Novel Approach for Fault Detection and Diagnosis of Sensors in Air Handling Units Using Wavelet Energy Entropy

A novel fault diagnosis method for sensors in air handling unit（AHU） using wavelet energy entropy was presented. Instead of directly comparing the numerous data under noise conditiom, the wavelet energy entropy residual was compared in the proposed method. Three.level wavelet analysis was used to decompose the measurement data under both fault-free and faulty operation conditions. The concept of Shannon entropy was referred to define wavelet energy entropy of the wavelet coefficients. The sensor faults were diagnosed by comparing the deviation of the wavelet energy entropy of the measured signal and the estimated one with the preset threshold. Testing results showed that the wavelet energy entropy was sensitive to diagnose the biased faults. The wavelet energy entropy residuals exceed the threshold significantly when faults occur. In addition, the severer the faults were, the larger the residuals would be. The results prove that the proposed method is feasible and effective for the fault detection and diagnosis of the sensors.

无线传感器网络中的近似Unit Delaunay功率控制算法

提出一种新几何结构AUDT,将其作为无线传感器网络的底层逻辑拓扑后,每个节点依据最远的逻辑邻居调整到最小发射功率;AUDT从理论上保证网络拓扑的双向连通、平面、逻辑邻居有界及延迟性能的上界等。仿真实验显示,AUDT与其他相似算法相比,在网络延迟相当的情况下,可以获得更小的发射功率和通信干扰,特别是其构造通信开销已经达到最小。

Experimental Study of Air Conditioning Unit of Evaporative Cooling Assisted Mechanical Refrigeration

The evaporative cooling,which assists the refrigeration machinery air-conditioning systems test-rig,has been designed.Its structure and working principle were described,and the performance test was conducted and analyzed.The test shows that making full use of the evaporative cooling "free cooling" in Spring and Autumn seasons can fully meet the requirements of air-conditioned comfort through the switch of the function in different seasons.Taking into account the evaporative cooling fan and pump energy consumption,compared with the traditional mechanical refrigeration system,more than 80 percent of energy can be saved,and the energy efficiency ratio of the Unit(EER)is as high as 7.63.Using the two stages of indirect evaporative cooling to pre-cool the new wind in summer,under the conditions of the same air supply temperature requirements,0.83 kg/s chilled water saved can be equivalent to the traditional mechanical refrigeration system,and when the new wind ratio up to 50 percent,more than 10 percent load was reduced in mechanical refrigeration system.The overall EER increased about 35 percent.

Soft Sensor for Inputs and Parameters Using Nonlinear Singular State Observer in Chemical Processes

Chemical processes are usually nonlinear singular systems.In this study,a soft sensor using nonlinear singular state observer is established for unknown inputs and uncertain model parameters in chemical processes,which are augmented as state variables.Based on the observability of the singular system,this paper presents a simplified observability criterion under certain conditions for unknown inputs and uncertain model parameters.When the observability is satisfied,the unknown inputs and the uncertain model parameters are estimated online by the soft sensor using augmented nonlinear singular state observer.The riser reactor of fluid catalytic cracking unit is used as an example for analysis and simulation.With the catalyst circulation rate as the only unknown input without model error,one temperature sensor at the riser reactor outlet will ensure the correct estimation for the catalyst circulation rate.However,when uncertain model parameters also exist,additional temperature sensors must be used to ensure correct estimation for unknown inputs and uncertain model parameters of chemical processes.

Cathodic Protection Remote Monitoring Based on Wireless Sensor Network

Cathodic Protection system is an efficient system used for protecting the underground metal objects from corrosion. In this paper the use of Cathodic Protection (CP) system and how they can be developed to simulate corrosion control solution was illustrated. The aim of developing a Cathodic Protection system is to provide control over oil pipelines and to reduce the incidence of corrosion. The proposed system integrates the technology of wireless sensor Network (WSN) in order to collect potential data and to realize remote data transmission. In this system each WSN receives the data from the environment and forwards it to a Remote Terminal Unit (RTU). Then each RTU forwards it to its base station (BS). In this work Labview 2010 program was used, due to its high potentials. In addition it contains a Tool Kit that supports the wireless sensor network. In this simulation used many cases study to test and monitoring data and get optimum results, least time delay and high speed to prevent corrosion.

Sensor Network Structure Recognition Based on P-law

A sensor graph network is a sensor network model organized according to graph network structure.Structural unit and signal propagation of core nodes are the basic characteristics of sensor graph networks.In sensor networks,network structure recognition is the basis for accurate identification and effective prediction and control of node states.Aiming at the problems of difficult global structure identification and poor interpretability in complex sensor graph networks,based on the characteristics of sensor networks,a method is proposed to firstly unitize the graph network structure and then expand the unit based on the signal transmission path of the core node.This method which builds on unit patulousness and core node signal propagation(called p-law)can rapidly and effectively achieve the global structure identification of a sensor graph network.Different from the traditional graph network structure recognition algorithms such as modularity maximization and spectral clustering,the proposed method reveals the natural evolution process and law of graph network subgroup generation.Experimental results confirm the effectiveness,accuracy and rationality of the proposed method and suggest that our method can be a new approach for graph network global structure recognition.

Hybrid Fault Diagnosis and Isolation for Component and Sensor of APU in a Distributed Control System

This paper addresses the gas path component and sensor fault diagnosis and isolation(FDI) for the auxiliary power unit(APU). A nonlinear dynamic model and a distributed state estimator are combined for the distributed control system. The distributed extended Kalman filter(DEKF)is served as a state estimator,which is utilized to estimate the gas path components’ flow capacity. The DEKF includes one main filter and five sub-filter groups related to five sensors of APU and each sub-filter yields local state flow capacity. The main filter collects and fuses the local state information,and then the state estimations are feedback to the sub-filters. The packet loss model is introduced in the DEKF algorithm in the APU distributed control architecture. FDI strategy with a performance index named weight sum of squared residuals(WSSR) is designed and used to identify the APU sensor fault by removing one sub-filter each time. The very sensor fault occurs as its performance index WSSR is different from the remaining sub-filter combinations. And the estimated value of the soft redundancy replaces the fault sensor measurement to isolate the fault measurement. It is worth noting that the proposed approach serves for not only the sensor failure but also the hybrid fault issue of APU gas path components and sensors. The simulation and comparison are systematically carried out by using the APU test data,and the superiority of the proposed methodology is verified.

基于单片机的超声波测距系统设计

基于超声波测距的渡越时间检测原理,实现对近距离障碍物无接触的实时测量,本文提出一种基于STC89C52RC单片机实现温度补偿的超声波测距的设计方法。该超声波测距系统工作稳定,同时具有低成本、高精度、微型化等优点,可应用在道路限高、液位测量、倒车雷达预警、机器人自动避障、工业场地等应用场合,具有一定实际应用价值。

改进TCN结合Bi-GRU的人体动作识别方法

针对传统人体动作识别方法特征提取不完善和泛化性能不足导致识别精度不高的问题,提出一种基于深度学习的动作识别模型。改进了传统时域卷积网络(TCN),逐层指数级缩减空洞率,优化了时域卷积的残差结构,实现在浅层网络中提取到长时间间隔数据之间的时域特征和规范网络输出。重构结构进一步结合双向门控循环单元网络(Bi-GRU),提取数据局部特征输入到全连接层整合特征并进行Softmax分类。实验表明,提出的模型在自建数据集和公开数据集UCI-HAR上保持较低参数量的同时,准确率分别达到99.61%和94.16%,具备可靠的识别性能。

配电网单相接地故障的电场检测装置研制

为了提升电场检测精度,研制配电网单相接地故障的电场检测装置。利用低通调理电路采集传感单元在电场中产生的感应电压,实时计算出线路附近的电场强度。使用COMSOL软件对输电线路单相接地故障情况进行有限元仿真,观察空间电场的变化情况。搭建工频均匀电场试验平台,对PCB传感单元进行电场强度测量校正,验证COMSOL电场强度仿真值。利用验证后的仿真值对平行半圆板电场测量传感单元进行校正,建立输电线路模型,对输电线路发生单相接地故障前后的电场强度进行测量。实验结果表明,当线路A相发生接地故障时,其周围电场强度迅速减小,而B、C两相周围电场强度增大,与仿真结果一致,电场检测精度较高。

基于COA-GRU的低成本气体传感器数据修正方法

针对低成本气体传感器在受到温度、湿度、压力、气体交叉干扰等影响时检测精度低的问题,提出了一种长鼻浣熊-门控循环单元神经网络(COA-GRU)的修正模型,用于提高传感器检测精度。首先,根据低成本传感器的非线性特性构建了GRU修正模型;其次,利用COA算法解决修正模型的多局部极值以及参数组合寻优问题;最后,利用低成本传感器组以及H200D气体检测装置的实测数据对该方法进行了仿真实验。结果表明,使用COA-GRU修正模型后,SO_(2)、CO、NO_(2)、CO_(2)传感器的平均绝对误差分别降低了72.0%、28.4%、29.6%、13.5%,能够有效提高低成本传感器的检测精度。

基于IMU与激光雷达融合的无人弹药补给车SLAM系统研究

针对单一传感器建图精度低、实时性不足的问题,将IMU融合到激光雷达SLAM算法中。首先,采用手眼标定方法对2种传感器坐标系外参进行标定,实现传感器在时间与空间上的对齐。然后,结合因子图优化模型,解决在建图过程中产生的漂移现象,并将IMU融合到激光雷达LeGO-LOAM算法中。最后,在室外场景下搭建了无人弹药补给车SLAM实验平台,分别进行了LeGO-LOAM算法融合IMU前后的建图和定位试验。结果表明,融合IMU后的SLAM算法建图和定位精度都明显提高,满足了在未知环境下无人弹药补给车建图和定位的性能要求。

基于IMSGP-WEDI的水电机组故障预警方法

水电机组故障预警指标对于机组早期故障预警时间影响较大,而当前预警指标多基于单传感器信息构建,且其特征信息单一,难以更全面地表征机组运行状态,针对此问题,提出了一种基于集成多传感器遗传规划(integrated multi-sensor genetic programming,IMSGP)与权重欧式距离指标(weighted euclidean distance index,WEDI)的水电机组故障预警方法。首先,将多传感器信号进行预处理,剔除干扰信息;然后从预处理后的信号中提取多元特征,构建原始预警特征集;接下来利用复合检测指数(composite detection index,CDI)进行特征选择,并利用IMSGP进行特征构造;最后结合主成分分析(principal component analysis,PCA)与欧式距离构建WEDI,判别机组异常状态。通过对水电机组实测数据的分析,证明了提出的方法可及时发现早期故障,实现故障预警。

An ultra-thin bolt tension sensor and online monitoring system:For application in hydropower plant unit

The condition of bolted connections significantly affects the structural safety.However,conventional bolt tension sensors fail to provide precise measurements due to their bulky size or inadequate stability.This study employs the piezoresistive effect of crystalline silicon material to fabricate an ultrathin sensor.The sensor exhibits a linear relationship between pressure and voltage,an exceptional stability at varying temperatures,and a superior resistance to corrosion,making it adaptable and user-friendly for applications of high-strength bolt tension monitoring.A monitoring system,incorporating the proposed sensor,has also been developed.This system provides real-time display of bolt tension and enables the assessment of sensor and structural conditions,including bolt loosening or component failure.The efficacy of the proposed sensor and monitoring system was validated through a project carried out at the Xiluodu Hydropower Plant.According to the results,the sensor and online monitoring system effectively gauged and proficiently conveyed and stored bolt tension data.In addition,correlations were created between bolt tensions and essential unit parameters,such as water head,active power,and pressures at vital points,facilitating anomaly detection and early warning.

高速动车组进出隧道前照灯自动开关装置研究

本文针对山区高速铁路昼间行驶隧道多,高速动车组进出隧道需要司机频繁开关前照灯而造成很大的负担和一定的劳动强度等问题,研制出高速动车组进出隧道前照灯自动开关装置,光敏电阻传感器根据隧道内外光照强度不同,单片机自动控制高速动车组前照灯开启或关闭,减轻动车组驾驶员的工作强度,达到高铁运输安全的目的。

Auto-Tuning Parameters of Fractional PID Controller Design for Air-Conditioning Fan Coil Unit

The traditional integer order PID controller manipulates the air-conditioning fan coil unit(FCU)that offers cooliug and heatins loads to each air-conditioning room in summer and winter,respectivelv.In order to maintain a steady indoor temperature in summer and winter,the control quality cannot meet the related requirements of air-conditioning automation,such as large overshoot,large steady state error.long regulating time,etc.In view of these factors,this paper develops a fractional order PID controller to deal with such problem associated with FCU.Then,by varving mutation factor and crossover rate of basic differential evolution algorithmadaptivelv,a modified differential evolution algorithm(MDEA)is designed to tune the satisfactory values of five parameters of indoor temperature fractional order PID controller.This fractional order PID coutrol system is configured and the corresponding mumerical simulation is conducted by means of MATLAB software.The results indicate that the proposed fractional order PID control svstem and MDEA are reliable and the related control performance indexes meet with the related requirements of comfortable air-conditioning design and control criteria.

基于NodeMCU的智能浇灌系统设计

当前,智能化技术不断发展,为了提升现代农田的智能化管理水平,设计以NodeMCU(节点微控制单元)作为中央控制器的智能浇灌系统,实现农田灌溉智能化。YL-69土壤湿度传感器用于检测土壤含水量,DHT11温湿度传感器用于检测环境温度和湿度,中央控制器负责数据的采集与处理,通过内置的ESP8266芯片连接Wi-Fi将数据无线传输至网页端进行实时监测与控制,并对比系统设置的阈值指标,反馈至微控制器,微控制器通过控制继电器打开水泵对土地浇灌,并且搭建数据库进行数据记录与分析。该系统结构简单,可靠性较高,可实现浇灌的无人化管理,具有重要的现实意义。

Enhancing Human Action Recognition with Adaptive Hybrid Deep Attentive Networks and Archerfish Optimization

In recent years,wearable devices-based Human Activity Recognition(HAR)models have received significant attention.Previously developed HAR models use hand-crafted features to recognize human activities,leading to the extraction of basic features.The images captured by wearable sensors contain advanced features,allowing them to be analyzed by deep learning algorithms to enhance the detection and recognition of human actions.Poor lighting and limited sensor capabilities can impact data quality,making the recognition of human actions a challenging task.The unimodal-based HAR approaches are not suitable in a real-time environment.Therefore,an updated HAR model is developed using multiple types of data and an advanced deep-learning approach.Firstly,the required signals and sensor data are accumulated from the standard databases.From these signals,the wave features are retrieved.Then the extracted wave features and sensor data are given as the input to recognize the human activity.An Adaptive Hybrid Deep Attentive Network(AHDAN)is developed by incorporating a“1D Convolutional Neural Network(1DCNN)”with a“Gated Recurrent Unit(GRU)”for the human activity recognition process.Additionally,the Enhanced Archerfish Hunting Optimizer(EAHO)is suggested to fine-tune the network parameters for enhancing the recognition process.An experimental evaluation is performed on various deep learning networks and heuristic algorithms to confirm the effectiveness of the proposed HAR model.The EAHO-based HAR model outperforms traditional deep learning networks with an accuracy of 95.36,95.25 for recall,95.48 for specificity,and 95.47 for precision,respectively.The result proved that the developed model is effective in recognizing human action by taking less time.Additionally,it reduces the computation complexity and overfitting issue through using an optimization approach.

基于多传感器信息融合和CNN-BIGRU-Attention模型的液压防水阀故障诊断方法

在建筑工程领域,尤其是在工程搅拌设备中,涉及到复杂液压工作介质,液压防水阀往往会出现不同程度的故障。此外,恶劣的工作环境和复杂的噪声背景使得对液压防水阀的故障进行诊断变得困难。为了解决这一难题,提出了一种基于多传感器信息融合和卷积神经网络-双向门控循环单元-自注意力机制(CNN-BIGRU-Attention)模型的防水阀故障诊断方法。首先,考虑到单一传感器振动信号难以充分表达故障特征,该方法使用了3个传感器采集含噪声的振动信号,并进行了必要的预处理;其次,提取了信号的16个时域特征、5个频域特征以及3个时频域特征,并利用熵权法进行了特征融合,达到了增强特征的目的;然后,将融合的多维特征集输入到CNN-BIGRU-Attention模型中进行了特征识别;最后,利用实际的液压防水阀故障诊断实验,验证了该方法的有效性。研究结果表明:采用多传感器提取的特征更为全面,信息融合有助于捕捉更完整的隐藏数据特征,从而显著提高诊断的准确率;相较于其他特征识别方法,采用CNN-BIGRU-Attention模型的液压防水阀故障诊断准确率可分别提高6.7%、4.6%和14.2%,达到了96.86%,证明了该方法的有效性。该方法将先进的机器学习技术与实际工程应用相结合,为建筑工程问题提供了一种新颖、有效的解决方案。

基于GRU和XGBoost算法的中小流域流量预测研究

中小流域的流量预测对于水资源管理和防洪工程等具有重要意义,因此针对传统小流域流量预测模型精度不高、实时性不强的问题,提出一种基于门控循环神经网络(Gate Recurrent Unit,GRU)和XGBoost的中小流域流量预测方法。在该项研究中,首先利用多传感器协同传输技术采集了流域内的水文数据,如降雨量和径流量,并依据GRU算法捕获数据在时序维度的关联关系。然后,将GRU模型提取的时序特征作为XGBoost模型的输入,利用XGBoost算法实现中小流域流量的预测。最后,在某真实小流域场景中采集流量数据,并根据预测数据与实际观测数据的对比和分析,评估所提出方法的有效性和准确性。实验结果显示,基于GRU和XGBoost算法的中小流域流量预测方法能够较为准确地预测流域的流量变化,为水资源管理和防洪工程提供可靠的决策依据。