基于小波分解的设备状态预测方法

首次提出将小波分解应用于非平稳时间序列的预测中,通过小波分解将非平稳时间序列分解为多层近似意义上的平稳时间序列,并且用AR(n)模型对分解后的时间序列进行预测,进而得到最终的预测结果.将该方法应用于压缩机轴承座磨损的趋势预测中,通过与基于BP网络的预测方法相比较表明:该方法预测精度高,而且预测速度快,可以有效地应用设备状态的预测和设备故障趋势的分析中.     

减少矫直机故障停机时间的对策

针对矫直机拨料机构故障停机时间长严重影响生产的现状,从人、机、料、法、环等多方面分析了故障原因,最终确定主要原因,并制定相应的对策与改进措施,实现故障时间的全面压缩,保障设备正常运转,生产稳定运行。     

ARIMA模型在有色金属价格预测中的应用

以1959—2010年共52年度的平均结算价格为计算对象,采用时间序列B-J法,建立了铜金属价格预测模型;通过对52年的铜金属价格数据进行平稳化处理和自相关分析,确定了ARIMA(2,1,2)为铜金属价格预测模型,并对预测模型进行了检验;其模型检验结果表明,预测结果总体误差较小,可用于外推预测。将该模型运用于2011—2025年的铜金属价格预测中,预测结果显示,铜金属价格总体呈上升趋势,其中在2012年出现峰值,随后有小幅度回落,以后基本处于小幅度震荡的平稳状态。     

浅谈设备故障分析方法

随着宝钢一体化进程的逐步推进.对设备管理人员提出了新的要求,对设备的故障管理显得尤为重要.设备正常运行是保证正常生产的前提,只有对已发生的故障进行客观、科学的分析,总结经验教训,才能有效降低设备故障,提高设备运行效率.作者以国际知名大企业案例为依据,比较详细地论述了故障分析典型方法.     

用时间序列分析预测焦炉煤气发生量

一、前言 日本三菱化成公司,作为节能技术开发的一部分,在焦炉煤气(COG)净化工程上引入了分散型仪表系统和过程计算机,并对煤气净化设备的最佳化控制进行了研究。第一步,开发了COG发生量预报以及脱硫、轻油捕集设备的数学模型。将这些     

45^t化铁炉鼓风机故障预测

故障诊断技术除了能寻找设备的缺陷和隐患外，还可根据设备现有状况预测设备状态发展趋势和故障发生时间，据以确定设备检修日期。     

运用ARIMA模型预测烧结矿成分

利用目前广泛应用的自回归积分移动平均法（Auto Regressive Integrated Moving Average）建立ARIMA（p,d,q）模型,用以预测烧结矿化学成分。由于其建模过程复杂,特别是模型结构阶次识别与检验繁琐,故使用专业软件EVIEWS5．0完成建模过程,构建了TFe,FeO,Ro的ARIMA模型。通过严格检验,模型拟合度高,拟合效果特别显著,残差为白噪声序列。用模型超前12步（24小时）预测烧结矿成分,其预报结果完全适合生产要求,实际应用取得了明显效果。     

运用劣化趋势图诊断风机设备故障的点检方法

介绍了运用劣化趋势图预测风机发生故障的时间,使设备运行处于受控状态,从而降低了设备维修费用,发挥了设备的最大功效。     

Time Series Prediction of Chimney Foundation Settlement by Neural Networks

Neural network (NN) models for time series forecasting were initially used in economic fields. In this paper, NN models for time series forecasting are introduced for use in forecasting the settlement of chimney foundations. The data sets used in the NN models were measured in the field. Seven models with different input series are developed to determine the optimal structure of the network. In evaluating the network performance, the network model that uses the previous nine months’ settlement values as input is selected as the optimal model. The analysis results demonstrate that the settlement values predicted by the optimal model are in good agreement with the field measurements. In addition, as the number of data points in the input series increases, the NN performance clearly improves, and this improvement stops after the input series has increased to a certain extent. This demonstrates that the time-series-based NN model can also be successfully applied to predict foundation settlement.     

高炉煤气发生量时间序列预测模型的开发

采用时间序列预测模型建模方法,包括时间序列模型阶次判定、参数估计、模型检验,对莱钢高炉煤气总发生量进行预测。结果表明,模型预测准确,平均误差为1.8748%,对煤气发生量的短期趋势预报有一定参考价值,能够为制定合理的煤气使用计划提供依据,可提高钢铁企业节能降耗水平。     

Time Series Models for Forecasting Construction Costs Using Time Series Indexes

Construction often involves considerable time gaps between cost estimation and on-site operations. In addition, many operations are performed over a considerable period of time. Accordingly, estimating construction costs must consider the trend of costs in the market, where construction costs normally change over time. Insight into the trend of construction costs in the market, therefore, is beneficial, even critical, to the effective cost management of construction projects. In an effort to support such insight development, two time series models were built by analyzing time series index data and comparing them with existing methods in the present study. The developed time series models accurately predict construction cost indexes. In particular, the models respond sensitively and swiftly to a quick, large change of costs, which allows for accurate forecasting over the short- and long-term periods. Overall, the models are effective for understanding the trend of construction costs.     

冲击凿岩数学模型及活塞失效分析

应用应力波理论,建立了冲击凿岩的理论模型,并应用该模型对活塞的受力及失效形式进行了分析,找出了活塞失效的真正原因,提出了活塞选材的判据。     

Time Series Analysis of ENR Construction Cost Index

Every month, Engineering News-Record (ENR) publishes the construction cost index (CCI), which is a weighted aggregate index of the 20-city average prices of construction activities. Although CCI increases over the long term, it is subject to considerable short-term variations, which make it problematic for cost estimators to prepare accurate bids for contractors or engineering estimates for owner organizations. The ability to predict construction cost trends can result in more-accurate bids and avoid under- or overestimation. This paper summarizes and compares the applicability and predictability of various univariate time series approach for in-sample and out-of-sample forecastings of CCI. It is shown that the seasonal autoregressive integrated moving-average model is the most-accurate time series approach for in-sample forecasting of CCI, while the Holt-Winters exponential smoothing model is the most-accurate time series approach for out-of-sample forecasting of CCI. It is also shown that several time series models provide more-accurate out-of-sample forecasts than the ENR’s subject matter experts’ CCI forecast. Cost estimators can benefit from CCI forecasting by incorporating predicted price variations in their estimates and preparing more-accurate bids for contractors and budgets for owners. Owners and contractors can use CCI forecasting in reducing construction costs by better-timed project execution.     

Streamflow Properties from Time Series of Surface Velocity and Stage

Time series of surface velocity and stage have been collected simultaneously. Surface velocity was measured using an array of newly developed continuous-wave microwave sensors. Stage was obtained from the standard U.S. Geological Survey (USGS) measurements. The depth of the river was measured several times during our experiments using sounding weights. The data clearly showed that the point of zero flow was not the bottom at the measurement site, indicating that a downstream control exists. Fathometer measurements confirmed this finding. A model of the surface velocity expected at a site having a downstream control was developed. The model showed that the standard form for the friction velocity does not apply to sites where a downstream control exists. This model fit our measured surface velocity versus stage plots very well with reasonable values of the parameters. Discharges computed using the surface velocities and measured depths matched the USGS rating curve for the site. Values of depth-weighted mean velocities derived from our data did not agree with those expected from Manning’s equation due to the downstream control. These results suggest that if real-time surface velocities were available at a gauging station, unstable stream beds could be monitored.     

基于非线性预测模型的单神经元自适应PID板形控制

为了解决传统PID板形控制精度低、速度慢、抗干扰能力差等问题,将BP神经网络和单神经元引入到板形的控制中,提出一种基于BP神经网络预测模型的单神经元自适应PID控制的板形控制策略。利用BP神经网络的非线性逼近能力和单神经元的自学习、自适应能力,通过两者的有机结合寻找一个最佳的P、I、D非线性组合控制律,实现对带钢板形缺陷的有效控制。仿真实验结果表明,该控制算法能很好地跟踪板形的目标设定值,提高了系统的控制精度,加快了系统的响应速度,并且具备较强的抗干扰能力。     

Time‐Series Models for Nonlinear Systems

Discrete time‐series models can be used for the dynamic response prediction of linear structures. When structural nonlinearities are present, it may be possible to modify the form of the discrete time‐series model to account for the nonlinearities. One approach is to allow the model parameters to become functions of state. This paper explores some possible forms of the parameter functions for various nonlinear structures. Numerical case studies using both a Duffing oscillator and a combined viscous and coulomb damped oscillator are presented. Also, experimental data from a highly nonlinear aircraft landing gear strut are used to evaluate different model forms. The results from these studies show the potential for future applications of nonlinear time‐series models.     

Model for Predicting Financial Performance of Development and Construction Corporations

Performance forecasting is central to aligning an organization’s operations with its strategic direction. Despite the panoply of approaches to performance predictions, relatively few published studies address model development of financial performance predictions for the construction industry. By analyzing the preceding relationship between financial and economic variables and financial performance, this paper proposes an innovative approach to predicting firm financial performance. First, hypothesis tests using data for 42 development and construction corporations listed in the construction sector of the Taiwan Stock Exchange between 1997 Q1 and 2006 Q4 uncover useful relationships between financial performance and financial and economic variables. Second, based on these relationships, a three-stage mathematical modeling procedure is used for cross-sectional model estimation, which is subsequently refined to create firm-specific financial performance-forecasting models for four sample firms. The out-of-sample forecasting accuracy is evaluated using mean absolute percentage error (MAPE). The results show that the cross-sectional model explains 78.9% of the variation in the cross-sectional performance data, and the MAPE values in the forecasting models range from 9.54 to 19.69%.     

Real-Time Detection of Sanitary Sewer Overflows Using Neural Networks and Time Series Analysis

Sanitary sewer overflows (SSOs) are becoming of increasing concern as a health risk. Utilities and regulators have taken preventive measures but many overflows still occur and are not identifiable, especially in access-challenged locations. Several mathematical approaches are presented for detecting if a disruption in the system is impending or occurring based on measurements at one or more locations in the system. Time series analysis and neural networks are used as prediction tools for expected depths and flows for single measurement locations and a neural network is developed for a multiple monitor system. Control limit theory is applied in all cases for identifying significant deviations of measured values from the expected values that suggest a SSO is occurring. Data from Pima County Wastewater Management’s monitoring system are used in two case studies.