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.

Improved Process Monitoring Using Nonlinear Principal Component Models

This paper presents two new approaches for use in complete process monitoring. The firstconcerns the identification of nonlinear principal component models. This involves the application of linear
principal component analysis (PCA), prior to the identification of a modified autoassociative neural network (AAN) as the required nonlinear PCA (NLPCA) model. The benefits are that (i) the number of the reduced set of linear principal components (PCs) is smaller than the number of recorded process variables, and (ii) the set of PCs is better conditioned as redundant information is removed. The result is a new set of input data for a modified neural representation, referred to as a T2T network. The T2T NLPCA model is then used for complete process monitoring, involving fault detection, identification and isolation. The second approach introduces a new variable reconstruction algorithm, developed from the T2T NLPCA model. Variable reconstruction can enhance the findings of the contribution charts still widely used in industry by reconstructing the outputs from faulty sensors to produce more accurate fault isolation. These ideas are illustrated using recorded industrial data relating to developing cracks in an industrial glass melter process. A comparison of linear and nonlinear models, together with the combined use of contribution charts and variable reconstruction, is presented.

Improved principal component monitoring using the local approach

This paper shows that current multivariate statistical monitoring technology may not detect incipient changes in the variable covariance structure nor changes in the geometry of the underlying variable decomposition. To overcome these deficiencies, the local approach is incorporated into the multivariate statistical monitoring framework to define two new univariate statistics for fault detection. Fault isolation is achieved by constructing a fault diagnosis chart which reveals changes in the covariance structure resulting from the presence of a fault. A theoretical analysis is presented and the proposed monitoring approach is exemplified using application studies involving recorded data from two complex industrial processes. © 2007 Elsevier Ltd. All rights reserved.

Improved fault diagnosis in multivariate systems using regression-based reconstruction

Subspace monitoring has recently been proposed as a condition monitoring tool that requires considerably fewer variables to be analysed compared to dynamic principal component analysis (PCA). This paper analyses subspace monitoring in identifying and isolating fault conditions, which reveals that the existing work suffers from inherent limitations if complex fault senarios arise. Based on the assumption that the fault signature is deterministic while the monitored variables are stochastic, the paper introduces a regression-based reconstruction technique to overcome these limitations. The utility of the proposed fault identification and isolation method is shown using a simulation example and the analysis of experimental data from an industrial reactive distillation unit.

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.

Analysis of voltage source converter-based high-voltage direct current under DC line-to-earth fault

DC line faults on high-voltage direct current (HVDC) systems utilising voltage source converters (VSCs) are a major issue for multi-terminal HVDC systems in which complete isolation of the faulted system is not a viable option. Of these faults, single line-to-earth faults are the most common fault scenario. To better understand the system under such faults, this study analyses the behaviour of HVDC systems based on both conventional two-level converter and multilevel modular converter technology, experiencing a permanent line-to-earth fault. Operation of the proposed system under two different earthing configurations of converter side AC transformer earthed with converter unearthed, and both converter and AC transformer unearthed, was analysed and simulated, with particular attention paid to the converter operation. It was observed that the development of potential earth loops within the system as a result of DC line-to-earth faults leads to substantial overcurrent and results in oscillations depending on the earthing configuration.

Reproductive isolation and genetic differentiation of ferox trout from sympatric brown trout in Loch Aweand Loch Laggan, Scotland

Molecular marker studies reported here, involving allozymes, mitochondrial DNA and microsatellites, demonstrate that ferox brown trout Salmo trutta in Lochs Awe and Laggan, Scotland, are reproductively isolated and genetically distinct from co-occurring brown trout. Ferox were shown to spawn primarily, and possibly solely, in a single large river in each lake system making them particularly vulnerable to environmental changes. Although a low level of introgression seems to have occurred with sympatric brown trout, possibly as a result of human-induced habitat alterations and stocking, ferox trout in these two lakes meet the requirements for classification as a distinct biological, phylogenetic and morphological species. It is proposed that the scientific name Salmo ferox Jardine, 1835, as already applied to Lough Melvin (Ireland) ferox, should be extended to Awe and Laggan ferox.

The isolation and secondary functionalisation of fac-tris2,2'-bipyridine complexes of ruthenium(II)

Fac-ruthenium(II) tris-(5-carboxy-2,2'-bipyridine) has been synthesised as a single geometric isomer for the first time, and proves to be a good "building-block" to introduce new functionality with retention of the isomeric integrity.

The isolation and purification of tris-2,2'-bipyridine complexes of ruthenium(II) containing unsymmetrical ligands

Monomeric ruthenium(II) complexes [Ru(L)3]2+ containing unsymmetric bipyridine ligands [Where L = 5-methyl-2,2'-bipyridine (L1), 5-ethyl-2,2'-bipyridine (L2), 5-propyl-2,2'-bipyridine (L3), 5-(2-methylpropyl)-2,2'-bipyridine (L4), 5-(2,2-dimethylpropyl)-2,2'-bipyridine (L5) and 5-(carbomethoxy)-2,2'-bipyridine (L6)] have been studied and the meridional and facial isomers isolated by the use of cation-exchange column chromatography (SP Sephadex C-25) eluting with either sodium toluene-4-sulfonate or sodium hexanoate. The relative yield of the facial isomer was found to decrease with increasing steric bulk, preventing the isolation of fac-[Ru(L5)3]2+. The two isomeric forms were characterized by 1H NMR, with the complexes [Ru(L1-3)3]2+ demonstrating an unusually large coupling between the H6 and H4 protons. Crystals suitable for X-ray structural analysis of [Ru(L1)3]2+ were obtained as a mixture of the meridional and facial isomers, indicating that separation of this isomeric mixture could not be achieved by fractional crystallisation. The optical isomers of the complex [Ru(L3)3]2+ were chromatographically separated on SP Sephadex C-25 relying upon the inherent chirality of the support. It is apparent that chiral interactions can inhibit geometric isomer separation using this technique.