Method of diagnosing imminent or existing damage to a vehicle component generating airborne and / or structure-borne noise emissions

Abstract

The invention relates to a method for diagnosing damage to a vehicle component generating airborne and / or structure-borne noise emissions, wherein data about these emissions are used for diagnosis, in which in a first method step (M1) - the measured airborne and / or structure-borne noise emissions are converted into airborne and / or structure-borne noise data, - at the same time, the current operating situation of the vehicle is determined by recording operating situation data, - and the detected data is stored in a computer, in which in a second method step (M2) - the airborne and / or structure-borne sound data is transformed using mathematical methods in such a way that the first transformed airborne and / or structure-borne sound data generated in this way can be analysed and evaluated, and in which in a third method step (M3) a normalisation process is carried out, - in which a reference data set associated with the current operating situation data is determined, which contains such transformed reference airborne and / or structure-borne noise data that would be formed in this operating situation for a vehicle without damage, - in which this reference data set is compared with the first transformed airborne and / or structure-borne sound data to generate normalised transformed airborne and / or structure-borne sound data, and - in which a detected difference gives an indication of airborne and / or structure-borne noise emitted by a defective vehicle component. The invention also relates to a diagnosis system, with which the method can be operated.

Description

[0001] Method for diagnosing impending or existing damage to a vehicle component generating airborne and / or structure-borne noise emissions

[0002] The invention relates to a method for diagnosing damage to a vehicle component generating airborne and / or structure-borne sound emissions. Data about these emissions are used for diagnosis. In a first step of the method, the measured airborne and / or structure-borne sound emissions are converted into airborne and / or structure-borne sound data. In parallel, the current operating situation of the vehicle is determined by acquiring operating situation data, and the determined data is stored in a computer. Furthermore, the invention relates to a vehicle diagnostic system for implementing such a method.

[0003] Diagnostic systems for vibroacoustic monitoring of unwanted or unexpected noises, vibrations, and shocks in vehicles (NVH for short) are designed to detect impending functional impairments or damage to vehicles as early as possible and, ideally, to enable predictive maintenance to prevent vehicle downtime and high follow-up costs due to component damage. Various versions of such NVH diagnostic systems are already available.

[0004] In the following description, defective vehicle components or vehicle components exhibiting damage are understood not only to mean defective vehicle components, but also those that exhibit severe wear resulting from their operational function.

[0005] For example, damaged vehicle components on commercial vehicles regularly cause significant costs and organizational overhead for fleet owners due to subsequent vehicle maintenance and vehicle downtime. Furthermore, damage to vehicle components often leads to dangerous situations on the road. For example, wheel or tire detachment on trucks causes accidents. Ensuring safety through early detection of component damage and preventing injuries to third parties is therefore an important aspect and goal.

[0006] Since commercial vehicles, in particular, are comparatively long and wide, as well as noisy during operation, a driver of such a vehicle often cannot determine with their own senses, for example, by hearing a noise caused by a component or group of components, i.e., airborne noise, whether this noise is normal or indicates existing or imminent damage. To make matters worse, the frequency spectrum and volume of such noises depend on the respective operating situations and the vehicle load, making it difficult or even impossible to distinguish between normal operating behavior and defective operating behavior of such a vehicle component or even an entire vehicle component.

[0007] In a vehicle, mechanical defects in vehicle components in particular can arise due to external influences, improper use, excessive stress, faulty manufacturing or assembly, and / or wear and tear. Vehicle components, such as rotating or reciprocating components such as bearings, friction elements, shafts, or springs, which are installed in chassis systems, braking systems, drive systems, or suspension systems, usually cause unusual airborne and / or structure-borne noise emissions when defective. These can occur temporarily or permanently during ferry operation, but they often cannot be immediately located. Even during a visit to a workshop, it is often difficult or involves considerable effort for the service personnel there to reproduce unwanted airborne and / or structure-borne noise emissions detected by a driver in order to identify a vehicle component that is generating these unwanted emissions.

[0008] DE 199 14 533 A1 discloses a vibroacoustic diagnostic system for detecting damage to motor vehicle components, in which sound or vibration spectra are recorded and evaluated. Target spectra for a specific operating condition, ideally for all operating conditions, are stored in a memory. These spectra are generated in an initial operating phase depending on a distance traveled, an operating time, or a number of operating conditions. The determined sound or vibration spectra and the stored target spectra are compared. A deviation is displayed if a previously defined limit is exceeded.

[0009] DE 10 2007 051 261 A1 discloses a method for the acoustic assessment of a motor vehicle. Airborne noise emissions from the vehicle are recorded using at least one microphone and preprocessed in an evaluation unit. Preprocessing of the acquired data includes normalization to reference variables and / or sorting of the acoustic data. To determine features, an analysis using one or more known mathematical methods, such as Fourier analysis, is applied to the preprocessed data. The features are classified and compared with a reference classification. The reference classification is determined model-based, adaptive, or through learning.

[0010] The comparison of the classification result with the reference classification can be carried out in the vehicle itself or by means of pattern recognition in a central database computer.

[0011] DE 10 2016 208 048 A1 describes a method for generating diagnostic data for an automobile, in which an acoustic waveform generated by an automotive component is captured and converted into an electrical waveform data signal. An outlier pattern is identified in the waveform data signal. Pattern identification involves a comparison with a library of normal vehicle sound distribution patterns. The identified pattern is classified as indicative of a selected performance problem, for example, in a drive motor, transmission, or braking system.

[0012] DE 10 2017 200 372 A1 discloses a method for diagnosing a motor vehicle, in which an acoustic signal generated by a vehicle component is recorded using at least one microphone located outside the driver's cab. Furthermore, at least one operating state of the vehicle is recorded, which may include information about the vehicle speed, wheel speed, brake application, a transmission shift, and engine speed, among other things. The acoustic signal is first classified according to frequency components, constancy of an amplitude, and the presence of a second, pulsating amplitude. The classified acoustic signal is then correlated with at least one of the vehicle states. From this correlation, a cause for the acoustic signal is determined.Data about vehicle states is transmitted between a local database in the vehicle and a central database outside the vehicle via a network and an antenna to create and execute a learning process.

[0013] DE 10 2017 214 841 A1 discloses a system for locating and identifying mechanical damage to a vehicle. A data processing device is configured to perform triangulation based on the microphone signals from at least three microphones to locate mechanical damage to the vehicle and identify the vehicle component exhibiting the mechanical damage. The microphone signals are compared with a speed, a wheel speed, and / or an engine speed of the vehicle, as well as with an acoustic reference model, to determine the type and / or severity of the damage.

[0014] US 10417 837 B2 discloses a method for identifying vibrations of wheel-tire assemblies and drivetrain components of a vehicle. The method comprises determining a signal strength of a vibration in one or more frequency ranges, analyzing first- and second-order vibrations as well as natural frequencies of components. A controller receives a sensor response from a plurality of sensors indicating a vibration. The sensor response also includes, among other things, a wheel speed, an engine speed, a transmission input shaft speed, and a transmission output shaft speed. A controller processes the sensor measurements and determines the frequency range using a real-time Fast Fourier Transform and / or spectral analysis.The known method determines whether the vibration is a temporary problem originating from a condition outside the vehicle and is due to road conditions or ambient temperature, or whether it is a recurring problem originating from inside the vehicle and whether the vibration is due to a condition of a tire or wheel, a drivetrain and / or an engine of the vehicle.

[0015] Furthermore, EP 3 242 118 A1 discloses a sensor system for monitoring the mechanical integrity of a vehicle axle and the wear of its components, as well as for distinguishing between several axle failure modes. Monitoring the mechanical integrity of the vehicle axle and the wear of its components is based on the temporal and spectral analysis of measurement data from vibration sensors arranged on the vehicle axle.

[0016] Given longer maintenance intervals and a growing shortage of experienced drivers, automobile manufacturers and fleet managers are now taking a more proactive role in vehicle monitoring, particularly in the monitoring of safety-relevant vehicle areas such as wheel-side vehicle components such as tires, rims, and wheel bearings. Automated vehicle monitoring with predictive, variable scheduling of maintenance intervals is becoming increasingly important. Future diagnostic systems, particularly in the commercial vehicle sector, are expected to go beyond today's standard tire pressure monitoring and use extensive vibroacoustic monitoring to enable the most comprehensive possible condition and wear detection of chassis assemblies. Furthermore, future autonomous vehicles, which no longer have a human driver on board, are expected to be fully automatically monitored using vibroacoustic technology.Therefore, there is a need for advanced vibroacoustic vehicle diagnostic systems that can monitor a vehicle's vibroacoustic condition both with and without driver assistance and transmit it to a data-based or computer-based cloud. These systems should mimic human acoustic and tactile perception to a certain extent.

[0017] Against this background, the invention is based on the object of presenting a method for the vibroacoustic diagnosis of a vehicle, which enables continuous and largely complete monitoring of a vehicle's vibroacoustic condition as well as a precise and specific vibroacoustic diagnosis of various vehicle components, and which also reliably avoids misdiagnoses. In particular, such a method should be suitable for implementation on a vehicle with a high degree of automation, regardless of whether a driver is present or not. A further object is to present a diagnostic system for implementing such a method.

[0018] The method-related problem is solved by a method having the features of claim 1. The features of the diagnostic system are defined in the independent device claim. Advantageous embodiments and further developments are mentioned in the respective associated dependent claims.

[0019] The invention therefore initially relates to a method for diagnosing an impending or already existing damage to a vehicle component generating airborne and / or structure-borne sound emissions, wherein data about this airborne and / or structure-borne sound emission are used for the diagnosis. In a first method step M1 of this method,

[0020] - the measured airborne and / or structure-borne sound emissions are converted into airborne and / or structure-borne sound data,

[0021] - at the same time, the current operating situation of the vehicle is determined by recording operating situation data,

[0022] - and the collected data is stored in a computer for further processing.

[0023] In a second stage of the procedure M2,

[0024] - the airborne and / or structure-borne sound data are transformed using mathematical methods, such as filtering or spectra generation, in such a way that the first transformed airborne and / or structure-borne sound data thus generated can subsequently be analyzed and evaluated.

[0025] In a third process step M3, a standardization process is carried out,

[0026] - in which a reference data set is determined which is assigned to the current operating situation data and which contains such transformed reference airborne and / or structure-borne sound data which would be generated in this operating situation with a faultless vehicle,

[0027] - in which, to generate standardised transformed airborne and / or structure-borne sound data, this reference data set is compared with the first transformed airborne and / or structure-borne sound data, and - in which a determined difference provides at least an indication of emitted airborne and / or structure-borne sound from a defective vehicle component.

[0028] This method enables continuous and largely complete monitoring of a vehicle's vibroacoustic condition, as well as precise and specific vibroacoustic diagnostics of various vehicle components, reliably preventing misdiagnosis. This method can be implemented on a vehicle with a high degree of automation, regardless of whether a driver is present or not.

[0029] Accordingly, in the first process step M1, the measured values ​​of the respective sensors are converted into airborne and / or structure-borne sound data and stored. Subsequently, in the second process step M2, the airborne and / or structure-borne sound data are transformed in an electronic evaluation unit using signal processing methods into airborne and / or structure-borne sound data suitable for the subsequent analysis, without evaluation. For example, in this second process step M2, frequency spectra, order spectra, and / or an order cepstrum, as well as spectra and other transformations of envelope curves of the unsteady phase or the unsteady frequency of the detected vibrations, are generated.

[0030] In a third process step M3, a standardization process is then carried out, in which a reference data set associated with the current operating situation data is first determined. This reference data set is vehicle-type-specific and / or vehicle-specific. It takes into account the driving situation data relevant during data collection, such as the driving speed, the wheel diameter of the vehicle wheels, the engaged transmission gear, the engine speed, the axle loads, the brake pressure, the steering wheel angle, and so on. The reference data set therefore contains the transformed reference airborne and / or structure-borne sound data that would be generated in this operating situation for a healthy vehicle. In the further course of this standardization process, the aforementioned reference data set is compared with the first transformed airborne and / or structure-borne sound data to generate standardized transformed airborne and / or structure-borne sound data.The difference determined in this comparison provides at least an indication that a defective vehicle component generates airborne and / or structure-borne noise that indicates the damage.

[0031] The standardization process can also utilize environmental information, i.e., information from the vehicle's surroundings, if available. Overall, the airborne and / or structure-borne sound data determined by the measurement and then transformed are comparable with the transformed airborne and / or structure-borne sound data that would be determined in a comparable operating situation of the vehicle with undamaged vehicle components. The standardization process therefore ensures that, when evaluating the measured airborne and / or structure-borne sound, the dependence of the measured values ​​on the current vehicle operating condition as well as on external influences is taken into account.

[0032] In the third process step (M3), deviations are calculated between the first transformed airborne and / or structure-borne sound data based on the current measurement of the airborne and / or structure-borne sound and the transformed airborne and / or structure-borne sound data that would actually be expected in the current operating situation of the vehicle with undamaged vehicle components. The determined deviation provides at least an indication of whether or not the emitted and measured airborne and / or structure-borne sound was generated by a defective vehicle component or by impending component damage.

[0033] To carry out a standardization process, according to a first variant, it can be provided that second transformed airborne and / or structure-borne sound data are determined from the operating situation data by means of a physical-mathematical model, that in order to generate standardized transformed airborne and / or structure-borne sound data, the second transformed airborne and / or structure-borne sound data are compared with the first transformed airborne and / or structure-borne sound data, and that a determined difference provides at least an indication of emitted airborne and / or structure-borne sound of a defective vehicle component.

[0034] Accordingly, second transformed airborne and / or structure-borne sound data are generated from the determined operating situation data using a physical-mathematical model. These second transformed airborne and / or structure-borne sound data represent the transformed airborne and / or structure-borne sound data to be expected in the current operating situation for a damage-free vehicle. These second transformed airborne and / or structure-borne sound data are then compared with the first transformed airborne and / or structure-borne sound data. The deviations thus determined are also referred to here as standardized transformed airborne and / or structure-borne sound data. They provide at least an indication of whether or not the emitted and measured airborne and / or structure-borne sound was generated by a defective vehicle component or impending component damage.

[0035] To carry out a standardization process, according to a second variant, it can be provided that third transformed airborne and / or structure-borne sound data are determined from a portion of the first transformed airborne and / or structure-borne sound data and the operating situation data by means of a physical-mathematical model, that in order to generate standardized transformed airborne and / or structure-borne sound data, the third transformed airborne and / or structure-borne sound data are compared with the first transformed airborne and / or structure-borne sound data, and that a determined difference provides at least an indication of emitted airborne and / or structure-borne sound from a defective vehicle component.

[0036] In this second variant, the aforementioned portion of the first transformed airborne and / or structure-borne sound data represents, for example, the portion that represents the propagation of sound and / or vibrations in the Y-direction and / or Z-direction in a Cartesian coordinate system. The remainder of the first transformed airborne and / or structure-borne sound data therefore relates to the propagation of airborne and / or structure-borne sound in the X-direction and is only used to generate the standardized transformed airborne and / or structure-borne sound data after the third transformed airborne and / or structure-borne sound data has been generated.

[0037] To carry out the standardization process, it can also be provided that the transformed airborne or structure-borne sound data are compared with similarly transformed airborne or structure-borne sound data that were previously determined on a vehicle, ideally the same vehicle, with undamaged components or component groups. The generation of such a comparison database, which serves to create the aforementioned reference data set, takes place, for example, during test drives with a production vehicle at the vehicle manufacturer. However, it can also be provided that the vehicle computer of each production vehicle learns and saves its own comparison database regarding undamaged components or component groups during an initial operating phase at its owner's site, for example, during an initial 5,000 km drive, or sends this comparison database to the aforementioned cloud for storage so that other identical vehicles can use it.With both of these approaches, it is possible to supplement the comparison database with additional measurements during subsequent vehicle operation, for example, during a further 5,000 km of driving. This makes sensor data samples or data patterns with derived curves or spectra available for as many operating situations as possible.

[0038] Through the aforementioned standardization process and subsequent pattern recognition that determines a component or vehicle location, comparability of all previously recorded and evaluated airborne and / or structure-borne sound data is achieved. Using a self-learning process, these patterns for various vehicle components can be continuously stored and updated in a database. This creates a vibroacoustic component library for the vehicle in question and all other vehicles of this type.

[0039] Known signal processing methods such as Fourier analysis, statistics, neural networks, and artificial intelligence can be used to detect and evaluate patterns in standardized airborne and / or structure-borne sound data. Corresponding computational algorithms can be implemented in a computer program within the electronic evaluation unit. It is also possible to execute the process independently of the vehicle's cloud integration in a local evaluation unit within the vehicle, and to store information in a local data storage device within the vehicle, which can be retrieved later during a workshop visit and updated if necessary.

[0040] The method according to the invention is therefore suitable for determining, based on acquired airborne and / or structure-borne sound data, whether a vehicle component is technically sound, whether damage to a vehicle component is imminent, or whether damage has already occurred. If an airborne and / or structure-borne sound sensor is arranged sufficiently close to a specific vehicle component, for example, close to a wheel bearing, it is possible to determine, based solely on its airborne and / or structure-borne sound emissions, without any further process steps, whether this particular wheel bearing is still sound, whether bearing damage is imminent, or whether the wheel bearing is already defective.

[0041] According to another advantageous development of the method, additional vehicle-external data is recorded and taken into account in the described diagnosis. Such vehicle-external data represents, for example, environmental conditions, meteorological data, satellite-based positioning data, and / or road condition data that influence or at least could influence the operation of the vehicle.

[0042] Using precise location information regarding the quality of the road currently being traveled, as well as current, weather-related poor road conditions, airborne or structure-borne noise emissions that were initially recorded as deviations from the normal state, but which only occurred as a deviation from this normal state due to the current conditions, can be more easily detected and filtered out, or can be specifically compared with data from such ambient conditions. This can significantly reduce the number of misdiagnoses to a minimum.

[0043] Furthermore, when driving on a damaged road, the diagnostic method according to the invention can be used specifically to check, for example, a vehicle's chassis components. On the other hand, in the event of reliably detected component damage, precise knowledge of the vehicle's position and the topographical environment in which the vehicle is located or was located at the time the damage was reported can help fleet management to initiate any necessary measures.

[0044] If an airborne and / or structure-borne sound sensor is arranged comparatively far away from a vehicle component to be diagnosed and / or an overlap of the airborne and / or structure-borne sound emissions of several vehicle components is to be expected, for example if the sensor is arranged in the middle of a vehicle axle at a distance from the right-hand and left-hand wheel bearings, and a defective vehicle component or wheel bearing is nevertheless to be detected based on its airborne and / or structure-borne sound emissions, then according to a fourth method section M4 the standardized transformed airborne and / or structure-borne sound data can be examined to determine whether predetermined, for example rare and / or component-specific patterns can be identified in these standardized transformed airborne and / or structure-borne sound data.If at least one such pattern is detected, at least one pattern characteristic value is generated to describe and / or quantify the determined pattern, and the at least one pattern characteristic value is assigned to the underlying transformed airborne and / or structure-borne sound data and data derived therefrom and stored in connection with these.

[0045] Such pattern parameters can be, for example, the frequency, amplitude, and phase of harmonic oscillations or carrier frequencies in the standardized, transformed airborne and / or structure-borne sound data. Likewise, the frequency and amplitude of a modulated signal in the case of a detected amplitude or frequency modulation. It should be noted at this point that, in the strict sense, the pattern does not yet provide any information about amplitudes. Since the pattern is identified from the standardized data, this pattern is more likely the amplitude deviation from the expected amplitude value. However, if the standardization of the transformed airborne and / or structure-borne sound data only occurs after the pattern recognition step, this is the actual amplitude. Quantities derived from such patterns, such as the average amplitude value, can also serve as pattern parameters.

[0046] According to the fourth method step M4, the aforementioned pattern recognition is used to identify specific patterns in the standardized, transformed airborne and / or structure-borne sound data. Pattern recognition could also be performed before the step of standardizing the transformed airborne and / or structure-borne sound data, provided that the consideration of operating situation-related data and / or vehicle-external data can be omitted.

[0047] In the fourth process step (M4), important properties and dependencies of the standardized airborne and / or structure-borne sound data are determined. These represent the deviation between the transformed airborne and / or structure-borne sound data generated from a current measurement and the transformed airborne and / or structure-borne sound data expected for undamaged vehicle components under the same operating situation. Based on this information, an assignment to a vehicle area can therefore be made if necessary. However, a specific vehicle component does not necessarily have to be identified in this step.

[0048] One or more mathematical methods can be used to accurately distinguish an airborne and / or structure-borne sound pattern of a vehicle component that corresponds to a normal condition from an airborne and / or structure-borne sound pattern that deviates from this and may indicate component damage.

[0049] In order to identify a pattern in the standardized transformed airborne and / or structure-borne sound data of a vehicle component, or to identify a specific vehicle component based on a determined pattern in airborne and / or structure-borne sound data, different areas of the vehicle and different formations of airborne and / or structure-borne sound emissions can be considered.

[0050] Patterns recognizable in transformed and / or standardized airborne and / or structure-borne sound data may have been caused by the natural frequencies and / or movements of vehicle components. A vehicle component may begin to vibrate at one or more natural frequencies if, for example, it is impacted by an impulse force or stimulated to vibrate by a suitable periodic oscillation. A pattern of airborne and / or structure-borne sound data from a component may therefore exhibit a frequency range with these natural frequencies. If the vehicle component is actively moving, in addition to patterns of natural frequencies, patterns may also occur in other frequency ranges in which the respective airborne and / or structure-borne sound is effective.

[0051] For example, a brake disc vibration can occur in multiple frequency ranges, depending on whether the vibration is due to the brake disc's natural frequency or to a wheel speed-dependent influence. Another example could be a rotating vehicle tire, which, when vibrating due to an imbalance and a certain speed, emits airborne and / or structure-borne noise in a different frequency range and with a different intensity distribution than another vehicle component, such as a rotating transmission shaft with an imbalance. The airborne and / or structure-borne noise emissions of different vehicle components therefore differ in the frequency ranges in which they are generated, in the amplitudes that can be detected at different frequencies, as well as in their time-dependent and / or movement-dependent characteristics.In addition, for example, damage frequencies typical of wheel bearings can be particularly prominent as constant orders of the wheel speed, while damage frequencies of a transmission shaft can be identified as constant orders of the transmission shaft speed. This results in distinguishable, component-specific airborne and / or structure-borne sound patterns.

[0052] A frequency spectrum of a vehicle's airborne and / or structure-borne noise represents a snapshot of the vehicle's operating status and its ambient conditions at any given time during ferry operation. Rotating vehicle components, in particular, generate vibrations in vehicles that correspond to their rotational speed. Since the rotational speed of the vehicle component changes during travel depending on the vehicle speed and other parameters, such as the gear ratio in an engaged gear or the clutch position of a starting clutch, the airborne and / or structure-borne noise pattern in question appears blurred when viewed over a longer period of time. This makes it difficult to identify a pattern in the airborne and / or structure-borne noise data.

[0053] However, if all information about the vehicle's operating status and ambient conditions has been recorded or is known, virtually constant and clearly interpretable airborne and / or structure-borne sound patterns for the respective vehicle components result from processing the relevant signals and taking the data into account in the electronic evaluation unit using one or more of the analysis methods described above. Using this analysis, an airborne and / or structure-borne sound emission can be reliably assigned to a vehicle area or vehicle component group. This identification is interpreted as an indication of a possible cause for the respective airborne and / or structure-borne sound emission, but, as already mentioned, is not yet sufficient for a reliable, specific assignment to a vehicle component. This is carried out, if necessary, in a fifth process step M5.According to this fifth method step M5, the determined at least one characteristic value of a pattern is assigned to a specific vehicle component and / or a specific damage and / or a specific location on the vehicle and / or a recognized pattern stored in the database from previous measurements. This can be, for example, a left- or right-hand wheel bearing on a vehicle axle, or a previously recognized and now recognized pattern in the airborne and / or structure-borne sound emissions of this wheel bearing.

[0054] In the fifth method step M5, which is required if necessary, the at least one pattern characteristic value assigned to the identified pattern is used. For this assignment, previously known or currently available component-specific and / or driving operation-specific information and / or associated pattern characteristic values ​​from previous airborne and / or structure-borne sound measurements are also used. This additional information can, for example, be certain driving maneuvers or loading situations that are correlated with a pattern in the standardized, transformed airborne or structure-borne sound data or that change their characteristics in terms of frequency and amplitude. Further information can be known higher-order airborne and / or structure-borne sound vibrations contained in an airborne and / or structure-borne sound emission.Known natural frequencies of vehicle components and / or statistical probability values ​​regarding the maximum service life or the frequency of damage to the respective vehicle components can also be usable information.

[0055] Matching can also be performed using data from previously identified patterns stored in the database. This not only allows the currently identified pattern to be matched to a pattern stored in the database if the specified distance criteria for at least one pattern characteristic are exceeded, but also allows, after successful matching of the current pattern, the matching of the already known pattern with regard to the associated component, damage, and location to be adopted for the current pattern. This matching also enables the evaluation of pattern variation under different operating situations, for example, in order to match the currently identified pattern to a specific vehicle component.

[0056] Finally, but not ultimately, values ​​can also be used with regard to the energy which an airborne and / or structure-borne sound emission of a vehicle component or a group of components typically has during its operation, and / or which causes or has caused the vehicle component or group of components in question to vibrate, so that it emits an airborne and / or structure-borne sound emission.

[0057] In addition, the localization of an airborne or structure-borne sound signal can be facilitated by a sensor device comprising multiple airborne or structure-borne sound sensors. Accordingly, a plurality of airborne and / or structure-borne sound sensors can be distributed throughout the vehicle at various positions or in multiple vehicle areas. If only one of several airborne and / or structure-borne sound sensors detects a specific airborne and / or structure-borne sound, the location of this airborne and / or structure-borne sound emission can be relatively easily localized as being close to the sensor.If, however, an airborne and / or structure-borne sound emission is detected simultaneously by multiple sensors and the origin of this emission is therefore unclear, for example, airborne and / or structure-borne sound emissions from a right-hand and a left-hand wheel bearing on a vehicle axle, the distance of the emission source from the sensor can be determined by evaluating a measured time difference between the onset of the airborne and / or structure-borne sound emission. In another case, if the signal strength of at least one of the airborne and / or structure-borne sound sensors is very weak and / or heavily overlaid by other emission sources, this airborne and / or structure-borne sound sensor can be the one located furthest from the vehicle component to be monitored.

[0058] As already mentioned, the method steps for pattern recognition M4 and pattern evaluation M5 can be carried out before or after the third method step M3, i.e. before or after the standardization process. Thus, according to this embodiment of the method, it can be provided that before or after the third method step M3, in a fourth method step M4, the transformed and optionally standardized airborne and / or structure-borne sound data are examined to determine whether at least one pattern can be identified in these transformed and optionally standardized airborne and / or structure-borne sound data. If such a pattern has been identified, at least one pattern characteristic value is generated to describe and / or quantify the determined pattern, and the at least one pattern characteristic value is assigned to these transformed and optionally standardized airborne and / or structure-borne sound data and to the data derived therefrom.

[0059] Accordingly, in these cases, the standardization process initially creates a reference data set associated with the current operating situation data, which contains at least one reference sample characteristic value that would be generated in this operating situation with a non-damaged vehicle. Subsequently, to generate at least one standardized sample characteristic value, the reference sample characteristic value is compared with at least one currently determined sample characteristic value. The difference determined in this comparison also provides at least an indication that a defective vehicle component is generating airborne and / or structure-borne noise indicating damage and, as a result of the process, is causing at least one sample characteristic value.

[0060] In a sixth process step M6, which can be used as needed, the transformed airborne and / or structure-borne sound data and the data derived from them, in particular the at least one associated pattern characteristic value, are classified in such a way that if no vehicle component was detected and the pattern could not be assigned to a known pattern, the process run with regard to damage detection is saved in the computer as inconclusive. If a pattern was identified from previous measurements, the current monitoring result is assigned to this known pattern, and the associated data is saved in the computer.

[0061] If a vehicle component is detected, the current monitoring result is assigned to that vehicle component, and the associated data is stored in the computer. A damage report and / or a damage signal are generated when component damage or impending component damage is detected. The damage report is stored in the computer, and the damage signal is output in a human-perceptible manner.

[0062] To assess damage or another cause of airborne and / or structure-borne sound emissions, the temporal progression of the airborne and / or structure-borne sound data or, in the case of the derived, transformed and, if necessary, standardized airborne and / or structure-borne sound data, the amplitude distribution is preferably evaluated. These include, for example, continuously increasing or decreasing amplitudes, a single amplitude occurrence, a temporal shift of individual frequencies, or a change in individual aspects of an entire pattern or the corresponding pattern characteristics. For example, a change in the number of detected harmonics of a fundamental frequency can be detected, as can a change in the amplitudes or frequencies.

[0063] When classifying the transformed airborne and / or structure-borne sound data and data derived from them in accordance with the sixth procedural step M6, a predefined damage threshold and / or a predefined damage type can also be considered. For example, it can be stipulated that presumed impending damage is only recorded if the transformed airborne and / or structure-borne sound data or the information derived from them allows the presumed impending damage to be identified with sufficient clarity. By considering a damage threshold, it can be ensured that unnecessary warning signals are not issued and that immediate measures are not initiated unless they are absolutely necessary or at least not required soon.If necessary, a low damage threshold can initially be set in the procedure, if this threshold is exceeded, an indication is sufficient that a vehicle component which has generated an unusual airborne and / or structure-borne sound emission has been detected and is initially being monitored further.

[0064] According to another refinement of the method, even unidentified patterns of the transformed and, if applicable, standardized airborne and / or structure-borne sound data are provisionally assigned to a (by definition, unknown) airborne or structure-borne sound source for subsequent processing and stored in a computer database for unidentified patterns. Possible subsequent processing can take place, for example, during a visit to a specialist workshop. Accordingly, it can be provided that all indications of damage, even those that are not clear, are stored and, if necessary, analyzed later, and their development over time is monitored and evaluated.

[0065] The database mentioned can be a local database within the vehicle, a central database outside the vehicle, or a combination of both. The database(s) are advantageously continuously updated with the information obtained.

[0066] In the event of a component replacement, the relevant component history is reinitialized. Even if a physical-mathematical normalization function is changed, the previous component history of the respective vehicle component can be updated through a recalculation.

[0067] Preferably, a replacement of a vehicle component detected by the method can be confirmed by an input from a driver on an electronic graphical user interface in the vehicle and / or by the fleet management and / or a commissioned workshop in order to initialize this specific, newly installed vehicle component for the first time.

[0068] The component history data of the previous vehicle component are preferably not deleted from the database, but are permanently stored and marked as old diagnostic data for possible later checking of a large number of identical and defective vehicle components.

[0069] In cases where a pattern in the acquired airborne or structure-borne sound data cannot initially be identified, this pattern is provisionally assigned to an unknown airborne or structure-borne sound source for later processing, as mentioned above. Subsequent data sets of airborne and / or structure-borne sound data can then be searched for this older pattern and / or changes to this pattern. If necessary, at least one critical change in the pattern can be recorded in this way. By continuously updating the on-board and / or external database, it may be possible in a later run of the process to identify and classify a vehicle component as the cause of an airborne and / or structure-borne sound emission using initially unclear airborne and / or structure-borne sound data.

[0070] To reduce the load on the vehicle computer, the procedure can be configured to only be run when predetermined vehicle operating situations occur. For this purpose, operating situations are selected in which particularly good airborne and / or structure-borne noise emissions are expected for the diagnosis to be performed, in terms of their intensity and clarity. The airborne and / or structure-borne noise data generated from these data are clearly the easiest and most reliable way to perform the described diagnostic procedures.

[0071] A further advantage, which arises from operating the method only in selected operating situations, is the reduced processing activity of a corresponding vehicle computer and / or an external cloud computer, so that this can be less powerful and therefore more cost-effective. Furthermore, by operating the method in selected operating situations and using an external computer in a cloud, the radio data transfer between the respective vehicle and the cloud is significantly reduced. This is not an insignificant aspect, especially if many thousands of vehicles will be operated with the described method in the future. Furthermore, the costs for data transfer between the transmitting vehicle and the stationary receiver are kept comparatively low.

[0072] It can be provided that the method is only operated when predetermined limit values ​​of at least one value of the vehicle's operating situation data are undershot, reached or exceeded.

[0073] For example, it can be provided that the process is only operated when the vehicle

[0074] - negotiates a curve with a predetermined small curve radius and in a predetermined direction (left turn or right turn), and / or - negotiates an uphill or downhill section with a predetermined gradient, and / or

[0075] - driving on a route with known severe road damage, and / or

[0076] - travels a route at predetermined high or low outside temperatures, and / or

[0077] - is accelerated positively or negatively, and / or

[0078] - is accelerated positively immediately after a negative acceleration, and / or

[0079] - is driven at a constant, specified speed, and / or

[0080] - rolls without propulsion, and / or

[0081] - no rain or snowfall occurs, and / or

[0082] - has fallen below or exceeded a predetermined axle load.

[0083] For example, when cornering, the operating noise of the wheel bearings and the resulting airborne and / or structure-borne noise emissions on the inside and / or outside wheels of the vehicle can be specifically examined. During sharp deceleration, the operating noise of the vehicle's brakes can be examined on each side and axle of the vehicle. Alternatively, when driving on a road with known severe road surface damage, the operating noise of the vehicle's suspension components or certain attachments to the vehicle body can be examined.

[0084] Another embodiment of the method provides that, based on a diagnosed defect in a vehicle component, a warning is sent to a driver and / or a vehicle fleet management system is notified, and / or a control signal is transmitted to a control unit. A warning signal can be an acoustic, vibration-mechanical, or optical signal, the latter being formed, for example, by a warning light or a text message on a display device.

[0085] Accordingly, if component fatigue or damage to a vehicle component is identified as the cause of airborne and / or structure-borne noise emissions generated by this component and recorded by a sensor device, a warning and / or warning signal can optionally be issued. For example, a driver can receive a warning stating that their vehicle or the identified vehicle component should be inspected as soon as possible. If the vehicle is part of a commercial vehicle fleet, a fleet management team can be notified of this warning in order to initiate suitable measures, for example, further vehicle scheduling. It may also be sufficient if such a warning is only made available for electronic collection by employees of the vehicle operator. Furthermore, it can be provided that a warning is sent to all vehicles of the same design.

[0086] As mentioned, a corresponding control signal can also be transmitted to a vehicle control unit to change a current vehicle operating state and adapt it to the new situation. Following driver approval, the vehicle's electronic control unit can intervene in the vehicle's operation, for example, by braking the vehicle, reducing its speed, or bringing it to a standstill.

[0087] Particularly in vehicles without a driver, a vibroacoustic diagnosis can automatically initiate appropriate action. For example, the control unit can bring the vehicle to a safe stop by appropriately controlling drivetrain components, steering systems, and / or braking systems, and activate hazard warning lights or similar devices. This can be done to avoid expensive consequential damage in the event of tire damage detected or imminent due to a tire tread beginning to detach, or in the event of an imminent fire due to overheating of a grinding wheel bearing, or other safety-relevant component events, preventing accidents and / or minimizing the risk to the driver and other road users. At the same time, fleet management is informed of the process.

[0088] Additionally or alternatively, in other situations, it is possible to forgo updating the stored patterns characterizing faultless operation for this vehicle component as a reference source for the comparisons according to the method. It is also possible to forgo issuing a warning and acoustic and / or visual warning signal, and to only log an impending, relatively minor damage electronically. Furthermore, it is considered advantageous if the diagnostic method according to the invention independently initiates a new airborne and / or structure-borne sound-related test of at least one vehicle component if airborne and / or structure-borne sound data determined during a previous diagnostic process indicated impending or already existing component damage and patterns derived from this were determined.It is advantageous if this procedure is performed using the diagnostic system at least every time the vehicle is driven. This ensures the diagnostic system's proper operational readiness. However, the diagnostic system can also be operated as needed, for example, during a workshop visit or at a vehicle fleet's location on a chassis dynamometer.

[0089] Furthermore, it can advantageously be provided that the method is carried out in an electronic evaluation unit which is or can be connected by signal technology to first sensor means for determining airborne or structure-borne sound emissions and to second sensor means for detecting operating situation data of the vehicle representing the current operating situation, wherein the electronic evaluation unit is arranged in the vehicle and communicates with a computer-supported cloud arranged outside the vehicle with regard to data exchange.

[0090] Alternatively, it can be provided that the method is carried out in an electronic evaluation unit which is or can be connected by signal technology to first sensor means for determining airborne or structure-borne sound emissions, to second sensor means for recording operating situation data of the vehicle representing the current operating situation, and in which the electronic evaluation unit is integrated in a computer-supported cloud and communicates via a transmitting and receiving device which is or can be connected by signal technology to the said sensor means.

[0091] Furthermore, it can be provided that the aforementioned electronic evaluation units can be signal-connected to third sensor means for determining vehicle-external data. With regard to the diagnosis, the method can be further refined to determine whether impending damage or damage that has already occurred to an identified vehicle component is determined based on a change in the determined pattern of the transformed airborne or structure-borne sound data and / or the standardized transformed airborne and / or structure-borne sound data occurring within a short period of time and / or becoming increasingly rapid.

[0092] Experience has shown that certain mechanical changes to a vehicle component observed over a defined period of time correlate with a specific change in acoustic and mechanical vibration behavior. Taking into account the recorded information on the vehicle condition and the information on the ambient conditions, an abnormal airborne and / or structure-borne sound pattern can therefore be interpreted as a characteristic change in or on the vehicle component in question. Several causes are possible, which can be analyzed in more detail based on previous airborne and / or structure-borne sound patterns in the emission behavior of the vehicle component.Accordingly, when observing a previously identified airborne and / or structure-borne sound pattern, it can be assumed with a high degree of probability that damage to the vehicle component is imminent or has already occurred if increasingly rapid and stronger changes in the associated airborne and / or structure-borne sound pattern of the vehicle component are detected within a short period of time.

[0093] The presence of a measurement error or an adverse environmental influence, such as very poor road conditions, can be determined based on singular and / or randomly occurring changes in the determined pattern of the transformed and, if applicable, standardized airborne or structure-borne sound data of an identified vehicle component. Associated airborne or structure-borne sound data are deleted or simply logged.

[0094] Furthermore, as indicated, it can be provided that a replacement of an identified vehicle component is detected on the basis of a significant deviation of a pattern of the transformed and, if applicable, standardized airborne or structure-borne sound data, which deviation was determined at the beginning of a journey and repeatedly confirmed, in comparison with at least one most recently determined and / or stored pattern of the transformed and, if applicable, standardized airborne or structure-borne sound data of this vehicle component.

[0095] The presence of a running-in phase for an identified vehicle component after its recent installation in the vehicle can be inferred based on a slow and / or decreasing change in a determined pattern of the transformed and, where applicable, standardized airborne or structure-borne sound data for this vehicle component. Such a running-in phase is detectable after a defective vehicle component has been replaced with a new or repaired vehicle component. During the initial operating phase of a new vehicle or a new vehicle component, characteristic running-in noises or vibrations may occur, which decrease and disappear completely after some time. Changes in usage and environmental conditions can also trigger such an adjustment process, which is reflected in a slowly changing vibration characteristic.This can be detected by a change in the pattern of the recorded airborne and / or structure-borne sound data.

[0096] A defective brake disc or other defective wearing part of a braking system is detected according to the procedure if a change in the natural frequency of the brake disc or the amplitudes of the wheel speed orders has been determined on the basis of the transformed and, if applicable, standardized airborne or structure-borne sound data, and if the determined pattern of the transformed and, if applicable, standardized airborne or structure-borne sound data is only generated during braking operations.

[0097] The presence of a defective suspension of the vehicle body on the vehicle axles can be determined according to the method if a change in the natural frequency of at least one component of the suspension is detected based on the transformed and, if applicable, standardized airborne or structure-borne sound data, and if the determined pattern of the transformed and, if applicable, standardized airborne or structure-borne sound data is generated at least during sharp braking of the vehicle accompanied by a pitching movement of the vehicle body or when driving on a damaged road surface. A defective rolling bearing, such as a wheel bearing or a transmission shaft bearing, is detected if the determined pattern of the transformed and, if applicable, standardized airborne or structure-borne sound data was generated by at least one harmonic occurring at a specific frequency in the airborne and / or structure-borne sound emission of this rolling bearing.Even if the damage frequencies of a wheel bearing are unknown, damage to such a wheel bearing can still be detected if non-integer wheel speed orders are significantly increased and / or a change in the pattern of the transformed and, if applicable, standardized airborne and / or structure-borne sound data is detected during cornering or under varying axle loads. Such an axle load variation can also occur due to the rotation of a bearing component under a constant weight force.

[0098] To achieve the device-related task, the invention relates to a diagnostic system for diagnosing a possible defect in at least one vehicle component generating an airborne and / or structure-borne sound emission, with first sensor means which have at least one airborne and / or structure-borne sound sensor arranged in the vehicle, which is designed to detect at least one airborne or structure-borne sound emission generated by at least one vehicle component to be determined, with second sensor means which are designed to detect current operating situation data of the vehicle, with data storage means which have a local database arranged in the respective vehicle and / or a central database arranged in a cloud external to the vehicle, and which are designed at least to store operating situation data of the vehicle as well as airborne and / or structure-borne sound data in the form of temporally successive sensor data,Frequency spectra and / or spectrograms as well as quantities of this data derived therefrom, and with at least one electronic evaluation unit which is arranged on the vehicle or in the cloud outside the vehicle and is designed to communicate with the sensor means, with sensors and devices external to the vehicle and with the data storage means, and in which a computer program is stored in the electronic evaluation unit which enables the detection of a vehicle component emitting airborne and / or structure-borne sound and an assessment of any identified damage to this vehicle component or another cause of this airborne and / or structure-borne sound emission, wherein the detected airborne and / or structure-borne sound data can be standardized with current operating situation data of the vehicle and vehicle-external data, patterns in the airborne and / or structure-borne sound data can be identified,identified patterns can be assigned to a respective vehicle component and a defect in the vehicle component in question can be classified.

[0099] The described design and arrangement of this vibroacoustic diagnostic system enables comprehensive airborne and / or structure-borne noise monitoring of a vehicle. Defective vehicle components can be accurately identified and, if necessary, the urgency of repair measures determined. Other causes of airborne and / or structure-borne noise emissions can be effectively assessed. The current operating situation of the vehicle is always taken into account. The first sensor means can be all types of airborne and structure-borne noise sensors, such as microphones, vibration sensors, accelerometers, or displacement sensors. The second sensor means can be all types of sensors that can measure the current operating conditions of the vehicle, such as speed sensors, acceleration sensors, rpm sensors, force sensors, torque sensors, pressure sensors, etc.

[0100] A vehicle equipped in this way, for example as part of a vehicle fleet, can be in constant information-technology contact with a cloud remote from the vehicle, where the process is carried out fully automatically. The cloud can contain powerful computers and data storage with large storage capacities. If these resources are shared within a network, a less expensive computer with a correspondingly smaller data storage in a vehicle may be sufficient to carry out the process, or at least parts of it. In addition, many vehicles can benefit from a large database of identical vehicle models or similar vehicle types. Deviations between individual vehicles can be reliably detected.Important messages can be received by a fleet management and / or a driver in order to initiate necessary measures immediately if necessary and / or to prepare planned measures in a timely and cost-effective manner.

[0101] Finally, the invention also relates to a vehicle, such as a commercial vehicle or a passenger car, with a diagnostic system for vibroacoustic diagnosis of components of the vehicle, wherein the diagnostic system is constructed according to at least the device claim and is operable to carry out a method according to one of the method claims.

[0102] The invention is explained in more detail below with reference to an exemplary embodiment illustrated in the accompanying drawing. In the drawing, the single figure schematically shows a diagnostic system 1 of a vehicle 2 for carrying out a method according to the invention. As shown, the diagnostic system 1 uses three data sources for the vibroacoustic diagnosis of the vehicle 2, namely data on the airborne sound and / or structure-borne sound emissions from vehicle components, operating situation data, and data from vehicle-external sensors and devices 9. In addition, six interacting method sections M1, M2, M3, M4, M5, M6 are schematically illustrated in the figure, each representing a plurality of method steps.

[0103] As shown, vehicle 2 is a commercial vehicle in the form of a semi-trailer truck with a towing vehicle 2a and a trailer 2b coupled to it. In this example, vehicle 2 is part of a logistics company's vehicle fleet. The diagnostic system 1 of vehicle 2 has first sensor means 3 in the form of at least one airborne and / or structure-borne sound sensor. In the present case, for example, several sensors (not shown) for detecting structure-borne sound emissions and several microphones for detecting airborne sound emissions are distributed throughout vehicle 2. Furthermore, second sensor means 4 in the form of operating state sensors are arranged on vehicle 2, which can be designed, for example, as rotational speed sensors, force sensors, speed sensors, acceleration sensors, pressure sensors, and other sensors.These second sensor means 4 serve to record current operating situation data of the vehicle 2, such as the driving speed, the steering angle, the wheel speeds, the vehicle acceleration, the axle load, the brake pressure, etc.

[0104] The first and second sensor means 3, 4 are arranged in such close proximity to the respectively assigned components or component groups that, on the one hand, a clearly assignable sensor signal of good signal strength and good signal quality can be achieved, and on the other hand, the function of the respective component or component group is not impaired. For example, a structure-borne sound sensor for sensing bearing vibrations will be arranged relatively close to a wheel bearing. A speed sensor for determining the speed of a vehicle wheel will preferably be placed close to a brake disc having speed sensor elements. An airborne sound sensor can also be arranged further away from the vehicle component to be monitored if space is limited on the vehicle.

[0105] The sensor devices 3, 4 are known in terms of their design and functionality and therefore need not be described further here. Naturally, for cost reasons, as few sensors as possible are placed on the vehicle, both to save manufacturing costs and to be able to measure all airborne and / or structure-borne noise emissions from relevant vehicle components.

[0106] The sensor means 3, 4 can also be designed as sensor combinations, in which, for example, a speed sensor and a microphone, i.e., an airborne sound sensor, or a speed sensor and a structure-borne sound sensor are arranged in a common housing. It is also possible for such a sensor combination to consist of two sensors, each with its own housing, with these two housings being firmly connected to each other. Regarding sensor combinations, reference is made to WO2023 / 036576 A1 by the same applicant.

[0107] The diagnostic system 1 also includes an electronic evaluation unit 5 and, optionally, an electronic graphical user interface 6 with a display unit 6a and an input unit 6b. The graphical user interface 6 is designed for the visual display of messages, for example, warnings regarding vehicle components identified as defective, and for the input of control commands to the evaluation unit 5, as well as for the bidirectional transmission of messages between a driver and a fleet manager. Such an electronic user interface 6 can be the user interface of a smartphone or a telematics unit that is in communication with the electronic evaluation unit 5.

[0108] The evaluation unit 5, together with the electronic user interface 6, can be arranged in the vehicle 2 as shown. However, it can also be configured in a cloud 7 in the computer system there as an external evaluation unit 7a, which also includes a central, external database 7b.

[0109] The evaluation unit 5 has a computer 5a in which a computer program for executing a method according to the invention is stored. The computer program is used to evaluate the airborne and / or structure-borne sound data acquired by the first sensor means 3, taking into account the operating situation data acquired by the second sensor means 4. Preferably, data from vehicle-external sensors and devices 9 that influence or at least can influence the operation of the vehicle are also used. This data obtained externally from the vehicle can be, for example, vehicle position data, meteorological data from the vehicle's surroundings, and road condition data.

[0110] The computer 5a is connected to a local, vehicle-based database 5b, which is thereby assigned to the evaluation unit 5 in the vehicle 2. Optionally, the computer 5a can also be wirelessly connected to the database 7b of the cloud 7. The local database 5b is designed to store air and / or body shell emission data and data derived therefrom, as well as to store current operating situation data and the associated data from the vehicle-external sensors and devices 9.

[0111] The figure illustrates with directional arrows that, during a method sequence, the measured values ​​of the vehicle-mounted sensors 3, 4 and the vehicle-external sensors and / or devices 9 are fed to the vehicle-mounted evaluation unit 5. There, the measured values ​​of the sensors 3, 4 are received in a first method step M1 of the diagnostic method according to the invention and, if not already done previously, converted into digital airborne and / or structure-borne sound data. In parallel, the current operating situation of the vehicle is determined by acquiring operating situation data, which is fed to the evaluation unit 5. In addition, data from the vehicle-external sensors and / or devices 9 is generated and received by the vehicle-mounted evaluation unit 5. The vehicle-related and vehicle-external data thus determined are then stored in the database 5b of the evaluation unit 5 of the diagnostic system 1 for further processing.To reduce the amount of incoming data to be processed in the first process step M1, it can be provided that only data from specifically selected sensors and / or selected external devices are accepted and stored. For example, it can be provided that only if a previous diagnostic process provides an initial, albeit slight, indication of a defective braking system, all available sensor data in this regard are accepted and stored for the subsequent diagnostic process.

[0112] In a second process step M2, the airborne and / or structure-borne sound data are transformed using mathematical methods so that the resulting first transformed airborne and / or structure-borne sound data can then be further analyzed and evaluated. Thus, in the second process step M2, only the received and temporarily stored data is transformed. Various methods can be used for data transformation. These include, for example:

[0113] - Filtering (highpass, lowpass, bandpass, notch, bandstop),

[0114] - Formation of frequency and order spectra with regard to amplitude and phase,

[0115] - Envelope analysis,

[0116] - Detecting a non-stationary phase and a non-stationary frequency (for example based on the Hilbert transformation),

[0117] - Generating a cepstrum to detect periodic structures in frequency spectra,

[0118] - Continuous and Discrete Wavelet Transformation,

[0119] - singular value decomposition,

[0120] - Empirical and Dynamic Mode Decomposition,

[0121] - Autocorrelation.

[0122] In a third process step M3, a standardization process is then carried out, in which a reference data set associated with the current operating situation data is first determined. This reference data set is vehicle-type-specific and / or vehicle-specific and takes into account the driving situation data relevant during data collection, such as driving speed, wheel diameter of the vehicle wheels, engaged transmission gear, engine speed, axle loads, brake pressure, steering wheel angle, and so on. The reference data set therefore contains such transformed reference airborne and / or structure-borne sound data that would be generated in this operating situation for a healthy vehicle. In the further course of this standardization process, the aforementioned reference data set is compared with the first transformed airborne and / or structure-borne sound data to generate standardized transformed airborne and / or structure-borne sound data.The difference determined by this comparison at least provides an indication that a defective vehicle component is generating airborne and / or structure-borne noise that indicates the damage. However, it is already possible to identify a defective vehicle component at this point.

[0123] In the third process step (M3), the current transformed airborne and structure-borne sound data are compared with the transformed airborne and structure-borne sound data that would have been generated for the vehicle as a whole or for a vehicle component under investigation in a comparable operating situation, assuming that the component was in a healthy condition. Simply put, this comparison determines a distance value that indicates how and to what extent the determined transformed airborne and structure-borne sound data deviate from the expected transformed airborne and structure-borne sound data.For example, a frequency spectrum of the transformed airborne and structure-borne sound data of a defective vehicle component will differ from a frequency spectrum of transformed airborne and structure-borne sound data that was or would be generated in a comparable operating situation of the vehicle on the basis of technically flawless, i.e. undamaged, vehicle components.

[0124] Mathematical methods for this normalization step can include, for example, Hottelings T 2 , Bayesian approaches and machine learning methods such as one-class support vector machines, autoencoders or neural networks can be used. With the Hottelings T 2Using this method, a probability distribution of measured data can be determined and deviations evaluated. Conditional probabilities, such as expected airborne and / or structure-borne sound data or sample parameters dependent on determined vehicle operating situation data, can also be taken into account. Bayesian approaches can also be used to make conditional probability statements. The one-class support vector machine method and autoencoder can be used to identify anomalies in measured data. The terms machine learning methods and neural networks refer to computer procedures designed to enable independent computer learning. Different metrics can be used as the deviation value, some of which are predetermined by the respective mathematical method. For example, with an autoencoder, the reconstruction error is used to quantify deviations.

[0125] The third process step (M3) ensures that the transformed airborne and structure-borne sound data are evaluated taking into account the operating situation data available at the time of their determination, and preferably taking external effects into account. For example, the variable influence of load-dependent vehicle axle loads, vehicle speed, current brake pressure, road conditions, and weather conditions on the vehicle component being observed can be virtually eliminated from the transformed airborne and structure-borne sound data. If, for example, the airborne sound data from an airborne sound sensor is to be assessed with regard to possible wheel bearing damage, the airborne sound emissions generated by hailstones or raindrops hitting the vehicle during data acquisition will be disregarded.

[0126] By additionally considering only a portion of the respectively determined transformed airborne and / or structure-borne sound data to generate expected third transformed airborne and / or structure-borne sound data, unusual vibrations can be detected depending on other vibration characteristics. For example, it can be determined whether, during a vehicle's deceleration, the vibrations in the current direction of travel deviate unusually strongly from the vibrations actually expected for the vibrations in the current direction of travel based on knowledge of the vibrations in the vehicle's transverse direction. Thus, the portion of the transformed airborne and / or structure-borne sound data to be examined can only include those transformed airborne and / or structure-borne sound data that act in one direction of the vehicle's Cartesian coordinate system (X, Y, Z), for example in the X direction, i.e., the longitudinal direction of the vehicle.As mentioned above, the normalization process can also be performed using transformed data from outside the vehicle. However, it is also possible to use a previously determined mathematical function applicable to the respective operating situation and the vehicle component to be diagnosed. This mathematical function, applied to the initially determined transformed airborne and structure-borne sound data, removes the influence of unwanted or irrelevant sound emissions on the respective diagnostic process.

[0127] The airborne and structure-borne sound data processed in this way can then be made available for further analysis to identify specific patterns in the transformed airborne and structure-borne sound data or data derived from them. Such further investigations can include amplitude changes, amplitude modulations, frequency shifts, and the detection of new vibration frequencies that are not caused by the vehicle's specific operating situation or by external influences.

[0128] The standardization process can also be described differently: The transformed airborne and / or structure-borne sound data, or data derived from them, are standardized by comparing them with associated operating situation data or data derived from them, and, if necessary, with external vehicle data, using a standardization process. In a flowchart, a standardization module would be present in which the standardization process takes place. The input variables to the standardization module are the transformed airborne and / or structure-borne sound data, as well as data representing the current operating situations of the vehicle during the measurement of airborne and / or structure-borne sound emissions, and, if necessary, external vehicle information.In simple terms, such a normalization module generates a distance value as an output value, which indicates the extent to which the currently transformed airborne and / or structure-borne sound data deviate from the airborne and / or structure-borne sound data actually expected for this operating situation. For example, for a braking operation with a brake pedal deflected by 30%, the value indicates how much the amplitude spectrum of the airborne and / or structure-borne sound data determined during this braking operation deviates from the amplitude spectrum expected for such a braking operation. If the measured values ​​of an airborne and / or structure-borne sound sensor are analyzed, which is very clearly assigned to a specific vehicle component, for example, is located very close to it, it can already be determined after this third process step M3 whether the vehicle component in question is technically OK or not.If this clear assignment is not given, the diagnostic procedure is continued with a fourth procedure step M4 according to the figure.

[0129] In this fourth method step M4, the transformed and, if applicable, standardized airborne and / or structure-borne sound data are examined to determine whether at least one single pattern can be identified in these transformed airborne and / or structure-borne sound data or data derived therefrom, and if such a pattern is identified, at least one pattern characteristic value is generated and stored in an assigned manner to describe and / or quantify the determined pattern of these transformed and, if applicable, standardized airborne and / or structure-borne sound data.

[0130] The software of the fourth process step (M4) is therefore used to examine the airborne and / or structure-borne sound data or data derived from them to determine whether, and if so, which patterns can be identified in this data. These patterns can be, for example, singular amplitude maxima, multiple equally spaced amplitude maxima, or equidistant secondary amplitude maxima in a frequency spectrum or cepstrum. The patterns can indicate, for example, resonances, harmonic oscillations, a sequence of periodic pulse excitations, amplitude modulation, phase modulation, and / or frequency modulation in the temporal progression of the acquired airborne and structure-borne sound data or in the values ​​derived from them as described.

[0131] Using the software of a fifth method step M5, the at least one determined pattern characteristic is then assigned to a specific vehicle component, a specific damage, and / or a specific location on the vehicle and / or to recognized patterns from previous measurements stored in the database. For example, engine-related, transmission-related, or chassis-related patterns can be searched for. To enable their identification, the following information is preferably used: - Data on the driving maneuver that took place when the airborne and structure-borne sound data were measured,

[0132] - Data on whether the determined, at least one pattern of the transformed and, where applicable, standardised airborne and / or structure-borne sound data changed during the period in which the airborne and structure-borne sound data were measured, for example a change in the axle load on at least one of the vehicle axles,

[0133] - data on whether the determined, at least one pattern of the transformed and, where appropriate, standardised airborne and / or structure-borne sound data has changed compared to a previous determination of this pattern, and whether this change can be reconciled with a driving condition, for example a change in the axle load,

[0134] - Data on the vibration frequencies of airborne and structure-borne emissions or the quantities derived therefrom during the period in which the airborne and / or structure-borne sound data were measured,

[0135] - Data on the presence of known damaging frequencies or damaging orders in the airborne and / or structure-borne sound data or the quantities derived therefrom during the period in which the airborne and structure-borne sound data were measured,

[0136] - Data on the orders of rotational speeds of rotating vehicle components during the determination of airborne and structure-borne sound data,

[0137] - Data on the energy content of the measured airborne and / or structure-borne sound or airborne and / or structure-borne sound pattern,

[0138] - Data on the presence of amplitude, phase and / or frequency modulations in the airborne and structure-borne sound data,

[0139] - data on the frequency band in which the mentioned modulations can be detected,

[0140] - Data on whether the detected pattern is present in the airborne and structure-borne sound data from multiple sensors, and

[0141] - Data on whether the amplitude values ​​of the airborne and structure-borne sound data from multiple sensors differ.

[0142] The result of such an assignment of a detected pattern in transformed and, if necessary, standardized airborne and / or structure-borne sound data then provides an indication of a specific vehicle component, a specific vehicle location, and / or a specific damage to a vehicle component. However, the diagnostic process described may also simply reveal that something is not working properly on the vehicle, for example, when the vehicle is negotiating a tight left turn.

[0143] The software of a sixth method step M6 is used, if necessary, to classify the transformed and, if applicable, standardized airborne and / or structure-borne sound data and the at least one assigned pattern characteristic value in such a way that, if no vehicle component was detected and the pattern is not known from at least one previous measurement, the method run is saved in the computer as having no result with regard to a damage search, if a pattern is known from previous measurements, the current monitoring result is assigned to this known pattern and the associated data is saved in the computer, if a vehicle component is detected, the current monitoring result is assigned to this vehicle component and saved in the computer, and if an existing component damage or impending component damage is detected, a damage report and / or a warning signal are generated and sent out.

[0144] If the performed diagnosis has determined which vehicle component or vehicle location is damaged or developing, the damage can be classified accordingly using the software of the sixth process step M6. An unidentified airborne or structure-borne sound pattern is provisionally assigned to an airborne or structure-borne sound source designated as "unknown" for later assessment, and the database 5b, 7b is continuously updated with the recorded airborne or structure-borne sound patterns.

[0145] Based on the diagnosis, it can also be determined which vehicle component already has damage or is showing such a damage trend that it makes replacing this vehicle component with a replacement component seem sensible. The identified damage or the impending damage to the assigned vehicle component is therefore assigned to its component history. This component history is stored in the vehicle-side database 5b and / or in the central database 7b of a cloud 7 as a log or component history. In this log, all anomalies assigned to the respective vehicle component are stored, for example, in chronological order. For example, this log lists that the vehicle component already showed an anomaly of damage during a previous diagnostic process.However, this report may also note that the vehicle component was newly installed in the vehicle during a maintenance visit to a workshop and replaced with a defective vehicle component.

[0146] However, the classification of the diagnostic result can also mean that the alleged damage is actually measurement noise or is clearly due to environmental influences, which can be recorded as non-critical component behavior.

[0147] The classification of the diagnostic result can also be carried out using machine learning methods using several parameters of different identified patterns.

[0148] As the figure illustrates and has already been indicated, the computer 5a is wirelessly connected to a computer-based, vehicle-external cloud 7, which, in addition to the necessary radio communication means, as mentioned, has a vehicle-external computer 7a and a central database 7b. This central database 7b can store, for example, vehicle-specific information. This vehicle-specific information includes, for example, general information regarding the respective vehicle type as well as environmental information, such as data from a satellite-based navigation system regarding the current vehicle position and current weather data and / or road condition data associated with this position.

[0149] In an example scenario, the method is initiated when vehicle 2 is started. Vehicle 2 is continuously vibroacoustically monitored by diagnostic system 1. While driving, the driver notices an unusual noise coming from the vehicle that he cannot identify. At the same time, an airborne sound sensor in the first sensor means 3, in the form of a microphone, has been measuring the noise since the vehicle was started. The airborne sound data thus determined are received by the computer 5a of vehicle 2 in the first method step M1 and stored as a digital signal curve of airborne sound data in the database 5b of evaluation unit 5. In evaluation unit 5, this airborne sound data is then transformed, i.e. converted, into a frequency spectrum in accordance with the second method step M2.

[0150] In the subsequent third process step M3, a standardization process is carried out in which the currently generated airborne sound data is compared with stored or newly determined reference airborne sound data that would be generated in a healthy vehicle. For this purpose, an associated reference data set is determined for the currently determined operating situation data, which contains the transformed reference airborne sound data that would be generated in this operating situation in a healthy vehicle. To generate standardized transformed airborne sound data, this reference data set is compared with the first transformed airborne sound data. The result of this comparison, i.e. the difference between the transformed airborne sound data of the reference data set and the currently transformed airborne sound data, provides at least an indication of the airborne sound emitted by a defective vehicle component.In this way, in favorable structural situations on the vehicle, a defective component or the location of a defective component can be identified without further investigations.

[0151] The standardized airborne sound data is then subjected to a frequency and intensity analysis. It turns out that the current airborne sound pattern best matches known and stored airborne sound patterns from the "wheel area" component group, which essentially depend on the wheel speed, at various wheel speeds. Airborne sound emissions are constantly present while the vehicle is moving and are therefore not limited to specific processes such as braking maneuvers or other events. The determined frequency spectrum does not contain any known natural frequencies of a brake disc. Instead, harmonics can be identified in the frequency spectrum that correspond to historical data from stored harmonics typical for a wheel bearing of this type. In addition, statistical information is available on the maximum service life of a wheel bearing of the type used in vehicle 2.

[0152] After reviewing all relevant data, a wheel bearing is identified as the source of the unusual airborne noise. The driver sees a notification on the display unit 6a of their graphical user interface 6 indicating that a wheel bearing may be worn and is under observation. If applicable, the specific wheel bearing could be identified and displayed. This information is also sent to a fleet manager. The fleet manager is already scheduling an inspection of the wheel bearing for the next service appointment. The same procedure would, of course, also be followed if the driver had not noticed any initial signs of potential damage.

[0153] The driver interrupts the journey to unload vehicle 2 at the destination. After unloading the vehicle, the process restarts when the vehicle sets off. The airborne noise emissions, which are still present and determined by diagnostic system 1, are automatically standardized using the current operating situation data, particularly taking into account the now lower axle load. As a result, the intensity of the detected damage appears to decrease again, but due to the standardization of the current airborne noise data, this does not negatively impact the diagnosis of the existing damage. For the now lower axle load, the emitted airborne noise is still noticeable.

[0154] The previously almost constant, rather quiet noise becomes increasingly louder and changes rapidly during the subsequent journey. The evaluation unit 5 detects this critical change and sends the warning signal "Wheel bearing damage" to the graphical user interface 6, which displays this information on its display unit 6a, requiring urgent action. The fleet manager then arranges for an immediate workshop visit, during which the defective wheel bearing is replaced.

[0155] The completed component replacement is then entered into the databases 5b, 7b, for example, via the input unit 6b of the graphical user interface 6. The component history is adjusted and initially ignored, and the new component's data is initialized. The unusual noise has now disappeared. If necessary, an airborne and / or structure-borne noise pattern characteristic of a running-in phase of the new wheel bearing is temporarily detected and classified as normal. List of reference symbols (part of the description):

[0156] 1 diagnostic system

[0157] 2 vehicles

[0158] 2a towing vehicle

[0159] 2b Trailer vehicle

[0160] 3 First sensor means, airborne and / or structure-borne sound sensor

[0161] 4 Second sensor means, operating status sensor

[0162] 5 Electronic evaluation unit in the vehicle

[0163] 5a Computer

[0164] 5b Data storage means, local database on the vehicle

[0165] 6 Graphical user interface

[0166] 6a Display unit of the graphical user interface

[0167] 6b Input unit of the graphical user interface

[0168] 7 Cloud

[0169] 7a Electronic evaluation unit in the cloud

[0170] 7b Data storage means, central database in the cloud

[0171] 9 Vehicle-external sensors and devices

[0172] M1 First process step, generate and save measurement data

[0173] M2 Second step, transforming measurement data

[0174] M3 Third procedural step, standardize measurement data

[0175] M4 Fourth stage of the procedure, identification of a pattern

[0176] M5 Fifth stage of the procedure, assignment of the sample

[0177] M6 Sixth stage of the procedure, classification of the sample

Claims

Patent claims:

1. A method for diagnosing impending or existing damage to a vehicle component generating airborne and / or structure-borne sound emissions, wherein data on said airborne and / or structure-borne sound emissions are used for diagnosis, in which in a first method step (M1) - the measured airborne and / or structure-borne sound emissions are converted into airborne and / or structure-borne sound data, - the current operating situation of the vehicle is determined in parallel by recording operating situation data, - and the data obtained are stored in a computer for further processing, in which in a second process step (M2) - the airborne and / or structure-borne sound data are transformed using mathematical methods in such a way that the first transformed airborne and / or structure-borne sound data thus generated can subsequently be analyzed and evaluated, and in which a standardization process is carried out in a third process step (M3), - in which a reference data set is determined which is assigned to the current operating situation data and which contains such transformed reference airborne and / or structure-borne sound data which would be generated in this operating situation for a non-damaged vehicle, - in which, to generate standardised transformed airborne and / or structure-borne sound data, this reference data set is compared with the first transformed airborne and / or structure-borne sound data, and - where a difference detected provides at least an indication of airborne and / or structure-borne noise emitted by a defective vehicle component.

2. Method according to claim 1, characterized in that second transformed airborne and / or structure-borne sound data are determined from the operating situation data by means of a physical-mathematical model, that in order to generate standardized transformed airborne and / or structure-borne sound data, the second transformed airborne and / or structure-borne sound data are compared with the first transformed airborne and / or structure-borne sound data. be compensated, and that a difference determined provides at least an indication of airborne and / or structure-borne noise emitted by a defective vehicle component.

3. Method according to claim 1, characterized in that third transformed airborne and / or structure-borne sound data are determined from a portion of the first transformed airborne and / or structure-borne sound data and the operating situation data by means of a physical-mathematical model, that in order to generate standardized transformed airborne and / or structure-borne sound data, the third transformed airborne and / or structure-borne sound data are compared with the first transformed airborne and / or structure-borne sound data, and that a determined difference provides at least an indication of emitted airborne and / or structure-borne sound of a defective vehicle component.

4. Method according to one of the preceding claims, characterized in that additional vehicle-external data are recorded and taken into account in the diagnosis, which represent environmental conditions and / or satellite-based position data and / or road condition data which influence or at least can influence the operation of the vehicle.

5. Method according to one of the preceding claims, characterized in that before or after the third method step (M3) in a fourth method step (M4) the transformed and optionally standardized airborne and / or structure-borne sound data are examined to determine whether at least one pattern can be recognized in these transformed and optionally standardized airborne and / or structure-borne sound data, and if such a pattern has been recognized, at least one pattern characteristic value is generated to describe and / or quantify the determined pattern and the at least one pattern characteristic value is assigned to these transformed and optionally standardized airborne and / or structure-borne sound data and to the data derived therefrom.

6. The method according to claim 5, characterized in that in a fifth method step (M5) immediately following the fourth method step (M4), the at least one determined pattern characteristic value is assigned to a specific vehicle component and / or a specific damage and / or a specific location on the vehicle and / or recognized patterns of previous measurements stored in the database.

7. Method according to one of the preceding claims, characterized in that in a sixth method section (M6) a classification of the transformed airborne and / or structure-borne sound data and data derived therefrom, such as at least one assigned pattern characteristic value, takes place in such a way that if no vehicle component was detected and the pattern is not known from previous measurements, the method run is stored in the computer as having no result with regard to a damage search, that if a pattern of previous measurements is detected, the current monitoring result is assigned to this pattern and the associated data is stored in the computer, that if a vehicle component is detected, the current monitoring result is assigned to this vehicle component and stored in the computer, and that a damage report and / or a damage signal are generated if component damage or impending component damage is detected.

8. Method according to one of the preceding claims, characterized in that an unidentified pattern of the transformed and optionally standardized airborne and / or structure-borne sound data is provisionally assigned to an unknown airborne or structure-borne sound source for later further processing and is stored in a database (5b, 7b) for unidentified patterns.

9. Method according to one of the preceding claims, characterized in that the method is operated only when predetermined limit values ​​of at least one value of the operating situation data of the vehicle are undershot, reached or exceeded.

10. Method according to claim 9, characterized in that the method is only operated when the vehicle - driving around a curve with a predetermined small curve radius and in a predetermined direction (left turn or right turn), and / or - travels uphill or downhill with a predetermined gradient, and / or - driving on a route with known severe road damage, and / or - travels a route at predetermined high or low outside temperatures, and / or - is accelerated positively or negatively, and / or - is accelerated positively immediately after a negative acceleration, and / or - is driven at a constant, specified speed, and / or - rolls without propulsion, and / or - no rain or snowfall occurs, and / or - has fallen below or exceeded a predetermined axle load.

11. Method according to one of the preceding claims, characterized in that, due to a diagnosed defect in a vehicle component, a warning is sent to a driver, and / or a vehicle fleet management is notified, and / or a control signal is transmitted to a control unit.

12. Method according to one of the preceding claims, characterized in that the method is carried out in an electronic evaluation unit (5) which is provided with first sensor means (3) for determining airborne or structure-borne sound emissions, with second sensor means (4) for recording operating situation data of the vehicle (2) representing the current operating situation, wherein the electronic evaluation unit (5) is arranged in the vehicle (2) and communicates with a computer-supported cloud (7) arranged outside the vehicle (2) with regard to a data exchange.

13. The method according to one of claims 1 to 11, characterized in that the method is carried out in an electronic evaluation unit (7a) which is provided with first sensor means (3) for determining airborne or structure-borne sound emissions, with second sensor means (4) for recording operating situation data of the vehicle (2) representing the current operating situation, wherein the electronic evaluation unit (7a) is integrated in a computer-supported cloud (7) and communicates via a transmitting and receiving device which is or can be connected to the said sensor means (3, 4) by means of signals.

14. Method according to one of claims 12 to 13, characterized in that the electronic evaluation unit (5, 7a) can be connected by signal technology to third sensor means (9) for determining vehicle-external data.

15. Method according to one of the preceding claims, characterized in that an inference is made as to impending damage or damage that has already occurred to an identified vehicle component on the basis of a change in the determined pattern of the transformed airborne and / or structure-borne sound data and / or the standardized transformed airborne and / or structure-borne sound data occurring within a short period of time and / or becoming increasingly faster.

16. Method according to one of the preceding claims, characterized in that a measurement error is inferred on the basis of singular and / or randomly occurring changes in the determined pattern of the transformed and optionally standardized airborne or structure-borne sound data of an identified vehicle component.

17. Method according to one of the preceding claims, characterized in that a replacement of an identified vehicle component is recognized on the basis of a significant deviation of a pattern of the transformed and optionally standardized airborne or structure-borne sound data, which deviation was determined at the beginning of a journey and repeatedly confirmed, in comparison with at least one most recently determined and / or stored pattern of the transformed and optionally standardized airborne or structure-borne sound data of this vehicle component.

18. Method according to one of the preceding claims, characterized in that a running-in phase of an identified vehicle component after its installation in the vehicle is inferred on the basis of a slow and / or decreasing change in a determined pattern of the transformed and optionally standardized airborne or structure-borne sound data of this vehicle component.

19. Method according to one of the preceding claims, characterized in that a conclusion is drawn about a defective brake disc or another wearing part of a brake system if a change in the natural frequency of the brake disc or in the amplitudes of the wheel speed orders has been determined on the basis of the transformed and optionally standardized airborne or structure-borne sound data, and if the determined pattern of the transformed and optionally standardized airborne or structure-borne sound data is only generated during braking operations.

20. Method according to one of the preceding claims, characterized in that a conclusion is drawn about a defective suspension of the vehicle body on the vehicle axles if a change in the natural frequency of at least one component of the suspension is detected on the basis of the transformed and optionally standardized airborne or structure-borne sound data, and if the determined pattern of the transformed and optionally standardized airborne or structure-borne sound data is generated at least during a sharp braking of the vehicle accompanied by a pitching movement of the vehicle body or when driving on a damaged road surface.

21. Method according to one of the preceding claims, characterized in that a defective rolling bearing, such as a wheel bearing or a transmission shaft bearing, is detected if the determined pattern of the transformed and standardized airborne and / or structure-borne sound data was generated by at least one harmonic occurring at a specific frequency in the airborne and / or structure-borne sound emission of this rolling bearing.

22. Diagnostic system (1) for diagnosing a possible defect in at least one vehicle component (2) generating an airborne and / or structure-borne sound emission, with first sensor means (3) which have at least one airborne and / or structure-borne sound sensor arranged in the vehicle, which is designed to detect at least one airborne or structure-borne sound emission generated by at least one vehicle component to be determined, with second sensor means (4) which are designed to detect current operating situation data of the vehicle (2), with data storage means which have a local database (5b) arranged in the respective vehicle (2) and / or a central database (7b) arranged in a cloud (7) external to the vehicle, and which are designed at least to store operating situation data of the vehicle (2) as well as airborne and / or structure-borne sound data in the form of chronologically successive sensor data,Frequency spectra and / or spectrograms as well as quantities of these data derived therefrom, and with at least one electronic evaluation unit (5, 7a), which is arranged on the vehicle (2) or in the cloud (7) outside the vehicle (2) and is designed for communication with the sensor means (3, 4), with vehicle-external sensors and devices (9) and with the data storage means (5b, 7b), and in which a computer program is stored in the electronic evaluation unit (5, 7a), which Determination of a vehicle component emitting airborne and / or structure-borne sound as well as an assessment of any identified damage to this vehicle component or any other cause of this airborne and / or structure-borne sound emission, wherein the determined airborne and / or structure-borne sound data can be standardized with current operating situation data of the vehicle (2) and vehicle-external data, patterns in the airborne and / or structure-borne sound data can be identified, identified patterns can be assigned to a respective vehicle component and a defectiveness of the vehicle component in question can be classified.

23. Vehicle (2), such as a commercial vehicle or passenger car, with a diagnostic system (1) for its vibroacoustic diagnosis, which is constructed according to at least one device claim and operable to carry out a method according to one of the method claims.

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