DE102012206836A1 - Method for forecasting an error or for detecting errors in a transport machine and transport machine - Google Patents

Abstract

A method for forecasting a fault in a haulage machine is described, comprising: (2) detecting at least one type of time-domain detection values in a first haulage machine, storing (2) the detection values or storing frequency spectra of the detection values, waiting for, preferably (4), examining (6) the frequency spectra stored before the occurrence of the error, or frequency spectrums calculated from the detection values stored before the occurrence of the error, which is detectable without the stored detection values or the stored frequency spectra, for at least one characteristic which is the Indicates the occurrence of the error early, using the feature for early detection of the error or for error detection in the first transport machine or in a second transport machine.

Description

The invention relates to a method for detecting errors, in particular to a method for the early prognosis of an error. In addition, an associated transport machine should be indicated, e.g. a vehicle, an airplane or a ship.

• – Erfassen mindestens einer Art von Erfassungswerten im Zeitbereich in einer ersten Transportmaschine,
• – Speichern der Erfassungswerte oder Speichern von Frequenzspektren der Erfassungswerte,
• – Warten auf einen Fehler, vorzugsweise auf einen ohne die gespeicherten Erfassungswerte oder die gespeicherten Frequenzspektren erfassbaren Fehler,
• – Untersuchen der vor dem Auftreten des Fehlers gespeicherten Frequenzspektren oder von aus den vor dem Auftreten des Fehlers gespeicherten Erfassungswerten berechneten Frequenzspektren auf mindestens ein Merkmal hin, welches das Auftreten des Fehlers frühzeitig anzeigt,
• – Verwenden des Merkmals zur Früherfassung des Fehlers oder zur Fehlererfassung in der ersten Transportmaschine oder in einer zweiten Transportmaschine.

The invention relates to a method for forecasting an error in a transport machine, comprising:

• Detecting at least one type of detection values in the time domain in a first transport machine,
• Storing the detection values or storing frequency spectra of the detection values,
• Waiting for an error, preferably a fault detectable without the stored detection values or the stored frequency spectra,
• Examining the frequency spectra stored prior to the occurrence of the fault or frequency spectrums calculated from the detection values stored prior to the occurrence of the fault, for at least one characteristic indicative of the occurrence of the fault,
• Use of the feature for early detection of the error or for error detection in the first transport machine or in a second transport machine.

• – einem Bordnetz, das mindestens zwei Teile enthält, einer zwischen den mindestens zwei Teilen angeordneten Trennvorrichtung, mit der die beiden Teile elektrisch voneinander getrennt werden können,
• – einer ersten Erfassungseinrichtung an dem Bordnetz, vorzugsweise an einem ersten Teil der mindestens zwei Teile,
• – einer Steuereinheit, die mit der Trennvorrichtung oder mit einer Warneinheit verbunden ist, und die abhängig von den mit der Erfassungseinrichtung erfassten Erfassungswerten die Trennvorrichtung oder die Warneinheit ansteuert.

Furthermore, the invention relates to a transport machine, with:

• - a vehicle electrical system containing at least two parts, a separating device arranged between the at least two parts, with which the two parts can be electrically separated from each other,
• A first detection device on the vehicle electrical system, preferably on a first part of the at least two parts,
• - A control unit, which is connected to the separating device or with a warning unit, and which controls the separating device or the warning unit depending on the detected values detected by the detection device.

It is an object of the exemplary embodiments to provide a method for forecasting an error in a transport machine, wherein the method should be particularly easy to implement. In addition, a transport machine is to be specified, which is particularly suitable for the application of methods with error prognosis.

This object is achieved by a method according to claim 1 and by a transport machine according to the independent device claim. Further developments are specified in the subclaims.

• – Erfassen mindestens einer Art von Erfassungswerten im Zeitbereich in einer ersten Transportmaschine,
• – Speichern der Erfassungswerte oder Speichern von Frequenzspektren der Erfassungswerte,
• – Warten auf einen, vorzugsweise ohne die gespeicherten Erfassungswerte oder die gespeicherten Frequenzspektren erfassbaren, Fehler,
• – Untersuchen der vor dem Auftreten des Fehlers gespeicherten Frequenzspektren oder von aus den vor dem Auftreten des Fehlers gespeicherten Erfassungswerten berechneten Frequenzspektren auf mindestens ein Merkmal hin, welches das Auftreten des Fehlers frühzeitig anzeigt,
• – Verwenden des Merkmals zur Früherfassung des Fehlers oder zur Fehlererfassung in der ersten Transportmaschine oder in einer zweiten Transportmaschine.

One embodiment relates to a method for forecasting an error in a haulage machine, comprising:

• Detecting at least one type of detection values in the time domain in a first transport machine,
• Storing the detection values or storing frequency spectra of the detection values,
• Waiting for a fault, preferably without the stored detection values or the stored frequency spectra, to be detected,
• Examining the frequency spectra stored prior to the occurrence of the fault or frequency spectrums calculated from the detection values stored prior to the occurrence of the fault, for at least one characteristic indicative of the occurrence of the fault,
• Use of the feature for early detection of the error or for error detection in the first transport machine or in a second transport machine.

The difficulty in forecasting errors consists, for example, in determining characteristic features which occur in the run-up to an error which, for example, would lead to the triggering of other safety devices. At a minimum, there should be a high probability between the occurrence of the feature and the occurrence of the error. If the probability is 100 percent, then there is a 1: 1 relationship or a deterministic or direct relationship.

The characteristic feature occurs, for example, only after several years of operation. If it is then possible to associate an error with a characteristic feature from data that has been stored for a while, then this characteristic feature can be used in the vehicle repaired after the fault and, above all, in other vehicles of the same design for the early detection or prognosis of errors. This is possible by changing the operating software in a simple way, for example. In a workshop or via a wireless network.

The same applies to electromagnetic radiation in the electrical system in case of failure or imminent errors of connected to the electrical system modules or assemblies that are located close to the electrical system.

The determination of a characteristic feature or a characteristic feature group can not be over-estimated considering the general cost pressure, which is often associated with quality defects. Instead of Global recalls can be used to selectively predict faults and only the vehicles actually affected need to be phased into the workshops.

The detection values can be stored. Alternatively, however, frequency spectra of the detection values are stored to reduce the amount of data, for example by a factor less than 10. Also, older data may be erased or lighted out. For example. Deletes data older than one year. In a multiple storage per month, after the expiration of a period of time, which is, for example, between a month and a year, stored for the out-of-period months, only one record for each month.

Thus, data that has already been acquired and stored before the occurrence of a major error is used to determine the characteristic feature for the early detection of the error or for error detection. For example, data older than one day, older than one week, older than one month, or even older than one year may be used relative to the day the larger error occurred.

The detection values can be detected at a vehicle electrical system of the first transport machine. In on-board systems, early detection is particularly important because, for example, the power and voltage supply for important units must be secured. Faults in on-board networks can quickly lead to particularly serious errors, e.g. to fires. Even if conventional security measures would take effect, such as fuses or circuit breakers, another measure is preferable, since the classical safety measures only trigger currents or voltages that are possibly at least an order of magnitude higher than in fault-free operation, which can lead to accompanying damage.

In addition, faults in on-board networks are particularly difficult to locate, let alone predict, so that the proposed method opens up new application areas.

In particular, in the on-board networks of electric cars, the discharge of a battery can be prevented by the early detection of errors. This can greatly increase the acceptance of electric cars because they rely on a charged battery for driving.

The method may be performed on a variety of vehicles, e.g. also for all vehicles of a manufacturer. The database can be chosen as large as possible, since it is initially unknown in which vehicle the errors will occur. Alternatively, a relevant sample is used. If characteristics for early detection or error detection have then been determined on the basis of the random sample, these features can also be used in predicted vehicles for prognosis, in particular in vehicles that did not belong to the random sample.

Likewise, a wide range of frequencies in the spectrum can be detected, for example a frequency range that covers more than 4 or more than 5 powers of ten. Thus, a frequency range can be used, for example, 0 Hz to 10 MHz (megahertz) or even up to 100 MHz. The largest frequency may typically be less than 1 GHZ. But even 50 percent or 80 percent of these ranges can be sufficient, especially if the percentages contain the upper frequency range. Again, it is not known or difficult to estimate where features change. Thus, a wide range ensures that changes are actually recorded.

The acquisition of the detection values can be effected on a physically present vehicle / transport machine or on a physically existing subsystem of the vehicle / transport machine or in a simulation of the first transport machine or a simulation of a part of the first transport machine.

Simulation methods are becoming increasingly important and used as early as possible in the development process, see HIL (Hardware In The Loop), etc.

A probability measure may be determined that indicates the probability with which the early detection of the error is correct. On the one hand, there is a certain dispersion of the detection values and, on the other hand, features or feature combinations can also be associated with different errors or errors of different severity.

An error which can not be detected without the stored detection values or the stored frequency spectra can be specifically incorporated into a part of the transport machine or into a simulation of at least part of the transport machine. In a vehicle electrical system, for example, the insulation of a cable can be selectively thinned. Alternatively, a cable can be heavily loaded mechanically, for example by pressure, by kinking or by pulling. As a result of the load on the cable, it may then come after a certain time to larger errors, such as cable break or short circuit. In a simulation, for example, an aging of the insulation could be simulated or a higher temperature, which lowers the insulation capacity.

The detection values or the frequency spectra can be stored outside the transport machine.

In question, for example, a transmission via radio or a reading during maintenance, e.g. in a factory. The proposed method can be carried out inexpensively with M2M (Machine to Machine) methods, e.g. via existing radio networks or via specially designed radio networks. Thus, an existing mobile network could be used, such as UMTS (Universal Mobile Telecommunications System) or LTE (Long Term Evolution).

The detection values or the spectra are collected for several transport machines, e.g. for more than 10, for more than 100 or for more than 1000 transport machines. Thus, a database can be created for the vehicles of a vehicle type of a manufacturer or for all vehicles of a manufacturer with the same on-board networks.

Alternatively or additionally, however, the detection values or the spectra are also stored in the transport machine, for example in order to avoid transmission via public radio networks or to design them effectively, for example the data is transmitted only on a monthly basis.

A current profile or a voltage profile or both a current profile and a voltage profile can be detected. If both current and voltage are detected, errors can easily be predicted, which are associated with an increase or decrease in electrical power or impedance.

The fault may be a cable break or cable short. Especially with these two types of error early detection is possible on the basis of characteristic frequency patterns. Before a cable break or short circuits, even sporadic sparking or isolated breakdowns may occur, which are particularly associated with charging processes or high-frequency discharge processes.

The error may also relate to an electromagnetic radiation of a device of the transport machine in the electrical system of the transport machine. This is based on the knowledge that in the vehicle manufacturing due to the increasing requirements of EMC (electromagnetic compatibility) for error-free operation high-frequency emissions of control units are basically avoided or kept small, but occur or amplified in case of error or in the run-up to an error occur and can then be recorded.

A feature of the frequency spectrum in the range of a frequency greater than 1 megahertz can be used. These frequencies occur, for example, amplified only in the run-up to a cable break or short circuit or as a result of faulty electromagnetic radiation. The feature may be at a frequency less than 100 MHz (megahertz).

The detection values can be detected at a vehicle electrical system of the first transport machine. The electrical system can contain at least two parts, between which a separating device is arranged, through which the two parts can be electrically separated from each other.

Thus, when predicting an error or when an error occurs by disconnecting a part in which the error occurs, the error can be prevented. For example. The driver can be given more time until the next workshop visit, for example at the expense of a reduced redundancy or a restriction of the driving comfort.

In the case of early detection or error detection, a warning may be issued and / or at least one of the separation devices ( 46 ) can be operated. Thus, when detecting an error, a decision is made as to which measures are to be initiated.

• – einem Bordnetz, das mindestens zwei Teile enthält, einer zwischen den mindestens zwei Teilen angeordneten Trennvorrichtung, mit der die beiden Teile elektrisch voneinander getrennt werden können,
• – einer ersten Erfassungseinrichtung an dem Bordnetz, vorzugsweise an einem ersten Teil der mindestens zwei Teile,
• – einer Steuereinheit, die mit der Trennvorrichtung oder mit einer Warneinheit verbunden ist, und die abhängig von mit der Erfassungseinrichtung erfassten Erfassungswerten die Trennvorrichtung oder die Warneinheit ansteuert.

An embodiment also relates to a transport machine, comprising:

• - a vehicle electrical system containing at least two parts, a separating device arranged between the at least two parts, with which the two parts can be electrically separated from each other,
• A first detection device on the vehicle electrical system, preferably on a first part of the at least two parts,
• - A control unit, which is connected to the separating device or with a warning unit, and which controls the separating device or the warning unit depending on detection values detected by the detection device.

This transport machine is particularly suitable for methods for error prediction, because can be switched off in a predicted error parts of the electrical system, so that the predicted error can not occur at all. Additionally or alternatively, an automatic warning message can be generated. For the rest, the technical effects mentioned above for the method apply. Also, a targeted shutdown of individual segments to locate the error or signal indicating the error.

The two parts can be redundant to each other. At the two parts can preferably be connected to each other redundant units of the transport machine. Redundancy is increasingly being used for automatic driver assistance systems, in particular for assistance systems which engage in vehicle control for a longer period, e.g. for more than a second or more than 3 seconds. Examples of such systems are: steering assistant, brake assist, overtake assistant, etc., i. Control units with central control tasks, control units for a steering system, control units for braking the transport machine.

Depending on the topology, for example, in the case of in-line networks intermeshed beyond a ring structure, the redundancy can still be ensured despite the separation of a faulty segment.

In one embodiment, the transport machine also includes a memory device for storing comparison features, comparison spectra, detection values acquired by the detection device / sensor, or spectra calculated from the detection values.

The signals detected by the detection unit can be transformed in one embodiment into the frequency domain. A transmitting unit may then send the signals detected by the detecting unit or the transformed signals to a collecting unit, e.g. to a server which is also referred to as a service providing computer.

In one embodiment, there is a communication connection, in particular a bus system, which lies between the control unit and the separation unit or the separation units. The same data transmission system or another data transmission system can be used for the transmission of the detection values, in particular before carrying out the transformation into the frequency domain.

In a next embodiment, in addition to the separation unit, there is at least one further separation unit between the two parts in the vehicle electrical system. The further separation unit may switch on occurrence of the predicted or predicted error or other serious errors and is e.g. a fuse or a circuit breaker. This avoids mispredictions and errors that are difficult or impossible to forecast.

In another embodiment, an error is initially predicted. Subsequently, signal propagation time measurements are carried out in the vehicle electrical system in order to determine a segment of the electrical system in which the cause of the signals whose occurrence has caused the forecast of the fault. This segment can be separated from the rest of the vehicle electrical system as a precaution and / or it can be issued a warning message. A warning message is particularly sufficient in cases where the expected error is less serious or in which the error is expected only after a certain time, for example, in a period that is greater than a week.

In other words, a protection concept for on-board systems is specified by means of pattern recognition in the frequency domain.

It is about detecting and / or forecasting errors in on-board networks of vehicles or other transport machines, regardless of the network topology used, i. for simple topologies, such as star networks, as well as for ring networks or heavily meshed networks. This applies in particular to the detection and / or prognosis of short circuits and resulting parallel and serial discharges or arcs as well as over- or under-voltages or interference couplings.

It must be ensured in the vehicle or in the transport machine that these errors are avoided by suitable mechanisms and countermeasures are implemented in order to reduce or avoid damage to the equipment. Faulty components should be switched off reliably in order to avoid fault propagation in the vehicle electrical system and thus a failure of the entire system.

In the vehicle or in the transport machine errors in on-board networks, in particular short circuits, are recognized and repaired by fuses and circuit breakers usually. In the event of a short circuit, the fuse will cause a higher current than permitted during normal operation. With fuses this leads to a thermal overload and thus to the separation of the nets. In a circuit breaker, the extra energy is used to stimulate a switch and thus turn off the affected network. These guards are usually housed in vehicles / transport machines in a central fuse box.

An undervoltage protection is usually integrated in the individual components of the vehicle, so that after detection of an undervoltage in the device, the component switches off or via an implemented energy buffer in the components, for example in the form of batteries or Buffer capacitors is installed, the other function can be maintained over a period of time.

Surge protection is usually provided by a diode or surge arrester, e.g. Surge Arrester or lightning discharge protection, made, which becomes conductive from a certain voltage.

All of these mechanisms only detect errors. It is not possible for you to predict mistakes.

The invention relates to, for example, vehicles / transport machines with electrical wiring systems, which allows the detection of errors in on-board networks of vehicles, in particular short circuits, on the basis of frequency patterns. The frequency patterns result from the detection of electrical characteristics, e.g. Current and voltage, and their mapping in the frequency domain. The basis for detecting or detecting errors in on-board networks is that each component has a characteristic fingerprint in the frequency domain. Typical errors, such as Serial arcs occurring in the event of a cable break - precursor to a short circuit - also show a characteristic frequency response.

• – Abtasten des Stroms und der Spannung an definierten Stellen im Bordnetz eines Fahrzeugs/einer Transportmaschine. Dies sollte möglichst verteilt über das Fahrzeug geschehen.
• – Transformation der abgetasteten Werte in den Frequenzbereich durch eine Fourier-Transformation oder ähnliche Verfahren.
• – Identifikation und Vergleich mit charakteristischen Merkmalen bzw. Fingerabdrücken der Geräte und Fehlern. Anhand von Frequenzänderungen und -verschiebungen und Amplitudenänderungen lässt sich mit geeigneten Methoden, z.B. Vorwissen oder statistischen Methoden, ein möglicher Fehler, z.B. im Leitungsnetz oder in der Komponente, z.B. Alterung, erkennen oder gar prognostizieren.
• – Einleiten von Gegenmaßnahmen. z.B. Information des Benutzers bis hin zur Abschaltung von betroffenen Netzsegmenten.

A suitable procedure for fault finding and troubleshooting in a vehicle electrical system is, for example, in the following procedure:

• - Sampling of the current and the voltage at defined locations in the electrical system of a vehicle / a transport machine. This should be done as possible distributed over the vehicle.
• - Transformation of the sampled values in the frequency domain by a Fourier transform or similar methods.
• - Identification and comparison with characteristic features or fingerprints of the devices and errors. By means of frequency changes and shifts and changes in amplitude, a possible error, eg in the line network or in the component, eg aging, can be detected or even predicted with suitable methods, eg previous knowledge or statistical methods.
• - initiate countermeasures. Eg information of the user up to the shutdown of affected network segments.

In advance, it is necessary to record a data basis of the frequency patterns, as well as the characterization of individual components and defect images. This can also happen at runtime. For this purpose, fault scenarios and associated frequency images are recorded and evaluated for optimization.

Subsequently, these evaluations can be centrally collected, analyzed and correlated with each other to improve the database regarding the functioning of on-board networks and their components. The resulting error syndromes can be used for the proactive diagnosis of errors in other vehicles / transport machines with similar on-board networks / components and for the further development of on-board networks.

The method described is suitable for detecting errors which are particularly difficult to detect, such as e.g. Cable breaks, i. serial short circuits, as well as typical parallel short circuits, e.g. The chafing of cables causes one to predict ahead of time. The aim is to prevent damage to other components of the electrical system or the entire system and the necessary replacement of components and lines, e.g. are subject to aging, that they can be consumed more efficiently.

The state of components in the electrical system can be monitored and recorded so that the quality of the electrical system and its components, in particular its availability, can be improved.

Interference couplings, e.g. with regard to electromagnetic compatibility, can be measured and stored at runtime and analyzed at runtime or after operation, and detected errors can be mitigated or remedied by countermeasures.

In the following, embodiments of the invention will be explained with reference to the accompanying drawings. Show:

1 Method steps for the construction of a database with characteristic features, eg for the prognosis of errors,

2 the chronology during the construction and use of the database,

3 an electrical system of a vehicle, eg a passenger car,

4 an infrastructure for generating the database,

5 Procedural steps in the error monitoring and / or the prognosis of errors,

6 a typical spectrum for components and / or consumers on the electrical system,

7 a typical spectrum when an error occurs, eg a short circuit,

8th a voltage curve and a current waveform at a detection device on the electrical system, and

9 the calculated from the voltage curve and the current waveform spectrum with error feature.

The 1 shows method steps for the construction of a database with characteristic features, eg for the prognosis of errors. The process begins in one process step 1 , hereinafter also referred to as step.

In a subsequent step 2 , First, in a variety of cars / vehicles detection data in the time domain are detected and then transformed into the frequency domain, for example., Voltage data, current data, performance data, etc. The transformed data is then transmitted via a radio network or in another way, eg via a service or maintenance network by means of wired transmission, to one or more storage computers and stored there.

Alternatively, only the data in the time domain can be transmitted and stored. A transformation into the frequency domain then takes place at a central location or at several central locations. In this case, after saving, only the data relevant for an error detection or error prognosis can be transformed, for example, only the data of a vehicle within a specific period before the occurrence of a fault in this vehicle.

By dots are procedural steps 3 indicated in which further data from the plurality of vehicles are transmitted to the database. The timing of data generation is described below by reference to the 2 explained in more detail. Spectra or data are transmitted in the time domain.

In one process step 4 If an error occurs in one of the vehicles. It will be in one step 5 read the data of this vehicle from the database, preferably the spectra stored for this vehicle, in particular the spectra in a certain time before the occurrence of the error, eg within a week or within a month recalculated from the date of the error. However, it is also possible to use data from the vehicle which are located further back, in order, for example, to have a basis of comparison for a course of the spectra without errors. Alternatively, it is also possible to resort to the spectra transmitted by other vehicles, for example to have comparison values for error-free spectra.

In one step 6 Characteristic features of the spectrum are sought, which allow time as far back as possible from the time of error nevertheless a reliable detection of a deviation. This examination can be done manually, computer assisted or automatically. In particular, statistical evaluations can be carried out.

When searching for characteristic features, it is preferable to examine a plurality of vehicles with the same error, so that, if necessary, further maintenance is required before the determined characteristic feature for forecasting errors can be released. It must therefore be ensured that the deviations found in the spectrum are actually related to the error that has occurred and that the change in the spectrum did not occur only in the one vehicle due to its individual production or its individual properties. However, probabilities of occurrence of the error in the chosen change may also be used.

If a characteristic feature has been determined, then this can be used for the error prognosis and the error detection in future cases, which will be described below with reference to FIG 5 is explained in more detail.

The search of characteristic features can be carried out with the aid of the service providing computer on which the database is stored. Alternatively, another computer or another data processing system can be used for the examination.

The method for determining the features is in one step 7 completed.

The 2 shows the time sequence during the construction and use of the database. On a timeline 8th is the time t applied. For example. a record is always generated when the vehicle has been running for 10 minutes, but at most once a day. Alternatively, the data are, for example, detected shortly after starting, eg within 30 Seconds.

The acquisition of the data may also be tied to other conditions, such as driving at a certain speed or in a certain range of speed.

In other embodiments, data is continuously collected while the vehicle is traveling. Even at standstill of the vehicle detection is possible.

At a time t1, for example, a data set DS1a is generated for a first vehicle. The structure of the records DS1a, etc. will be described below with reference to 4 explained in more detail. After that, the first vehicle, for example, does not drive for a few days, so that no data records are recorded. Only at a time t2 is a data set DS1b generated for the first vehicle. One day later, a data set DS1c is generated for the first vehicle.

At a time t4, which is after the time t3, a data set DS2a for a second vehicle is generated and stored centrally or in the vehicle. The generation of further data records is by interrupting the timeline 8th indicated.

At a time t5, another set of data DS1d for the first vehicle is generated and stored, e.g. more than two weeks after time t4.

At a time t6, an error occurs in the first vehicle, for example, on the same day as the record DS1d has been generated. It will then be the top of the hand 1 explained step 6 to search for characteristic features. The software for error detection is then updated on the basis of the feature found in the first vehicle and in the second vehicle, so that the feature found in the forecast of errors in the electrical system of these vehicles can be considered in the future.

Subsequently, at time points t7 and t8, further data sets DS2b or DS1e are generated by the second vehicle or the first vehicle and transmitted to the database, for example to find deviations in other errors that occur later.

The data sets DS1a to Ds1e as well as the data sets DS2a and Ds2b have the following at the hand of the 4 explained structure.

• – Segmente 12 bis 26, die jeweils eine Pluspolleitung und ggf. eine Minuspolleitung enthalten, und die in der genannten Reihenfolge in einer Ringstruktur verschaltet sind,
• – bspw. zwei Batterien bzw. Akkumulatoren 28 und 30, kurz Akku genannt, wobei der Akku 28 am Segment 26 und der Akku 30 am Segment 18 angeschlossen ist,
• – Trennvorrichtungen 32 bis 46, z.B. mechanische, elektromechanische oder elektronische Schalter, z.B. Leistungs-Feldeffekttransistoren, insbesondere MOSFET's (Metal Oxide Semiconductor Field Effect Transistor), die in dieser Reihenfolge zwischen die Segmente 12 bis 26 geschaltet sind,
• – eine Datenübertragungsverbindung bzw. ein Bussystem 50a, 50b zur Ansteuerung der Trennvorrichtungen 32 bis 46 über Steuerleitungen 52 bis 64 und 51 und zur Abfrage von Strom und/oder Spannungssensoren über Sensorleitungen 72 bis 82, sowie
• – eine Steuer- und Kommunikationseinheit SE am Bussystem 50a, 50b.

The 3 shows a wiring system 10 a vehicle, eg a passenger vehicle 110 , please refer 4 , The electrical system 10 includes:

• - Segments 12 to 26 which each contain a plus lead and possibly a minus lead, and which are connected in the order mentioned in a ring structure,
• - For example, two batteries or accumulators 28 and 30 , called battery for short, with the battery 28 on the segment 26 and the battery 30 on the segment 18 connected,
• - Separators 32 to 46 For example, mechanical, electromechanical or electronic switches, eg power field effect transistors, in particular MOSFETs (Metal Oxide Semiconductor Field Effect Transistor), which in this order between the segments 12 to 26 are switched,
• A data transmission connection or a bus system 50a . 50b for controlling the separation devices 32 to 46 via control lines 52 to 64 and 51 and for interrogating current and / or voltage sensors via sensor lines 72 to 82 , such as
• - A control and communication unit SE on the bus system 50a . 50b ,

Usually, the body is used as a return conductor in an automobile. An explicit return conductor is thus not necessary, but can e.g. be more advantageous with regard to interference couplings.

The segments 12 and 24 are mutually redundant and serve, for example, the power supply of control units with central tasks. These control units can in turn each be redundant, ie two control units on the segment 12 and two control units on the segment 24 ,

The segments 14 and 22 are also redundant to each other and serve the power of control units to support the steering of the vehicle, see, for example, the control units 90 and 92 on the segment 22 ,

The segments 16 and 38 are also redundant to each other and serve the power of control units, which serve, for example, the automatic braking of the vehicle.

The measured data for the on hand of the 1 explained methods are, for example, only on the segment 12 detected. Alternatively, measurement data can be sent to all segments 12 to 16 . 20 to 24 and optionally also on the segments 18 and 26 be recorded. The equipment of each segment 12 to 16 . 20 to 24 but with a detection unit is, for example, only required for the detection of runtime differences in the localization of a segment in which an error is approaching.

In another embodiment, a more meshed topology is chosen instead of a ring topology. Even with on-board networks without redundancy, the invention is used.

The 4 shows an infrastructure 100 for generating the database. To the infrastructure 100 count cars or vehicles 110 to 114 , which are equipped with transmitting units, such as transmitting unit 102 of the vehicle 110 , Furthermore counts to the infrastructure 100 a server 116 ,

• – einen Speicher Mem, z.B. einen RAM (Random Access Memory) und/oder einen ROM (Read Only Memory),
• – einen Prozessor MP, z.B. einen Mikroprozessor oder einen Mikrocontroller, der Befehle abarbeitet, die im Speicher Mem gespeichert sind,
• – eine Sende-/Empfangseinheit R/S (Receive/Send), wobei der Sendeteil optional ist,
• – einen bidirektionalen Datenbus 18 zwischen der Sende-/Empfangseinheit R/S und dem Prozessor MP, und
• – einen Daten-/Befehlsbus 20 zwischen dem Prozessor MP und dem Speicher Mem.

The server 116 is a service providing computer (DER), which contains, for example, the following components:

• A memory Mem, for example a RAM (Random Access Memory) and / or a ROM (Read Only Memory),
• A processor MP, eg a microprocessor or a microcontroller, which executes instructions stored in the memory Mem,
• A transmitting / receiving unit R / S (Receive / Send), the transmitting part being optional,
• - a bidirectional data bus 18 between the transmitting / receiving unit R / S and the processor MP, and
• - a data / command bus 20 between the processor MP and the memory Mem.

The memory Mem stores a plurality of data records for a plurality of vehicles, see the two illustrated data sets DS1a and DS2a. The record DS1a relates to the vehicle 110 , The data set DS2a, on the other hand, concerns the vehicle 112 ,

• – ein Identifikationsdatum, z.B. ID1, spezifiziert das Fahrzeug 110, 112, 114, von dem der Datensatz kommt,
• – Spektrendaten, z.B. Sp1, enthalten das Spektrum für die innerhalb eines vorgegebenen Zeitfensters erfassten Werte in dem betreffenden Fahrzeug 110, 112 oder 114.
• – ein Zeitdatum, z.B. T1, gibt den Tag und/oder die Uhrzeit an, an dem der Datensatz erzeugt worden ist.

The data sets, see DS1a, DS2a, have the same data structure:

• An identification date, eg ID1, specifies the vehicle 110 . 112 . 114 from which the dataset comes,
• Spectral data, eg Sp1, contain the spectrum for the values detected within a given time window in the relevant vehicle 110 . 112 or 114 ,
• A time date, eg T1, indicates the day and / or time at which the data record was generated.

The records DS1a, DS1b, etc., DS2a, Ds2b, etc. are from the vehicles 110 to 114 to the server 116 transmitted, see data transmission path 122 . 124 respectively. 126 , The records DS1a, etc. may also be electronically encrypted and / or electronically signed by the vehicles 110 to 114 to the server 116 be transmitted.

Alternatively, a data transfer from the server 116 towards the vehicles, for example to confirm the reception of the data records, to update program components of the vehicles 110 to 114 or for other purposes.

The 5 shows process steps in the error monitoring and / or the prognosis of errors. The process begins in one process step 200 , hereinafter also referred to as step. In a subsequent step 202 Typical patterns or characteristics for the prognosis are determined, as described above with reference to FIG 1 explained. These features are stored in a program, during its execution by a processor, in particular the steps explained below 206 and 208 be executed. This processor can be arranged in the vehicle or else outside the vehicle, for example in a service-providing computer of a workshop or a service center.

In one step 202 become detection values on the electrical system 10 detected, in particular, the sampling of voltage U (t) and current I (t) at defined points of the electrical system 10 carried out, for example, on the segment 12 or 22 ,

In one step 204 Thereafter, the transformation of the information from U / I (impedance) by Fourier transformation in the frequency domain. Alternatively, the power U · I can be transformed, only the voltage or only the current profile. For example, a signal processor which is contained in the control and communication unit SE is used for the transformation. The transformation can also be performed outside the vehicle, eg in a service or maintenance center.

In one step 206 a comparison of the in step 204 calculated frequency range (amplitude response) with typical patterns, as in step 201 have been deposited, and from this identification of errors or prognosis of errors in the step 208 , The identification or prognosis of errors thus takes place in the frequency domain.

Will in step 208 determined that there is no error, the method is, for example, in the process step 202 continued, for example, without a break or the next start of the vehicle, the next day of the trip or the next month of travel. Thus, the process is in a loop from the process steps 202 to 208 , This loop is in step 208 leave only when an error is detected or predicted. However, the steps can 202 to 208 also be carried out only during maintenance.

In case of an error follows immediately after the step 208 a step 210 the initiation of countermeasures, for example, the shutdown of the affected segment of the electrical system 10 and / or the issuing of warning messages to the driver of the vehicle. The segment to be disconnected can be determined, for example, via transit time measurements, in which several of the sensors are included in the electrical system. Alternatively, it is tried in turn or according to a search strategy which segment 12 to 26 the faulty segment is or the segment for which an error is predicted. If the procedure is only performed during maintenance, the error can be rectified immediately.

The procedure is then in one step 212 completed. Alternatively, the method can also be used in step 202 continue, see arrow 214 , for example, to forecast further errors.

The 6 shows a typical spectrum 220 for components and / or consumers on the electrical system. On the horizontal axis, the frequency is plotted with negative and positive values. The spectrum 220 has three maxima in its left part. The right part of the spectrum 220 also has three maxima, of which, however, two maxima have larger amplitude values than the maxima on the left side.

The 7 shows a typical spectrum 230 when an error occurs, eg a short circuit. Compared to the spectrum 220 has the spectrum 230 a similar course on the left side, ie three maxima. The right side of the spectrum 230 on the other hand has only two maxima, with the first maxima in the range of the first two maxima of the spectrum 220 and where the third maximum at high frequencies is on the right side of the spectrum 230 the corresponding maximum of the spectrum 220 significantly surpassed at this frequency and even represents the global maximum. The global maximum was in the investigation 6 , please refer 1 , assigned to an error that occurred later or had already occurred.

The 8th shows a voltage curve 250 and a current waveform 252 at a detection device on the electrical system 10 , as he, for example, in the step 202 , please refer 5 , is determined in the time domain. Initially, the voltage is at low levels, then increases and then drops again. The current starts at large values, then drops and rises again several times in alternation.

The 9 shows that from the voltage curve 250 , please refer 8th , and the current flow 252 , see also 8th , in step 204 , please refer 5 , calculated spectrum 260 with error feature 262 , On the basis of the error feature 262 ie, high amplitude at a high frequency, then an error is predicted or detected. The spectrum 260 is slightly different compared to the spectrum 230 of the 7 , which is due to vehicle-specific deviations.

Current and voltage can, for example, be multiplied only in the time domain, whereupon the spectrum is then calculated. Alternatively, first the spectrum of the voltage profile and the spectrum of the current profile are calculated. Thereafter, a convolution of the spectra in the frequency domain. A calculation of the impedance spectrum is possible in two ways. Likewise, only the voltage spectrum or only the current spectrum can be used for error analysis or for predicting errors.

• – die Lage und der Amplitudenwert eines globalen Minimums,
• – die Lage und der Amplitudenwert eines globalen Maximums,
• – die Anzahl von lokalen Minima bzw. lokalen Maxima,
• – die Lage und der Amplitudenwert eines lokalen Minimums bzw. lokalen Maximums,
• – die Lage eines Wendepunktes,
• – die Verschiebung der genannten Merkmale auf der Frequenzachse,
• – u.ä. Merkmale.

Suitable error characteristics of the frequency spectrum are, for example:

• The position and the amplitude value of a global minimum,
• The position and the amplitude value of a global maximum,
• The number of local minima or local maxima,
• The position and the amplitude value of a local minimum or local maximum,
• - the location of a turning point,
• The displacement of said features on the frequency axis,
• - and similar Characteristics.

The signal energy, i. the area under the curve in a given frequency range is a typical characteristic that can be evaluated in the frequency domain and is often much more meaningful than, for example, a minimum or maximum consideration. The steepness of the curve or the waviness of the curve may be other criteria, in particular relative to a certain frequency range.

The transformation into the frequency domain may, for example, be a Fourier transformation, a Laplace transformation, a Z transformation or another transformation.

By measuring the transit time of signals, it is also possible to determine in which segment of a vehicle electrical system an error occurs. This makes it possible to switch off this segment in a targeted manner. Alternatively, it can be determined by probing in which segment the error lies, e.g. the segments are sequentially separated from the electrical system. If a faulty segment is switched off, then the fault feature on other segments can no longer be detected, whereby the faulty segment is determined. It is also possible to use a strategy which leads to a faster detection of the defective segment, e.g. Disconnecting the half-board electrical system, determining in which part the fault lies and then continue to search in this half, e.g. by dividing this half again into two segments of approximately equal size, etc.

The on-board electrical systems, for example, carry voltages less than 100 volts or even less than 60 volts but greater than 5 volts or greater than 10 volts. In electric vehicles, however, the methods described can also be used in the power grid. In this case, the power grid may carry voltages greater than 100 volts or greater than 400 volts. The voltages in the drive network can typically be less than 1000 volts. Due to the use of batteries or rechargeable batteries, mainly DC mains are used. In contrast to signal lines, the voltage on DC on-board networks is comparatively constant and fluctuates, for example, by less than plus / minus 10 percent of a mean or nominal value. However, the methods are not limited to DC networks and can thus also be used in AC networks.

The embodiments explained with reference to the figures can be combined in particular with the exemplary embodiments mentioned in the introduction. The embodiments are not to scale and are not restrictive. Modifications in the context of expert action are possible. Although the invention has been further illustrated and described in detail by the preferred embodiment, the invention is not limited by the disclosed examples, and other variations can be derived therefrom by those skilled in the art without departing from the scope of the invention.

Claims (15)

Method for forecasting an error or for detecting errors in a transport machine ( 110 to 114 ), with capture ( 2 ) at least one type of detection values in the time domain in a first transport machine ( 110 ), To save ( 2 ) of the detection values or storage of frequency spectra of the detection values, waiting for an error ( 4 ), Examine ( 6 ) the frequency spectra or examinations ( 6 ) of frequency spectrums calculated from the detection values stored prior to the occurrence of the error, for at least one characteristic indicating the occurrence of the error ( 4 ) early indicates Use ( 206 ) of the feature for early detection of the error or for error detection in the first transport machine ( 110 ) or in a second transport machine ( 112 ). Method according to claim 1, wherein the detection values on a vehicle electrical system ( 10 ) of the first transport machine ( 110 ). The method of claim 1 or 2, wherein the method is for a plurality of vehicles ( 110 to 114 ) is carried out. Method according to one of the preceding claims, wherein the detection of the detection values in a simulation of the first transport machine ( 110 ) or a simulation of a part of the first transport machine ( 110 ) he follows. Method according to one of the preceding claims, wherein a probability measure is determined, which indicates with which probability the early detection of the error or the error detection is correct. Method according to one of the preceding claims, in which a specific error which can not be detected without the stored detection values or the stored frequency spectra is transferred to a part of the transport machine ( 110 ) or in a simulation of at least part of the transport machine ( 110 ) is installed. Method according to one of the preceding claims, wherein the detection values or the frequency spectra outside the transport machine ( 110 ) get saved. Method according to one of the preceding claims, wherein a current profile ( 252 ) or a voltage curve ( 250 ) or both a current course ( 252 ) as well as a voltage curve ( 250 ). The method of any one of the preceding claims, wherein the fault relates to a cable break or a cable short. Method according to one of the preceding claims, wherein the fault is an electromagnetic radiation of a device of the transport machine ( 110 ) in the electrical system ( 10 ) of the transport machine ( 110 ). Method according to one of the preceding claims, wherein a feature of a frequency spectrum is used, which is at frequencies greater than 1 megahertz. Method according to one of the preceding claims, wherein the detection values on a vehicle electrical system ( 10 ) of the first transport machine ( 110 ) and the on-board network ( 10 ) at least two parts ( 12 . 24 ), between which a separating device ( 46 ), through which the two parts ( 12 . 24 ) can be electrically separated from each other. Method according to claim 12, wherein in the case of early detection or error detection a warning is issued and / or at least one of the separation devices ( 46 ) is pressed. Transport machine ( 110 ), with a vehicle electrical system ( 10 ) containing at least two parts ( 12 . 24 ), one between the at least two parts ( 12 . 24 ) arranged separating device ( 46 ), with which the two parts ( 12 . 24 ) can be electrically separated from each other, a first detection device ( 72 ) on the electrical system ( 10 ), a control unit (SE) connected to the separation device ( 51 ) or is connected to a warning unit, and dependent on the with the detection device ( 72 ) detected detection values, the separation device ( 46 ) or the warning unit activates. Transport machine ( 110 ) according to claim 14, wherein the two parts ( 12 . 24 ) are redundant to each other, and wherein at the two parts ( 12 . 24 ) preferably mutually redundant units of the transport machine ( 110 ) are connected.

Images