This paper investigates the characteristics of a non-stationary time series, which exists in mechanical fault diagnosis. Combining the characteristics with predictive efficiency, the limitation of the ARIMA model pred...This paper investigates the characteristics of a non-stationary time series, which exists in mechanical fault diagnosis. Combining the characteristics with predictive efficiency, the limitation of the ARIMA model prediction method is analyzed. This model often is applied in the prediction of a non-stationary times series in present. Thus, a wavelet prediction method is introduced to solve non-stationary problems. The Mallat method, often used in signal processing, results form the decimation or the retention of one out of every two samples. Its advantage is that just enough information is kept to allow the exact reconstruction of the input series, but the disadvantage is a time-varying series on line cannot be pursued. Therefore, the authors present another method, à Trous method, which can be applied for recursive prediction in real-time sampling procedure.展开更多
The combustion characteristics and particulate emission during combustion of Thai lignite with 30% of secondary air to total air （SA：TA） in a fixed bed combustor have been investigated in real-time. The results hav...The combustion characteristics and particulate emission during combustion of Thai lignite with 30% of secondary air to total air （SA：TA） in a fixed bed combustor have been investigated in real-time. The results have shown that particle formation is governed by competing reaction between the formation of the nucleated sized-particles （Dp 〈 0.1μm） and the coagulated particle （Dp 0.1-1 μm）. Temperature and burning rate are the highest priority factors to control the emission of particulate. Furthermore, the co-firing of coal/rice husks at 60：40% mass fraction with 10%SA：TA could be the alternative options to further reduction of particulate and to be recommended.展开更多
Consider a semiparametric regression model with linear time series errors Y_k= x′ _kβ + g(t_k) + ε_k, 1 ≤ k ≤ n, where Y_k's are responses, x_k =(x_(k1),x_(k2),···,x_(kp))′ and t_k ∈ T is con...Consider a semiparametric regression model with linear time series errors Y_k= x′ _kβ + g(t_k) + ε_k, 1 ≤ k ≤ n, where Y_k's are responses, x_k =(x_(k1),x_(k2),···,x_(kp))′ and t_k ∈ T is contained in R are fixed design points, β =(β_1,β_2,···,β_p)′ is an unknown parameter vector, g(·) is an unknown bounded real-valuedfunction defined on a compact subset T of the real line R, and ε_k is a linear process given byε_k = ∑ from j=0 to ∞ of ψ_je_(k-j), ψ_0=1, where ∑ from j=0 to ∞ of |ψ_j| < ∞, and e_j,j=0, +-1, +-2,···, ard i.i.d. random variables. In this paper we establish the asymptoticnormality of the least squares estimator of β, a smooth estimator of g(·), and estimators of theautocovariance and autocorrelation functions of the linear process ε_k.展开更多
基金Sponsored by the National High Technology Research and Development Program of China (Grant No.2002AA721063).
文摘This paper investigates the characteristics of a non-stationary time series, which exists in mechanical fault diagnosis. Combining the characteristics with predictive efficiency, the limitation of the ARIMA model prediction method is analyzed. This model often is applied in the prediction of a non-stationary times series in present. Thus, a wavelet prediction method is introduced to solve non-stationary problems. The Mallat method, often used in signal processing, results form the decimation or the retention of one out of every two samples. Its advantage is that just enough information is kept to allow the exact reconstruction of the input series, but the disadvantage is a time-varying series on line cannot be pursued. Therefore, the authors present another method, à Trous method, which can be applied for recursive prediction in real-time sampling procedure.
文摘The combustion characteristics and particulate emission during combustion of Thai lignite with 30% of secondary air to total air （SA：TA） in a fixed bed combustor have been investigated in real-time. The results have shown that particle formation is governed by competing reaction between the formation of the nucleated sized-particles （Dp 〈 0.1μm） and the coagulated particle （Dp 0.1-1 μm）. Temperature and burning rate are the highest priority factors to control the emission of particulate. Furthermore, the co-firing of coal/rice husks at 60：40% mass fraction with 10%SA：TA could be the alternative options to further reduction of particulate and to be recommended.
基金CHEN Min's work is supported by Grant No. 70221001 and No. 70331001 from NNSFC and Grant No. KZCX2-SW-118 from CAS.
文摘Consider a semiparametric regression model with linear time series errors Y_k= x′ _kβ + g(t_k) + ε_k, 1 ≤ k ≤ n, where Y_k's are responses, x_k =(x_(k1),x_(k2),···,x_(kp))′ and t_k ∈ T is contained in R are fixed design points, β =(β_1,β_2,···,β_p)′ is an unknown parameter vector, g(·) is an unknown bounded real-valuedfunction defined on a compact subset T of the real line R, and ε_k is a linear process given byε_k = ∑ from j=0 to ∞ of ψ_je_(k-j), ψ_0=1, where ∑ from j=0 to ∞ of |ψ_j| < ∞, and e_j,j=0, +-1, +-2,···, ard i.i.d. random variables. In this paper we establish the asymptoticnormality of the least squares estimator of β, a smooth estimator of g(·), and estimators of theautocovariance and autocorrelation functions of the linear process ε_k.
