DE102020124194A1 - METHOD FOR PREPROCESSING ERROR SIGNALS AND COMPUTER PROGRAM PRODUCT - Google Patents

Abstract

Examples relate to a method (10) for pre-processing error signals (22) for evaluating the error signals (22). The method (10) comprises receiving (11) an error signal (22) from at least one error signal source, selecting (12) at least one piece of error information from the error signal (22) and providing (13) the selected error information to a system for error evaluation .

Description

technical field

Exemplary embodiments relate to a method for pre-processing error signals for evaluating the error signals. Further exemplary embodiments relate to a computer program product.

background

More and more different types of control devices are used in modern motor vehicles. These can cause errors if they are not working properly. Vehicle diagnostics can be complicated and time-consuming, especially when there are a large number of control units. In the case of complex problems with various error memory entries on different control units, a simultaneous diagnosis of these problems should be carried out automatically according to the rules based on all available information.

Error chains of complex error patterns can generate a large number of error memory entries, some of them on different control units. There are sometimes different qualities of error memory (e.g. not available; in debouncing; entered; in healing; healed), which must be evaluated. An evaluation with many error memory entries and different types of error memory entries can lead to problems with the error diagnosis, e.g. with regard to the duration of the evaluation or the success of the evaluation.

Systems for error evaluation of complex devices are known. For example, errors can lead to redundant error information that can be evaluated in the system. Such systems can be used to infer a causal or causal error, in particular a physically remediable error, from a large amount of error information in a complex device. As a result, maintenance or repair of the complex device can be simplified.

In vehicles, there may be different error sources that each output error information in different ways (e.g. different error debounce times). The error system in the vehicle can differ from other known complex systems. Therefore, known systems for error evaluation often cannot be used successfully in complex vehicle architectures.

Summary

It is an object of the present disclosure to provide improved concepts for evaluating error signals in vehicles.

This object is solved according to the subject matter of the independent patent claims. Further advantageous embodiments are described in the dependent patent claims, the following description and in connection with the figures.

Accordingly, a method for pre-processing error signals for an evaluation of the error signals is proposed. The method includes receiving an error signal from at least one error signal source. The method also includes selecting at least one item of error information from the error signal. The method also includes providing the selected error information to a system for error evaluation.

According to the method, it is proposed to analyze error signals before processing by means of an error evaluation system. Error signals can be or cause entries in error memories, for example. It is possible not to have to transmit all error signals to the system for error evaluation. For example, the selection or preselection of the error signals to be checked can enable the error analysis system to process the error diagnosis more efficiently.

The selection of specific error information makes it possible, for example, through appropriate control, to only take into account specific error aspects (e.g. a related error group) in the error diagnosis, while other error information that occurs can remain unconsidered. For example, acute errors can be selected and diagnosed, or historical errors (e.g., which are some time ago) can be selected and diagnosed, e.g. for long-term diagnoses. Alternatively or additionally, a prognosis of errors that are likely to occur in the future can be controlled and implemented by the selection. This means that error evaluation can be carried out more efficiently, since, for example, not all available error information is taken into account.

For example, it can be provided that when the error information is selected, at least error information that indicates currently existing errors is selected. Such a selection can, for example, limit the amount of information that is sent to the System for error evaluation are reduced, so that a faster error evaluation can be possible. The selection of acute errors can also make it easier to find the original error (eg to understand the error chain) if an original error affects various control units.

For example, as an alternative or in addition, it can be provided that when selecting the error information, such error information is selected that indicates errors that existed in the past. This makes it possible, for example, to include errors that occurred further back in the diagnosis of the system for error evaluation. An error that occurred a long time ago may have caused further, acute errors as the origin of the error, so that e.g. taking into account the acute errors cannot be sufficient to determine the source of the error.

For example, it can alternatively or additionally be provided that, when selecting the error information, such error information is selected that occurred at least several times in the past within a predetermined period of time. For example, a frequency counter can be used that counts the frequency of an error state (e.g. error entry active). In order to increment the frequency counter, it may be necessary for each debouncing state to be fully run through (e.g. an error entry is only made when a limit value for an error display has been exceeded by a minimum period of time - e.g. a debouncing period). Selecting common faults for diagnostics can result in testing those functions that regularly fail to function properly or whose failure, albeit infrequent, causes a significant system change.

For example, as an alternative or in addition, it can be provided that when selecting the error information, such error information is selected that indicates errors that will occur with a predetermined probability in the future. For example, error information in a control unit can have a function that warns when an error limit is likely to be exceeded soon (e.g. by using a warning value below an error limit; e.g. using a gradient of the error signal to predict the development of the error signal). Thanks to the error prediction function, the error diagnosis can, for example, also take into account errors that have not yet occurred but are likely to occur soon. As a result, the system (e.g. complex system; e.g. vehicle) and/or its operator (e.g. human or machine control) can be prepared for the occurrence of the error, for example, and an associated incorrect function can be compensated for. For example, a control unit that is likely to have a fault soon can be replaced before the fault actually occurs. Alternatively, for example, a driver of a vehicle with knowledge about components that are gradually failing can adjust his driving behavior if necessary until the next maintenance opportunity, in order to prevent a breakdown.

For example, it can be provided that a gradient of the error signal is evaluated for the prediction of such errors. A small gradient (e.g. only moderately increasing signal value of the error signal) suggests that it will take even longer before the error will occur. On the other hand, a steep gradient (e.g. sharply increasing signal value) suggests that the error will occur relatively soon. As mentioned, a warning threshold can be used as an alternative or in addition, which indicates that the error signal is close to the error limit value (e.g. the value of the warning threshold can be 90% of the error limit value or 80% or 70% of the error limit value). It can be provided that the starting point of the error signal is compared by gradient with the distance to the debouncing threshold (e.g. error limit value).

For example, according to the method, provision can be made to take into account when the error signal is already above a predetermined limit value, so that the error can be expected to be debounced soon. This can be carried out in particular in connection with a gradient analysis of the error signal, so that it is more likely that an error entry will be triggered if the gradient is positive. In the case of safety-critical applications or control units, such a prediction can, for example, mean that protective measures can be taken (e.g. in a vehicle warning that a function could very likely fail soon; e.g. initiating braking in a vehicle to ensure safety even with to be able to guarantee a failed function).

For example, it can be provided that error information that indicates a probable end of the error within a predetermined period of time is not selected for the provision for error evaluation. Such error information can, for example, relate to a healing phase with a steeply decreasing gradient of the error signal. In such cases, it can be expected, for example, that the affected error will soon be inactive or no longer effective. In such a case, therefore, there is no need to take the corresponding error value into account so that the error evaluation system can be operated more efficiently, for example, since it does not have to evaluate irrelevant error signals.

For example, it can be provided that error signals from a large number of error signal sources are used when selecting the error information. Advantageously, the method can be used for complex systems with a large number of components or devices with error signals (e.g. error memories). The method can make it possible to provide an efficient and targeted analysis of errors even in complex systems. In this way, for example, an actual source of error can be found faster or more reliably if there are a large number of potential sources of error.

For example, it can be provided that at least two of the error signal sources each have a different error sampling rate or error detection rate, with the error signal sources being evaluated at a common point in time. It may be the case that the errors or error information are detected or recorded at different points in time. The pre-selection of a number of error signals at a common point in time can enable more uniform processing of the error signals for the error diagnosis.

A further aspect relates to a computer program product with a program code which is designed to carry out the method proposed above or below when the program runs on a processor or a programmable hardware component. Such a computer program product or program can be used, for example, on an on-board computer of a vehicle or on a diagnostic device or diagnostic system, so that fault diagnosis for the vehicle can be improved.

Accordingly, depending on particular implementation needs, embodiments of the disclosure may be implemented in hardware or in software. The implementation may be performed using a digital storage medium such as a floppy disk, a DVD, a Blu-ray disk, a CD, a ROM, a PROM, an EPROM, an EEPROM or a FLASH memory, a hard disk or other magnetic or optical memory, on which electronically readable control signals are stored, which can interact or interact with a programmable hardware component in such a way that the respective method is carried out.

A programmable hardware component can be formed by a processor, a computer processor (CPU = central processing unit), a graphics processor (GPU = graphics processing unit), a computer, a computer system, an ASIC, an IC, a SoC, a programmable logic element or an FPGA be.

The digital storage medium can therefore be machine or computer readable. Some embodiments thus include a data carrier that has electronically readable control signals that are able to interact with a programmable computer system or a programmable hardware component in such a way that one of the methods described herein is carried out. An embodiment is thus a data carrier (or a digital storage medium or a computer-readable medium) on which the program for performing one of the methods described herein is recorded.

In general, it is accordingly provided that embodiments of the present disclosure are implemented as a program, firmware, computer program or computer program product with a program code or as data, the program code or the data being operative to carry out one of the methods when the program is based on a Processor or a programmable hardware component expires. The program code or the data can also be stored, for example, on a machine-readable carrier or data carrier. The program code or data may be in the form of source code, machine code or byte code, as well as other intermediate code, among others.

character list

• 1 ein Flussdiagramm eines Verfahrens zur Vorverarbeitung von Fehlersignalen;
• 2 ein schematisches Beispiel eines Signalwertverlaufs bei einer Fehlerentstehung;
• 3 ein schematisches Beispiel eines Systems mit mehreren Fehlerspeichern; und
• 4 ein schematisches Beispiel einer Möglichkeit zur Rekonstruktion eines Fehlerauftretens.

Exemplary embodiments are explained in more detail below with reference to the enclosed figures. Show it:

• 1 a flowchart of a method for pre-processing error signals;
• 2 a schematic example of a signal value profile when an error occurs;
• 3 a schematic example of a system with multiple error memories; and
• 4 a schematic example of a possibility for the reconstruction of an error occurrence.

description

Various embodiments will now be described in more detail with reference to the accompanying drawings, in which some embodiments are illustrated. In the figures, the thickness dimensions of lines, Layers and/or regions may be exaggerated for clarity. In the following description of the accompanying figures, which only show a few exemplary embodiments, the same reference symbols can designate the same or comparable components.

An element that is referred to as being “connected” or “coupled” to another element, may be directly connected or coupled to the other element, or that there may be intervening elements. Unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meanings as those of ordinary skill in the art to which the exemplary embodiments pertain.

1 shows a flowchart of a method 10 for pre-processing error signals. The method 10 includes receiving 11 an error signal from at least one error signal source and selecting 12 at least one item of error information from the error signal. The method 10 also includes providing 13 the selected error information to a system for error evaluation.

The proposed method can make it possible to also use existing systems for error evaluation of complex systems in other complex systems. So it may be possible that different systems use different systematics for error diagnosis. Selecting (step 12) certain fault information may result in compatibility of a diagnostic tool with different systems (e.g., an in-vehicle system).

As a result, existing systems for error evaluation can be used effectively for a large number of different systems in an efficient manner.

2 shows a schematic example 20 of a signal value profile when an error occurs. A signal value 21 of an error signal 22 is shown over a time axis 23 . In various phases of the display, the signal value 21 moves above or below a limit value 24, which triggers debouncing of an error when it is exceeded.

In a first state A, the system is in perfect condition (e.g. no error entry in the error memory). For an entry in the fault memory, the limit value must be exceeded for a certain minimum period. A second state B' represents the case where the limit has not been exceeded long enough to cause an entry into the fault memory (e.g. incomplete debouncing). Another state B, on the other hand, represents the minimum duration for which the signal value 21 must exceed the limit value 24 in order for an error entry to be triggered (e.g. completed debouncing).

In error status C, an error entry (e.g. Diagnostic Trouble Code, DTC for short) in the error memory is active. If the limit value 24 is subsequently undershot, a healing state D occurs in which the error entry becomes inactive, for example. After a predetermined duration of the healing state D, the cured state E finally occurs, in which the (e.g. inactive) error entry in the error memory can be removed. The healed state E can, for example, correspond to the initial state A (without an error entry).

For example, the debouncing time periods (e.g. for complete debouncing) can differ in different types of systems. For example, in some systems a debounce period cannot be provided, so that an error is always entered immediately when the limit value 24 is exceeded. The debounce durations can also differ for different functions of systems. This can, for example, result in different times when the error is entered, even if the actual error (e.g. defined by exceeding limit value 24) occurred at the same time.

In addition to limit value 24, a lower warning value (not shown) can be used. If this warning value is exceeded, a warning can be given, for example, that there is a possibility that the limit value 24 can be exceeded in the near future. For such a prognosis of exceeding the limit value, an increase in the error signal can also be detected and analyzed (e.g. a strong increase can indicate that the limit value 24 may soon be exceeded; e.g. a weak increase can indicate that the limit value 24 will only reach a certain point , more distant point in time can be exceeded). In this way, the status of the error signal can be better evaluated and the possibility of forecasting and/or analyzing the occurrence of the error can be improved.

Further details and aspects are mentioned in connection with the embodiments described above or below. This in 2 The embodiment shown may have one or more optional additional features corresponding to one or more aspects associated with the proposed concept or with one or more of the above (e.g 1 ) or below (e.g 3-4 ) described embodiments are mentioned.

3 shows a schematic example 30 of a system with multiple fault memories. The system includes several electronic control units (ECU) ECU1, ECU2 to ECUx. The control units can have one or more fault memories DTC1.1 to DTCx.y.

In 3 is a time progression t of different states A to E (cf. 2 ) the error memory DTC1.1 to DTCx.y is shown. When an error condition C occurs for the first time, an error entry is made, which is indicated schematically by a black dot in 3 is marked. When the healed state E is reached, the error memory is exported, which is indicated schematically with a black circle in 3 is marked. The discharge can take place, for example, after a healing state D, eg with several healing stages D′, D″. The healing stages can indicate, for example, the distance between the error signal and limit value 24 and/or a gradient of the error signal.

At a first point in time t1, the error memory is read out for the first time. In the example shown, the fault memories DTC1.1, DTC2.1 and DTC2.2 are active at this point in time t1. If a frequency counter is used, it can also be read out that the error signal associated with error memory DTC2.1 has indicated an error three times (e.g. going through state B three times until complete debouncing) and the error signal associated with error memory DTC2.2 has indicated an error twice.

At a second point in time t2, the error memory is read out a second time. In the example shown, the fault memories DTC1.1, DTC1.2 and DTC2.1 are active at this point in time t2. On the other hand, fault memory is DTC2.2. inactive, but no discharge has taken place.

The various error entries read out can have different urgencies. For example, a prioritization can take place, which can be used for the selection according to the method. At time t1, all error entries can be equally urgent. At time t2, the error entry DTC 1.1 can be classified as historical (e.g. already present at time t1) and the error entry DTC1.2 can be classified as a new error entry (e.g. not yet available at time t1). Furthermore, an improvement in the state of the fault signal from fault memory DTC2.2 can be detected, since the fault entry is no longer active, in contrast to the previous point in time t1.

For example, error memory evaluations can be prioritized. Acute errors (e.g. with error status C; e.g. when limit values are exceeded) can have a high priority. Priority 1 (e.g. highest priority) can be present if the fault memory is active and/or is present again and again (e.g. high frequency; a frequency counter (HfK) can be incremented from the entry of the DTC in order to document a fault that has occurred several times). . A priority 2 may exist when the fault log is active but no longer incrementing and/or transitioning to healing with state D.

Historical and imminent errors can have a medium priority. A priority 3 can exist if a fault memory is inactive, but the fault was present several times (e.g. high frequency) and/or healing has just started (e.g. high value of a healing counter; a healing counter (LGZ) can be decremented from the entry of the DTC to document a measure of the number of healing cycles completed before the DTC is discharged). A priority 4 may exist when the fault memory is inactive but has been active for a predetermined period of time. A value of the frequency counter can be low and/or a value of the healing counter can be low. A priority 5 can exist if the error memory is inactive, but an imminent activation of the error memory is forecast (e.g. with current debouncing; e.g. status B; e.g. error in debouncing after exceeding limit value).

A forecast of pending errors can have a low priority. For example, a priority 6 can exist if the fault memory is inactive, but a limit value monitoring shows a preliminary violation (e.g. use of the warning value; e.g. transition from state A to state B; e.g. limit value violation with a frequency counter value of zero).

Further details and aspects are mentioned in connection with the embodiments described above or below. This in 3 The embodiment shown may have one or more optional additional features corresponding to one or more aspects mentioned in connection with the proposed concept or with one or more above (e.g 1-2 ) or below (e.g 4 ) described embodiments are mentioned.

4 shows a schematic example 40 of a possibility for the reconstruction of an error occurrence. The time course of a fault memory DTC of a control unit ECU is shown.

A first entry in the fault memory (state C) is marked with a black dot. This time can be defined as the first entry time EEZP.

The first entry time EEZP can be defined as a calendar date or operating hour value from environmental conditions at the time of entry of the DTC [h]. Before the error was entered, the limit value was exceeded for a predetermined debounce duration EPD. This can be determined for DTC from application values (e.g. in seconds s). The debouncing time, which is a time period 41 before the error entry, can be determined from the debouncing duration of the first entry time EEZP. The formula EPZ= EEZP -EPD applies. Frequency counter HfK and logistics counter LGZ can be present as numerical values of environmental conditions. A period of time 42 after the error entry, healing D (healing start HB) can begin. The formula HB= EEZP + HfK* EPD applies (the assumption applies that if an error does not occur again, it will be cured). An average operating time MBZ per drive cycle can be determined by reading it out in [h] or from the operating time/number of drive cycles. The end of healing HE (state E) takes place a period of time 43 after the error has been entered. The formula HE= HB + (INI -LGZ)*MBZ applies (the assumption here is: the system (e.g. vehicle) is used for about MZB [h] per healing cycle and a start value INI of the LGZ decrementation comes from application constants.

The example shown shows that the sequence of error occurrence can be easily estimated from the time of the first entry EEZP. Several sequences can be placed in a chronological context in order to trace back the triggering event. This can be advantageous for the selection of the error information according to the proposed method in order to forward this to the system for error evaluation.

Further details and aspects are mentioned in connection with the embodiments described above or below. This in 4 The embodiment shown may have one or more optional additional features corresponding to one or more aspects considered in connection with the proposed concept or with one or more above (e.g 1-3 ) or in the exemplary embodiments described below.

Examples relate to a concept for using error memories of different quality in diagnostic reasoning. A prioritization of the error memory (which are the most important ones?) is proposed, which can be used for diagnostic closing (e.g. for selecting the relevant error information). This prioritization can be used as input for the estimation of subsequent, e.g. cost-optimal work steps (testing, repair, looking elsewhere). Such information can be used as input, e.g. for any automated set of rules for a diagnosis. A temporal contextualization is provided for classifying the error occurrence over time (when did healing and debouncing occur? How long were the intervals between them?). The temporal contextualization can enable traces of errors (various DTCs occur together) to be traced back and their interaction with each other to be made available for Big Data processing. For example, complex systems can be better analyzed with regard to sources of error.

The proposed concept uses e.g. assumptions about the occurrence of the physical error DTC in a layer model (e.g. states A to E) of the error origin. The proposed concept uses e.g. a prioritization of the error memory entries based on an acuteness measure (e.g. HfK and LGZ) and/or a classification for the acute diagnosis, the historical diagnosis and prognosis (e.g. with different prioritization). The proposed concept enables, for example, the reconstruction of the error occurrence over time for each DTC with regard to the time of debouncing, the start of healing and/or the end of healing. Prioritization can also be based on hierarchy information, for example, whereby a "component fault memory" can be preferable to a "functional failure fault memory" because it can be used to refer directly to a component to be replaced. In other words, it can be provided that first error information that indicates an error in a component, part or assembly is processed and then (e.g. if no such error information is currently available) other error information is processed or taken into account, e.g indicate an error in a function. A ranking of the processing of error information can thus be made possible.

Claims (11)

Method (10) for the pre-processing of error signals (22) for an evaluation of the error signals (22), the method (10) comprising: receiving (11) an error signal (22) from at least one error signal source; Selecting (12) at least one item of error information from the error signal (22); and Providing (13) the selected error information to a system for error evaluation. The method (10) according to claim 1 , wherein when selecting (12) the error information at least such error information is selected that indicates currently existing errors. The method (10) according to claim 1 or 2 , wherein when selecting (12) the error information, such error information is selected that indicates errors that existed in the past. The method (10) according to one of the preceding claims, wherein when selecting (12) the error information such error information is selected that occurred at least several times in the past within a predetermined period of time. The method (10) according to any one of the preceding claims, wherein when selecting the error information, error information is selected which indicates errors which will occur with a predetermined probability in the future. The method (10) according to claim 5 , wherein a gradient of the error signal (22) is evaluated for the prediction of such errors. The method (10) according to claim 5 or 6 , wherein the error signal (22) is already above a predetermined limit value (24), so that it can be expected that the error will soon be debounced. The method (10) according to any one of the preceding claims, wherein error information which indicates a probable end of the error within a predetermined period of time is not selected for the provision for error evaluation. The method (10) according to any one of the preceding claims, wherein error signals (22) from a plurality of error signal sources are used when selecting (12) the error information. The method (10) according to one of the preceding claims, wherein at least two of the error signal sources each have a different error sampling rate, the error signal sources being evaluated at a common point in time (t1, t2). Computer program product with a program code which is designed to carry out the method (10) according to one of the preceding claims when the program runs on a processor or a programmable hardware component.

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