煤巷过断层破碎带施工工艺与支护技术实践

W1313回风顺槽在里程332 m处揭露正断层,对此采取了调整巷道顶板锚网索支护参数,采用圆木大板棚复合支护等措施,并加强了顶板、通风、掘锚机破岩、爆破作业和作业人员的安全管理,确保工作面过断层破碎带时的安全施工。     

考虑不确定性的大坝监测信息异常诊断方法及应用

大坝监测资料分析是一个充满不确定性的复杂非线性问题,常规的分析方法较少考虑监测信息中的不确定性问题,导致分析结果的合理性存疑。因此,基于信息熵理论的重要指标——近似熵研究大坝监测信息中的不确定性问题,利用实际监测数据选取近似熵算法的关键参数,进而评估大坝的运行状态。通过分析混凝土坝坝顶水平位移和土石坝测压管水位,发现近似熵算法能在若干监测信息不确定性条件下较有效地识别大坝监测数据的异常。     

基于人工神经网络的柴油机失火故障诊断

车载诊断系统在诊断失火故障时,采用基于曲轴段角加速度和阈值规则相结合的方法,该方法在内燃机高速轻载运行时诊断单缸完全失火工况存在一定的局限性。通过对比分析失火和正常工况下曲轴瞬时转速的幅频和相频特征,提取不同谐次的幅值和相位信息,结合人工神经网络作为故障模式识别工具,得到了一种改善方法。通过台架实验,对此改善方法进行了单缸完全失火、两缸完全失火和单缸一定程度失火的故障诊断测试。结果表明,在实验条件下该方法可以有效识别不同的失火模式,并可在单缸失火模式下实现失火程度判别。同时,该方法通过少量工况数据训练神经网络,即可实现一定转速范围内的失火诊断,可行性强,可用于发动机失火故障在线诊断。     

改进ILoG算子的故障检测方法

针对强背景噪声干扰下轮对轴承故障特征微弱、难以准确检测的问题,提出了一种自适应改进高斯拉普拉斯(improved Laplacian of Gaussian,简称ILoG)算子的微弱故障检测方法。ILoG算子滤波器具有优良的信号突变特征检测能力,将其用于轮对轴承故障信号的冲击特征检测,同时利用水循环算法(water cycle algorithm,简称WCA)的寻优特性,并行搜寻筛选最佳的ILoG算子影响参数,通过对参数优化后ILoG算子滤波后信号做进一步包络解调分析,提取出轮对轴承微弱的故障特征信息。对实际轮对轴承外圈和内圈故障信号分析的结果表明,该方法可以有效检测出轴承微弱故障特征频率,故障检测效果优于小波阈值和多尺度形态学差值滤波方法。     

Numerical modeling of the hydraulic blade pitch actuator in a spar‐type floating wind turbine considering fault conditions and their effects on global dynamic responses

This paper deals with numerical modeling of the hydraulic blade pitch actuator and its effect on the dynamic responses of a floating spar‐type wind turbine under valve fault conditions. A spar‐type floating wind turbine concept is modeled and simulated using an aero‐hydro‐servo‐elastic simulation tool (Simo‐Riflex [SR]). Because the blade pitch system has the highest failure rate, a numerical model of the hydraulic blade pitch actuator with/without valve faults is developed and linked to SR to study the effects of faults on global responses of the spar‐type floating wind turbine for different faults, fault magnitudes, and environmental conditions. The consequence of valve faults in the pitch actuator is that the blade cannot be pitched to the desired angle, so there may be a delay in the response due to excessive friction and the wrong voltage, or slit lock may cause runaway blade pitch. A short circuit may cause the blade to get stuck at a particular pitch angle. These faults contribute to rotor imbalance, which result in different effects on the turbine structure and the platform motions. The proposed method for combining global and hydraulic actuator models is demonstrated in case studies with stochastic wind and wave conditions and different types of valve faults.     

SFEP文本因果关系提取及其与SFN转化研究

为将系统故障演化过程(system fault evolution process，SFEP)的文本描述转化为空间故障网络(space fault network，SFN)结构，用于故障分析，本文提出SFEP文本因果关系提取方法，及其与SFN基本结构的转化方法。首先给出SFEP中事件的几种典型因果关系。随后提出因果关系与SFN基本结构的转化流程。本文方法围绕着关键字和因果关系组模式展开，通过模型的不断学习补充和丰富关键字和组模式。最终使方法具备将SFEP文本转化为SFN结构的能力。以飞机起落架故障发生过程文本为例进行了应用，实验结果表明该方法可用于SFEP文本中的因果关系分析，并得到了理想的SFN。完善的关键字和组模式有利于使用计算机智能处理SFEP的SFN。     

Scalable Distributed Sensor Fault Diagnosis for Smart Buildings

The enormous energy use of the building sector and the requirements for indoor living quality that aim to improve occupants’ productivity and health, prioritize Smart Buildings as an emerging technology. The Heating, Ventilation and Air-Conditioning (HVAC) system is considered one of the most critical and essential parts in buildings since it consumes the largest amount of energy and is responsible for humans comfort. Due to the intermittent operation of HVAC systems, faults are more likely to occur, possibly increasing eventually building’s energy consumption and/or downgrading indoor living quality. The complexity and large scale nature of HVAC systems complicate the diagnosis of faults in a centralized framework. This paper presents a distributed intelligent fault diagnosis algorithm for detecting and isolating multiple sensor faults in large-scale HVAC systems. Modeling the HVAC system as a network of interconnected subsystems allows the design of a set of distributed sensor fault diagnosis agents capable of isolating multiple sensor faults by applying a combinatorial decision logic and diagnostic reasoning. The performance of the proposed method is investigated with respect to robustness, fault detectability and scalability. Simulations are used to illustrate the effectiveness of the proposed method in the presence of multiple sensor faults applied to a 83-zone HVAC system and to evaluate the sensitivity of the method with respect to sensor noise variance.      

断层应力区巷道顶板支护优化设计

1208运巷在过F4断层时,巷道顶板出现破碎、下沉以及局部冒顶现象,不仅降低了巷道掘进效率,而且威胁施工安全。通过分析原巷道支护设计存在的主要问题,对原支护进行优化,提出了“注浆锚棚+纵向桁架锚索”等联合支护措施。通过实际应用效果观察发现,支护设计优化后,加强了顶板稳定性,防止了顶板冒落事故发生。     

基于混合时间序列卷积神经网络的轴承故障诊断

在传统滚动轴承故障诊断中,绝大多数方法采用了从振动信号提取特征的诊断模式,但是这种模式必然会使原始信号降维进而导致故障信息的丢失。卷积神经网络(CNN)通过权重共享和稀疏连接直接对原始信号进行操作,实现自适应特征提取,最大化保留故障信息。受CNN原理启发,开发出了一种基于工业振动信号特征的新型诊断框架,称之为混合时间序列CNN(HTS-CNN)。首先,利用估计总体比例的方法自适应确定模型训练样本数目;其次,通过对时间序列片段进行随机组合的方式,使模型能够提取非相邻信号特征;最后,利用Softmax激活函数在模型输出端执行多分类任务。通过对凯斯西储大学及CUT-2平台轴承数据进行分析,实验结果表明:该方法能够准确、有效的对滚动轴承故障进行分类。     

基于VMD瞬时能量与GA- RBF的滚动轴承故障诊断

针对滚动轴承早期故障的有效识别,提出了一种基于VMD瞬时能量与GA优化的RBF神经网络的滚动轴承故障诊断方法,可以有效对滚动故障做出诊断。首先,VMD将滚动轴承振动信号进行分解成合适数目的本证模态函数;其次,计算本证模态函数分量的瞬时能量并组成特征向量;最后,将特征向量输入到GA优化的RBF神经网络实现轴承故障识别。通过滚动轴承故障诊断实验对该方法进行验证。结果表明,该方法识别滚动轴承故障的准确率为96.43%,较默认参数的RBF神经网络和EEMD瞬时能量与GA-RBF神经网络有明显的提高,证明了所提方法的可行性。