yielded a demand to improve energy-efficient scheduling algorithms and e...     

应用神经网络和AE信号对磨削烧伤的在线检测

磨削烧伤是磨削过程中常见缺陷之一,严重影响被加工零件质量和使用寿命,运用RBF神经网络和AE传感器实现了磨削过程中磨削烧伤的在线检测,通过分析磨削加工中AE信号的特性,计算240～400kHz内的信号有效值,峭度和歪度,处理后作为神经网络的输入向量,完成磨削烧伤的在线识别,通过比较在线识别结果和离线检测结果,证明了该在线检测系统具有较高的准确性.     

基于深度学习的复杂观测条件下工频磁异常探测

复杂观测条件下使用工频磁场探测人员、车辆、飞行目标等多类型目标造成的磁场扰动时,受到复杂环境下电磁噪声、供电设备及外来物体扰动等影响,工频磁场扰动信号具有噪声多、干扰强等特征,为有效削弱噪声及干扰对工频磁场扰动信号的影响,实现工频磁场扰动探测,该文利用实验数据对复杂观测条件下的磁场扰动信号进行特征分析,提出了一种基于深度学习的工频磁场异常探测方法,通过提取正常状态与有扰动状态的信号序列,将该信号输入神经网络训练,得到准确检测工频磁场异常信号的网络模型。实验结果表明,该方法的识别准确率在80%以上。