WO2006133865A1 - Priorisation dynamique d'operations de verification dans un diagnostic d'atelier - Google Patents

Priorisation dynamique d'operations de verification dans un diagnostic d'atelier Download PDF

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Publication number
WO2006133865A1
WO2006133865A1 PCT/EP2006/005572 EP2006005572W WO2006133865A1 WO 2006133865 A1 WO2006133865 A1 WO 2006133865A1 EP 2006005572 W EP2006005572 W EP 2006005572W WO 2006133865 A1 WO2006133865 A1 WO 2006133865A1
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test
error
diagnostic
computer
candidate
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PCT/EP2006/005572
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German (de)
English (en)
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Martin Konieczny
Harald Renninger
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Daimlerchrysler Ag
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Publication of WO2006133865A1 publication Critical patent/WO2006133865A1/fr

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    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B23/00—Testing or monitoring of control systems or parts thereof
    • G05B23/02—Electric testing or monitoring
    • G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
    • G05B23/0208—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterized by the configuration of the monitoring system
    • G05B23/0216—Human interface functionality, e.g. monitoring system providing help to the user in the selection of tests or in its configuration
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B23/00—Testing or monitoring of control systems or parts thereof
    • G05B23/02—Electric testing or monitoring
    • G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
    • G05B23/0218—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults
    • G05B23/0221—Preprocessing measurements, e.g. data collection rate adjustment; Standardization of measurements; Time series or signal analysis, e.g. frequency analysis or wavelets; Trustworthiness of measurements; Indexes therefor; Measurements using easily measured parameters to estimate parameters difficult to measure; Virtual sensor creation; De-noising; Sensor fusion; Unconventional preprocessing inherently present in specific fault detection methods like PCA-based methods

Definitions

  • the diagnostic system of the present invention employs a decision-making process based on entropy and cost evaluations of tests, with the aim of being able to suggest optimal tests to the service technician at each time of a diagnostic session.
  • the test domain, the relations between tests and components to be tested can be modeled using a rule-based knowledge base, a mathematical-physical model of system diagnostics or Bayesian networks and compiled into an executable computer program.
  • the focus of this invention lies in the determination of the error candidates and in the construction of the decision function for the test step selection.
  • An example of a system diagnosis is disclosed in the German patent application DE 195 23 483 Al.
  • the characteristic of the system diagnosis is the mapping of the system to be diagnosed into at least one physical-mathematical model that can be implemented and processed with computer assistance.
  • the modeling comprises a structural model and an impact model, which is often referred to as a behavioral model.
  • the structure model depicts the physical relationships of the individual components of the technical system, and the behavioral model maps the functions of the individual components of the system.
  • a knowledge base which is essentially a rule table from If / then conditions, which in turn can be mapped to data tuples, the diagnostic knowledge relevant for the system diagnostics is stored. With the system diagnostics a fault detection and by recourse to the knowledge base a computer-aided troubleshooting can be carried out.
  • System diagnostics has two major disadvantages.
  • the modeling is extremely expensive for larger technical systems, such as a motor vehicle, if all possible causes of faults are to be controlled by the system.
  • Even more difficult to handle in system diagnostics are ambiguous system states, eg if a recorded error code can have several causes that can not be further processed by the system diagnostics due to the lack of sufficient error environment data or insufficient information about the current system state.
  • the system diagnostics then stops at this point without diagnostic result.
  • Another disadvantage of the system diagnosis is its basic unsuitability for the processing of experience of the service technician. Nor can Customer information on defective functions or on intact functions in the diagnostic process.
  • Diagnostic systems of the aforementioned type have the further disadvantage that they very quickly become very complex and the necessary modeling effort, calculation effort and calculation effort for larger technical systems increases exponentially with the number of error possibilities of the individual components of the overall system.
  • all possible checks must be mapped into a static test step tree for diagnostics.
  • the technician must always check the entire modeled system. At no time is the attempt made to identify, identify or even narrow down a component. Only the statistical failure probabilities of system components are considered. Information about actual errors or malfunctions can not be processed by the system.
  • the design of the system assumes that the decomposition can always be done and that the individual components of the decomposition have no influence on the fault condition of the components connected to them. In other words, it is assumed that the decompositions are equivalent in terms of the fault condition of the system. However, this is an assumption that applies only to a few systems, perhaps telephone networks. For most networked systems, component failure also causes malfunction in the rest of the system.
  • This earlier application of the applicant mainly contains an interactively operating diagnostic program, in which the service technician can set a focus for the further, automated troubleshooting by the diagnostic program within a troubleshooting space of the diagnostic program initially identified as potentially defective components or functions identified.
  • the focus can be set by restricting to an error code or by restricting to a function. After setting the focus, a limited focus amount is selected that matches the selected focus of possible error candidates. In this case, the individual error candidates experience - by offsetting different probabilities for the occurrence of an error code, for the probability of failure of a component or a Function and, if necessary, for the presence of an error image -, a weighting.
  • the diagnostic system has the additional possibility of processing error images.
  • Fault images here are combinations of several fault codes that are specific to the failure of a particular component and thus can provide a direct indication of the defective component.
  • the error images can be formed from a combination of active and non-active error codes.
  • the non-active error codes can provide particularly valuable information on non-defective components and thus limit the number of possible error candidates.
  • a new focus can be set by the service technician and thus a new focus set with weighted error candidates can be generated.
  • the object according to the invention arises for setting up a test step sequence for a set of already weighted and identified error candidates which the preselected candidate for the error can be examined as effectively as possible.
  • the solution succeeds mainly with a diagnostic system that generates an error candidate quantity first by means of system queries and by processing the queried system messages or system states with a diagnostic program.
  • the error candidates in this candidate error quantity are already prioritized with regard to error probability.
  • This prioritization can be taken into account when setting up a test step tree and helps to build the most efficient test step tree possible in the respective faulty system state. Basically, the prioritization of the error candidates is not necessary. For example, all error candidates of the candidate error quantity may also have the same error probabilities.
  • the checks that are used to model the relations between the tests to be performed and the error candidates to be checked are used to determine by computer the tests relevant for the checking of the error candidates and to be evaluated by means of a program module for the test step prioritization.
  • the evaluation is carried out with a decision function, which evaluates the individual tests with regard to their information content for the current diagnosis problem in the form of the current candidate error quantity.
  • the decision function in addition to the evaluation of the information content of the individual tests, the decision function also contains a cost evaluation of the individual tests.
  • the two assessments can be used to assess the efficiency of the two be summarized. The efficiency of the test is then determined by the quotient of the information content and the cost of carrying out the individual test.
  • test step prioritization contains the decision function an evaluation and selection of the test steps with regard to the expected costs of further diagnosis, the so-called Expected Costs of Diagnosis.
  • test domain is modeled by a Bayes network.
  • the check domain is modeled using a rule-based knowledge base.
  • the check domain is modeled with a mathematical-physical model of the technical system.
  • the clamping of the test step tree takes place automatically and dynamically.
  • Automatic means that after the check step prioritization, the tests to be performed are displayed to the service technician by the diagnostic system, without the service technician having to search out the checks.
  • Dynamic clamping basically means two aspects.
  • the Test step prioritization dynamically with the number of currently suspected error candidates and second dynamically with the number of tests already performed and proposed.
  • the diagnostic system has a feedback option with which the service technician can enter the result of the tests carried out as evidence in the diagnostic system. After evidence has been entered, the diagnostic system updates the candidate defect quantity if an error candidate was found, and the step prioritization deletes the already used tests.
  • Test step prioritization and diagnostics system can be tracked dynamically to the progress of the diagnostics session. If only one single candidate is found, the search ends and prioritization is unnecessary.
  • the check step prioritization starts with a prioritized list of error candidates, which was determined by a diagnostic system in a diagnostic run as erroneously suspected components and prioritized with regard to their error probabilities.
  • the check step prioritization starts with a unweighted candidate list.
  • This basic stage has the advantage that even commercially available diagnostic systems, such as those discussed in connection with the prior art in FIG. 1, can be used to determine the error candidates. Details of the invention will be explained in more detail below with reference to graphical representations.
  • FIG. 1 A typical workshop diagnostic system of the prior art
  • Fig. 2 is a block diagram of a diagnostic system, as in the earlier German patent application DE
  • FIG. 3 is a schematic diagram of the invention
  • Figure 1 a situation is shown schematically, as it is known today in motor vehicle workshops.
  • a computer-aided diagnostic tester 1 is connected via a standardized diagnostic interface 2 to the communication network 3 for the control units 4 in the motor vehicle.
  • Known diagnostic testers are z.
  • the DAS system from DaimlerChrysler or the BMW DIS system.
  • the control units 4 installed in the motor vehicle are in communication with each other, for example via a data bus.
  • a common data bus in motor vehicles here is the so-called CAN bus (for Control Area Network).
  • Each of the installed control units in the motor vehicle has the ability to self-diagnose in addition to the communication interfaces.
  • error memory As part of the self-diagnosis of the ECUs detected by the diagnostic routine in the ECUs errors in codified form as so-called error codes from the ECU software in specially reserved memory areas, so-called error memory, written. In the schematic representation of FIG. 1, these reserved, non-volatile memory areas are designated FS (for error memory).
  • FS for error memory
  • the standard for the Keyword Protocol 2000 includes two different application options. On the one hand, the standard stipulates that the communication between the diagnostic tester and the control units is effected via a gateway 5, which is eg. B. binds the motor vehicle CAN bus to the diagnostic interface 2, or takes place, as usual, the error memory of the control units via the so-called. K and L lines and read via the normalized diagnostic interface 2 directly into the diagnostic tester and stored can be.
  • a gateway 5 which is eg. B. binds the motor vehicle CAN bus to the diagnostic interface 2, or takes place, as usual, the error memory of the control units via the so-called.
  • K and L lines and read via the normalized diagnostic interface 2 directly into the diagnostic tester and stored can be.
  • FIG. 1 shows the more modern form of access via a CAN bus and thus via a gateway.
  • the invention is of concern only that there is at least one way to be able to read the error memory of the control units with a diagnostic tester.
  • the transmitted contents of the control device memory, in particular error codes and status data of the control devices are further processed in a diagnostic session with an implemented diagnostic program.
  • the diagnostic program also includes the option of manually inputting further information that is important for a diagnosis via a computer workstation as a human-machine interface.
  • FIG. 2 shows a block diagram of the most important program modules and functions of a diagnostic system according to the invention realized with these program modules.
  • the individual program modules are integrated into a higher-level sequence control of the entire diagnostic system. This sequence control takes over the, call of the individual program modules at the respectively necessary time.
  • the diagnostic system processes error codes FC and inputs by a service technician as input variables.
  • the service technician makes his inputs from a VDU workstation 200, which is typically equipped with a screen and a computer keyboard, each connected to the computer system 201 of the diagnostic system. Via a further interface 202, the computer system can be connected to the motor vehicle to be diagnosed. About the OBD socket (On Board Diagnosis), the control units contained in the motor vehicle can be addressed.
  • OBD socket On Board Diagnosis
  • the self-diagnosis routines of the control units can be started and thereby functional tests of the individual control units are started and it can be accessed and read current system state data from the motor vehicle.
  • One possibility of the technical realization was discussed in connection with FIG.
  • the diagnostic program implemented on the computer system is characterized among other things by a modular structure.
  • the programming and the configuration of the diagnostic system are structured.
  • a first program module 210 according to its function called rule table evaluation, retrieved from the motor vehicle data, such as error codes and system status data to the individual components installed in the motor vehicle, read and processed.
  • the further processing includes checking of rule tables stored in a knowledge base 211.
  • the rule tables contain the diagnostic knowledge relevant for the technical system to be diagnosed. This knowledge is stored, for example, in compressed form in data tuples.
  • the data tuples depict the relationships between the information contained in them.
  • a data tuple is stored for each diagnostic rule.
  • Each data tuple consists of a component identifier Ki, an error code FCi, a symptom sympi as an indication of an affected technical function, and a system status Stat.
  • the rule table evaluation then takes place in such a way that all the stored data tuples are looked up in the entirety, which data tuples contain the error code (s) read in and which components Ki and functions Sympi are called in the identified data tuples and thus from the observed one Error FCi may be affected.
  • the component identifications found in this way are recorded and combined into a first quantity of error candidates and stored.
  • These component identifiers Ki indicate which component or also which function of the technical system can be responsible for the observed error code or for the observed error symptom.
  • the result of this first scouring of the knowledge base is a first set of suspicious components that have been identified based on the identification via the error codes FCi.
  • the troubleshooting space formed by the first candidate set is further tensioned.
  • the possible sources of error are now enhanced by the relevant functions that may be involved in the motor vehicle.
  • the rule tables are searched again, this time not according to detected error codes, but according to the possibly already affected by the error codes components Ki.
  • the components that are to be affected are the possibly affected Sympi functions. These two quantities do not have to be identical. Because it is possible that an error code refers to a component that is relevant for several functions.
  • the result of this second pass through the knowledge base is a supplemented candidate list 214, which now also contains possibly faulty functions in addition to the possibly faulty components.
  • Screen workstation 200 performed a query 215 and displayed whether for further processing already detected error codes or the determined possibly affected functions are to be displayed.
  • the service technician is offered the opportunity to set a focus for the further diagnostic procedure.
  • the focus is set by either selecting a displayed error code or a displayed, suspicious function Sympi by means of graphical menu control, and using it for further processing by the diagnostic program. If the focus is set, further data processing is restricted to this focus. This means that not all detected error codes, suspicious candidates or suspicious functions are considered, but only those that fall under the chosen focus.
  • the individual error candidates are subjected to a weighting in a further program module or method step 217.
  • the probabilities of the error codes FCi For the weighting, the probabilities of the error codes FCi, the probability of the occurrence of sympi and possibly the probability of the occurrence of error images must be calculated. For this purpose, probabilities must be provided which indicate with what certainty a defective component or a candidate Ki causes an error code (P (FCiIKj)), a malfunction (P (Sympi
  • the relative error weighting g (Kj) of a component itself is needed. This information is needed to calculate the prioritized or weighted candidate list.
  • the conditional probabilities are easy to estimate. Most of them are set to "1", but sometimes unsafe symptoms or error codes may take on values less than 1.
  • the error weights g (Kj) can be selected from empirical knowledge, for example, between one and one hundred and represent a relative failure parameter.
  • the current field events can also be taken into account via these error weights g (Kj) by calculating these weights by means of offsetting Error frequencies are adapted.
  • All probabilities and error weights are stored in the database of the diagnostic system 218. Appropriately, these can be filed and bedatet together with the component list, the feature or symptom list and the error image list. These lists are created in the construction of a motor vehicle and therefore need only be supplemented by the knowledge of experience with regard to the conditional probabilities and the error weights. Expediently, the data is model-specific. However, cross-product databases can also be created and used. For cross-database databases, however, the possibility of a series-specific selection, e.g. be kept in the form of an upstream master table.
  • G is a normalization quantity and is determined in a preparatory step or by calculation eg as the sum of all weights g (Kj).
  • the a posteriori error probability or priority Prio (Ki) of a component Ki results from the following product:
  • Pr io (Ki) (g (Ki) / G) - fj P (FQ 1 Ki) -] J P (Sympk ⁇ Ki) ⁇ Y [P (FBl ⁇ Ki)
  • This priority is still not standardized and can alternatively be normalized by dividing by the sum of all candidate priorities.
  • the above-discussed diagnostic system which is within the disclosure of the invention described herein, provides a prioritized candidate list, and is the preferred diagnostic system with which test step prioritization can be performed.
  • the inventive system provides a prioritized candidate list, and is the preferred diagnostic system with which test step prioritization can be performed.
  • Test step prioritization with each diagnostic system that provides an error candidate quantity as a result of diagnosis performed.
  • the final step in the workshop diagnosis is component testing or functional testing, with the goal of complete fault isolation for the quickest possible remedy. Until this time, all available information, such as vehicle diagnoses or customer complaints will be evaluated largely automatically.
  • the focus of the inventive test step prioritization described here lies in the creation of an optimized test sequence.
  • the test step prioritization 220 starts from an error candidate quantity whose error candidates have already been identified and named in a preliminary stage. This situation is illustrated by the block diagram of FIG. 3.
  • a non-prioritized candidate defect quantity 330 is determined by a basic version diagnostic system.
  • a weighted candidate list 340 is determined by a diagnostic system.
  • the software module 350 of the check step prioritization 220 for the determined error candidates determines the relevant checks stored in a knowledge base or in a database 360 for checking the individual suspicious candidates. Once all the relevant tests have been determined, the individual tests are evaluated, prioritized and placed in a test step sequence by the test step prioritization with regard to information content and / or costs.
  • the prioritization of the test steps can hereby be tracked recursively and dynamically to the progress of the diagnostic session, in that the tests already performed by the service technician are entered into the system and the used tests are deleted from the test step sequence.
  • the consumption of a proposed check usually leads to a new prioritization of a weighted check step list 370, which is currently updated in each case to the status of the diagnostic session.
  • the diagnostic system in a first stage creates a list of the suspect components, the candidate defect quantity. Now the diagnostics program can select those checks from a test step database that are relevant for checking the suspicious components. That is, those checks that have at least one reference to the suspected components.
  • the amount of all stored in the database checks is PS.
  • the relevant tests are ps element of PS.
  • the basic key question for determining the best test at the current time is according to the invention: how well does the test ps in the current diagnostic problem, which consists of a candidate error rate, help further?
  • a diagnostic program Decision function proposed, which evaluates the relevant tests with regard to their respective information content for the respective diagnostic problem, or which evaluates the relevant tests for the respective diagnostic problem with regard to the respective costs for carrying out the test, or which evaluates the tests relating to efficiency relevant to the respective diagnostic problem, the efficiency is formed by the quotient of information content and costs.
  • the information content of a test step can e.g. from his Entropie Ent determine.
  • Efficiency Eff then is the ratio of the information content of a test step ps to its cost:
  • the entropy of a test step is understood as the following probability sum:
  • the information content of a test step becomes greatest when its entropy becomes greatest.
  • the entropy becomes greatest when a test step has many outputs, ie many possible test results, and when the individual ones Test results have a possible uncertain outcome. This corresponds to the fact that with this examination I gain the most information, which at the same time checks as many error candidates as possible and of which I least know how the examination ends. Conversely, the exam has the lowest information content, which has only a few outputs and of which I know the result with great certainty already.
  • the decision step for the check step selection is formed by the expected cost of the test steps remaining after the selected check step for the respective remainder of the prospective diagnostic session.
  • This decision function also includes the entropy criterion, which, however, is complemented by a refined and extended cost consideration and cost estimate.
  • the refined and expanded cost estimate calculates, at each current test step, the expected cost of the remaining test, i. which costs are still to be expected, after which a previous test has already been carried out.
  • the designation of the decision function as ECD (Extended Cost of Diagnosis) is based on the term ECR, the Extended Cost of Repair, which is occasionally used in the scientific literature.
  • j is a run index and n is the number of tests remaining after the current test step ps.
  • the estimated cost EstCosts consists of the entropy of the remaining diagnostic problem multiplied by the average cost of testing AvCosts.
  • the latter are the arithmetic mean of the cost of all remaining exams.
  • the respective upcoming test step to be selected is then selected from the possible test steps by selecting in each case that test step which minimizes the expected cost of diagnosis of the respectively remaining diagnostic session.
  • Probabilities p (ps) and the variables derived therefrom of the respectively relevant test steps ps are derived from the Prioritization of the error candidate list is calculated.
  • the relevant test steps are determined for the determined error candidates Ki.
  • the test step ps simultaneously checks two candidates K1 and K2, the test step having the result "not OK” (NOK) if one of the two candidates proves to be faulty and has the result "OK” if both Candidates deliver the test result without errors.
  • the probable outcome of the relevant test steps then results from the error candidate probabilities as follows:
  • Another criterion for a check step prioritization is the cost of a test step to be performed. These costs are known as empirical values e.g. known as so-called labor values, and are assigned during the creation of the test step database and assigned to the individual test steps. Thereby, the decision step function for the check step prioritization may be refined from an entropy to an efficiency selection or a cost estimate according to the expected cost of diagnosis.
  • the decision function for the check step prioritization of the diagnostic system is then either one
  • Test step prioritization based on the information content of the individual relevant test step the test step with the highest information content receives the highest priority, or the decision function prioritizes the individual test steps based on the efficiency defined above, the test step giving the highest priority having the highest efficiency, or a cost estimate corresponding to the expected costs of Diagnosis used.
  • test step sequence can be set up automatically by computer calculations and computer selection.
  • the service technician can be proposed a test step sequence and displayed on a display of a diagnostic system, which begins with the highest priority test step and the subsequent test steps with decreasing prioritization followed.
  • the process flow for a base system with test step prioritization is shown in the block diagram of FIG.
  • the relevant diagnostic steps are determined by the diagnostic program supplemented according to the invention in a subsequent method step 420 and evaluated and prioritized eg with the aforementioned evaluation scheme with regard to efficiency, information content or cost estimate.
  • the service technician or the fitter can select from the displayed checking steps a checking step, for example by menu control, and perform this on the motor vehicle.
  • the result of the test step entered into the diagnostic system and carried out with the newly gained knowledge of a recalculation of the weights of the suspect candidate.
  • the recalculation leads in particular to the deletion of all error candidates already tested and found to be in order from the list of error candidates.
  • the released weights grow in this case the not yet tested error candidate.
  • the test step prioritization is also recalculated and performed, so that the procedure for the check step prioritization is recursive, with the aim as quickly as possible to obtain an error candidate with a probability of error close to 100%. Illustrated in method step 450 of the block diagram.
  • a problem with the check step prioritization may be the more accurate determination of the information content of a check step, if one wants to apply this information content to the current diagnostic session.
  • the above-described uniform distribution of the test outputs is only a first approximation, which, however, on closer inspection may vary with the current diagnostic problem. In particular, already performed tests and the knowledge gained from them can increase or decrease the information content and the value of tests that have not yet been carried out. If you want to adapt the information content of a test step to the progress of the diagnostic session, you need a flexible calculation tool that is able to deal with probabilities. Probability networks and in particular so-called Bayes networks are suitable here. Bayes networks have been used successfully in the fields of fault diagnosis, decision support, reliability analyzes, risk assessment, etc.
  • the Bayes networks serve Here, the representation of relationships and sizes that may be subject to uncertainties or are known only with probability.
  • the so-called propagation methods offer the opportunity to process new information, such as exam results, consistently and thereby calculate the impact on other variables.
  • the Bayes network now serves to represent the relations between testing and tested components and to calculate more accurately the probabilities required for the test step prioritizations and to adapt them to the progress of the diagnostic session.
  • Error candidate list is set up a diagnostic node that contains a state for each suspicious component with a probability that corresponds to the weighting of the error candidate. Now those checks from the check step database can be selected which are relevant, ie which check the suspicious components. The selected test steps are prioritized and selected with one of the decision functions. A test step is still available if it has not already been performed, or if the test output is not already certain. For the test tree construction then both output options of the test, component in order or component not in order, must be present. Then, all relevant unused exams are recursively continued. Such a method is called myoptic or short-sighted, since only the next step is optimized.
  • test tree If the test tree is spanned, the results of the test steps that have been carried out can be entered as evidence in the Bayes network as evidence, and the effect on the remaining components can be calculated and to the remaining components again a test tree are spanned, which selects the most prioritized test steps with the previously described myoptischen method.
  • test step tree which is graphically displayed to the service technician.
  • a graphic representation of such a test step tree is shown in FIG.
  • the states of the determined error candidates are in the form of weights. These are the candidates Kl, K2, K3, K4, K5.
  • the best test step PS1 is selected for the present diagnostic problem.
  • the exam result is reflected back.
  • OK candidate Kl is in order
  • test step PS4 for checking the component K4.
  • PS4 N0K
  • component K4 is identified as faulty. If the check with test step PS4 reveals that component K4 is OK, PS2 continues with the next best test step, which checks component K2 and identifies it at test output Not OK K2. If the test result is OK, it is continued with the best remaining test step. In the example chosen, this is the test step PS3 for checking the component K3.
  • the test result is not so important, because in any case, the last remaining, suspect component K5 must be examined. As a rule, a test is still due for this last review, but this is no longer shown in FIG. It may also be that a restart of the diagnostic system after the Repairs performed the components Kl and K4 shows that so that the error candidate K2, K3, K5 are no longer suspected.
  • Realistic testing problems can include around 50 suspicious components. Each component has at least one individual test. In addition, there are functional tests that cover more than one component. Thus, the number of tests is generally greater than the number of components.
  • the branches of a test step tree are balanced according to their entropy and cost.
  • the error probability and thus the weighting in the candidate error quantity grows to a few mostly one component. Namely, the error probabilities of all of the components already tested in the correct order become zero and enter into the diagnostic program as a check result, so that as the diagnostic candidate progresses and the update of the weighted candidate error quantity by recursive recalculation by the diagnostic program becomes more acute, the diagnostic focus becomes ever sharper.
  • the information content or efficiency of these composite checks is usually very high, as they examine multiple components and therefore provide a large information gain.
  • the composite checks are therefore among the highly prioritized checking steps for an error candidate quantity.
  • the diagnostic session In particular, at the customer meeting in the vehicle acceptance, a current cost estimate for each remaining diagnostic session are determined. For this purpose, the complete test step tree is determined for the error candidate quantity and the costs of the determined test steps are summed up. This cost sum then sets an upper limit on the costs that may possibly be expected in the present diagnosis problem.
  • the service technician is offered a complete test step tree by the diagnostic system in which the individual test steps with regard to efficiency, information content or expected costs of diagnosis are prioritized. Ultimately, it is up to the service technician to decide in which order he performs which test step. The service technician does not have to start with the highest prioritized check of the test step tree.
  • the repairs carried out can be logged with the diagnostic system. This protocol can then be used to suggest tests or test steps to ensure that the installed components or the repair are rechecked to ensure that the repair has been successful and no new faults have been incorporated.
  • the logging function can continuously log the current status of the vehicle in the repair workshop. This can be determined continuously, which components of the motor vehicle are just removed.
  • the dynamic costs of the diagnostic session also depend on the current build state of the vehicle, so that the protocol function can also be used for cost estimation.
  • the tracking of the Verbausches of the vehicle in the Repair succeeds in logging which test steps have already been performed with which test exit.

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  • Test And Diagnosis Of Digital Computers (AREA)

Abstract

L'invention concerne un système de diagnostic qui génère un nombre de défaillances possibles au moyen d'un programme de diagnostic, d'abord par consultation de système et par traitement des informations de système demandées ou des états de système consultés. Les défaillances possibles dans ce nombre de défaillances possibles sont classées par priorité, de préférence relativement à leur éventualité. Cette priorisation peut être prise en compte lors de l'élaboration d'une arborescence d'opérations de vérification et elle permet de créer une arborescence d'opérations de vérification la plus efficace possible dans l'état de système défectueux correspondant. La priorisation des défaillances possibles n'est toutefois pas indispensable en soi. Par exemple, toutes les défaillances possibles d'un nombre de défaillances possibles peuvent avoir la même probabilité. Pour les défaillances possibles déterminées, les vérifications adéquates sont définies par ordinateur au moyen du domaine de contrôle, dans lequel les relations entre vérifications à réaliser et défaillances possibles à contrôler sont modélisées, ces vérifications étant soumises à une évaluation pour la priorisation au moyen d'un module du programme. L'évaluation est réalisée par une fonction de décision qui évalue chaque vérification relativement à son contenu informationnel pour le problème de diagnostic en cours sous forme d'un nombre de défaillances possibles momentané.
PCT/EP2006/005572 2005-06-14 2006-06-09 Priorisation dynamique d'operations de verification dans un diagnostic d'atelier WO2006133865A1 (fr)

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DE102005027378.5 2005-06-14

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