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International Congress Motor Vehicles & Motors 2012

Kragujevac, October 3rd-5th, 2012

MVM2012-039 Petrović Saša1 Perić Sreten2 Mitrović Melanija3 Lozanović-Šajić Jasmina4

STATISTICAL ENGINE CRANKSHAFT ROTATION ANALYSIS

ABSTRACT: Increasing requirements for a fuel economy, exhaust emissions and the output performance and

also the complexity of the automotive engines necessitate the development of a new generation of the engine control functionality. It is known that engine revolution analysis is excellent tool for this purpose. Main goal in this paper it’s to defining membership function for value distribution crank shaft signal for fault decision expert system based on fuzzy logic. In combination with another data (for example O2 sensor or intake manifold embedded sensors) the Quality decision may be strengthen.

KEYWORDS: statistical analysis, engine rotation, fault diagnosis.

INTRODUCTION

Increasing requirements for a fuel economy exhaust emissions and the output performance and also the complexity of the automotive engines necessitate the development of a new generation of the engine control functionality. Engine torque estimation function is an important function for an engine torque model, misfire diagnostics and dependability. The engine torque estimation function is based on monitoring of the cylinder individual fluctuations of the high resolution engine speed signal [1]. The engine speed signal is based on the measurements of a passage time between two subsequent teeth on a crank wheel. The passage time decreases as the rotational speed increases thus the time interval errors increase. Moreover, low frequency oscillations from the power train and high frequency oscillations due to the crankshaft torsion, together with vibrations induced by the road, act as disturbances on the crankshaft. These disturbances influence directly the performance of the engine speed signal and consequently the torque monitoring function. Many misfire diagnostic functions utilize a low rate sampling of the engine crankshaft speed. Typically, the crankshaft speed is sampled once per cylinder firing event. The engine speed can be approximated by a trigonometric polynomial due to the periodic nature of both engine rotational dynamics and combustion forces as functions of a crank angle [2]. Misfire is the state of an engine where the combustion does not occur due to the errors in fueling or ignition. As a consequence, such misfires affect long term performance of the exhaust emission control system. The misfires cause changes in the crankshaft rate of rotation, because the misfired cylinder is not able to provide the torque. Engine misfire diagnostic functions are based on monitoring of the cylinder individual fluctuations of the high resolution engine speed signal or a passage time between subsequent teeth on a crank wheel. The high resolution engine speed signal is calculated as a ratio of the length of the angular segment on the crank wheel and the passage time for this segment. The passage time becomes less as the rotational speed raises, thereby time interval errors rise.

1 Petrović Saša, M.Sc., Department of Logistics (J-4) General Staff of SAF Belgrade, Serbia, [email protected] 2 Perić Sreten, Ph.D., University of Defence, Military Academy, Belgrade, Serbia, [email protected] 3 Mitrovic Melanija, Ph.D., Faculty of Mechanical Engineering University of Nis, [email protected] 3 Lozanovic-Sajic Jasmina, Ph.D., Faculty of Mechanical Engineering, Innovation Center,University of Belgrade [email protected]

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Usually, measured signal in most cases are transformed to frequency signal. In this paper we analyzed measured signal directly, without any transformation.

Figure 1. Types of sensors: a) variable reluctance sensor; b) Hall effect sensor

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700

800

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1000

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1300

1400

Time/sample priod

RP

M

Figure 2. The estimate circular velocity on the basis of data from sensors

PROBLEM STATEMENT

As a rule, a passage time between two teeth on a crank wheel is measured in production engines. The high resolution engine speed signal is then calculated as a ratio of the length of the angular segment on the crank wheel and the passage time for this segment. For example, the AVL CONCERTO software tools which also can be used for evaluation of the frequency contents of the engine signals require expensive license which should be annually renewed.

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Figure 3. Meny of AVL CONCERTO

We prepare hardware for measurement signal value in voltage from engine crankshaft sensor on real engine. For this, we use universal measurement toll QUANTUM MX-840 and engine embedeed crank shaft speed sensor [3]. The sampling rate it’s very high in interest for high resolution of measurement.

Figure 4. The applied measuring system for data acquisition

Suppose that there is a set of the Crank Angle synchronized data ly , l = 1,…, n (n < 15000) measured at the

following points: x , 1 2, 2 ,.... nx x x n , Δ=0,000417 s.

Misfire state it is controlled manually, for any cylinder (1st to 4th).

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Table 1. Measured data

normal state 1st cyl disabled 2nd cyl disabled 3th cyl disabled 4th cyl disabled

0 -0.34135 0 3.025295 0 -6.24184 0 -2.08619 0 -2.22722

0.000417 3.674666 0.000417 1.620199 0.000417 -11.3549 0.000417 -3.41265 0.000417 -5.53553

0.000833 0.289516 0.000833 -1.45069 0.000833 -2.03844 0.000833 -4.56775 0.000833 -3.39072

0.00125 -1.05096 0.00125 4.253419 0.00125 1.352602 0.00125 -0.94344 0.00125 -3.59625

0.001667 3.290856 0.001667 -0.63544 0.001667 10.69681 0.001667 -6.3695 0.001667 -4.99481

0.002083 0.85035 0.002083 1.482191 0.002083 3.597733 0.002083 -0.25947 0.002083 -2.19339

0.0025 -0.86523 0.0025 2.984 0.0025 2.470534 0.0025 -6.91866 0.0025 -6.96948

0.002917 3.151368 0.002917 -1.86904 0.002917 -3.30137 0.002917 -0.93836 0.002917 -0.89454

0.003333 1.211538 0.003333 3.738023 0.003333 3.078322 0.003333 -5.12115 0.003333 -7.48536

0.00375 -1.40347 0.00375 0.649114 0.00375 -2.19125 0.00375 -3.04726 0.00375 -0.53651

0.004167 3.067888 0.004167 0.193866 0.004167 0.889037 0.004167 -2.96356 0.004167 -6.83391

THE RESULTS OF ANALYSIS AND DISCUSSION

Measured signal it is not transformed in rotation speed or frequency signal, and its evaluated directly.

Figure 5. Interpretation of signals

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Figure 6. Normal working mode and one cylinder disabled

In next few figures are shown histograms of data distribution in normal mode or when some cylinders are disabled.

Figure 7. Signal data distribution

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And summary:

Figure 8. Data distribution for different variants of the engine work (normal work and one cylinder disabled)

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Normal work analysis for different sample of data:

Figure 9. Data distribution for the different sample sizes

And when 2nd cylinder are disabled:

Figure 10. Data distribution when is the second cylinder disabled

All measurements are performed by engine rotation speed of 1500 RPM.

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Figure 11. Uneven distribution of the measuring signal for normal engine work

Figure 12. Uneven distribution of the measuring signal for one cylinder disabled

In this sense exist one problem. When rotation speed rise, signal amplitude and frequency also raised [3] and normally, distribution may be different.

Verifying data First off all, we evaluated signal distribution for different samples [4]. Normal working condition are shown in figure 13.

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Figure 13. Analysis of different sample size (data)- function density for normal distribution

It is clear that function density for normal distribution it is same for different samples (3 groups). Table 2. Normal working

A1: Mean: 0.100009 Variance: 6.5596

A2: Mean: -0.121623 Variance: 7.46086

A3: Mean: -0.120445 Variance: 7.08218

3nd cyl disabled (for example):

Figure 14. Analysis of different sample size (data)- function density for normal distribution (for 3 cylinder disabled)

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Mean values and variance of measured signal are shown in figure 15.

Figure 15. Function density distribution data for all variants of the engine work

nwc: Mean -0.118227 Variance 6.80575 (normal working condition) Dis1: Mean -0.978992 Variance 7.69948 (1st cylinder disabled) Dis2: Mean -0.194629 Variance 8.35745 (2nd cylinder disabled) Dis3: Mean -0.529648 Variance 8.44994 (3rd cylinder disabled) Dis4: Mean -0.264108 Variance 8.6931 (4th cylinder disabled)

Estimation (simulated) for different rotation speed in reason of nature crank shaft sensor signal (induction sensor). In interest it is value of mean.

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5sin(x) vs 5sin(2x)

sin(x)

5sin(2x)

Figure 16. Simulated signal from the engine crankshaft sensor for different working conditions

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Figure 17. Function density distribution data for simulated modes of the engine work

sin( )x , Mean: -1.72582e-005≈0 Variance: 0.500253

5sin(2 )x , Mean: -0.000172676≈0 Variance: 12.5063

After fitting data distribution histogram, it is clear that mean value and form of fitting curve are good start point for defining membership function (MF) for fuzzy sets which may be used in expert system [5], [6], [7]. This expert system then must be capable to make decision in case of misfire. One of mayor problem it is a calibration, especially when we see figure 15.

Figure 18. Model systems for the detection of misfire

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CONCLUSIONS

Automotive makers are not published detailed engine onboard system for fault diagnosis. Complex algorithms for evaluation engine proper or improper work are secret, for reason of expensive development, researching and competition. It is known that engine revolution analysis is excellent tool for this purpose. Capability for capture real data in working conditions, and analyzing them with existing software (for example Matlab) in combination with simulating failures enabled possibility for education, scientific and other tasks. Measurement data (signal) can be analyzed, transformed, filtered, and combined with commercial software. Main goal in this paper it is how to determining membership function (MF) for data distribution signal for fault decision expert system based on fuzzy logic. In combination with another data (for example O2 sensor or intake manifold embedded sensors) the Quality of decision may be strength. One of the key techniques used in this paper is the statistical techniques. A periodic nature of the engine rotational dynamics and a cycle-to-cycle variability allows the presentation of the engine signals as statistical signals utilizing such statistical variables as mean values and standard deviations. These statistical methods are the most future prospective methods for a new generation of robust engine functionality.

REFERENCES

[1] U. Kiencke and L. Nielsen: Automotive Control Systems, For Engine, Driveline, and Vehicle, ISBN 3-540-23139-0.

[2] Alexander A. Stotsky: Automotive Engines, control, estimation, statistical detection, ISBN 978-3-642-00164-2, 2009

[3] M. Popović: Senzori i merenja, (IV издање), 2004.

[4] Statistic Tolbox Matlab User Guide

[5] T. Denton: Advanced Automotive Fault Diagnosis, ISBN-13: 978-0-75-066991-7.

[6] S.L. Kendal: An Introduction to Knowledge Engineering, ISBN 13: 978-1-84628-475-5.

[7] Allan W.M. Bonnick: Automotive computer controlled systems, ISBN 0 7506 5089 3.