Method for determining chassis assembly status and vehicle system

Abstract

The invention relates to a method for determining the state of a component of an individual chassis of an individual vehicle, comprising the following steps: - providing a first data set of vehicle data, which comprises at least load data and / or wear data of the same or similar type of chassis of the individual vehicle over the entire service life, - generating an individual second data set in the individual vehicle as a target state by detecting the vehicle data up to a previously specified first mileage and / or a determined age of the individual vehicle, - detecting currently measured vehicle data from a previously specified second mileage and / or a determined age of the individual vehicle, - comparing the currently measured vehicle data with the first data set and the second data set in order to determine the state. Furthermore, the invention relates to a vehicle system and a vehicle.

Description

Technical Field

[0001] This invention relates to a method and vehicle system for determining the state of components of an individual chassis of a vehicle. The invention also relates to a vehicle. Background Technology

[0002] Vehicles typically have onboard diagnostic systems that monitor vehicle components and systems, for example, for cyclical inspection purposes. To this end, the system compares vehicle data such as mileage. Furthermore, vehicle diagnostic systems have sensors for monitoring, which report faults in the vehicle or component operation. However, such vehicle diagnostic systems only report when parameters exceed acceptable ranges.

[0003] To date, there is no way to predict possible failure scenarios or failures that may occur due to factors such as wear and tear.

[0004] However, component wear can seriously compromise the reliability of the component and the more complex system in which it is integrated. In fact, excessive wear of a basic component can damage the entire assembly of components in which that basic component is integrated. Furthermore, excessive wear of brake pads, for example, can cause dangerous damage to brake assembly components, thereby compromising vehicle safety.

[0005] DE 10 2018 119652A1 discloses a vehicle having a chassis with at least one chassis component and a driver assistance system for detecting the state of the chassis component, wherein the driver assistance system has an evaluation unit and a first acoustic sensor that detects sound generated by the chassis component during the vehicle's operation and converts it into an electrical signal, and the evaluation unit uses the electrical signal to determine at least one piece of information about the state of the chassis component. Summary of the Invention

[0006] Therefore, the object of the present invention is to provide a method and vehicle system in which the state of the chassis can be determined more accurately, thereby thereby, for example, improving driving safety. Furthermore, the object is to provide a vehicle.

[0007] This objective is achieved by a method having the features of claim 1, a vehicle system having the features of claim 9, and a vehicle having the features of claim 14.

[0008] Advantageous improvements that can be used alone or in combination with each other are described in the dependent claims and the specification.

[0009] This objective is achieved through a method for determining the state of components of an individual vehicle's chassis, comprising the following steps:

[0010] - Provide a first dataset of vehicle data, which includes at least load data and / or wear data of the same or similar type of chassis for individual vehicles throughout their service life;

[0011] - As a target state, a second dataset for each individual vehicle is generated by detecting vehicle data up to the previously specified first mileage and / or determined vehicle age.

[0012] -Detect the currently measured vehicle data starting from the previously specified second mileage and / or determined vehicle age of the individual vehicle;

[0013] - Compare the currently measured vehicle data with the first and second datasets to determine the status.

[0014] The two axles and their components of a vehicle, as well as the single axle and its components, can preferably be understood as the chassis.

[0015] According to the present invention, current assessments of chassis condition primarily rely on data from current detections using accelerometers. Here, changes in vibration behavior are analyzed. However, previous investigations have not yielded any clear indicators for accurately assessing chassis wear. The replacement of worn and new chassis components allows for a virtually unlimited number of possible combinations.

[0016] However, if the assessment is incorrect, especially when important components such as wheel suspension / brakes are directly or indirectly affected, there may be driving safety risks.

[0017] In this regard, the present invention recognizes that it is insufficient to make wear judgments or assessments of condition solely based on currently detected data. According to the present invention, a first dataset of vehicle data is provided, which includes at least load data and / or wear data of chassis of the same or similar type for individual vehicles throughout their service life. This can be determined on a test track.

[0018] The first dataset is provided from vehicles with similar or identical chassis.

[0019] According to the invention, it is also recognized that, in order to determine the target state, it is necessary to learn the target state individually for each vehicle. Therefore, if a vehicle leaves the assembly line as a new car, the target state is learned by detecting vehicle data. This learning process remains active over a certain time and / or mileage. Here, for example, a second dataset is generated based on detected vibrations. Here, according to the invention, it is recognized that not only must the target state be detected generally, but also on a per-vehicle basis, because the configurations of a large number of individuals generate different target datasets. Then, starting from a previously defined second mileage and / or a determined vehicle age for the individual vehicle, current vehicle data is measured or detected. The second mileage may, for example, be shortly after the first mileage.

[0020] Furthermore, according to the present invention, the currently measured vehicle data is then compared with the first dataset and the second dataset. That is, according to the present invention, the current vehicle data is compared to target data or target state to a certain extent, and the current vehicle data is compared with the first dataset as the actual state during the service life period.

[0021] A state value is generated by comparing the actual state during the service life with the individually generated target state. Based on this state value, a reliable judgment about the actual condition of the chassis can be made. Thus, for example, at the end of the service life, a judgment of "complete wear" can be generated more reliably based primarily on the first dataset and with the help of the second dataset. Furthermore, by comparing the two datasets, judgments such as "80% complete wear" or "20% corresponds to a new state" can be made.

[0022] This invention enables a clear and reliable assessment of chassis condition by using, to a certain extent, the new condition of an individual vehicle as a basis, as well as information about the entire service life of similar or identical chassis. It assesses both the distance from this new condition and compares it with a first dataset representing the service life. This allows for a reliable judgment of the condition.

[0023] In a further design, a second dataset is generated by assigning the currently measured vehicle data to a road surface cluster representing the road surface, or by creating a new road surface cluster for previously unknown road surfaces and assigning the currently measured vehicle data to the newly created road surface cluster. For improved contrast or comparison, road surfaces are preferably clustered. If a road surface has not yet been detected, a new cluster is created and stored together with the recorded vehicle data as a second dataset. This allows for learning from the improved second dataset, and the chassis status can be determined more accurately later.

[0024] In a further preferred design, the first dataset is provided as a generally generated reference dataset by using the same or similar chassis types tested on a test bench during long-term testing.

[0025] The initial dataset can be easily determined through long-term testing on the test bench. Specific frequency ranges can be readily identified using this test bench, for example, generating footprints that can be used for later comparisons.

[0026] Preferably, such a first dataset can be generated through purely analytical methods as well as through artificial neural networks or another machine learning method. Generating the first dataset on a test bench also offers the advantage of being able to replace parts. Thus, for example, brake pads can be replaced periodically, which then generate additional vibrations / frequencies along with the aging, partially worn surrounding parts. In this way, the first dataset can be expanded and improved, thereby allowing for a better determination of the current chassis condition of individual vehicles in subsequent comparisons.

[0027] In a further design, a separate reference dataset is generated for each of the two axles. This allows for more targeted comparisons between current vehicle data recorded in front of the vehicle and current vehicle data recorded behind the vehicle.

[0028] In another embodiment, at least the vibration amplitude of vehicle motion under characteristic road surface conditions is used as the currently measured vehicle data and as a second dataset. In this way, specific frequency ranges can be generated and assigned based on the identified road surface.

[0029] In further design, the actual state is determined by comparing the currently measured vehicle data with a first dataset, and the target state is determined by comparing the currently measured vehicle data with a second dataset. The current wear of the chassis components is determined based on a target-actual-comparison approach. These comparisons between the datasets and the currently measured vehicle data can also be weighted differently. Therefore, the current wear of the chassis or its components can be determined. By comparing the data with datasets characterizing service life cycles with corresponding wear and datasets representing the new state, a reliable assessment of the chassis's condition can be achieved.

[0030] Further improvements will allow for the continuous or adaptive measurement of current vehicle data. This can be adaptively performed, for example, based on the vehicle's age or mileage, or as certain conditions occur.

[0031] In addition, the vehicle data currently measured may include the vehicle's mileage and / or age. This allows for the verification of the reasonableness of comparisons or results. Mileage can be used, for example, to represent an average set of loads, and age can be used to represent, for example, the aging of rubber bearings.

[0032] This objective is also achieved by a vehicle system for determining the state of components of an individual vehicle's chassis, the vehicle system comprising:

[0033] - A storage unit for providing a first dataset of vehicle data, wherein the first dataset includes at least load data and / or wear data of the same or similar type of chassis of individual vehicles throughout their service life;

[0034] - A storage unit for providing a second dataset of an individual in an individual vehicle, wherein the second dataset of the individual contains vehicle data measured by one or more sensors up to the previously specified first mileage and / or determined age of the individual vehicle as a target state;

[0035] - A sensor system for detecting currently measured vehicle data from a previously specified second mileage and / or a determined vehicle age for an individual vehicle;

[0036] - Comparison unit, used to compare the currently measured vehicle data with the first dataset and the second dataset to determine the status.

[0037] The advantages of this method can also be applied to vehicle systems.

[0038] A sensor system can consist of multiple different sensors and various sensor types.

[0039] The comparator unit can be designed as a processor.

[0040] In a further design, the sensor system is designed to detect road surfaces. Furthermore, a processor is preferably provided to generate a second dataset by assigning currently measured vehicle data to a road surface cluster representing the road surface, or by creating a new road surface cluster for previously unknown road surfaces and assigning the currently measured vehicle data to the newly created road surface cluster, and storing the second dataset in a storage unit. For this purpose, the processor is connected to the sensor system and the storage unit for communication. Alternatively, the processor, comparison unit, and storage unit can also be designed as modules.

[0041] In a further design, the comparison unit is designed to determine the actual state based on a comparison between the currently measured vehicle data and a first dataset, and to determine the target state based on a comparison between the currently measured vehicle data and a second dataset. It also determines the current wear of the chassis components through a target-actual-comparison process.

[0042] In further design, the sensor system was designed to continuously or adaptively measure current vehicle data.

[0043] In addition, the vehicle data currently being measured may include the vehicle's mileage and / or age.

[0044] Furthermore, this objective is achieved by a vehicle having the vehicle system described above. Attached Figure Description

[0045] Other features and advantages of the invention will become apparent from the following description with reference to the accompanying drawings. Wherein, schematically:

[0046] Figure 1 An example design of the method according to the invention is shown; and

[0047] Figure 2 The evaluation of the tie rod over a portion of its service life within the frequency range is shown; and

[0048] Figure 3 A vehicle system according to the present invention is shown. Detailed Implementation

[0049] Figure 1 A design of a method for determining the state of components of an individual chassis of an individual vehicle, according to the present invention, is shown.

[0050] First, a first dataset is generated in preparatory step S0. This first dataset is generated based on the same or similar chassis installed in individual vehicles. This first dataset is generated through long-term testing at a test track, reflecting the service life cycle of the chassis from a new state to complete wear. Through long-term testing at the test track, specific vibrations and frequencies (frequency ranges) can be identified, each belonging to a specific state of the chassis. Therefore, when braking occurs under conditions of existing wear, a significant squeaking noise will be generated.

[0051] Such long-term testing makes it easy to identify certain frequency ranges, i.e., generate footprints that can be considered for later comparisons. The first dataset can be generated through purely analytical methods, or through artificial neural networks or another machine learning method.

[0052] However, worn parts can be replaced on the test track through replacement. This replacement, along with the subsequently generated first dataset, specifically vibration and frequency data, is stored. This first dataset can then be expanded or completed. Therefore, the first dataset contains information about new parts, worn components, and worn but "still usable" components, thus comprehensively reflecting frequency / vibration and load data throughout the chassis's entire service life. This first dataset, thus generated, can help to more accurately determine the actual chassis condition in an individual vehicle when later compared with measured vehicle data from that vehicle to the current chassis condition.

[0053] Generating the initial dataset on the test bench also offers the advantage of allowing for targeted and proactive component replacement. For example, brake pads can be replaced periodically, and these pads, along with surrounding worn and partially worn components, generate additional vibrations / frequencies. This allows the initial dataset to be expanded and improved. Essentially, this initial dataset is a general-purpose dataset.

[0054] This allows for the inexpensive generation of simplified, general-purpose data. By generating such a first dataset, reliability can be guaranteed when creating / introducing a second dataset, for example, by considering the use of the first dataset for credibility.

[0055] Figure 2 An evaluation of the tie rod over a portion of its service life is shown within its frequency range. The frequency range at the tie rod is 3.5 to 4.5 Hz.

[0056] Initially, an 86% increase in frequency and damage was observed. After replacing the tie rod at approximately 100,000 km at time point T1, the frequency decreased by about 9%. As operation continued, the frequency increased further by 28%. With the engine hydraulic bearing replaced at approximately 200,000 km at time point T2, the frequency decreased by about 24%. Over the course of its service life, increasingly severe damage occurred, and the determined frequency increased further by 15%.

[0057] By creating a general dataset on the test bench, an individual footprint, i.e., the frequency curve of the corresponding chassis, can be created for each axle and can be considered for comparison.

[0058] Creating the initial dataset on the test bench offers the following advantages: it allows for accurate understanding of the replacement of various components and a better determination of how frequency ranges respond to them. For example, it also identifies how sensitive different components are to frequency / vibration failures.

[0059] Furthermore, by creating the first dataset in this way, the entire lifespan of the chassis can be easily and cost-effectively covered. The first dataset has now been integrated into individual vehicles with the same or similar chassis.

[0060] In the first step S1, as the target state, a second dataset for each individual vehicle is generated by detecting vehicle data up to the previously specified first mileage and / or determined vehicle age. This takes into account the configuration / characteristics of each / many vehicles that generate individual vibrations / frequencies.

[0061] This ensures individual learning processes based on different configuration schemes.

[0062] If a vehicle rolls off the assembly line, the target state is learned to generate a second dataset. This learning process remains active for a certain period of time or a certain number of kilometers traveled.

[0063] To generate a second dataset that is as representative as possible, the road surface is detected, identified, and grouped into clusters by a sensor system. This sensor system can consist of sensors such as cameras and radar, and also includes information from navigation systems. Thus, for example, it can be divided into gravel roads, asphalt surfaces, and new / old road surfaces. Preferably, these can be further subdivided based on weather conditions.

[0064] If new vibrations / frequencies are detected during the learning process, they will be assigned to one of these clusters for later comparison. If no clusters representing the road surface already exist, a new cluster will be created, for example, by the processor.

[0065] As a result, an improved second dataset can be learned, thus allowing for a more accurate determination of the chassis's condition.

[0066] In the second step S2, current vehicle data, namely vibration and frequency, is detected using sensors. These sensors could be, for example, accelerometers. The current vehicle data is detected from a previously defined second mileage and / or a determined vehicle age for the individual vehicle. This vehicle age and second mileage are determined after the first mileage.

[0067] The detection of current vehicle data can be performed by the vehicle continuously or adaptively.

[0068] In the third step S3, the currently measured vehicle data is compared with the first dataset and the second dataset. To a certain extent, by comparing the current vehicle data with the first dataset, the actual condition can be determined. This comparison helps determine the wear and tear on the vehicle components.

[0069] The comparison between the current vehicle data and the second dataset corresponds to the comparison between the current state and the target (new) state. To a certain extent, the differences from the new state are determined, and the target state is formulated based on the comparison between the currently measured vehicle data and the second dataset.

[0070] The current wear of chassis components is determined based on target-actual-comparison.

[0071] Therefore, a target-actual-comparison can be achieved to obtain a judgment about the chassis condition. This comparison is made both to a new condition and, in this case, to, for example, a final condition (complete wear) or a service life condition. Thus, the result could be, for example, "80% complete wear" or "20% corresponds to a new condition".

[0072] Furthermore, different weights can be applied to the datasets. For example, in the case of newer vehicles, the comparison with the second dataset can be incorporated to a greater extent into the final result compared to the case of older vehicles.

[0073] In addition, parameters such as vehicle mileage or total vehicle age can be considered to check for reasonableness. Mileage can be used to represent an average load set, while vehicle age can be used to represent, for example, the aging of rubber bearings.

[0074] The method according to the present invention allows for a clear and reliable assessment of the chassis condition. This can improve driving safety or facilitate the timely replacement of worn parts.

[0075] Figure 3 A vehicle system 1 according to the invention is illustrated for determining the state of components of an individual chassis of an individual vehicle.

[0076] Vehicle system 1 includes a storage unit 2 for providing a dataset of vehicle data, wherein the first dataset reflects load and / or wear data of the same or similar chassis type for individual vehicles throughout their service life. Preferably, this first dataset is generated cost-effectively on a test track during long-term testing.

[0077] Storage unit 2 can be integrated into the controller, for example. The second dataset for each individual is stored in storage unit 2 via sensor system 3. The second dataset is generated by the vehicle's own sensor system 3. The second dataset for each individual is generated up to a predetermined first mileage and / or a determined vehicle age, and is determined to be the target state. Based on the corresponding road surface, the second dataset is divided into multiple clusters, which reflect the corresponding road surface.

[0078] Current vehicle data can be detected or measured using the same sensor system 3. This vehicle data can be measured continuously or adaptively. Vehicle data typically includes vibrations and their frequencies. This vehicle data can be detected, for example, using an accelerometer.

[0079] A target-to-actual comparison can be performed in comparison unit 4 based on the first and second datasets. Comparison unit 4 can be designed as a processor. The processor can also be integrated into the controller.

[0080] In addition, an output unit 5 can be provided. This output unit can be, for example, a display or a cockpit display. If the target-actual-comparison value is determined to be higher than the predetermined value, the comparison unit 4 can be designed to issue a warning message or suggest scheduling an inspection via the output unit 5.

[0081] List of reference numerals

[0082] 1. Vehicle System

[0083] 2 storage units

[0084] 3. Sensor System

[0085] 4 Comparison Unit

[0086] 5 Output Units

[0087] T1 and T2 time points

[0088] Steps S0-S3.

Claims

1. Method for determining the state of components of an individual chassis of an individual vehicle, characterized in that: - a first data set of vehicle data is provided, which at least comprises load data and / or wear data of the same or similar type of chassis of the individual vehicle over the entire service life; - an individual second data set is generated in the individual vehicle as a target state by detecting the vehicle data up to a previously specified first mileage and / or a determined age of the individual vehicle; - current measured vehicle data is detected from a previously specified second mileage and / or a determined age of the individual vehicle; - the current measured vehicle data is compared with the first data set and with the second data set in order to determine the state of components of the individual chassis of the individual vehicle.

2. Method according to claim 1, characterized in that the second data set is generated by assigning the current measured vehicle data to a road surface cluster representing a road surface, or by creating a new road surface cluster for a previously unknown road surface and assigning the current measured vehicle data to the newly created road surface cluster.

3. The method according to claim 1 or 2, characterized in that, the first data set is provided as a generally generated reference data set by testing the same or similar chassis type on a test bench in long-term tests.

4. Method according to claim 3, characterized in that the reference data set is generated separately for each of the two axles of the same or similar chassis type.

5. Method according to claim 1 or 2, characterized in that at least the amplitude of the vibrations of the vehicle movement in characteristic road surface situations is used as current measured vehicle data and as second data set.

6. Method according to the preceding claim 1 or 2, characterized in that the actual state is formulated from the comparison between the current measured vehicle data and the first data set, and the target state is formulated from the comparison between the current measured vehicle data and the second data set, and the current wear of the components of the chassis is determined from the target-actual-comparison.

7. Method according to claim 1 or 2, characterized in that the current vehicle data is continuously or adaptively measured by the vehicle.

8. Method according to claim 1 or 2, characterized in that the current measured vehicle data comprises the driven kilometers and / or the age of the vehicle.

9. Vehicle system (1) for determining the state of components of an individual chassis of an individual vehicle, comprising: - a storage unit (2) for providing a first data set of vehicle data, wherein the first data set at least comprises load data and / or wear data of the same or similar type of chassis of the individual vehicle over the entire service life; - the storage unit (2) for providing an individual second data set in the individual vehicle, wherein the individual second data set contains vehicle data measured by one or more sensors up to a previously specified first mileage and / or a determined age of the individual vehicle as a target state; - a sensor system (3) for detecting currently measured vehicle data from a previously specified second mileage and / or a determined age of the individual vehicle; - a comparison unit (4) for comparing the currently measured vehicle data with the first data set and the second data set in order to determine a state of components of the individual chassis of the individual vehicle.

10. Vehicle system (1) according to claim 9, characterized in that the sensor system (3) is designed to detect a road surface, and a processor is provided for generating the second data set by assigning the currently measured vehicle data to a road surface cluster representing the road surface, or creating a new road surface cluster for a previously unknown road surface and assigning the currently measured vehicle data to the newly created road surface cluster, and storing the second data set in the storage unit (2).

11. Vehicle system (1) according to the preceding claims 9 or 10, characterized in that the comparison unit (4) is designed to formulate an actual state from the comparison between the currently measured vehicle data and the first data set, and to formulate a target state from the comparison between the currently measured vehicle data and the second data set, and to determine a current wear of components of the chassis from a target-actual comparison.

12. Vehicle system (1) according to the preceding claims 9 or 10, characterized in that the sensor system (3) is designed to measure the current vehicle data continuously or adaptively.

13. Vehicle system (1) according to the preceding claims 9 or 10, characterized in that the currently measured vehicle data include the driven kilometers and / or the age of the vehicle.

14. Vehicle having a vehicle system (1) according to any of the preceding claims 9 to 13.

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