Robust fault detection of turbofan engines subject to adaptive controllers via a Total Measurable Fault Information Residual (ToMFIR) technique

This paper provides a new design of robust fault detection for turbofan engines with adaptive controllers. The critical issue is that the adaptive controllers can depress the faulty effects such that the actual system outputs remain the pre-specified values, making it difficult to detect faults/failures. To solve this problem, a Total Measurable Fault Information Residual (ToMFIR) technique with the aid of system transformation is adopted to detect faults in turbofan engines with adaptive controllers. This design is a ToMFIR-redundancy-based robust fault detection. The ToMFIR is first introduced and existing results are also summarized. The Detailed design process of the ToMFIRs is presented and a turbofan engine model is simulated to verify the effectiveness of the proposed ToMFIR-based fault-detection strategy.     

Lifetime commissioning as a tool to achieve energy‐efficient solutions

Quality control of the complete energy system is necessary if energy‐efficient solutions are to be met. To perform good building operation and quality control of a given system, it is necessary to have information about building systems and assessment tools. The paper presents Norwegian lifetime commissioning (LTC) procedures that are enabling follow‐up of the building performance during the building lifetime by establishing a generic framework on building performance data. Further, three developed assessment tools are presented: inspection algorithm for ventilation system, mass balance inspection algorithm for consumer substation, and advanced method for improved measurement of heat pump performance based on data fusion technique. The LTC procedures were tested on two case buildings. The results showed that 20% of all the defined building performance data can be monitored by BEMS. Using the mass balance inspection algorithm, it was found that fault in mass balance prevented implantation of desired temperature control for floor heating system. For heat pump performance, measurement of differential water temperature can be very erroneous. Hence, use of compressor electrical signal can give more precise data on heat pump performance. Comparative analysis showed that detailed monitoring system helps tracking energy use and fault detection in operation. Yearly and hourly profiles of energy consumption with separated use and energy carriers are given in the paper. Copyright © 2011 John Wiley & Sons, Ltd.     

Direct estimation of voltage phasor, frequency and its rate of change using Newton''s iterative method

A new approach to the design of a digital algorithm for direct estimation of voltage phasor, frequency and its rate of change is presented. The algorithm derived is based on Newton's iterative method, very commonly used in the field of unconstrained optimization studies. A five-parameter voltage model was assumed, so the result of the estimation was a parameter vector, consisting of the following unknown parameters of the voltage signal processed: its DC component, magnitude, phase angle, frequency and its rate of change. To demonstrate the performance of the algorithm, off-line computer simulation results are presented. The algorithm showed high measurement accuracy over a wide range of frequency changes. The algorithm ‘order two convergence’ provided a very good dynamic response as well as a fast algorithm adaptability. The new algorithm seems to be a particularly useful tool in the field of frequency relaying as well as in various aspects of power engineering applications.     

Model order selection of damped sinusoids in noise by predictivedensities

We develop a procedure for the order selection of damped sinusoidal models based on the maximum a posteriori (MAP) criterion. The proposed method merges the concept of predictive densities with Bayesian inference to arrive at a complex multidimensional integral whose solution is achieved by way of the efficient Monte Carlo importance sampling technique. The importance function, a multivariate Cauchy probability density, is employed to produce stratified samples over the hypersurfaces support region. Centrality location parameters for the Cauchy are resolved by exploiting the special structure of the compressed likelihood function (CLF) and applying the fast maximum likelihood (FML) procedure of Umesh and Tufts. Simulation results allow for a comparison between our method and the singular value decomposition (SVD) based information theoretic criteria of Reddy and Biradar (see IEEE Trans. Signal Processing, vol.41, no.9, p.2872-81, 1993)     

Supervised clustering of label ranking data using label preference information

This paper studies supervised clustering in the context of label ranking data. The goal is to partition the feature space into K clusters, such that they are compact in both the feature and label ranking space. This type of clustering has many potential applications. For example, in target marketing we might want to come up with K different offers or marketing strategies for our target audience. Thus, we aim at clustering the customers’ feature space into K clusters by leveraging the revealed or stated, potentially incomplete customer preferences over products, such that the preferences of customers within one cluster are more similar to each other than to those of customers in other clusters. We establish several baseline algorithms and propose two principled algorithms for supervised clustering. In the first baseline, the clusters are created in an unsupervised manner, followed by assigning a representative label ranking to each cluster. In the second baseline, the label ranking space is clustered first, followed by partitioning the feature space based on the central rankings. In the third baseline, clustering is applied on a new feature space consisting of both features and label rankings, followed by mapping back to the original feature and ranking space. The RankTree principled approach is based on a Ranking Tree algorithm previously proposed for label ranking prediction. Our modification starts with K random label rankings and iteratively splits the feature space to minimize the ranking loss, followed by re-calculation of the K rankings based on cluster assignments. The MM-PL approach is a multi-prototype supervised clustering algorithm based on the Plackett-Luce (PL) probabilistic ranking model. It represents each cluster with a union of Voronoi cells that are defined by a set of prototypes, and assign each cluster with a set of PL label scores that determine the cluster central ranking. Cluster membership and ranking prediction for a new instance are determined by cluster membership of its nearest prototype. The unknown cluster PL parameters and prototype positions are learned by minimizing the ranking loss, based on two variants of the expectation-maximization algorithm. Evaluation of the proposed algorithms was conducted on synthetic and real-life label ranking data by considering several measures of cluster goodness: (1) cluster compactness in feature space, (2) cluster compactness in label ranking space and (3) label ranking prediction loss. Experimental results demonstrate that the proposed MM-PL and RankTree models are superior to the baseline models. Further, MM-PL is has shown to be much better than other algorithms at handling situations with significant fraction of missing label preferences.     

Distance protection and fault location utilizing only phase currentphasors

Two numerical algorithms for fault location and distance protection which use data from one end of a transmission line are presented. Both algorithms require only current signals as input data. Voltage signals are unnecessary for determining the unknown distance to the fault. The solution for the most frequent phase to ground fault is presented. The algorithms are relatively simple and easy to be implemented in the on-line application. The algorithms allow for accurate calculation of the fault location irrespective of the fault resistance and load. To illustrate the features of the new algorithms, steady-state and dynamic tests are presented     

Perfect sampling: a review and applications to signal processing

Markov chain Monte Carlo (MCMC) sampling methods have gained much popularity among researchers in signal processing. The Gibbs and the Metropolis-Hastings (1954, 1970) algorithms, which are the two most popular MCMC methods, have already been employed in resolving a wide variety of signal processing problems. A drawback of these algorithms is that in general, they cannot guarantee that the samples are drawn exactly from a target distribution. New Markov chain-based methods have been proposed, and they produce samples that are guaranteed to come from the desired distribution. They are referred to as perfect samplers. We review some of them, with the emphasis being given to the algorithm coupling from the past (CFTP). We also provide two signal processing examples where we apply perfect sampling. In the first, we use perfect sampling for restoration of binary images and, in the second, for multiuser detection of CDMA signals     

Gaussian sum particle filtering

We use the Gaussian particle filter to build several types of Gaussian sum particle filters. These filters approximate the filtering and predictive distributions by weighted Gaussian mixtures and are basically banks of Gaussian particle filters. Then, we extend the use of Gaussian particle filters and Gaussian sum particle filters to dynamic state space (DSS) models with non-Gaussian noise. With non-Gaussian noise approximated by Gaussian mixtures, the non-Gaussian noise models are approximated by banks of Gaussian noise models, and Gaussian mixture filters are developed using algorithms developed for Gaussian noise DSS models. As a result, problems involving heavy-tailed densities can be conveniently addressed. Simulations are presented to exhibit the application of the framework developed herein, and the performance of the algorithms is examined.     

Order selection of autoregressive models

The problem of determining the order of autoregressive models by Bayesian predictive densities is addressed. A criterion employing noninformative prior densities of the model parameters is derived. Simulation results which demonstrate the good performance of the criterion are presented. Comparisons with four popular approaches verify its superiority in many cases     

A complete quantitative model of the isothermal vapor phase epitaxy of (Hg,Cd)Te

A quantitative model of isothermal vapor phase epitaxy is proposed. It can be applied to both closed and open tube systems. This model enables the prediction of compositional profiles of the layers grown by isothermal vapor phase epitaxy with dependence on the growth parameters and thermodynamical data of the (Hg,Cd)Te system. The dependence of compositional profiles of the ISOVPE layers on temperature and time of deposition, source to substrate spacing, mercury and inert gas pressures are discussed for both solid and liquid sources. Modification of the compositional profiles by the postgrowth annealing has also been studied. The proper choice of growth and annealing parameters makes the optimization of the profiles possible. The calculated profiles are compared with the experimental data and a satisfactory quantitative fit is found in most cases. The possible reasons for remaining discrepancies are discussed.