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.

Testing solar forcing of pervasive Holocene climate cycles

The temporal and spatial extent of Holocene climate change is an area of considerable uncertainty, with solar forcing recently proposed to be the origin of cycles identified in the North Atlantic region. To address these issues we have developed an annually resolved record of changes in Irish bog tree populations over the last 7468 years which, together with radiocarbon-dated bog and lake-edge populations, extend the dataset back to 9000 yr ago. The Irish trees underpin the internationally accepted radiocarbon calibration curve, used to derive a proxy of solar activity, and allow us to test solar forcing of Holocene climate change. Tree populations and age structures provide unambiguous evidence of major shifts in Holocene surface moisture, with a dominant cyclicity of 800 yr, similar to marine cycles in the North Atlantic, indicating significant changes in the latitude and intensity of zonal atmospheric circulation across the region. The cycles, however, are not coherent with changes in solar activity (both being on the same absolute timescale), indicating that Holocene North Atlantic climate variability at the millennial and centennial scale is not driven by a linear response to changes in solar activity.