WO2018215403A1

WO2018215403A1 - Measuring data-based method for detecting a measuring point stability of an internal combustion engine

Info

Publication number: WO2018215403A1
Application number: PCT/EP2018/063289
Authority: WO
Inventors: Job von Rango
Original assignee: Von Rango Job
Priority date: 2017-05-22
Filing date: 2018-05-22
Publication date: 2018-11-29
Also published as: DE102017111082A1; DE102018111668A1

Abstract

The invention relates to a method for operating a testing device (1) for testing an internal combustion engine (2) in real time, wherein the testing device (1) has a memory (4) and at least one sensor (5) for detecting at least one state variable of the internal combustion engine (2) and wherein measured sensor values (32.1, 32.2, 32.3) are used to determine a stability value of the sensor values (32.1, 32.2, 32.3) which is a measure for a correlation between the sensor values (32.1, 32.2, 32.3).

Description

Measurement-based method for detecting a measuring point stability of an internal combustion engine

The invention relates to a method for operating a test apparatus for

Examination of an internal combustion engine, wherein the test apparatus comprises a sensor for detecting a state variable of the internal combustion engine.

EP 2 078 945 A2 discloses such a method. In this

Method, a quality feature is determined, which is a measure of noise or a scattering of sensor values detected with the sensor. In EP 2 078 945 A2 it is proposed to determine values of

Quality characteristics such as the standard error, the mean square deviation, the coefficient of variation and / or the median deviation. Here, user-defined limits are used to check individual quality characteristics for a product to be tested

Internal combustion engine adapted. The disadvantage here is that the limits must be reentered for each test object and these can also vary depending on the knowledge of a user. This may result in the process being performed unnecessarily long, depending on the knowledge of the user. The object of the present invention is therefore to provide a method for operating a testing device for an internal combustion engine, with which an unnecessarily long operation of the testing device for testing the

Internal combustion engine can be prevented.

This object is achieved by a method according to claim 1 and a test device according to claim 13. Further advantageous embodiments will become apparent from the dependent claims.

To solve this problem, a method for operating a test apparatus for checking an internal combustion engine in real time is proposed. The test apparatus has a memory and at least one sensor for detecting at least one state variable of the internal combustion engine. The method comprises the following steps. In a first step, the

Internal combustion engine operated at an operating point. In a second Step, several sensor values of the sensor are measured in a first period. In a third step, a first and / or second stability value of the sensor values are determined using a calculation rule. The

The calculation rule has as variable variables only the measured sensor values, the first and / or second stability value being a measure of a correlation between the sensor values. In a fourth step, it is checked whether a first and / or second stability criterion is fulfilled. The checking takes place with the aid of the first and / or second stability value and independently of a value of a parameter that can be set as a function of the internal combustion engine for influencing the first and / or second stability value and / or the first and / or second stability criterion. In a fifth step, a measurement is triggered if the first and / or second stability criterion is met, wherein at least one of the sensor values and / or a further sensor value measured with the sensor for detecting the operating point as a stationary operating point of the internal combustion engine in the Memory is stored.

Due to the fact that the calculation rule exclusively has the sensor values as variable variables, it is possible to dispense with a setting process for adapting the testing device to the internal combustion engine. A parameter which can be set as a function of the internal combustion engine for influencing one of the two stability values and / or one of the two stability criteria can be any parameter within the meaning of the invention which serves to adapt the calculation rule or the stability criteria to the one to be tested

Adapt internal combustion engine. In particular, the measurement also includes detection of further sensor values of a further sensor, which contains a further state variable of

Internal combustion engine measures. The measurement may also include sensing environmental conditions such as humidity, ambient pressure, and / or room temperature. In particular, a transient phase, which begins with a start of operation of the internal combustion engine in the operating point, can be terminated if the first and / or second stability criterion is met. During the measurement, the sensor values, the further sensor value and / or further sensor values measured with the sensor can be used to determine a stationary measured value. For example, the stationary measured value may be off an average of the other sensor values are calculated. Preferably, the stationary operating point can be described by the stationary measured value and further stationary measured values, which are obtained by means of further sensors according to the proposed method. The correlation between the sensor values means a relationship between the sensor values that can be described using a function without a random variable. The function may be, for example, a straight line or a periodic function which describes a measurement series formed from the sensor values. In any case, the correlation is not a stochastic one

or random distribution of the sensor values in the first period. A measure of the random distribution of the sensor values may be calculated with a standard deviation of the sensor values from an average of the sensor values in the first period and is referred to in the art as a possible quality characteristic of a series of measurements. With such a quality feature, however, no correlation between the sensor values of the measurement series can be detected.

The advantage of the method according to the invention is that with the determination of the first and / or second stability value, additional information about the measurement series can be obtained in addition to a quality feature, for example how high and / or significant the correlation between the sensor values is. This additional information can be used to make a decision about continuing to operate the internal combustion engine at the operating point and / or triggering the measurement of the internal combustion engine.

In an advantageous embodiment, the operation of the internal combustion engine is terminated at the operating point when the first and / or second stability criterion is met. In this case, a delay time may elapse between the termination of the operation and a point in time at which the first and / or second stability criterion is fulfilled. During the delay time, for example, the measurement can be performed. Advantageously, the operation of the internal combustion engine at the operating point can also be terminated if, on the basis of an evaluation of a value of a quality feature, for example by calculating a variance of the sensor values, this operation alone would not be terminated. This is above all makes sense if, due to the first stability value, a correlation of the sensor values is detected, which can be described by a constant function, and at the same time a variance of the sensor values lying above a specification is detected. If only the variance were evaluated as in the prior art, the operation might not be interrupted in this case. Often, however, a reduction in the variance can not be achieved in such a case, because an overall system consisting of the tester and the internal combustion engine is in a steady state. Therefore, with the proposed method, the test apparatus can be prevented from being unnecessarily long at the same operating point

To operate internal combustion engine.

With the proposed method, the measurement can be triggered if a variance of the sensor values lying above a specification is detected and the first and / or second stability criterion are met. This is particularly advantageous if a change in the quality of the series of measurements over time is very small, but the sensor values have no drift. Thus, it can be prevented that the test apparatus is operated unnecessarily long, if no improvement in the quality of the measurement series is to be expected.

The proposed method further has the advantage that user-defined inputs are not necessary and therefore a duration of the method is independent of a user's knowledge. This is brought about by the fact that the first or the second stability value is independent of a value of a parameter that can be set as a function of the internal combustion engine

Influencing the first and / or second stability value or the first and / or second stability criterion is determined.

In an advantageous embodiment, it is provided that the sensor values are used to approximate a time-dependent first function for describing a chronological progression of the sensor values in the first time period and for each of them

Sensor values a corresponding function value is determined with the first function. The first stability value may be a slope of the first function in this embodiment. A refinement of this refinement can provide that the first and / or second stability value is determined as a function of a first sum which has differences between in each case one of the sensor values and the function value corresponding to the sensor value. The first function can be determined by a regression method. Furthermore, the first function may be described by a model having individual subfunctions. The model is preferably linear or quadratic dependent on time, such as a linear polynomial model. This has the advantage that the approximation of the first function with the model can be performed in real time.

Advantageously, it may be checked whether the sensor values during the first period are increasingly deviating from a course of a constant function in the first period, i. the sensor values have a drift. The higher the slope of the first function, the higher the drift of the sensor values can be.

The advantage of approximating the first function with the sensor values is that using parameters describing the first function, such as slope, the correlation between the sensor values can be quantified. In an advantageous development, the first stability value is determined as a function of a second sum, which has differences between in each case one of the sensor values and an average value of the sensor values. The second sum provides a measure of dispersion of the sensor values, i. a criterion for determining a quality of the measurement series. In one variant, the first sum can be compared with the second sum. Here, the first stability value from a

Quotients of the first and the second sum are formed. Advantageously, the first stability value F, for a period j, in particular for the first period, is determined according to the following calculation rule:

n- i, _=] where a first mean square deviation MS _{J mM of} the sensor values y relative to the respective corresponding function values y as a function of the first sum of the respective squares of the differences between one of the sensor values y and one corresponding to the respective sensor value Function value y _{j + j is} formed. Before the first period, in or after which the operation of the internal combustion engine is preferably ended, there may be further periods during which further sensor values were measured.

The first index j denotes a number of a period, wherein for an initial period of time the first index assumes the value zero, the value following thereafter the value of one and, for the first period, preferably the maximum value of j. The second index i denotes a number of a detected sensor value in the period j, wherein the second index for the first sensor value in a period j takes the value one. This means that the same measurement of one of the sensor values can be used for a calculation of first stability values in different overlapping time periods.

From the first sum, dividing the first sum by a difference between a number n of the measured sensor values in a time period j and a number of degrees of freedom df _{moäel of} the model _computes the first mean square deviation MSV _model . If the first function is constant, linear or quadratic, then the number of degrees of freedom df _mM is equal to one, two or three.

The first stability value F. for a period of time j, in particular for the first

Period, is preferably a quotient of the first middle

square deviation MS _{J modA} and a second mean square

Deviation MS - _{data of} the sensor values y _{J + j of} the period j with respect to the

Average value y of the sensor values determined.

The second mean square deviation MS ^ _data is a function of the second sum of the respective squares of the differences between each one of the sensor values y. , and the average of the sensor values y. From the second sum, dividing the second sum by a difference between the number n of the sensor values in a period of time and the second mean square deviation MS _i . By comparing the first with the second sum, in particular with the aid of the first stability value, a significance of the detected drift of the sensor values as a function of the scatter of the sensor values can be determined. The lower the scatter, the more significant a drift of the sensor values can be detected with the first stability value. With the first stability value F, it is possible to check whether there are residuals between sensor values and the corresponding ones

Function values in the first period are significantly different to residuals between the sensor values and the mean value of the sensor values.

Advantageously, a statistical test method, such as an F-test method, is used to check whether the first stability criterion has been met. If the F-test method is used, it can be assumed as a null hypothesis that the sensor values assume such values over the first time period as has a function of a sum of a constant value and a Gaussian distributed over the first time period. Assuming this null hypothesis, a probability distribution can be determined which, for any values of the first stability value F, indicates a probability in each case. The probability distribution can be a Fisher distribution, ie F distribution. The F-distribution depends on the number of degrees of freedom df _{mM of} the

Model and a number of degrees of freedom n - l in the determination of the second mean square deviation MS - ^.

If a first value according to one of the variants described above is determined with the proposed method for the first stability value, then using the probability distribution a first probability can be determined for the first stability value to assume the first value if the null hypothesis is to apply. If the first probability lies below a first barrier stored in the memory, for example in the value of 0.01, then

assumed that the null hypothesis does not hold. In this case, the first applies

Stability criterion as not met. In the other case, the first stability criterion is fulfilled. A further embodiment provides that the first stability value is determined according to one of the variants described above and a comparison of the first

Stability value with a constant first comparison value, for example 0.95, is carried out. On the basis of a result of the comparison, it is checked whether the first stability criterion is fulfilled. So can the first stability criterion

for example, if the first stability value is greater than or equal to 0.95. In an advantageous variant, depending on a third sum, which has differences between in each case two of the sensor values which follow one another directly, the second stability value is determined. A development of this variant provides that the second stability value is determined as a function of the first and the third sum. Advantageously, the second stability value, for a period j, in particular for the first period, is obtained from a quotient with the first mean square deviation S _{; model} as a dividend and a reduced quadratic variance

MS _{j diff is} calculated from the differences of the immediately following sensor values y _{j + l} and y _{j + i +]} as a divisor according to the following calculation rule:

where the reduced quadratic variance SV _{diff depends} on the third

Sum is formed from the respective squares of the differences between a first sensor value y _{j + j} and a second sensor value y _M immediately following the first sensor value. From the third sum is by a

Dividing the third sum by the double difference between the number n of sensor values in a period j and one the reduced squared variance

MS _{j diff} calculated.

In the same way as for the first stability value, it can be checked by means of the F-test method, whether this is to the second stability value

corresponding second stability criterion is met. Here, the same null hypothesis is preferably used and a second probability for the calculated value of the second stability value under the assumption of

Zero hypothesis determined. Is the second probability below one in the Memory stored second barrier, for example, in the value of 0.01, it is assumed that the null hypothesis does not apply. In this case the second stability criterion is considered not fulfilled. In the other case, the second stability criterion is met. Alternatively it can be provided that a comparison of the second stability value is carried out with a constant second comparison value. Look at one

Result of the comparison is checked whether the second stability criterion is met. For example, the second stability criterion may be considered satisfied if the second stability value is greater than or equal to 250. The second comparison value is preferably significantly greater than one. This has the advantage that the second

Stability criterion is considered satisfied even if only relatively small fluctuations in the sensor values occur.

In a further embodiment, the second stability value, for a period j, in particular for the first period, is calculated from a quotient with the first mean square deviation MS _{j model} as divisor and the reduced quadratic variance MS _jm as a dividend according to the following

Calculation rule calculated:

^2y MS _mode]

In this embodiment, the second stability criterion is preferably met when the second stability value is less than or equal to 0.004.

The first and the second comparison value are each independent of a value of a parameter that can be set as a function of a test object, such as the internal combustion engine, for influencing the first or second parameter

Stability value or the first or second stability criterion. Preferably, the first and / or the second comparative value of the DUT and the

Tester independent values, i. universally accepted sizes.

Because the second stability value is formed as a function of the third sum, additional information about the measurement series with respect to a correlation of adjacent, in particular immediate, values can be used with the aid of the second stability value adjacent sensor values of the measurement series are obtained. Another advantage that results from using the reduced squared variance is the following. The reduced quadratic variance can be considered to be a correct estimate of a quadratic variance for a squared pure noise signal by a constant value if the sensor values are not correlated. However, if the sensor values are correlated, the reduced quadratic variance deviates from the quadratic variance, causing a change in the second stability value.

While with the first stability value, in particular with the aid of the first function, information about an approximated course of the sensor values during the first time period can be obtained, it is possible that the second stability value can be used to detect preferably all existing correlations between directly adjacent sensor values. For this reason, it is possible with the second stability value to detect a vibration-like course of the sensor values in the first time period. When calculating the first stability value, the oscillation-like course of the

Sensor values around a constant value the vibration may not be detected. Such a course can, for example, be caused by a swinging control loop, in which the sensor values are actual values. Advantageously, a warning signal is output when the second

Stability criterion is not met.

Alternatively, the second stability value may be a quotient with a

constant second value as a dividend and the reduced squared variance MS _{j dlff} can be calculated as a divisor. As a result, the first sum is not included in the calculation of the second stability value. This has the advantage that in the event that there is a very slight scattering of the sensor values and the first sum approaches a value of zero, the second stability value likewise does not approach zero. Thus, a correlation of the sensor values can be detected even with a very small scattering. Furthermore, calculating the second stability value according to this alternative may be faster than one

Calculation of the first stability value to be carried out as a

Approximation of the first function is not required and thus computing time can be saved. In particular, if on multiple channels the

Tester sensor values can be detected by multiple sensors, this can be May allow the proposed method to have real-time capability.

The second stability criterion according to this alternative is preferably satisfied when the second stability value for a time permanently stored in the memory is greater than a third barrier. The third bound preferably assumes a value in the range of one thousand to ten thousand, preferably the value two thousand. The fixed time preferably assumes a value in the range of ten to fifty seconds, preferably thirty seconds. It is also possible that the dividend is the second value or the first mean square deviation MS j _model , whichever is greater. A check of the second stability criterion can, depending on which of the two values is greater, according to the above-described

different types take place. Advantageously, with the second stability value, vibrations of the sensor values can be detected from a lower limit frequency, which depends on a duration of the first time period. In a variant, a duration of consecutive periods j can be changed to the lower one

Limit frequency change and a frequency range of detectable

To increase vibrations. The frequency range is limited by an upper limit frequency, which depends on a sampling rate at which the sensor values are detected.

In a further refinement, a third stability value is determined from a variance of the sensor values in the first period and a variance of the sensor values in a second period preceding the first. In this embodiment, the third stability value is a measure of a difference between the two variances. The third stability value is preferably selected from a root of a quotient which _divides the second mean-square deviation MS _{2 data of} the second time period as a dividend and the second mean-square deviation MS _{1 3 (3 of} the first time period

Divisor has calculated. The second mean square deviation MS _{2 data of} the second time period and the second mean square deviation MS ¹ , _{data of} the first time period can each be used as an estimate for the quadratic variance a _{2 Jala} of the second period or for the quadratic variance

of the first period.

If the third stability value lies between a lower and an upper limit, a third stability criterion is satisfied. The lower and upper limits are stored in the memory and preferably not changeable, in particular independent of the internal combustion engine to be tested. The third stability criterion can be checked according to the following formula:

wherein the lower and upper limits are preferably described by a fixed value r _M. The fixed value r _{MSStah [e} is preferably about equal to 1, the first

Using the third stability value, the measurement series can be assessed as stable if a temporal change in the variances of sensor values of chronologically consecutive periods is below a threshold value. The greater a time offset of the time periods, for example thirty seconds, the better a change in the variances can be detected. Preferably, the time offset is changed with multiple calculation of the third stability value.

By virtue of the fact that the third stability value can be changed only by the sensor values of the first and second time periods, when using this

Stability value also on an adjustment of the test apparatus in a

Change the internal combustion engine are waived.

In a further advantageous embodiment, it is provided that, depending on a difference between the mean value y. _{data of} the sensor values and to one of the sensor values, in particular to the last detected sensor value,

corresponding function value _{model is} checked, whether a fourth

Stability criterion is met. The fourth stability criterion is preferably satisfied when an amount of the difference, preferably an entire duration of the first period, is less than a fourth bound, which can be expressed by the following equation:

The fourth barrier will be advantageous from a product of a maximum

Resolution Ay _{tol of} the sensor, for example, seventeen grams per second, and a factor r _toX formed, the resolution and the factor are stored in the memory and are independent of the internal combustion engine. The function value j> _model corresponding to the sensor value can be determined with the first function. Particularly advantageous is the corresponding

Function value j> _model calculated with a third function. The third function is preferably approximated using sensor values which are acquired in a fourth period beginning before the first period. Thus, you can

Information of the sensor values of the first period with information of the

Sensor values are compared, which are detected before the beginning of the first period and in the form of the approximated third function.

It is possible that the first stability criterion is fulfilled and the fourth stability criterion is not met. This can occur if a model for describing the first function can be formed in the determination of the first stability criterion, but this is not significant, i. E. the first stability value deviates so little from the value one that in particular the null hypothesis can not be rejected with satisfactory certainty. In such a case, however, the fourth stability criterion can still be considered as not satisfied because, due to a current sensor value, the model can be changed automatically such that the difference between the mean value of the sensor values and the function value corresponding to the current sensor value exceeds the fourth limit. The use of the fourth stability criterion can thus further increase accuracy of information about the stability of the sensor value.

In a further development of the method, depending on a mean error Δy _{s data,} an average value of the sensor values and a difference between one

Mean y. ^ the sensor values and a setpoint of the sensor values or a

Average value y _{} ref} of variable setpoint values of the sensor values is checked to see whether a fifth criterion is fulfilled. The mean error is calculated from the root of a quotient with the second mean square deviation MS ■ _data as the dividend and the Number n of sensor values in a period j, especially in the first

Period, formed. The fifth criterion is preferably satisfied when an amount of the difference between the average of the sensor values and the mean value of the sensor

Target value less than a product of the mean error and a constant value, for example three, is what is described by the following formula:

The constant value r _ref is independent of a value of a parameter which can be set as a function of the internal combustion engine. The value r _ref is preferably equal to ten. A use of the fifth criterion has the advantage that, with a low mean error, it can be detected whether the mean value of the sensor values deviates from the desired value or from a fluctuating desired value. On the other hand, if only one quality criterion were used, such as the standard deviation of the target values, then such a deviation could not be detected. Therefore, the fifth criterion is preferably checked in addition to the first, second and / or third stability criterion.

A fluctuating setpoint, for example, can be caused by a complex control system with multiple controllers. For example, a NO _x emission value may fluctuate as a target value due to a variation of a position of an actuator of an exhaust gas recirculation valve. The fifth criterion can be used to check how strongly a deviation of the sensor values from

Reference value of random signal components, such as a fluctuating measured value or another fluctuating setpoint depends.

Advantageously, it is checked whether the first and / or second and the third and / or fourth stability criterion for a stored in the memory period is met. The period of time is preferably three to four seconds. If the first and / or second and the third and / or fourth stability criterion for the period of time is met, the measurement is triggered. Due to the fact that at least two stability criteria have to be fulfilled simultaneously during the time span, this can be done at the same time

Embodiment, the test of the stability of the sensor values made more accurate. Particularly advantageously, the measurement is triggered if at least the first and / or second stability criterion and a quality criterion which describes the quality of the measurement series are fulfilled. The quality criterion can be a

Standard deviation of the sensor values from the mean value of the sensor values in the first period. This makes it possible to check whether, on the one hand, the course of the sensor values in the first period is stable and, on the other hand, the sensor values have a low noise, and the measurements are triggered when both criteria are met.

A particular embodiment provides that the first function for the first period and a second function for a third period ending before the first period are each approximated using a regression method. The first and the second, preferably linear, function have at least one respective first and a second parameters. The two functions are determined by a respective first and second parameter value of the first and second parameters. Furthermore, the functions each describe a chronological progression of the sensor values in the first or third time period and are used to calculate the first and / or second stability value for the first or third time period according to the variants described above. The first period and the third period are divided into several, preferably three hundred, time intervals.

The first and the third time periods overlap, in particular such that the first time period begins and ends a time interval later than the third time period.

In the regression method, a fourth sum is formed for a calculation of the first and second parameter values of the first and second parameters of the second function and stored in the memory. The

Summands of the fourth sum have at least several, preferably all, of the sensor values detected in the third time period. For a calculation of the first and second parameter values of the first and second parameters of the first function, a fifth sum is formed using the stored fourth sum and a sensor value acquired after the third time period.

The regression method may be a method for minimizing error squares, in which the respective first parameter value b, and second parameter value of the first or second parameter of the first or second parameter second function can be calculated by transforming a normal equation. For this purpose, a first matrix M, with a first vector v, is preferably multiplied, as shown in the following formula:

Preferably, a number n of the time intervals of the first and third time periods and the duration of the two time periods are the same, for example thirty seconds.

This allows the first matrix to be used to calculate the two parameter values for both the first and third time periods. The first vector is dependent on the sensor values and different for both periods. A first component of the first vector v for the third period is formed from the fourth sum s _J ° _n and a second component of the first vector v, for the third period from a sixth sum s) ". A first component of the first vector v ₁ , for the first period, becomes the fifth sum of the sensor value y _{J + n + 1} detected after the third time period and a difference between the fourth sum S ° and the first detected sensor value v _x , in the third

Period determined. A second component of the first vector v, for the first

Period can be a seventh sum of a product of the detected after the third period sensor value y _{j + n + l} with the number n of the time intervals of the first period and a difference between the sixth sum s) "and the fourth sum S °

j. ".

Characterized in that in the calculation of the two components of the first vector v _{1 (} for the first period, the components of the first vector v for the third

Period, i. the fourth and sixth sums can be used, computing time can be saved and made the method real-time capable. The method preferably provides that sensor values are acquired in further periods of time that lie before the first and the third time periods, and these are detected by means of further

Functions are approximated. In order to be able to determine the parameter values for each period according to the variant described above, the sensor values first acquired in the respective period are stored in the memory, preferably in a ring memory of the memory.

In order to save storage space, the first detected sensor value y _x in the third period can be calculated using the fourth sum s ° _jn , for example by dividing the fourth

Sum S ° _n by the number n of the time intervals of the third period, are estimated. This makes it possible to dispense with storing the sensor values first acquired in the respective period.

In a further advantageous embodiment of the method, it is provided that the first and / or second stability value for the first time period is determined with the aid of the fifth sum, and a first and / or second stability value for the third time period is determined with the aid of the fourth sum. With this refinement, an arithmetic time can be shortened when updating the first and / or second stability value. Updating here means that the first and / or second stability value for the first period is calculated temporally after the first and / or second stability value for the third period. The computing time can be shortened in particular because the fifth sum is formed with the aid of the fourth sum and the fourth sum has been stored in the memory before the calculation of the fifth sum.

As already described, the first stability value for a period j, in particular for the third period, can be calculated from the quotient with the first mean square deviation MS _{j mM} as a dividend and a second mean square deviation MS ■ _data as a divisor. The third period is assigned the index j in the following. The first mean square deviation MS _{} raodel} for the third time period is preferably determined using an eighth sum P _n from the squares of the sensor values y _{J + i} and a scalar Ί for the third time period, as shown in the following formulas:

The scalar T for the third period can be calculated as a function of the fourth sum s' and the sixth sum SJ ¹ , n. In the event that the second

Function is a linear function, the result is advantageously the following

Calculation rule for the scalar. for the third period:

An advantageous embodiment may provide that the first mean square deviation MS _{] + λ model} for the first time period is calculated according to the following formulas in which the index j + 1 is assigned to the first time period:

In this variant, a scalar T _{J + 1} for the first period is calculated using the fifth sum S ° _n and the seventh sum S) ₊ ^ _n with:

^ / + 1, « ⁼ S), n ^~ S n + ^{H '} y _{j +} n +] By calculating the fifth and the seventh sum from the fourth and the sixth sum stored in the memory, calculation time can be saved when calculating the scalar T _{J +}} for the first time period.

Also, a ninth sum P _{+ ln can} be obtained using the eighth sum P _j ² _n stored in the memory. Thus, when calculating the first

Stability value for the third period, the fourth, sixth and eighth sum stored in the memory are used. Thus, it is not necessary to explicitly calculate the fifth, seventh and ninth sums by summing all the individual sensor values y _{j + l + i} measured in the first period after the fourth, sixth and eighth sums have already been calculated. This can be done in

Depending on predetermined hardware components of the test apparatus, in particular a control unit of the test apparatus, enable a real-time capability of the proposed method.

EXEMPLARY EMBODIMENT Further advantages, features and details of the invention emerge from the following description of at least one preferred exemplary embodiment and with reference to the figures. These show in:

1 shows a test apparatus for checking an internal combustion engine.

Fig. 2 shows individual steps of a method for operating the test system according to

Fig. 1;

3 shows a diagram with sensor values and modeled sensor values for

Calculating a first stability value;

4 shows a further diagram with sensor values and modeled sensor values for calculating a second stability value;

5 shows a further diagram with sensor values and modeled sensor values for calculating a second stability value.

Fig. 1 shows a test apparatus 1 for checking an internal combustion engine 2 in real time, wherein the test apparatus 1, a control unit 3 with a memory 4 and at least one sensor 5 for detecting at least one state variable of Internal combustion engine 2 has. The test apparatus 1 is suitable in an advantageous embodiment also for checking a machine system 6, which has the internal combustion engine 2 and an electric motor 7. The control unit 3 has a non-volatile computer-readable storage medium with information stored thereon, which, when executed by a processor of the control unit 3, effect implementation of the following method.

In Fig. 2, the individual steps of the proposed method are shown. In a first step 1 1, the internal combustion engine 2 in a

Operating point operated. In a second step 12, a plurality of sensor values of the sensor 5 are measured in a first time period 33. In a third step 13, a first stability value F, and / or second stability value, the

Sensor values determined with a calculation rule. The calculation rule has as variable variables exclusively the measured sensor values, the first and / or second stability value being a measure of a correlation between the sensor values. In a fourth step 14, it is checked whether a first and / or second stability criterion is fulfilled. The checking takes place with the aid of the first and / or second stability value and independently of a value of a parameter that can be set as a function of the internal combustion engine 2 for influencing the first and / or second stability value and / or the first and / or second stability criterion. In a fifth step 15, a measurement is triggered when the first and / or second stability criterion is met, wherein at least one of the sensor values and / or a further sensor value measured with the sensor for detecting the operating point as a stationary operating point of the internal combustion engine in the memory is stored. 3 shows a diagram with a time axis 31 and a measurement series from at least the sensor values 32.1, 32.2, 32.3, which are detected by the sensor 5 in the first time period 33. The sensor values 32.1, 32.2, 32.3, also referred to below as

Sensor values y _M , can be read qualitatively on an ordinate 34. The following describes how the first stability criterion can be checked. The first corresponding to the first stability criterion

Stability value F, for a period j, in particular for the first period 33, is calculated from a quotient of a first mean square deviation MS _{] modA of} the sensor values y _{J + i} relative to respective function values y _{j + j} and a second mean square deviation MS _{} data of} the sensor values y _{j + j of} the period j with respect to an average value 35 of the sensor values y _{+ i} . , in the

Also referred to below as mean value y _j , determined according to the following formula:

^ j.data "-" / modd ⁽ = 1

The second mean square deviation MS ^ _data is calculated as a function of the second sum from the respective squares of the differences between in each case one of the sensor values y _{J + 1} and the mean value 35. 3 shows how the sensor values 32.1, 32.2, 32.3 fluctuate around the mean value 35 of the sensor values. Based on the sensor values 32.1, 32.2, 32.3, a first function 36 for describing a time profile of the sensor values in the time period 33 is approximated and for each of the sensor values 32.1, 32.2, 32.3 of FIG

corresponding function value y, or 36.1, 36.2, 36.3, calculated using the first function 36. The first function 36 can be formed by a model with 2 degrees of freedom, ie df _modd is equal to two in this case.

From Fig. 3 it can be seen that the differences between the respective

Sensor value 32.1, 32.2, 32.3 and the mean 35 of the sensor values of the amount of higher than the corresponding differences between the respective sensor value 32.1, 32.2, 32.3 and the corresponding function value 36.1, 36.2, 36.3 are. Assuming a null hypothesis in which the sensor values 32.1, 32.2, 32.3 have no drift, a probability can be calculated for obtaining the calculated first stability value F i. If this probability is smaller than a confidence probability, then the null hypothesis is rejected and the first stability criterion is not fulfilled, as is the case in the example shown in FIG. 3. As a result, the first stability value deviates from one in this case. FIGS. 4 and 5 show an application of the second stability criterion. 4 shows a further diagram with a time axis 41 and a measurement series from at least the sensor values 42.1, 42.2, 42.3, which are detected in the first period 33 with the sensor 5. The sensor values 42.1, 42.2, 42.3, hereinafter also referred to as sensor values y _{j + j} , can be read qualitatively on an ordinate 44. The second corresponding to the second stability criterion

Stability value F, is calculated from a quotient with a first mean

square deviation MS. _model as a dividend and a reduced one

quadratic variance MS _{ί diff} from differences immediately following one another

Sensor values y and y _M , for example, a difference from the sensor value 42.1 and the sensor value 42.2, as a divisor according to the following

Calculation rule calculated:

In the example shown in FIG. 4, the sensor values 42.1, 42.2, 42.3 and 42.4 are correlated. As a result, the second stability value deviates from a value that the second stability value F would assume if the sensor values were uncorrelated. As for the first stability criterion, an F-test procedure can be used to determine that the second stability criterion is not met in this case. 5 shows an example of a measurement series of sensor values 52.1, 52.2, 52.3, 52.4, which are detected in the first period 33 with the sensor 5 and have no correlation and for which the second stability criterion is met. The sensor values 52.1, 52.2, 52.3, 52.4 shown in FIG. 5 and the respectively corresponding ones

Function values 56.1, 56.2, 56.3 are used in the same way for calculating the second stability value as the corresponding sensor values 42.1, 42.2, 42.3, 42.4 and the respective corresponding function values 46.1, 46.2, 46.3.

Claims

claims

1. A method for operating a test apparatus (1) for checking an internal combustion engine (2) in real time, wherein the test apparatus (1) has a memory (4) and at least one sensor (5) for detecting at least one state variable of the internal combustion engine (2), with the following steps:

Operating the internal combustion engine (2) at an operating point,

Measuring a plurality of sensor values (32.1, 32.2, 32.3) of the sensor (5) in a first period,

Determining a first and / or second stability value of

Sensor values (32.1, 32.2, 32.3) with a calculation rule which has as variable variables only the measured sensor values (32.1, 32.2, 32.3), the first and / or second stability value being a measure of a correlation between the sensor values (32.1, 32.2, 32.3),

Checking whether a first and / or second stability criterion is satisfied, wherein the checking using the first and / or second stability value and independently of a value of a parameter that can be set as a function of the internal combustion engine (2) influences the first and / or second stability value and / or the first and / or second stability criterion,

Triggering a measurement when the first and / or second

Stability criterion is met, wherein in the measurement of at least one of the sensor values (32.1, 32.2, 32.3) and / or a further sensor value measured with the sensor (5) for detecting the operating point as a stationary operating point of the internal combustion engine (2) in the memory (4 ) is stored.

2. The method according to claim 1, characterized in that the

Operating the internal combustion engine is terminated in the operating point when the first and / or second stability criterion is met.

3. The method according to claim 1 or 2, characterized in that with the sensor values (32.1, 32.2, 32.3) approximates a time-dependent first function for describing a time profile of the sensor values (32.1, 32.2, 32.3) in the first period, in each case one the sensor values (32.1, 32.2, 32.3) a corresponding function value determined with the first function and the first and / or second stability value in

Dependence on a first sum which has differences between in each case one of the sensor values (32.1, 32.2, 32.3) and the function value corresponding to the sensor value is determined.

Method according to one of the preceding claims, characterized in that the first stability value as a function of a second sum, which differences between each one of the

Sensor values (32.1, 32.2, 32.3) and an average value of the sensor values (32.1, 32.2, 32.3) is determined.

Method according to one of the preceding claims, characterized in that the second stability value is determined as a function of a third sum which has differences between in each case two of the sensor values (32.1, 32.2, 32.3, 32.4) which follow one another directly. 6. The method according to any one of the preceding claims, characterized

characterized in that a third stability value is determined from a variance of the sensor values (32.1, 32.2, 32.3) in the first period and a variance of the sensor values (32.1, 32.2, 32.3) in a second period preceding the first, wherein the third stability value is a Measure of a difference between the two variances is and a third

Stability criterion is checked as a function of the third stability value.

Method according to one of the preceding claims 4 to 6, characterized in that it is checked as a function of a difference between the mean value of the sensor values (32.1, 32.2, 32.3) and a function value corresponding to one of the sensor values (32.1, 32.2, 32.3) fourth stability criterion is met. Method according to one of the preceding claims, characterized in that in dependence on a mean error of an average value of the sensor values (32.1, 32.2, 32.3) and a difference between an average value of the sensor values (32.1, 32.2, 32.3) and a set value of the sensor values (32.1, 32.2, 32.3) or an average value of variable setpoint values of the sensor values (32.1, 32.2, 32.3), it is checked whether a fifth criterion is fulfilled.

9. The method according to any one of the preceding claims, characterized

characterized in that it is checked whether the first and / or second and the third and / or fourth stability criterion for a time stored in the memory (4) is satisfied and the measurement is triggered when the first and / or second and the third and / or or fourth stability criterion for the period of time is met.

10. The method according to any one of the preceding claims, characterized

characterized in that the measurement is triggered when a

Quality criterion, which describes a quality of the sensor values (32.1, 32.2, 32.3), is met.

Method according to one of the preceding claims 3 to 10, characterized in that the first function for the first period and a second function for a third period ending before the first period, wherein the first and the third period overlap, each approximates using a regression method in which, for a calculation of a first and a second parameter value of the second function, a fourth sum whose summands has at least a plurality of sensor values detected in the third time period is formed and stored in the memory and for a calculation of a first and a second parameter value of the first Function is a fifth sum using the stored fourth sum and a detected after the third period sensor value is formed.

A method according to claim 11, characterized in that the first and / or second stability value for the first period using the fifth sum is determined and a first and / or second stability value for the third period using the fourth sum is determined. Test device (1) for checking an internal combustion engine (2) in real time, wherein the test device (1) has a control unit (3) with a

Memory (4) and at least one sensor (5) for detecting at least one state variable of the internal combustion engine (2), wherein the control device (3) has a non-volatile computer-readable storage medium with information stored thereon, which in embodiments by a processor of the control device (3) effect an implementation of the method according to claim 1.