基于Lyapunov指数分析的液压泵故障诊断方法研究

提出了一种基于最大Lyapunov指数分析的微弱信号检测新方法,依据该方法对柱塞泵的脱靴故障进行了实验诊断.实验结果表明,故障压力信号的最大Lyapunov指数比无故障时的正常信号的小,可以作为故障发生与否的定量判据,为旋转机械故障诊断提供了一条新途径.     

水-乙二醇液压站故障分析与改造

根据水-乙二醇液压介质的应用特性,分析了某炼钢厂铸机液压站的故障,并对其液压系统进行了有效改造,此改造方法对铸机液压站的设计和维护有重要意义.     

Transient dynamical analysis of strain signals in sheet metal stamping processes

On-line monitoring of stamping processes can be carried out based on various sensors, such as force, strain, acceleration, proximity, and acoustic emission sensors. The strain sensor signal is the most favourite because of its effectiveness and acquisition cost as well as it contains rich information about the stamping process. The key problem of stamping monitoring is how to extract features from the strain signal to effectively detect the faults. The strain signal, however, is a transient signal that depends on many factors. In this paper, it is intended to address some new methods to analyse the transient strain signal with the objective of decomposing it in order to understand the dynamics of the stamping process and extract a malfunction signal for fault detection. A latent process model method, which is a combination of a time-varying auto-regression model and a dynamic linear model, is initially presented. Continuous wavelet transforms and a new discrete wavelet transform (maximum overlap discrete wavelet transform) are then addressed to project the transient signal into a time scale plan to represent the dynamical behaviour in a different way. Empirical mode decomposition is finally employed to decompose the transient signal into a finite and often small number of intrinsic mode functions (IMF). The advantage of this new method is that it is adaptive and highly efficient. The performance of the methods employed in this paper is reviewed using two real strain signals in a sheet metal stamping process. It is found that these methods can efficiently provide the energy–frequency–time distribution of the transient strain signal.     

The research of PNS based on micro inertial sensors

In order to reduce the dependence of pedestrian navigation on satellite navigation system and wireless communication, a new method is proposed to construct pedestrian navigation system (PNS) based on micro inertial technology. In this paper, the distributed system structure is arranged on human feet and trunk, and the key technologies, namely the system initial alignment, the error correction, the precise gait-phase detection, the effect and the inhibition of environmental magnetic field, have been studied. Besides, theories and applications of the key technologies are also discussed, and the performance of the PNS has been analyzed in the environment of electromagnetic interference. Experimental results show that the positioning error of the route with electromagnetic interference accounts for 2% of the travel distance. The key technologies proposed in the study can effectively improve the positioning accuracy of PNS, and independently achieve longer time personal navigation in the attenuate or even invalid global navigation satellite system (GNSS) and wireless communication signal environment.     

关联维数在往复压缩机气阀故障诊断中的应用

针对传统方法难以有效进行往复压缩机气阀故障诊断的现状,提出了应用关联维数对往复压缩机气阀故障进行诊断的方法。通过对气阀常见故障类型的诊断,表明关联维数可以有效地分析出往复压缩机气阀的常见故障。     

大型乙烯“三机”情况调研及分析

介绍了我国乙烯装置的现状,根据调研我国大型乙烯“三机”的情况,重点分析了乙烯“三机”存在的问题、原因及处理措施。     

An Extension Sample Classification‐Based Extreme Learning Machine Ensemble Method for Process Fault Diagnosis

In order to achieve higher accuracy and faster response in complex process fault diagnosis, an extension sample classification‐based extreme learning machine ensemble (ESC‐ELME) method is proposed. In the realization process, the extension sample classification is used to divide the fault types. For each fault type, a specific extreme learning machine (ELM) is established and trained independently. Then, all specific ELMs are integrated to determine which fault is happened by the majority voting method. The proposed ESC‐ELME method is compared with the traditional ELM and a duty‐oriented hierarchical artificial neural network in fault diagnosis of the Tennessee Eastman process. The results demonstrate that the proposed method provides higher diagnosis accuracy and faster response.     

歧口凹陷歧北低斜坡B90井区沙一下油气成藏条件研究

歧口凹陷歧北低斜坡B90井区,油气藏发育广泛,沙一下是该区勘探开发的重点。对研究区,沙一下油藏基本特征进行研究,分析其成藏条件,从而指导油气勘探。研究区油藏类型可分为构造-岩性油藏及岩性油气藏两种。研究区地层埋深较大,烃源岩成熟,生烃条件良好、源储一体,油气运移动力强。在运聚方向上的上倾尖灭砂体捕获油气而成藏。研究认为,B90井区,连接歧南、歧北斜坡,砂体叠置发育,连片分布,物性好,形成岩性上倾尖灭的岩性圈闭。这种岩性圈闭厚度大、条件好产量高,是大港油田重要的增储上产地区。     

Sliding mode based fault detection,reconstruction and fault tolerant control scheme for motor systems

The fault-tolerant control problem belongs to the domain of complex control systems in which inter-control-disciplinary information and expertise are required. This paper proposes an improved faults detection, reconstruction and fault-tolerant control (FTC) scheme for motor systems (MS) with typical faults. For this purpose, a sliding mode controller (SMC) with an integral sliding surface is adopted. This controller can make the output of system to track the desired position reference signal in finite-time and obtain a better dynamic response and anti-disturbance performance. But this controller cannot deal directly with total system failures. However an appropriate combination of the adopted SMC and sliding mode observer (SMO), later it is designed to on-line detect and reconstruct the faults and also to give a sensorless control strategy which can achieve tolerance to a wide class of total additive failures. The closed-loop stability is proved, using the Lyapunov stability theory. Simulation results in healthy and faulty conditions confirm the reliability of the suggested framework.     

基于EMD和分形的齿轮箱特征提取技术研究

齿轮箱故障诊断实质上是一个特征提取和状态识别的过程,在旋转机械研究中,其特征频率往往是已知或者可以计算的,而实际测量的机械振动信号往往含有故障特征以外的频率成分,为了避免多余信息的干扰,提出了结合EMD和分形几何算法的齿轮箱有效频率分量的特征提取方法。实验表明,该方法可以有效地计算出不同齿轮故障的特征值,实现了定量表征。