Optimal approximation of asymmetric type fractional-order bandpass Butterworth filter using decomposition technique

In this letter, the optimal rational approximation of fractional-order bandpass Butterworth filter (FOBBF) is presented. The transfer function of the FOBBF is decomposed into a multiplication of first-order and second-order terms. As a result, the design stability conditions can be easily satisfied using only the variable boundary constraints. The proposed technique generalizes the symmetric fractional-order roll-off characteristic as only a special case of the asymmetric one. Several examples are presented to validate the modeling efficacy.     

深部持续采动诱发断层活化滑移的灰色预测研究

深部持续采动诱发的断层活化滑移过程具有灰色系统的特征。基于某铜矿深部开采诱发断层活化滑移的现场实测数据,构建了GM(1,1)灰色预测模型。通过断层预测滑移量与实测滑移量对比分析,模型具有较高的精度。运用所构建的断层活化滑移灰色预测模型,对未来四期断层滑移量进行了预测,F2断层滑移量增速较快,与现场实际情况吻合。研究成果对于深部复杂采动影响下断层活化滑移量的预测具有重要的意义。     

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。     

脉冲电流对6016-T4铝合金板材力学性能的影响

为研究在电流辅助成形时脉冲电流的电流密度、占空比和脉冲频率3个因素对6016-T4铝合金板材力学性能的影响规律,设计了3因素5水平的正交实验。实验表明,对6016-T4铝合金力学性能影响的主次因素是:电流密度>占空比>脉冲频率。在此基础上,选用主次2个因素,即电流密度和脉冲频率,使用控制变量法进行进一步的实验研究。实验结果表明,电流密度对6016-T4铝合金力学性能影响较大,改变电流密度的同时带来了明显的焦耳热效应,该效应对材料有明显的软化作用,降低了材料的流动应力,但是,同时也降低了铝合金板的伸长率,这是因为过大的电流密度加剧了试样的主应变演化,进而促进了试样的断裂。而脉冲频率的变化对6016-T4铝合金板材的力学性能影响很小,这也印证了正交实验的结论。     

环氧丙烷产业现状及发展趋势

简述了全球及我国环氧丙烷产业的生产及供需现状;对环氧丙烷在聚醚多元醇、丙二醇/碳酸二甲酯、丙二醇醚及其酯类等领域的应用进行了分析;并对环氧丙烷生产技术进行了比较,提出了发展建议。     

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.