Misfire detection system and method using recursive median filtering for high data rate engine control system

An improved misfired detection system and method for high data rate engine control systems. A method of detecting misfires of individual cylinder events in an internal combustion engine is disclosed which includes the step of generating a high data rate velocity signal indicative of the torque behavior of the engine. The velocity signal is then filtered and separated into a plurality of non-interlaced homogeneous data sets wherein each of the data sets represents like portions of the internal combustion engine cycle. A median filter is then applied to each of the plurality of non-interlaced homogeneous data sets. In this manner, the data sets are normalized according to the applied recursive median filter and centered about a relative zero. This centering effect allows the signals to be recombined after filtering, thereby monitoring the nature of any deviations present in the signal. A threshold comparison of the resulting signal is then made to determine whether a misfire has occurred.

BACKGROUND OF THE INVENTION 
The present invention relates in general to detecting misfires occurring 
during normal operation of an internal combustion engine and, more 
specifically, to an improved signal processing system and method for 
detecting misfires in a reciprocating engine. 
Present engine control systems often include misfire detection systems. As 
emission standards become increasingly stringent, there is a need for 
accurate misfire detection and reporting under all engine operating 
conditions. 
Monitoring crankshaft engine angular velocity and crankshaft acceleration 
are preferred techniques for detecting misfires of individual cylinder 
firings during engine operation. Both of these measurements depend largely 
upon engine torque produced during the combustion process to determine 
misfiring of particular engine cylinders. Given the velocity or 
acceleration information, misfires are predicted by various signature 
analysis and/or spectral analysis methods. 
Several present misfire detection systems are well suited for detecting 
misfires in low data rate applications. Low data rate systems typically 
seek to detect misfires based on entire cylinder event intervals as 
measured by, for example, the profile ignition pulse (PIP) signal. In a 
four cylinder engine, the PIP signal actually indicates the approach to 
top dead center of two engine cylinders, one of which is approaching a 
power stroke and one of which is approaching an intake stroke. This is so 
because it takes two full crankshaft rotations to complete an engine 
cycle. In engines having more than four cylinders, however, the power 
strokes of different cylinders will overlap. Accordingly, associating 
crankshaft acceleration fluctuations with any particular cylinder becomes 
more difficult because the firing induced crankshaft accelerations occur 
over smaller rotational angles. 
Accordingly, in engines having more than four cylinders, it is desirable to 
monitor average crankshaft acceleration over smaller crankshaft rotational 
intervals for improved misfire detection. As a practical matter, however, 
the engine angular velocity and acceleration behavior is also affected by 
powertrain-related behaviors other than firing torque. These other 
conditions can significantly reduce the signal-noise ratio of the firing 
torque-related velocity or acceleration signal under analysis. In 
addition, under certain engine operating conditions, the noise exceeds the 
engine torque-related velocity or acceleration signal under analysis. 
Moreover, noise is introduced into the crankshaft acceleration signal due 
to crankshaft torsional vibrations, inertial torque due to reciprocating 
masses, and other mechanically induced vibrations on the engine's 
crankshaft. This noise can hide or mask any signatory behavior of a 
misfire event. 
One example of a high data rate misfire detection system with improved 
signal fidelity is disclosed in U.S. Pat. No. 5,515,720. In that patent, 
the improved fidelity acceleration signal is provided by a median filter 
operating on an acceleration signal where the median filter's rank is 
programmable dependent upon the determined operating condition of the 
engine. 
The data stream relied upon by such high data rate systems, however, 
comprise interlaced non-homogeneous data points. Accordingly, crankshaft 
acceleration signatures based upon such data can mask or hide misfire 
events. In other words, when data is sampled at higher rates, i.e., at 
smaller crankshaft rotational angles, the samples are interlaced and 
represent different portions of the engine cycle. When these interlaced 
non-homogeneous samples are coupled with heuristic-type filters, such as a 
median filter which is designed to determine some root nature of a signal 
or the difference between the measured value and the root nature of a 
signal, errors are often introduced. In addition, if the signal is 
modified prior to being operated upon by these rule-based filters, the 
root nature of the signal is also modified. 
Accordingly, there is a need for an improved misfire detection system and 
method which accounts for the small non-homogeneous angular sampling 
period of high data rate systems and the complications associated with 
interlaced data in heuristic-type filters in determining deviant 
accelerations or velocities to identify loss of combustion quality in 
internal combustion engines. 
SUMMARY OF THE INVENTION 
An object of the present invention is to provide an improved misfired 
detection system and method. 
The present invention is advantageous in that it accounts for errors 
introduced by second mode torsional vibration characteristics, the small 
non-homogeneous angular sampling period of high data rate systems, data 
skewing due to preprocessing such as low pass filtering, and problems 
associated with processing interlaced data in heuristic-type filters. 
In one aspect of the invention, a method of detecting misfires of 
individual cylinder events in an internal combustion engine is disclosed 
which includes the step of generating a velocity signal indicative of the 
torque behavior of the engine wherein the velocity signal comprises 
non-homogeneous interlaced data sets. The velocity signal is then filtered 
and separated into a plurality of non-interlaced homogeneous data sets 
wherein each of the data sets represents like portions of the internal 
combustion engine cycle. A median filter is then applied to each of the 
plurality of non-interlaced homogeneous data sets. The data sets are then 
re-interlaced, and a misfire indication is provided as a function of the 
re-interlaced velocity signal. In one aspect of the invention, the applied 
median filter is a recursive median filter. 
Another advantage of the present invention is that by separating the 
velocity signal into distinct non-interlaced homogeneous data sets, the 
data sets can be normalized. Once this data has been de-interlaced 
according to the applied heuristic, it can then be operated on by 
heuristic-type filters such as a median filter, mean filter, or logic 
filter. Since the effect of these heuristic-type filters is to normalize a 
data set according to the applied heuristic, the corresponding outputs of 
each homogeneous data set will be centered about the heuristic. This 
centering effect allows the signals to be recombined after the heuristic 
is applied since each data set will be centered about its relative zero. 
Thus, the nature of any deviations (i.e., misfires) is maintained. 
Other objects and advantages of the invention will become apparent upon 
reading the following detailed description and appended claims, and upon 
reference to the accompanying drawings.

DETAILED DESCRIPTION OF A PREFERRED EMBODIMENT 
In the typical four-stroke combustion engine, the four strokes include the 
intake stroke, the compression stroke, the power stroke, and the exhaust 
stroke. As shown in FIG. 1, the power strokes of the respective cylinders 
are arranged in a particular order according to crankshaft position. 
Furthermore, in any engine having more than four cylinders such as the six 
cylinder engine shown, the power strokes of different cylinders will 
overlap. This smoothes the crankshaft velocity and acceleration profile, 
but makes misfire detection associated with a particular cylinder more 
difficult. As can be seen in FIG. 1, one engine cycle is comprised of 
720.degree. of crankshaft rotation during which each cylinder passes 
through each of its four strokes. 
Curve 10 in FIG. 1 shows approximate acceleration fluctuation during engine 
operation. An acceleration peak 11 occurs during the firing interval of 
cylinder no. 1 and other maximums in the acceleration curve occur 
approximately corresponding to each other properly firing cylinder. When a 
misfire occurs such that no significant power is created by a cylinder 
during its firing interval, the crankshaft decelerates as indicated at 12. 
One embodiment of a high data rate system for gathering crankshaft velocity 
data is shown in FIG. 2. FIG. 2 shows a ten cylinder engine 20 with a 
crankshaft 22. A related engine control unit (ECU) 23 includes an input 
for receiving a top dead center (TDC) signal 25 from a camshaft rotational 
sensor 27 which indicates a TDC of cylinder no. 1 #29 of the ten cylinders 
30 of the engine 20. This TDC signal 25 is provided responsive to 
measuring a lobe 31 on the end of a camshaft 33. This camshaft rotational 
sensor 27 provides the TDC signal 25 to the ECU 23 every 720.degree. of 
crankshaft angular rotation. The TDC signal 25 is used by the ECU 23 to 
determine a starting point for measuring which of the cylinders 30 is 
currently firing and thus causing acceleration of the engine's crankshaft 
22. 
Another input to the ECU 23 is provided by an engine angular displacement 
sensor 35. This sensor measures engine angular displacement by sensing a 
tooth wheel 37 mounted on the engine's crankshaft 22. The engine angular 
displacement sensor 35 provides an engine angular displacement signal 39 
to the ECU 23 every 18.degree. of engine crankshaft rotation based on a 
tooth-space pattern on the toothed wheel 37. The engine angular 
displacement signal 39 is used by the ECU 23 to measure engine angular 
velocity, crankshaft acceleration, and also to identify the active 
cylinder in case of a misfire. 
The ECU 23 includes a central processing unit (CPU) such as a 
microprocessor 24 and associated memory 26. The microprocessor 24 converts 
the signals 25 and 39 from pulses to digital information representative of 
the information contained in the pulses used later in the executed method 
steps. 
FIG. 3 is a graph of the high data rate stream of information from which 
crankshaft velocity and acceleration are determined. The graph 40 
represents the engine angular displacement signal 39 resulting from the 
engine angular displacement sensor 35 and toothed wheel 37 for the engine 
20 of FIG. 2. As shown in FIG. 3, the engine angular displacement signal 
can be represented by a scuare wave signal 40 with edges corresponding to 
crankshaft angle. These edges are time-stamped at a given clock resolution 
which corresponds to some fixed angular interval of rotation. This angular 
interval is then divided by the time interval to determine a measured 
angular velocity. For a ten cylinder engine having a "40-1" toothed wheel, 
each combustion event 42 can be represented by four data samples (s.sub.0 
-s.sub.3, s.sub.4 -s.sub.7, etc.) wherein each data sample represents 
18.degree. of angular rotation (i.e., 360.degree. divided by 20 samples 
per wheel revolution). 
The crankshaft velocity signal is subject to many sources of variation, 
however. These include, but are not limited to the characteristics of the 
rotational interval (e.g., intake, compression, combustion, exhaust); the 
cylinder-to-cylinder combustion variations; the torsional vibrations of 
the crankshaft; the characteristics of balance mechanisms; and drive cycle 
variations. 
To remove these variations from the velocity signal, different filtering 
algorithms can be employed. These include low pass, band pass, or high 
pass filters depending upon the desired output. These filters introduce a 
smearing effect on the signal, however, which may not be desirable, and 
may affect a single measured sample or, under strong attenuation, many 
samples. This smearing alters the fundamental or root nature of the input 
signal and results in an altered filtered velocity signal. If this skewed 
signal or its derivatives are heuristically filtered, such as with a 
median filter, the root signal may be difficult to obtain and, therefore, 
the difference of any measured signal from that root signal may be less 
than optimal. This error can be demonstrated with reference to FIG. 4. 
FIG. 4 graphically represents the crankshaft acceleration based upon the 
interlaced and non-interlaced data sets of FIG. 3. As the engine speed 
increases as shown at 50, the engine angular displacement signal 40 (FIG. 
3) can be low pass filtered and median filtered to determine a root signal 
of crankshaft acceleration 52. The acceleration signal 52, however, 
represents a non-homogeneous interlaced data set. It is non-homogeneous in 
that the resulting signal 52 represents different portions of the cylinder 
events. In other words, C.sub.0 represents a different portion of the 
cylinder event than C.sub.1, and so on. Because the data stream is 
non-homogeneous, it inherently contains noise which can mask or hide a 
misfire event shown as 53 in the crankshaft acceleration signal 52. 
The present invention reduces the effects of velocity variations associated 
with the interlaced non-homogeneous data by normalizing the crankshaft 
acceleration signal. This is accomplished by sampling the interlaced 
non-homogeneous data stream 40 at only the crankshaft intervals of 
interest, or splitting data into channels wherein the samples in each 
channel correspond to homogeneous data sets based on like cylinder events 
or intervals. 
As shown in FIGS. 3 and 4, each cylinder event 42 can be divided into four 
channels C.sub.0, C.sub.1, C.sub.2, C.sub.3. A plot of the crankshaft 
acceleration with respect to each individual channel is shown in FIG. 4 as 
C.sub.0 -C.sub.3. Once the data has been de-interlaced, it can then be 
operated on by a heuristic-type filter, such as a median filter, mean 
filter, or logic filter. Since the effect of these heuristic-type filters 
is to normalize a data set according to the applied heuristic, the 
corresponding outputs of each channel are centered about the heuristic. 
This centering effect allows the signals to be recombined after the 
heuristic is applied, as shown in normalized acceleration profile of FIG. 
4. Since each channel is centered about its relative zero, the nature of 
any deviations will be maintained. This is shown, for example, by the 
deceleration 54 of channel C.sub.0 which was masked in the acceleration 
signal 52 resulting from the interlaced non-homogeneous data set. As can 
be seen, the deviation is readily apparent as deviation 56 in the 
normalized and recombined homogeneous non-interlaced data sets. Once the 
signals are recombined in this manner, additional computations may be 
performed or the signal may be compared to thresholds to detect deviations 
representing misfire events. 
One embodiment of the method steps required to detect misfires based upon 
the method described above is shown in FIG. 5. These method steps are 
continuously executed and start with step 101. In the preferred 
embodiment, the logic routine of FIG. 5 resides in the memory 26 of the 
ECU 23 as executed by the microprocessor 24 (FIG. 2). Of course, the logic 
could be executed by any peripheral device which may or may not reside in 
the ECU 24. 
In block 102, the engine angular displacement signal 39 is received from 
the engine angular displacement sensor 35 (FIG. 2) and is used to derive 
the angular velocity of the engine's crankshaft. This is a function of the 
known angular spacing of the teeth on the toothed wheel, and the time 
measured between the teeth. As mentioned above, this velocity signal 103 
is subject to many sources of variation. To remove some variations from 
the velocity signal 103, a filtering circuit is used in block 104. In this 
case, an order programmable low pass filter is used to attenuate the 
crankshaft torsional vibrations, inertial torque due to reciprocating 
masses, and other mechanically induced vibrations on the engine's 
crankshaft. The programmable low pass filter's order can be made variable 
as a function of engine speed and load to improve signal fidelity as is 
known in the art so that misfire detection capability can be enhanced. 
In order to further improve the signal characteristics before determining 
the crankshaft angular acceleration, the interlace data stream is split 
into separate channels at block 106. Once separated, each channel 
corresponds to a homogeneous data set based on some apriori knowledge, 
such as similar crankshaft angles, common engine cycle elements, or common 
torsional effects. For example, as shown in FIG. 3, for a ten cylinder 
engine, channel C.sub.0 would represent the first 18.degree. of rotation 
after TDC for each cylinder, channel c.sub.1, represents the next 
18.degree. of rotation, etc. In this manner, it is desirable to analyze 
like channels from cylinder event to cylinder event to detect deviations 
characteristic of misfires. Thus, the data set comprising all like 
channels, such as c.sub.0 is referred to as a homogeneous non-interlaced 
data set. 
In this case, the first heuristic applied separates the data into 
homogeneous non-interlaced data sets. For misfire detection, in this 
example, each of these data sets represents a similar portion of different 
cylinder events. In a similar manner, a different heuristic could, for 
example, separate the data sets to correspond to the intake valve opening 
of each cylinder. 
Once this data has been de-interlaced according to the applied heuristic at 
block 106, it can then be operated on by heuristic-type filters, such as a 
median filter, at step 108. A median filter is applied to each of the 
channels to normalize the data set and center the corresponding outputs. 
This centering effect allows the channels to be recombined in step 109 
after the median filter is applied so that deviations can be maintained 
and misfires can be detected. 
After the channels are recombined or re-interlaced at step 109, the 
crankshaft angular acceleration is determined at step 110. 
Alternatively, the reinterlaced data sets resulting from step 109 can be 
compared to threshold values to detect deviations representative of 
misfires. For improved misfire detection, however, it is preferable to 
determine the angular acceleration at step 110 and further processes the 
acceleration signal by a recursive median filter of selectable rank in 
step 112. Furthermore, the rank of the recursive median filter may be made 
dependent upon the pattern of misfire characteristic of he engine under 
analysis. 
Once the median filter has been applied in step 112, the oversampled data 
is reduced to one sample per combustion event such that a misfire can be 
associated with a particular cylinder. This is shown in step 114. 
At step 116, the data point is compared to a calibrated threshold value. If 
the data point deviates from the expected crankshaft acceleration by more 
than the calibrated threshold amount, it is determined to be a misfired 
event. 
For engine diagnostic purposes, misfire events are commonly stored in ECU 
memory 26. In addition, if misfires occur as a substantial percentage of 
all cylinder events, a malfunction indicator light can be activated at 
step 118 to alert the operator. 
In the misfire detection scheme just described, it is important that the 
median filter applied at step 108 converge to the root signal as quickly 
as possible. This allows the system to get to the root signal with the 
minimal number of computational steps. This is desirable if the system is 
to run in real-time. For this reason, the median filter applied at step 
108 is preferably a recursive median filter. One characteristic of a 
recursive filter is its rapid convergence to the root signal. As the name 
implies, in a recursive median filter, the filter output is fed back into 
the filter input For example, in a median filter having an odd number of 
sorted sample values, the mid or median value is used as the filter 
output. Thus, the output of a median filter y(A) is given by: 
EQU y(A)=median values of [x(A-N), . . . , x(A-1), x(A), x(A+1), . . . , x(A+N) 
] 
wherein N represents half the rank of the median filter and A is an index 
into the data stream. 
A recursive median filter, however, is defined by the following operation: 
EQU y(A)=median value of [y(A-N), . . . , y(A-2), x(A), x(A+1), . . . , x(A+N)] 
In this manner, the output y(A) is fed back into the filter allowing the 
recursive operation to generate a root output in a single pass through the 
data stream. 
Similarly, the median filter applied in step 112 is also preferably a 
recursive median filter of selectable rank. This is to create an enhanced 
difference between the median filtered acceleration values and the 
measured values. It will be seen that the present invention overcomes the 
drawbacks associated with processing interlaced non-homogeneous data 
points which are commonly associated with high data rate misfire detection 
systems. While the invention has been described in connection with one or 
more embodiments, it will be understood that the invention is not limited 
to those embodiments. For instance, the method steps of de-interlacing, 
filtering and re-interlacing (steps 106, 108, 109) could be performed at 
different or multiple times in the process. Specifically, these steps 
could be performed on the raw high data rate signal or the angular 
velocity signal prior to filtering (step 104). Alternatively, these steps 
could be performed after the angular acceleration has been determine (step 
110). Furthermore, these steps could be repeated in place of the median 
filter (step 112) depending upon the characteristic desired to be 
detected. Thus, the invention covers all alternatives, modifications, and 
equivalents, as may be included within the spirit and scope of the 
appended claims.