Clustering-Based Ensemble Method as an Alternative to Stacking

One of the most popular techniques of generating classifier ensembles is known as stacking which is based on a meta-learning approach. In this paper, we introduce an alternative method to stacking which is based on cluster analysis. Similar to stacking, instances from a validation set are initially classified by all base classifiers. The output of each classifier is subsequently considered as a new attribute of the instance. Following this, a validation set is divided into clusters according to the new attributes and a small subset of the original attributes of the instances. For each cluster, we find its centroid and calculate its class label. The collection of centroids is considered as a meta-classifier. Experimental results show that the new method outperformed all benchmark methods, namely Majority Voting, Stacking J48, Stacking LR, AdaBoost J48, and Random Forest, in 12 out of 22 data sets. The proposed method has two advantageous properties: it is very robust to relatively small training sets and it can be applied in semi-supervised learning problems. We provide a theoretical investigation regarding the proposed method. This demonstrates that for the method to be successful, the base classifiers applied in the ensemble should have greater than 50% accuracy levels.

Fault Diagnosis in Internal Combustion Engines Using Nonlinear Multivariate Statistics

This paper presents a statistical-based fault diagnosis scheme for application to internal combustion engines. The scheme relies on an identified model that describes the relationships between a set of recorded engine variables using principal component analysis (PCA). Since combustion cycles are complex in nature and produce nonlinear relationships between the recorded engine variables, the paper proposes the use of nonlinear PCA (NLPCA). The paper further justifies the use of NLPCA by comparing the model accuracy of the NLPCA model with that of a linear PCA model. A new nonlinear variable reconstruction algorithm and bivariate scatter plots are proposed for fault isolation, following the application of NLPCA. The proposed technique allows the diagnosis of different fault types under steady-state operating conditions. More precisely, nonlinear variable reconstruction can remove the fault signature from the recorded engine data, which allows the identification and isolation of the root cause of abnormal engine behaviour. The paper shows that this can lead to (i) an enhanced identification of potential root causes of abnormal events and (ii) the masking of faulty sensor readings. The effectiveness of the enhanced NLPCA based monitoring scheme is illustrated by its application to a sensor fault and a process fault. The sensor fault relates to a drift in the fuel flow reading, whilst the process fault relates to a partial blockage of the intercooler. These faults are introduced to a Volkswagen TDI 1.9 Litre diesel engine mounted on an experimental engine test bench facility.

A Phase-field Model for Computer Simulation of Lamellar Structure Formation in y-TiAI

In the present paper, a phase-field model is developed to simulate the formation and evolution of lamellar microstructure in γ-TiAl alloys. The mechanism of formation of TiAl lamellae proposed by Denquin and Naka is incorporated into the model. The model describes the formation and evolution of the face-centered cubic (fcc) stacking lamellar zone followed by the subsequent appearance and growth of the γ-phase, involving both the chemical composition change by atom transfer and the ordering of the fcc lattice. The thermodynamics of the model system and the interaction between the displacive and diffusional transformations are described by a non-equilibrium free energy formulated as a function of concentration and structural order parameter fields. The long-range elastic interactions, arising from the lattice misfit between the α, fcc (A1) and the various orientation variants of the γ-phase are taken into account by incorporating of the elastic strain energy into the total free energy. Simulation studies based on the model successfully predicted some essential features of the lamellar structure. It is found that the formation and evolution of the lamellar structure are predominantly controlled by the minimization of the elastic energy of the interfaces between the different fcc stacking groups, low-symmetry product phase γ and the high-symmetry α-phase, as well as between the various orientation variants of the product phase.

Environmental Isotopes as Indicators for Ground Water Recharge to Fractured Granite

To assess the contribution of accumulated winter precipitation and glacial meltwater to the recharge of deep ground water flow systems in fracture crystalline rocks, measurements of environmental isotope ratios, hydrochemical composition, and in situ parameters of ground water were performed in a deep tunnel. The measurements demonstrate the significance of these ground water recharge components for deep ground water flow systems in fractured granites of a high alpine catchment in the Central Alps, Switzerland. Hydrochemical and in situ parameters, as well as d18O in ground water samples collected in the tunnel, show only small temporal variations. The precipitation record of d18O shows seasonal variations of ~14‰ and a decrease of 0.23‰ ± 0.03‰ per 100 m elevation gain. d2H and d18O in precipitation are well correlated and plot close to the meteoric water line, as well as d2H and d18O in ground water samples, reflecting the meteoric origin of the latter. The depletion of 18O in ground water compared to 18O content in precipitation during the ground water recharge period indicates significant contributions from accumulated depleted winter precipitation to ground water recharge. The hydrochemical composition of the encountered ground water, Na-Ca-HCO3-SO4(-F), reflects an evolution of the ground water along the flowpath through the granite body. Observed tritium concentrations in ground water range from 2.6 to 16.6 TU, with the lowest values associated with a local negative temperature anomaly and anomalous depleted 18O in ground water. This demonstrates the effect of local ground water recharge from meltwater of submodern glacial ice. Such localized recharge from glaciated areas occurs along preferential flowpaths within the granite body that are mainly controlled by observed hydraulic active shear fractures and cataclastic faults.

Feasibility of online collaborative voltage stability control of power systems

This paper presents the results of feasibility study of a novel concept of power system on-line collaborative voltage stability control. The proposal of the on-line collaboration between power system controllers is to enhance their overall performance and efficiency to cope with the increasing operational uncertainty of modern power systems. In the paper, the framework of proposed on-line collaborative voltage stability control is firstly presented, which is based on the deployment of multi-agent systems and real-time communication for on-line collaborative control. Then two of the most important issues in implementing the proposed on-line collaborative voltage stability control are addressed: (1) Error-tolerant communication protocol for fast information exchange among multiple intelligent agents; (2) Deployment of multi-agent systems by using graph theory to implement power system post-emergency control. In the paper, the proposed on-line collaborative voltage stability control is tested in the example 10-machine 39-node New England power system. Results of feasibility study from simulation are given considering the low-probability power system cascading faults.

Nonlinear PCA with Local Approach for Diesel Engine Fault Detection and Diagnosis

This brief examines the application of nonlinear statistical process control to the detection and diagnosis of faults in automotive engines. In this statistical framework, the computed score variables may have a complicated nonparametric distri- bution function, which hampers statistical inference, notably for fault detection and diagnosis. This brief shows that introducing the statistical local approach into nonlinear statistical process control produces statistics that follow a normal distribution, thereby enabling a simple statistical inference for fault detection. Further, for fault diagnosis, this brief introduces a compensation scheme that approximates the fault condition signature. Experimental results from a Volkswagen 1.9-L turbo-charged diesel engine are included.

A 4500-year proxy climate record from peatlands in the North of Ireland: the identification of widespread summer ‘drought phases’?

Mid-to-late Holocene high-resolution testate amoebae-derived water table reconstructions from two peatlands in the North of Ireland are presented. The proxy climate records are dated and correlated using a combination of AMS 14C dating, spheroidal carbonaceous particles and tephrochronology. The reconstructions start prior to the Hekla 4 tephra isochron (2395–2279 BC) and thus span the last ~4500 years. The records are compiled by the process of tuning within chronological errors, standardisation and stacking. Comparisons are made to existing palaeoclimate records from peatlands in Northern Britain and Ireland and the compiled lake-level record for mid-latitude Europe. Four coherent dry phases are identified in the records at ca 1150–800 BC, 320 BC–AD 150, AD 250–470 and AD 1850–2000. Recent research has shown that peat-derived water table reconstructions reflect summer water deficit and therefore the dry phases are interpreted as periods with a higher frequency and/or greater magnitudes of summer drought. These ‘drought phases’ occur during periods of relatively low 14C production, which may add support to the hypothesis of persistent solar forcing of climate change during the Holocene. Any relationship with the North Atlantic stacked drift ice record is less clear. © 2009 Elsevier Ltd. All rights reserved.

Application of auto-associative neural networks to transient fault detection in an IC engine

The tailpipe emissions from automotive engines have been subject to steadily reducing legislative limits. This reduction has been achieved through the addition of sub-systems to the basic four-stroke engine which thereby increases its complexity. To ensure the entire system functions correctly, each system and / or sub-systems needs to be continuously monitored for the presence of any faults or malfunctions. This is a requirement detailed within the On-Board Diagnostic (OBD) legislation. To date, a physical model approach has been adopted by me automotive industry for the monitoring requirement of OBD legislation. However, this approach has restrictions from the available knowledge base and computational load required. A neural network technique incorporating Multivariant Statistical Process Control (MSPC) has been proposed as an alternative method of building interrelationships between the measured variables and monitoring the correct operation of the engine. Building upon earlier work for steady state fault detection, this paper details the use of non-linear models based on an Auto-associate Neural Network (ANN) for fault detection under transient engine operation. The theory and use of the technique is shown in this paper with the application to the detection of air leaks within the inlet manifold system of a modern gasoline engine whilst operated on a pseudo-drive cycle. Copyright © 2007 by ASME.

PLS modelling and fault detection on the Tennessee Eastman benchmark

This paper describes the application of multivariate regression techniques to the Tennessee Eastman benchmark process for modelling and fault detection. Two methods are applied : linear partial least squares, and a nonlinear variant of this procedure using a radial basis function inner relation. The performance of the RBF networks is enhanced through the use of a recently developed training algorithm which uses quasi-Newton optimization to ensure an efficient and parsimonious network; details of this algorithm can be found in this paper. The PLS and PLS/RBF methods are then used to create on-line inferential models of delayed process measurements. As these measurements relate to the final product composition, these models suggest that on-line statistical quality control analysis should be possible for this plant. The generation of `soft sensors' for these measurements has the further effect of introducing a redundant element into the system, redundancy which can then be used to generate a fault detection and isolation scheme for these sensors. This is achieved by arranging the sensors and models in a manner comparable to the dedicated estimator scheme of Clarke et al. 1975, IEEE Trans. Pero. Elect. Sys., AES-14R, 465-473. The effectiveness of this scheme is demonstrated on a series of simulated sensor and process faults, with full detection and isolation shown to be possible for sensor malfunctions, and detection feasible in the case of process faults. Suggestions for enhancing the diagnostic capacity in the latter case are covered towards the end of the paper.

Current transients in the small salient-pole alternator caused by sudden short-circuit and synchronisation events

Small salient-pole machines, in the range 30 kVA to 2 MVA, are often used in distributed generators, which in turn are likely to form the major constituent of power generation in power system islanding schemes or microgrids. In addition to power system faults, such as short-circuits, islanding contains an inherent risk of out-of-synchronism re-closure onto the main power system. To understand more fully the effect of these phenomena on a small salient-pole alternator, the armature and field currents from tests conducted on a 31.5 kVA machine are analysed. This study demonstrates that by resolving the voltage difference between the machine terminals and bus into direct and quadrature axis components, interesting properties of the transient currents are revealed. The presence of saliency and short time-constants cause intriguing differences between machine events such as out-of-phase synchronisations and sudden three-phase short-circuits.