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Diagnostic Method for 2-Stroke Cycle Diesel Engine 57

October 1999

Technical Papers

(3)

* Translated from Journal of MESJ Vol.33, No.10

(Manuscript received Dec. 15, 1997)

** Kobe University of Mercantile Marine (Kobe City)

*** Kobe Nippon Kisen Kaisha, Ltd. (Kobe City)**** Kawasaki Kisen Kaisha, Ltd. (Minato-ku Tokyo)

Diagnostic Method for 2-Stroke Cycle Diesel Engine*

by Measurement of Vibration on Cylinder-Jacket− Observation of Change in Normal Vibration Pattern− Ryuichi Kimura**, Wataru Terashima**, Noboru Nakai**,

Tetsuo Yamada***, Shou Takeda****

It is necessary to arrange the system to monitor the operational condition by various methods to achieve the safe

operation of the engine. Based on the idea that the information on the engine condition can be obtained from its

vibration signal, this paper carries out the experiment of the diesel engine of the actual ship. The vibration was

measured for a long time (7800 hours) by means of a vibration sensor on the cylinder jacket. The hand touch cannot recognize the time change of the vibration, but the three-dimensional expression of the vibration data through

the frequency analysis allows the good understanding of the change of the vibration. The vibration is always

changing if the vibration is observed on the long running hours of engine basis even in the stationary operation

(engine revolution: 103 rpm) of the diesel engine. We executed the statistical analysis as one method to estimate the

condition of engine by the information from the vibration.

All the data of vibration was recorded on a normal engine condition, as a result we could not examine on an

abnormal engine condition. Thus, the vibration data of low engine revolution (30 rpm) was used as the data of

abnormal condition in this analysis. As the result of the analysis, it is found that the abnormal can be recognized from

the above-mentioned engine condition. We succeeded to make the statistical model which can diagnose an engine

condition.

1. Introduction

The reliability of the main engine must be ex-

tremely high in order to ensure the safe navigation of

ships. It is thus necessary to locate the phenomena of

inconveniences and troubles even when they are small

and insignificant, and to pinpoint the causes and take

countermeasures therefor. We have known empirically

that the abnormal vibration and the abnormal sound

generated in a main engine are the symptom of possible

failures or accidents. It is understood from these fact-

findings that much information to indicate the state of

the engine is contained in the radiant noise and the

vibration of the engine. From such a viewpoint, the

paper has aimed at the construction1),2) of the monitor-

ing system capable of grasping the condition of the

engine by positively using these information.

In any equipment including marine diesel en-

gines, deterioration and abrasion are surely generated

in each part. The phenomena gradually increased in the

elapse of the running time, and eventually, leading tofailures or accidents. In order to prevent these failures

or accidents beforehand, it is necessary to grasp the

information related thereto from various angles.

In this experiment, the vibration of the side wall

part of the low-speed diesel engine was recorded for a

long time, and the condition that the vibration is changed

as the running time was examined. Firstly, the recorded

vibration was frequency-analyzed to store the data on

the vibration to construct the diagnosis system. The

analysis method of the running condition introduced

here includes the visual judgment, which grasps the

motion of the engine based on the information by

indicating the data of the radiant noise in a three-

dimensional manner. The statistical analysis is then

achieved in an objective manner based on the data

information, in addition to the visual judgment, the

results were evaluated and examined, and the diagnos-

tic system capable of coping with the phenomenon to be

changed with the time, was also considered.

2. Experiments

Two-stroke cycle diesel engine mounted on a carcarrier (19,000 dwt) was used as a test engine in this



Bulletin of the M.E.S.J., Vol. 27, No.2

58 Diagnostic Method for 2-Stroke Cycle Diesel Engine

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study. The specification of this engine is 8-cylinder and

16,320 kW in output. The data collection was started

immediately after the manufacture of the engine, and

the data obtained in one and a half years after the ship

went into service (the running time of the engine : 7800

hours). Fig. 1 is a section of the engine, a vibration

sensor is mounted on the part indicated by an arrow in

the figure, and the radiant sound was recorded at the

position away from the vibration sensor mounting

position by 5 cm in the vertical direction. And, at the

same time, the synchronous pulse signal to measure thesynchronization of the data on the vibration and the

radiant sound with the rotation of the engine was also

taped. The above-mentioned sensor was mounted on

No. 1 cylinder, and the cylinder was subjected to the

examination. The collected data on the vibration and

radiant sound was frequency-analyzed by an FFT ana-

lyzer. More specifically, the time of one cycle was

divided into 120 sections, and frequency-analyzed in

the measurement range of 20 kHz.

The time relationship between the synchronous

pulse signal generated for each cycle of the engine and

the opening/closing condition of each cylinder valve is

shown in the valve timing in Fig. 2. The figures at the

left end show the number of each cylinder, and the

completely black-covered parts E.V. on each line show

the opening condition of the exhaust valve. The ignition

order of the cylinders is 1-8-3-4-7-2-5-6.

3. Three-dimensional indication of soundlevel and vibration level

Figs. 3 to 6 show the spectrum of the radiant sound

and the vibration in the shop trial run of the engine in a

three-dimensional manner. Here, the spectrum imme-

diately after the manufacture of the engine is examined,and its temporal transition is also evaluated and exam-

ined. On the basis of the data of one cycle of the engine

(1 rotation of the crank shaft) obtained through the

frequency analysis, the three-dimensional figures of

the sound pressure level and the vibration level in the

light-and-shade pattern with the frequency (100-20

kHz) on the Y-axis and with the crank shaft angle (0-

360°) on the X-axis, the figure of the valve timing is

also indicated on the upper part of the three-dimen-

sional figure to explain both the behaviors of the piston

and the valve of each cylinder and the temporal change

in the three-dimensional figure. The scale of the soundpressure level or the vibration level is indicated in the

pattern below the three-dimensional figure.

The three-dimensional figures of the radiant sound

and the vibration in three hours after the shop trial run

was started, are shown in Fig. 3 and Fig. 4, respectively.

The result (Fig. 3) of the radiant sound shows thick lines

in the Y-axis direction in the ranges of A (0-40°), B (80-

120°), and C (210-250°). When reviewed together with

the figure of the valve timing above, it is proved that

these agree with that combustion in No. 1, No. 3 and

No. 2 cylinders. There fore, it is concluded that they are

attributable to the combustion. On the other hand, when

reviewed from the result (Fig. 4) of the vibration, a

Fig. 2 Valve timing

Fig. 1 Test engine




October 1999 (5)

completely different pattern is shown from that in Fig.

3. It is characterized in that the distribution of high level

values is present at 2, 4, 6, 8, 10, 12 and 14 kHz around

Point a (70°), Point b (160°) and Point c (295°). The

level change is characteristic, showing the bow-shaped

pattern which is symmetrical in the right-to-left direc-

tion around three crank angles. The bow-shaped pattern

is considered to be generated by the sliding of the piston

moving vertically with the cylinder liner wall. That is,

because the source of generation of the vibration passes

in the vicinity of the installation place of the sensor, the

change in the vibration appears as the change in the

frequency between in the approaching time and after

the passing. From this finding, the position of threecrank angles at the center of the pattern show the timing

when the piston passes the measurement point, and

from the figure of the valve timing, three points (a, b,

and c) indicates the passing timing of the piston of No.

1 cylinder is moved from the top dead center to the

bottom dead center, the passing timing of the piston of

No. 2 cylinder is moved from B.D.C to T.D.C. and the

passing timing of the piston of No.1 cylinder is moved

from B.D.C. to T.D.C.. In the same figure, light and

shade lines indicating the high level similar to that in

Fig. 3 can be seen in the ranges of the crank angles A,

B and C. These lines can be concluded to be thevibration attributable to the combustion of No. 1, No. 3

Fig. 4 Three-dimensional figure of vibration

(3 hours)

Fig. 3 Three-dimensional figure of radiant sound

(3 hours)

and No. 2 cylinders. When Fig. 3 is again compared

with Fig. 4, the change in the frequency can also be seen

at Points a, b and c similar to Fig. 4. This change does

not show a bow-shaped pattern as clear as that in the

vibration (Fig. 4), but shows that the sound by the piston

slide is indicated in a bow-shaped pattern. Figs. 5 and

6 are the three-dimensional figures indicating the radi-

ant sound and the vibration in ten hours after the shop

trial run was started. In Fig. 5 of the radiant sound, the

sound in the combustion appears in the ranges of A, B,

and C similar to Figs. 3 and 4. However, any bow-

shaped pattern attributable to the piston slide con-

firmed in Fig. 3 can hardly be confirmed. On the other

hand, in Fig. 6 to indicate the vibration, the vibrationcaused by the combustion appears in the ranges of A, B

and C, and the characteristic bow-shaped pattern attrib-

utable to the piston motion can be confirmed at 3 points

of a, b and c. However, compared with Fig. 4, the

vibration level is dropped by about 10 dB.

These four three-dimensional figures show that

the data on the radiant sound and the vibration in 3

hours and in 10 hours after the shop trial run was started,

is respectively changed. In particular, in the three-

dimensional figure of the radiant sound, the bow-

shaped pattern is almost in-recognizable in only 10

hours after the engine was started. It can be concludedto be difficult from the above-mentioned the radiant





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Fig. 5 Three-dimensional figure of radiant sound

(10 hours)


(10 hours)

sound results to detect the information by the slide. On

the other hand, regarding the vibration, the pattern to

indicate the slide of the piston is clearly shown, and it

may be said that the vibration is the source of informa-

tion suitable for understanding the slide condition of

the piston ring, etc. Accordingly, it is decided to exam-

ine the information on the vibration among the two

shown above in this study.

The change in the spectrum of the radiant sound

and the vibration in a short time was shown above, and

it was examined how the spectrum term changes if the

running time is further increased. The below-men-

tioned results are those obtained during the service after

the engine was mounted on the test ship. The dataimmediately after the engine was mounted to the data

in approximately one year and 6 months layer, were

used for the analysis. Here, the result after 190 hours,

2300 hours, and 6000 hours are shown in Figs. 7, 8 and

9. As mentioned above, these are the three-dimensional

figures of the vibration. In the result of Fig. 7, the

vibration by the slide is observed at Points a, b, and c

similar to the results of Figs. 4 and 6. However, the

condition was slightly changed in Figs. 8 and 9. For

example, a high level pattern generated in the range of

70-160° and 250-290° in crank angle of in Fig. 7, (a

band-like pattern of not less than 90dB generated overthe whole frequency band) is not present as the band-

like pattern in Fig. 8 where 2300 hours are elapsed.

Besides, the level is not more than 60 dB in the higher

frequency band (over 15 kHz), which is not indicated

by the pattern of the figure. This trend is intensified

with the time, and in Fig. 9 where 6000 hours are

elapsed, the pattern of not more than 60 dB (a white

pattern) is spread close to 10 kHz in a part of crank

angles in Fig. 9. The bow-shaped pattern by the slide

shows the trend that the level is dropped as the time is

elapsed through it is slight. For example, in Fig. 7, the

bow-shaped patterns distributed over higher frequency

bands (of not less than 15 kHz) are continuous at the

level of approximately 100 dB or over. However, in

Fig. 9, the bow-shaped pattern is reduced in size, andonly a part thereof is shown as dots.

As mentioned above, it is observed that the vibra-

tion level is changed by and by as the time is elapsed.

However, the data is for the normal running condition

of the engine, and does not indicate that the engine is in

the abnormal condition. In other words, even in the

normal running condition, the secular change always

works on the engine, and the degree of indication of the

secular change is shown in Figs. 7 to 9. Though the

factor is natural, the running time of the engine surely

affects each member of the engine, and the effect of the

abrasion, etc., is indicated more clearly. For the studyaiming at the construction of the abnormality monitor-




October 1999 (7)

ing system using the vibration, it is one of the important

objects to elucidate the regularity of the vibration

which is changed with the running time of the engine.

If the regularity can be understood, generation of an

abnormality in the changing normal pattern can be

understood. From the above-mentioned viewpoint, in

the following chapter, the event is examined by apply-

ing the statistical analysis.

4. Data analysis

In order to grasp a normal condition and an abnor-

mal condition of an engine from the pattern of the

vibration data to be changed with the service time of theengine, the relationship between the running time of the

engine and the vibration must be examined. Thus, the

possibility of explaining the relationship using the

multiple regression analysis was considered. The mul-

tiple regression analysis is a technique to explain the

dependent variables by explanatory variables of

multivariates, and in this study, the multiple regression

equation was obtained with the value of the vibration

data obtained through the frequency analysis as the

explanatory variable, and with the running time of the

engine as the dependent variable. In preparing the

above-mentioned three-dimensional figures, the re-quired number of the variables is 24000 in total, be-


(190 hours)

Fig. 8 Three-dimensional figure of vibration(2300 hours)


(6000 hours)





cause one cycle is composed of 120 sections and one

section is composed of 200 variables. The multivariate

analysis with using all variables is doubtful from both

the calculation time and the expected effect, and in this

study, it is decided to extract the data on the crank angle

capable of sufficiently explaining the above-mentionedrelationship from one cycle of the engine. As described

before, the bow-shaped vibration pattern is character-

istic and clearly changed with the running time of the

engine, the data in the vicinity of the crank angle is

examined. More specifically, the bow-shaped pattern

(the slide vibration of No. 1 cylinder) is generated

twice, i.e., when the piston is lowered and when the

piston is elevated, and the data at the bow-shaped top

point when the piston is lowered at the crank angle of

72° was adopted. At this angle, when 200 variables

obtained in one frequency analysis are referred to as the

data of one case, one case is constituted by allotting thespectrum values at 100 Hz, 200 Hz, ...., 20 kHz to the

explanatory variables of x1, x2, ..., x200. 85 cases of the

data under the same condition were prepared in the

analysis, and 22 groups data were prepared for each

different running time of the engine. When the ob-

served value is explained using a plurality of variables

x1, x2, ..., xp, the expected value of y is expressed by p

variables as follows.

E [y] = β0+β1 x1+β2 x2+………+β p x p (1)

The probability fluctuation part ε is added thereto,

and the observed value leads to as follows.

y = β0+β1 x1+β2 x2+………+β p x p+ε (2)

The equation (2) is the multiple regression equa-

tion of y to x1, x2, ..., xp, and the data is expressed as

indicated in Table 1.

The dependent variable of y is the running time

(runh) of the engine , and ε is the remainder of the

estimated value subtracted from the observed value. Inthis case, the multiple regression model and the re-

mainder are expressed by (3) and (4), respectively.

runh j = β0+β1 x1, j+β2 x2, j+……+βp xp, j+ε j (3)

where, (j= 1, 2,...., N), N: total case number

ε j = runh j- (β0+β1 x1, j+β2 x2, j+……+βp xp, j) (4)

The multiple regress analysis is the analytical

technique to obtain the estimated values b0, b1, b2, ....,

bp of β1, β2, ..., βp so that the sum of squares of theremainder is minimum, and the multiple regression

equation can be obtained as (5) below.

runh j = b0+b1 x1, j+b2 x2, j+……+bp xp, j (5)

The multiple correlation coefficient of the mul-

tiple regression equation obtained above is R =0.935,

and the decision coefficient R2 =0.873. It is indicated

that the multiple regression equation obtained from this

result applies well. The multiple correlation coefficient

is the result using 200 variables, but it can not be always

concluded that the analysis is achieved with only the

variables affecting the dependent variables among 200

explanatory variables. Thus, to select the variables to

be used for the analysis, the stepwise method is used.

The analysis is achieved with the input probability of

0.05 and the removal probability of 0.1 as for the

significant probability of the F-value in inputting or

removing the explanatory variables in/from the mul-

tiple regression equation, and 60 variables are finally

obtained. As a result of the analysis using the variables,

the multiple correlation coefficient R=0.928, and the

coefficient of decision R2=0.861. The rest of 140 vari-

ables except the selected 60 variables less affect the

dependent variables, and it can be concluded that 140

variable may be excepted.The average processing of the cases is taken in

order to decrease the noise component contained in the

data when the dependent variables are calculated using

the obtained multiple regression equation. Fig. 10 is the

figure to indicate the relationship between the average

number and the error. As shown in the figure, there is

not linear relationship between the average number and

the error, and the error time is rapidly reduced in the

range between 1 and 20, and then, gradually decreased.

As shown in the result, the error is 330 at the average

number of 100, and further reduction of the error can

not be expected. In this study, it is desired to reduce theerror as much as possible in obtaining the estimated

Table 1 Data constitution

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October 1999 (9)

value from the regression equation, but it is also neces-

sary to select an appropriate average number when

taking into consideration the calculation time. The

average number where the error is approximately 400

hours was adopted here.

Fig. 11 is a scatter diagram between the observed

value and the estimated value using the data of 30

addition-averages. The estimated value at each time of

observation is plotted with the mark with the esti-

mated value on the Y-axis (the estimated running time)

and the observed value (the observed running time) on

the X-axis. In the figure, the linear relationship between

the observed value and the estimated value is strong,and the accuracy of estimation is excellent, and it is

found that the obtained multiple regression equation

agrees well. The previously obtained multiple correla-

tion coefficient R of the model is 0.928, also indicating

the strong correlation from the numerical viewpoint. In

reality, the estimated value is distributed around the

regression line with the mean error of approximately

400 hours at any time of observation. Taking the above-

mentioned points into consideration, the multiple re-

gression model obtained in this chapter estimates the

running time of the engine in a transition mode under a

normal running condition from the vibration data of the

engine. That means, the multiple regression modelexplains the regularity of the vibration data to be

changed together with the running time of the engine

under the normal condition of the engine, and the

running time corresponding to the inputted vibration

data can be estimated thereby. However, if the engine

is not in a normal condition, an appropriate running

time can not be estimated. Because the normal model in

a transition mode is prepared, it can be understood

whether or not the running condition is normal when

the vibration data obtained at the engine side is ob-

tained.

As the test ship is engaged in an actual service, no

abnormality can be artificially generated. In this ex-

periment, no abnormalities were generated in the

engine, and no extraction of abnormalities of the engine

could be detected. Thus, in this experiment, an exami-

nation was made what result is obtained in the esti-

mated value in a different running condition from the

normal one by inputting the vibration data of the engine

at the engine speed of 30 rpm which is different from

the normal engine speed of 103 rpm. The vibration data

at different engine speed is the one where the running

condition of the engine is changed, and the engine is notin any abnormal condition, but from the viewpoint that

the vibration data is different from the normal one, it

was regarded as the abnormal condition. Fig. 12 is a

scatter diagram between the observed value and the

estimated value where the estimated value of the above-

mentioned data (hereinafter, referred to as abnormal

data) is plotted with (one sample at the lower left in

the figure), and the estimated value of the normal data

used in Fig. 11 are plotted with marks. As clearly

shown in the figure, the estimated value of the abnor-

mal data is estimated at the different position from that

of the group of the estimated value of the normal data.The estimated value of the running time of the engine

of the abnormal data was 1085 hours, and the error hour

from the observed value was 1831 hours. Based on the

negative estimated value, and the large error time, it can

be diagnosed that the abnormal data shows the running

condition different from the normal running condition.

Based on the fact that even a slightest change in the

running condition can be detected by the above-men-

tioned method, it is thus considered that a serious

abnormality can be sufficiently detected by the present

diagnostic method when it is generated in an actual

engine. This point will further be clarified through theanalysis of the changing data as the proceeds.

Fig. 11 Scatter diagram of observed value andestimated value

Fig. 10 Average Processing





Conclusion

Much information is included in the vibration

generated in one cycle of the engine, the vibration of the

side wall of the 2-cycle diesel engine for marine use was

obtained for a long time, and its characteristic was

examined, and also, it was examined how it was changed.

The radiant sound and the vibration in one cycle were

frequency-analyzed, and the sound and the vibration

attributable to the combustion and the slide of the

engine were clearly confirmed in a characteristic pat-

tern from the three-dimensional figure. In reviewing

the slide of the piston of the engine, it was judged from

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Fig. 12 Scatter diagram of observed value and

estimated value (normal and abnormal)

the pattern in the three-dimensional figure that the

information from the vibration directly propagated

from the engine body is more than that obtained from

the radiant sound. In this experiment, the information

from the vibration was used from the above-mentioned

reason, but it does not necessarily lead to that thediagnosis by the radiant sound is inferior. For example,

in a case of the extensive monitoring, the radiant sound

is more advantageous in the viewpoint of the simulta-

neous monitoring, and this point will also be examined.

The multivariate analysis was tried to examine the

relationship between the running time of the engine and

the change in the vibration. As a result, the normal

model for each engine running time could be prepared

by achieving the multiple regression analysis of the

vibration in a transition mode under the normal running

condition of the engine. In the example, the vibration

pattern under the normal condition is modeled, and thevibration pattern of the engine can be confirmed for the

running time, and it is possible to examine whether the

engine is in a normal running condition if the method is

advanced.

References

1) Kimura, Nakai & Kishimoto : Abnormal Sound

Detection by Neural Network in the Diesel En-

gine. Bulletin of the M.E.S.J.,Vol.26, No.1 (1998)

2) Kimura, Nakai & Mizutani : Diagnosis System of

Diesel Engine by Statistical Vibration Analysis.

Bulletin of the M.E.S.J., Vol.24, No.1 (1996)

Diesel 2 Stroke

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