Diesel Particulate Filter Diagnostics Using Correlation and Spectral Analysis

Surve, Pranati Ramdas. M.S.E.C.E., Purdue University, August 2008. Diesel Particulate Filter Diagnostics Using Correlation and Spectral Analysis. Major Professors: Peter H. Meckl and Venkataramanan Balakrishnan. Diesel Particulate Filters (DPF) are used to trap the harmful particulate matter (PM) present in the exhaust of diesel engines. The particulate matter is trapped in and on a porous ceramic substrate to keep PM emissions low. The onboard diagnostics requirements enforced by Environmental Protection Agency (EPA) require that the DPF perform well to keep emissions below certain specified levels. Further, should the DPF fail in any way, resulting in higher emission levels, this event must be detected by the engine control module. The objective of this work is to “detect failed DPF condition”. The temperature and pressure signals from transducers inserted into the inlet and outlet of the DPF are analyzed. The approach is to correlate the pre-DPF and post-DPF temperature and pressure signals and define the transfer function characteristics for nominal DPF behavior. Determining how these characteristics change as a result of filter failure forms the basis of a DPF fault detection algorithm. It is observed from the test data that for the pressure signal, other than the mean value signal (i.e., at zero frequency), most of the energy content is concentrated at the firing frequency of the engine. The dynamic pressure signals are used to determine the magnitude squared of the transfer function characteristics of DPF by energy spectral analysis. This approach can achieve a failure detection of lightly failed DPF which is not possible by current algorithms based on mean value pressure drop. The most significant contribution of this research is the extension of dynamic pressure signal analysis from steady-state engine operation to transient operating conditions.