It is desired to build the life distribution models of critical components (which are assumed to be non-repairable) of a repairable system as early as possible based on field failure data in order to optimize the oper...
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It is desired to build the life distribution models of critical components (which are assumed to be non-repairable) of a repairable system as early as possible based on field failure data in order to optimize the operation and maintenance decisions of the components. When the number of the systems under observation is large and the observation duration is relatively short, the samples obtained for modeling are large and heavily censored. For such samples, the classical parameter estimation methods (e.g. maximum likelihood method and least square method) do not provide robust estimates. To address this issue, this article develops a hybrid censoring index to quantitatively describe censoring characteristics of a data set, proposes a novel parameter estimation method based on information extracted from censored observations, and evaluates the accuracy and robustness of the proposed method through a numerical experiment. Its applicable range in terms of the hybrid censoring index is determined through an accuracy analysis. The experiment results show that the proposed approach provides much accurate estimates than the classical methods for heavily censored data. A real-world example is also included.
A stress-strength model usually has more than one failure mode since the component suffers at least two types of stresses, complicating the expression of the likelihood function and increasing the computational comple...
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A stress-strength model usually has more than one failure mode since the component suffers at least two types of stresses, complicating the expression of the likelihood function and increasing the computational complexity of the parameterestimation for general distributions (non-exponential distributions). A phase-type distribution (also known as a PH distribution) is dense and has closure properties, which makes it suitable to reduce the computational complexity of the stress-strength model. The traditional expectation-maximization (EM) method for estimating the parameters of the PH distribution cannot be used directly when the strength changes over time since the PH distribution is a continuous-time Markov process that must satisfy the relevant properties of the infinitesimal generator in the Markov state-space. Therefore, a parameter estimation method based on extending the Markov state-space with variable transition rates for the stress-strength model is proposed. Both failure and censored samples are considered. First, the stress-strength model based on the PH distribution is briefly introduced, and the likelihood functions for different failure modes are derived. Subsequently, the principle of the method is described in detail, the derivation process of the relevant equations is provided, and the limitations of the method are discussed. The performance of the method is evaluated using two simulation cases.
Generalised cubic phase function (GCPF)-based estimator is efficient in estimating the third-order coefficient for mono-component quadratic frequency-modulated (QFM) signal. However, it suffers from cross-terms and sp...
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Generalised cubic phase function (GCPF)-based estimator is efficient in estimating the third-order coefficient for mono-component quadratic frequency-modulated (QFM) signal. However, it suffers from cross-terms and spurious peaks when dealing with multi-component QFM signals. To efficiently estimate the third-order coefficients of multi-component QFM, a novel coherently integrated GCPF (CIGCPF) algorithm is proposed to enhance the auto-terms and suppress the cross-terms and spurious peaks. Comparisons with state-of-the-art algorithms indicate that CIGCPF not only solves the identification problem for multi-component QFM signals, but also can acquire higher anti-noise performance.
Effective ground thermal conductivity and borehole thermal resistance, which are key parameters in the design of borehole heat exchangers (BHEs), are often determined on the basis of in-situ thermal response tests.(TR...
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Effective ground thermal conductivity and borehole thermal resistance, which are key parameters in the design of borehole heat exchangers (BHEs), are often determined on the basis of in-situ thermal response tests.(TRTs). However, many disturbance factors can affect the accuracy of a TRT, e.g., voltage fluctuations from the power grid and oscillating external environments where a TRT rig is installed. Interpretation of TRT data is often done using the infinite line source (ILS) model, combined with the sequential plot method, because it is not only simple but also provides additional information about the estimation behavior and convergence. However, estimation behavior using the sequential method tends to fluctuate over time because the constant heat flux assumption is always violated as a result of the disturbance factors. As an alternative, a temporal superposition applied analytical model can be used in a recursive curve fitting manner, but this method cannot provide the additional information that sequential method can. In this study, as a solution for interpreting disturbed TRT data and to utilize additional information from the sequential plot method, we proposed an alternative method using a temporal superposition applied ILS model combined with the quasi-Newton optimization method. To verify the effectiveness, the proposed method was applied to in-situ TRTs and the results were compared with those from the conventional method in terms of the estimation stability and convergence speed. The results showed that, compared to the conventional sequential method using the ILS model, the proposed method yielded standard deviations for the effective thermal conductivity and borehole thermal resistance that were at least six times and four times lower, respectively. Moreover, the proposed method was able to achieve about four times faster convergence speeds. (C) 2015 Elsevier Ltd. All rights reserved.
In this paper, We have extracted and modeled the motion primitive of human hand in a simple 1-DOF rhythmic motion task, i. e. manipulating mass-spring-damper system. The experiment was carried out by using 1-DOF hapti...
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ISBN:
(纸本)9781479977871
In this paper, We have extracted and modeled the motion primitive of human hand in a simple 1-DOF rhythmic motion task, i. e. manipulating mass-spring-damper system. The experiment was carried out by using 1-DOF haptic box with virtual reality in Simulink environment. The interaction dynamics of haptic box and human which consists of hand and brain reveals the role of the human as an intelligent admittance. We tested 6 people who tried to combine motion primitives to produce smoother motion during learning process. In addition, we developed a novel identification method for modeling the rhythmic motion of hand in model space. It is shown that adaptive filter as a predictor of motion primitives with two parameters and two initial values appears as an ellipse in model space. The geometrical properties of ellipse are related to the parameters and initial values of adaptive filter that make it possible to identify the parameters of adaptive filter in model space.
In practice, the performance of distribution feeder parameterestimation is limited by the measurement conditions in distribution networks. An accurate mathematical model that considers limited phasor measurements in ...
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In practice, the performance of distribution feeder parameterestimation is limited by the measurement conditions in distribution networks. An accurate mathematical model that considers limited phasor measurements in distribution networks is necessary to estimate feeder parameters. This paper presents a set of modified parameterestimation models for unbalanced three-phase distribution feeders that only require the measurements of voltage amplitudes and power flows. To simplify the calculation process and improve the estimated results, a method combined with a radial basis function neural network (RBFNN) and multi-run optimization method (MRO), namely RBFNN-MRO, is proposed to calculate the parameters of distribution feeders. The relationship between the feeder parameters and the measurement data from the two terminals of the feeder can be mapped perfectly using the RBFNN. Furthermore, the random errors in the measurement device were eliminated using the proposed RBFNN-MRO algorithm. The RBFNN-MRO algorithm can limit the number of neurons in the hidden layer and substantially reduce the training time for each RBFNN. The feasibility of the proposed method was verified using four IEEE test systems. The proposed RBFNN-MRO and RBFNN methods were compared using the maximum absolute percentage error (MAPE) curves. The results reveal that the proposed RBFNN-MRO method has excellent potential for improving the accuracy of feeder parameterestimation even without synchronized phasor measurement.
In this article the dislocated hybrid synchronization between chaotic systems of different structures viz. real integer order and complex fractional order have been performed using two control methods viz. tracking co...
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In this article the dislocated hybrid synchronization between chaotic systems of different structures viz. real integer order and complex fractional order have been performed using two control methods viz. tracking control method and parameterestimation adaptive method. Illustrative example of the application of synchronization in secure communication has been performed. Numerical simulations have been performed using MATLAB which verify the efficacy of both the methods and help compare results.
A linear frequency modulation (LFM) signal is a typical non-stationary signal, and its parameterestimation has always been among the core issues in the field of signal processing. In this study, a novel method for pa...
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A linear frequency modulation (LFM) signal is a typical non-stationary signal, and its parameterestimation has always been among the core issues in the field of signal processing. In this study, a novel method for parameterestimation of LFM signal is proposed using the simplified linear canonical transform and evaporation-rate-based water cycle algorithm. According to the results obtained from the theoretical analysis and experimental simulation, the proposed method possesses the advantages of easy implementation, high precision and independence on initial values compared with the concise fractional Fourier transform (CFRFT)-based and fractional Fourier transform-based methods. Moreover, the proposed parameter estimation method is successfully applied to optical measurement with improving measurement accuracy.
A compressor map is usually represented by a limited number of feature points to speculate the entire operating range. Also, accurate compressor map models can be obtained quickly by using the appropriate methods. In ...
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A compressor map is usually represented by a limited number of feature points to speculate the entire operating range. Also, accurate compressor map models can be obtained quickly by using the appropriate methods. In this paper, 9351FA gas turbine is used as the research object, and a set of targeted compressor map speculation scheme is proposed. At 15 data points, high-precision compressor maps are obtained based on BP neural network, and this method is suitable for a large number of data points. At 6 data points, compressor maps are obtained based on the parameter estimation method, and this method is suitable for a small number of data points. The mean square deviation of the compressor map obtained by the neural network is about 0.002, while the minimum mean square deviation of the results of the parameter estimation method is 0.026 and the maximum mean square deviation is 0.088. Since the corrected speed line of 106.4 is almost vertical, the maximum error mean squared deviation and the maximum standard deviation occur on this line. Both methods are suitable for different sample sizes, and the speculated compressor maps are more reliable. The combination of the two methods can provide a set of reference methods for compressor map speculation.
This article proposes a parameterestimation technique for a self-saturation model of synchronous motors. The standstill self-identification method requires an equation-based flux saturation model for flux saturation ...
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This article proposes a parameterestimation technique for a self-saturation model of synchronous motors. The standstill self-identification method requires an equation-based flux saturation model for flux saturation modeling. The selected self-saturation model has the form of a polynomial representing a current with respect to a magnetic flux. This flux saturation model contains unknown parameters, including exponents, coefficients, and a current source for a permanent magnet. The exponent is a crucial parameter representing the nonlinear current and magnetic flux relationship. Therefore, the estimated flux saturation model only represents the flux saturation well if an appropriate exponent is selected. The proposed method can estimate the exponent of the self-saturation model for synchronous reluctance motors (SynRMs) using a linear least squares method (LSM). In addition, parameters of the self-saturation model for permanent magnet synchronous motors (PMSMs), including the exponents and a constant current source, can be estimated simultaneously. The rest of the coefficients of the flux saturation model are estimated using a linear LSM based on the pre-estimated exponents and a constant current source. The proposed parameter estimation method can be implemented in embedded systems. The performance of the proposed parameter estimation method was verified in 1.5-kW SynRM and 11-kW interior PMSM.
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