Localizing structural damage based on auto-regressive with exogenous input model parameters and residuals using a support vector machine based learning approach

Machine learning algorithms operating in an unsupervised fashion has emerged as promising tools for detecting structural damage in an automated fashion.Its essence relies on selecting appropriate features to train the model using the reference data set collected from the healthy structure and employing the trained model to identify outlier conditions representing the damaged state.In this paper,the coefficients and the residuals of the autoregressive model with exogenous input created using only the measured output signals are extracted as damage features.These features obtained at the baseline state for each sensor cluster are then utilized to train the one class support vector machine,an unsupervised classifier generating a decision function using only patterns belonging to this baseline state.Structural damage,once detected by the trained machine,a damage index based on comparison of the residuals between the trained class and the outlier state is implemented for localizing damage.The two-step damage assessment framework is first implemented on an eight degree-of-freedom numerical model with the effects of measurement noise integrated.Subsequently,vibration data collected from a one-story one-bay reinforced concrete frame inflicted with progressive levels of damage have been utilized to verify the accuracy and robustness of the proposed methodology.

基于NARX的蒸汽发生器液位异常检测方法

蒸汽发生器液位是评价核电机组运行状态的重要参数指标之一,由于传统预设固定液位报警阈值的监测方法无法在触发报警信号前及早发现异常,为蒸汽发生器液位建立异常检测模型很有必要。基于蒸汽发生器复杂非线性系统的特点,通过带外源输入的非线性自回归(nonlinear auto-regressive with exogenous inputs, NARX)方法研究了蒸汽发生器在正常工作模式下液位及相关参数间的耦合关系模型。模型以历史液位值和相关参数作为输入回归得到下一时刻的液位预测值,并通过预测值与实际观测值残差的大小,来判断蒸汽发生器多传感器系统当前工作状态是否异常。与触发预设液位阈值后再报警的传统状态监测方法相比,结果表明该方法能够检测到液位与相关参数间的耦合关系偏移,并在微小变化发生时就检测到异常,从而实现蒸汽发生器液位的状态监测和预警。同时经真实核电厂数据验证,可见该模型能够对液位实现准确的回归预测,并在依照真实故障类型构建的异常数据集验证实验中,取得了较好的异常检测效果。

Random dynamic analysis of vertical train–bridge systems under small probability by surrogate model and subset simulation with splitting

The response of the train–bridge system has an obvious random behavior.A high traffic density and a long maintenance period of a track will result in a substantial increase in the number of trains running on a bridge,and there is small likelihood that the maximum responses of the train and bridge happen in the total maintenance period of the track.Firstly,the coupling model of train–bridge systems is reviewed.Then,an ensemble method is presented,which can estimate the small probabilities of a dynamic system with stochastic excitations.The main idea of the ensemble method is to use the NARX(nonlinear autoregressive with exogenous input)model to replace the physical model and apply subset simulation with splitting to obtain the extreme distribution.Finally,the efficiency of the suggested method is compared with the direct Monte Carlo simulation method,and the probability exceedance of train responses under the vertical track irregularity is discussed.The results show that when the small probability of train responses under vertical track irregularity is estimated,the ensemble method can reduce both the calculation time of a single sample and the required number of samples.

Iterative learning control of SOFC based on ARX identification model

This paper presents an application of iterative learning control (ILC) technique to the voltage control of solid oxide fuel cell (SOFC) stack. To meet the demands of the control system design, an autoregressive model with exogenous input (ARX) is established. Firstly, by regulating the variation of the hydrogen flow rate proportional to that of the current, the fuel utilization of the SOFC is kept within its admissible range. Then, based on the ARX model, three kinds of ILC controllers, i.e. P-, PI- and PD-type are designed to keep the voltage at a desired level. Simulation results demonstrate the potential of the ARX model applied to the control of the SOFC, and prove the excellence of the ILC controllers for the voltage control of the SOFC.

Tool Condition Monitoring Based on Nonlinear Output Frequency Response Functions and Multivariate Control Chart

Tool condition monitoring(TCM)is a key technology for intelligent manufacturing.The objective is to monitor the tool operation status and detect tool breakage so that the tool can be changed in time to avoid significant damage to workpieces and reduce manufacturing costs.Recently,an innovative TCM approach based on sensor data modelling and model frequency analysis has been proposed.Different from traditional signal feature-based monitoring,the data from sensors are utilized to build a dynamic process model.Then,the nonlinear output frequency response functions,a concept which extends the linear system frequency response function to the nonlinear case,over the frequency range of the tooth passing frequency of the machining process are extracted to reveal tool health conditions.In order to extend the novel sensor data modelling and model frequency analysis to unsupervised condition monitoring of cutting tools,in the present study,a multivariate control chart is proposed for TCM based on the frequency domain properties of machining processes derived from the innovative sensor data modelling and model frequency analysis.The feature dimension is reduced by principal component analysis first.Then the moving average strategy is exploited to generate monitoring variables and overcome the effects of noises.The milling experiments of titanium alloys are conducted to verify the effectiveness of the proposed approach in detecting excessive flank wear of solid carbide end mills.The results demonstrate the advantages of the new approach over conventional TCM techniques and its potential in industrial applications.

Comparative analysis of time series neural network methods for three-way catalyst modeling

Relative Oxygen Level of the Three-Way Catalyst is an important parameter that affects the conversion efficiency of pollutants. ROL is a time-varying hidden state variable that is difficult to directly observe in practice. Therefore, it is common to use a method of clearing oxygen storage to simplify control in vehicles. However, this method negates the positive effects of ROL on pollutant treatment. ROL can be indirectly observed through modeling methods. Chemical modeling methods involve extensive computational requirements that cannot meet the demands of practical control. In contrast, time-series neural networks offer computational speed advantages when dealing with similar problems. Therefore, the ROL observation models using both NARX and LSTM neural networks are developed and compared in this study. The results indicate that the NARX neural network exhibits higher precision with a smaller number of neurons and time steps. The LSTM neural network demonstrates greater stability when dealing with data error fluctuations. In practical applications, the ROL model can monitor the TWC operating status and assist in the development of intelligent pollutant aftertreatment control strategies.

New Results on PWARX Model Identification Based on Clustering Approach

This paper deals with the problem of piecewise auto regressive systems with exogenous input(PWARX) model identification based on clustering solution. This problem involves both the estimation of the parameters of the affine sub-models and the hyper planes defining the partitions of the state-input regression. The existing identification methods present three main drawbacks which limit its effectiveness. First, most of them may converge to local minima in the case of poor initializations because they are based on the optimization using nonlinear criteria. Second, they use simple and ineffective techniques to remove outliers. Third, most of them assume that the number of sub-models is known a priori. To overcome these drawbacks, we suggest the use of the density-based spatial clustering of applications with noise(DBSCAN) algorithm. The results presented in this paper illustrate the performance of our methods in comparison with the existing approach. An application of the developed approach to an olive oil esterification reactor is also proposed in order to validate the simulation results.

Temporal variation in modal properties of a base-isolated building during an earthquake

Temporal variation of dynamical modal properties of a base-isolated building is investigated using earthquake records in the building. A batch processing least-squares estimation method is applied to segment-wise time-series data. To construct an input-output system,an auto-regressive model with exogenous input (ARX) of second-order including a forgetting coefficient as a weighting coefficient is used for the estimation of modal parameters. The fundamental and second natural frequencies and the damping ratios of the fundamental and second natural modes of the base-isolated building are identified in the time domain. The identified results are consistent with the results obtained from the micro-tremor vibration data,forced-vibration test data and earthquake records in the present base-isolated building in the case of taking into account the amplitude-dependency of the isolators and viscous dampers. It is finally pointed out that several factors,e.g.,amplitude dependency of the isolator and damper system and special characteristics of the series-type viscous damper system,may be related complicatedly with the temporal variation in modal properties of the above-mentioned system.