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.

Fast Response Exhaust Gas Temperature Measurement in IC Engines

The harsh environment presented by engines, particularly in the exhaust systems, often necessitates the use of robust and therefore low bandwidth temperature sensors. Consequently, high frequencies are attenuated in the output. One technique for addressing this problem involves measuring the gas temperature using two sensors with different time-constants and mathematically reconstructing the true gas temperature from the resulting signals. Such a technique has been applied in gas turbine, rocket motor and combustion research. A new reconstruction technique based on difference equations has been developed and its effectiveness proven theoretically. The algorithms have been successfully tested and proven on experimental data from a rig that produces cyclic temperature variations. These tests highlighted that the separation of the thermocouple junctions must be very small to ensure that both sensors are subjected to the same gas temperatures. Exhaust gas temperatures were recorded by an array of thermocouples during transient operation of a high performance two-stroke engine. The results show that the increase in bandwidth arising from the dual sensor technique allowed accurate measurement of exhaust gas temperature with relatively robust thermocouples. Finally, an array of very fine thermocouples (12.5 - 50 microns) was used to measure the in-cycle temperature variation in the exhaust.