基于ASOS-ELM的湿式球磨机负荷软测量方法

针对湿式球磨机在磨矿过程中内部负荷靠专家经验难以准确预测的问题,提出一种基于改进的共生生物搜索(ameliorated symbiotic organisms search,简称ASOS)-极限学习机(extreme learning machine,简称ELM)的磨机负荷软测量方法。首先,利用ELM算法建立磨机负荷软测量模型,运用ASOS算法优化软测量模型的隐含层参数;其次,以筒体振动与振声信号的特征信息构建磨机负荷特征向量,并将其作为软测量模型的输入,将磨机负荷参数作为输出;最后,通过磨矿负荷检测实验和对比分析表明,磨机负荷软测量模型的负荷参数预测准确率较高,泛化能力较强,为磨机磨矿效率的提高及控制优化提供了有益的指导。     

功率谱分析在筒式钢球磨煤机内存煤量测量中的应用研究

采用Welch功率谱估计法和切比雪夫窗,分析了噪音信号与筒式钢球磨煤机内存煤量的关系,分析了球磨机噪音信号的构成,提出了特征频段搜索的方法并根据能量-时间变化曲线确定了噪音信号的特征频段,实现了对球磨机内存煤量的测量.将结果应用到磨机负荷检测仪的研制中,取得较好的效果.     

球磨机理论临界转速的探讨

通过对磨球在球磨机筒体内的受力分析,说明了磨球自转实质,计算出磨球上升到不同位置时的速度,推导出球磨机的理论临界转速,对实际生产中球磨机运动参数的选取具有一定的参考意义。通过对磨球的力学分析,考虑到衬板与磨球之间的相对滑动及磨球半径对磨球速度的影响,推导出了磨机的理论临界转速。     

循环负荷与选粉效率实验设计与研究

针对由球磨机和配套选粉机组成的典型闭路粉磨系统,分析了循环负荷与选粉效率对粉磨过程以及粉磨效果的影响。在理论分析的基础上,以水泥粉磨为对象,研究设计了一种分析和确定闭路粉磨系统最佳循环负荷与选粉效率的实验方法。通过模拟闭路粉磨系统的粉磨流程,探索典型闭路粉磨系统循环负荷与选粉效率的最佳工艺参数,并根据实验结果,提出了此类粉磨系统循环负荷与选粉效率的最佳取值范围。为此类球磨粉磨系统循环负荷与选粉效率的参数优化提供了实验方法与数据基础。     

基于频域特征提取与信息融合的磨机负荷软测量

提出了基于频域特征提取与多传感器信息融合的磨机负荷(ML)软测量新方法。针对磨矿过程主要依靠人工经验定性判断ML状态,难以定量检测ML参数的现状,通过融合磨机筒体振动、振声及驱动电机电流信号,建立了以料球比、矿浆浓度、充填率为输出的ML软测量模型。该方法首先采用快速傅里叶变换(FFT)将时域振动及振声信号转换为频谱变量,再对频谱变量通过主元分析(PCA)进行谱特征提取,然后采用径向基函数(RBF)变换生成的激活矩阵实现谱特征的非线性映射,最后采用偏最小二乘(PLS)算法建立以谱特征、激活矩阵、电流信号为输入的回归模型,从而有效克服了多传感器信息之间及RBF变换引起的多重共线性等问题。实验表明,该方法能够较准确地检测ML参数,融合多传感器的软测量方法具有更好的预测效果。     

陶瓷球研磨精度的在线监控

利用光线示波器和频谱分析仪,对陶瓷球研磨加工过程中磨削力和振动信号进行了测试,从时域和频域两个角度进行分析论证。分析表明:对磨削力和振动信号的监测可以有效地监控整个研磨加工过程,控制加工精度,并能够生产出高精度的陶瓷球,满足使用需求。     

Vibration analysis of rolling element bearings with various defects under the action of an unbalanced force

In this paper, a method based on the finite element vibration analysis is presented for defect detection in rolling element bearings with single or multiple defects on different components of the bearing structure using the time and frequency domain parameters. A dynamic loading model is proposed in order to create the nodal excitation functions used in the finite element vibration analysis as external loading. A computer code written in Visual Basic programming language with a graphical user interface is developed to create the nodal excitations for different cases including the outer ring, inner ring or rolling element defects. Forced vibration analysis of a bearing structure is performed using the commercial finite element package I-DEAS under the action of an unbalanced force transferred to the structure via a ball bearing. Time and frequency domain parameters such as rms, crest factor, kurtosis and band energy ratio for the frequency spectrum of the enveloped signals are used to analyse the effect of the defect location and the number of defects on the time and frequency domain parameters. The role of the receiving point for vibration measurements is also investigated. The vibration data for various defect cases including the housing structure effect can be obtained using the finite element vibration analysis in order to develop an optimum monitoring method in condition monitoring studies.     

Characteristic recognition of chatter mark vibration in a rolling mill based on the non-dimensional parameters of the vibration signal

Detecting chatter mark vibration in rolling mills operating under normal working conditions is difficult. A novel characteristic recognition method of chatter mark vibration, where the non-dimensional parameters are calculated with time varying signals and kurtosis under normal rolling mill operating conditions, is presented in this paper. The character of the chatter mark vibration signal is obtained by calculating the kurtosis value of each vibration signal segment obtained by subdividing the raw time varying vibration signal. The probability density function is utilized to reveal obvious differences between signals with respect to the normal and chatter conditions. The method overcomes the limitation of traditional spectrum analysis, which is sensitive to working conditions. Numerical simulation and experimental results show that the proposed method has better recognition capability than traditional spectrum analysis.     

Experimental diagnostics of ball bearings using statistical and spectral methods

Vibration measurements and signal analysis is widely used for condition monitoring of ball bearings as their vibration signature reveals important information about the defect development within them. Time domain analysis of vibration signature such as peak-to-peak amplitude, root mean square, Crest factor and kurtosis indicates defects in ball bearings. However, these measures do not specify the position and/or nature of the defects. Each defect produces characteristic vibrations in ball bearings. Hence, examining the vibration spectrum may deliver information on the type of defects. In this paper a test rig is designed and a pair of brand new commercial ball bearings is installed. The bearings run throughout their lifespan under constant speed and loading conditions. Vibration signatures produced are recorded and statistical measures are calculated during the test. When anomalies are detected in the statistical measures, vibration spectra are obtained and examined to determine where the defect is on the running surfaces. At the end of the test, the ball bearings are disassembled in order to take microscopic photos of the defects.     

Monitoring of Ball Bearing Operation under Grease Lubrication Using a New Compound Diagnostic System Detecting Vibration and Acoustic Emission

Our new compound diagnostic system comprised of a compound sensor, a signal processor, and a personal computer installed signal processing software. The compound sensor made by an advanced sensor fusion technique was able to detect simultaneously the vibration acceleration and the acoustic emission by itself. The signal processor received a signal from the sensor and separated it into the vibration acceleration signal and the acoustic emission signal. The signal processor and the personal computer processed the acceleration signal and acoustic emission signal for diagnostic information. The rolling contact fatigue process of a ball bearing under grease lubrication was monitored using the compound system. The system outputs diagnostic information, for example, the means, the variance, the skewness, and the kurtosis of the vibration acceleration signal and the acoustic emission signal. In diagnosing the rolling contact fatigue failure, the root mean square (rms) value of the vibration acceleration was most effective, and the mean of the demodulated acoustic emission was second to the rms value of the acceleration in effectiveness. From the result of the evaluation, it became clear that the system was useful for diagnosing rolling contact bearings under grease lubrication.     

Mahlfeinheit,Verschleiss der Mahlbahn und Energieverbrauch bei der Kreislaufmahlung mineralischer Rohstoffe

As it is not feasible to apply operating results for grinding mills to other commercial plants, investigations were made in a laboratory mill to evaluate fineness, wear and energy consumption with different rocks, ores and wear materials. The wear process in a ball mill was analysed tribologically. Fineness, wear and specific energy consumption increase with mill speed. With increased feed rate there are decreases in fineness and energy consumption but the wear rate increases. The results of the laboratory tests may be applicable to commercial plants under certain circumstances.     

基于改进匹配追踪算法的特征提取及其应用

特征提取是机械设备状态监测和故障诊断过程中最基本、最关键的一部分,针对现有各种提取方法的不同缺点,提出一种自适应信号分解技术来实现旋转机械振动信号的特征提取。该方法是一种改进的匹配追踪算法,不需要构造任何参数表达的基函数,而是将观察信号分解为一系列波形的组合,这些波形由非参数波形估计方法计算而来,用以匹配信号的局部结构。非参数波形估计方法中模板信号的自适应调整使该方法也不需要具有任何信号的先验知识,因而在实际应用中具有更加良好的柔性和适应性。仿真信号和转子试验台试验信号验证该方法的可行性和有效性,即使是在噪声和信号中特征波形频带重叠的情况下也能将信号分离和提取出来。     

Application of higher order spectral features and support vector machines for bearing faults classification

Condition monitoring and fault diagnosis of rolling element bearings timely and accurately are very important to ensure the reliability of rotating machinery. This paper presents a novel pattern classification approach for bearings diagnostics, which combines the higher order spectra analysis features and support vector machine classifier. The use of non-linear features motivated by the higher order spectra has been reported to be a promising approach to analyze the non-linear and non-Gaussian characteristics of the mechanical vibration signals. The vibration bi-spectrum (third order spectrum) patterns are extracted as the feature vectors presenting different bearing faults. The extracted bi-spectrum features are subjected to principal component analysis for dimensionality reduction. These principal components were fed to support vector machine to distinguish four kinds of bearing faults covering different levels of severity for each fault type, which were measured in the experimental test bench running under different working conditions. In order to find the optimal parameters for the multi-class support vector machine model, a grid-search method in combination with 10-fold cross-validation has been used. Based on the correct classification of bearing patterns in the test set, in each fold the performance measures are computed. The average of these performance measures is computed to report the overall performance of the support vector machine classifier. In addition, in fault detection problems, the performance of a detection algorithm usually depends on the trade-off between robustness and sensitivity. The sensitivity and robustness of the proposed method are explored by running a series of experiments. A receiver operating characteristic (ROC) curve made the results more convincing. The results indicated that the proposed method can reliably identify different fault patterns of rolling element bearings based on vibration signals.     

Roller bearing safety region estimation and state identification based on LMD–PCA–LSSVM

The basic idea of safety region is introduced into roller bearing condition monitoring. Local mean decomposition (LMD), principal component analysis (PCA) and least square support vector machine (LSSVM) are used comprehensively for the estimation of the safety region and the identification of normal state and faulty state for the roller bearing operational status. First, the vibration acceleration data was segmented according to a certain time interval and then Product Functions (PFs) of each piece of the data were obtained by LMD. Based on this, statistics control limits T² and SPE were extracted by PCA as roller bearings’ state characteristics. Finally, LSSVM was used for the estimation of the safety region of the roller bearing operation state, and multi-class LSSVM was used for the identification of the four normal, ball fault, inner race fault and outer race fault states. The results show that both the safety region estimation and state identification are accurate, and confirm the validity of the LMD–PCA–LSSVM method.     

改进VPMCD方法在滚动轴承故障诊断中的应用

针对模式识别新方法VPMCD(variable predictive model based class discriminate)在参数估计过程中存在的缺陷,对VPMCD方法进行了改进,用主成分估计法代替原方法中的最小二乘法进行参数估计,消除了预测变量间存在多重线性相关性的影响,可以获得更加稳定的模型参数,从而提高模式识别的精度。采用局部特征尺度分解(LCD)方法对滚动轴承振动信号进行分解得到若干个单分量信号,提取各分量的近似熵组成故障特征向量作为改进VPMCD的输入,以改进VPMCD作为分类器对滚动轴承的工作状态和故障类型进行分类。对正常状态、外圈故障、内圈故障和滚动体故障四种不同工作状态和故障类型下的滚动轴承振动信号进行了分析,结果表明该方法有效。     

Laboratory studies on the wear of grinding media

Quartzite ore was ground in a laboratory mill at different mill speeds and under dry and wet conditions using hyper steel and high chromium cast iron balls. A study of ball dynamics in the mill and scanning electron microscopy of the used ball surface threw light on ball wear characteristics and the efficiency of the grinding processes.     

热连轧机自激振动诊断与振动机理分析

描述了热连轧机的现场多点振动测试方法，利用振动信号的功率谱分析和小波包分析等现代信号分析技术，深入研究了轧机自激振动机理并诊断了振源位置。分析结果表明，研究对象的轧机压下系统位移传感器定尺支座结构是引起系统不稳定的敏感部位。该结构参数不合理，是导致压下系统反馈信号严重失真,诱发轧机工作机座自激振动的根源。分析结果和诊断结论对于制定合理的结构动力学修改策略，消除轧机自激振动具有重要的意义。     

料球比对球磨机磨矿效果影响的试验研究

影响球磨机磨矿效率的因素很多。本文研究了在介质充填率和转速率一定时,利用球磨机样机,选取不同的料球比进行试验,通过试验数据,分析不同条件下磨矿产品的产率,得出在一定转速率和介质充填率下,球磨机料球比对磨矿效率的影响,从而为实际生产提供参考依据,为提高球磨机的磨矿效率提供可鉴之处。     

水下航行载体实航振动和噪声测量装置的研究

介绍了一种新型的水下航行载体实航振动和噪声测量装置,它包含一个多通道、连续高速采样、大存储容量的ＤＡＴ数字磁带记录仪。这套装置体积小,安装在水下航行载体内部,用可充电电池供电,自成独立系统。它不仅能测量实航全过程的振动和噪声,还能记录其他瞬态或稳态参数,具有很大的通用性。     

塔磨机运行参数优化匹配的仿真分析与试验研究

现有对塔磨机运行参数优化匹配研究不足,影响了塔磨机磨矿效率的提高及其工程应用,为此应用离散元法对某塔磨机主要的运行参数进行了仿真,对碰撞能量、碰撞频率、功率消耗等与磨矿性能相关的物理量进行了研究,并通过试验分析了各运行参数对矿料粒度的影响,验证了模拟分析的准确性。研究结果表明,在临界转速下,转速越高,研磨效率越高,该塔磨机最优的转速为210r/min;填充率过低则磨矿强度较低,填充率过高则能量利用率下降,综合考虑磨矿强度及能量利用率,该塔磨机的最优填充率为60%。