WO2023150854A1 - Système et procédé de génération de profil de données au moyen d'un dispositif embarqué dans un véhicule - Google Patents

Système et procédé de génération de profil de données au moyen d'un dispositif embarqué dans un véhicule Download PDF

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Publication number
WO2023150854A1
WO2023150854A1 PCT/BR2023/050051 BR2023050051W WO2023150854A1 WO 2023150854 A1 WO2023150854 A1 WO 2023150854A1 BR 2023050051 W BR2023050051 W BR 2023050051W WO 2023150854 A1 WO2023150854 A1 WO 2023150854A1
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Prior art keywords
data
vibration
vehicle
acceleration
acquisition device
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PCT/BR2023/050051
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English (en)
Portuguese (pt)
Inventor
Murilo Scherner LONGHI
Alessander Specht SCHMITZ
Leandro Luis CORSO
Joel Boaretto
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Instituto Hercílio Randon
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Publication of WO2023150854A1 publication Critical patent/WO2023150854A1/fr

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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
    • B60W40/02—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to ambient conditions
    • B60W40/06—Road conditions
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01P—MEASURING LINEAR OR ANGULAR SPEED, ACCELERATION, DECELERATION, OR SHOCK; INDICATING PRESENCE, ABSENCE, OR DIRECTION, OF MOVEMENT
    • G01P15/00—Measuring acceleration; Measuring deceleration; Measuring shock, i.e. sudden change of acceleration
    • G01P15/02—Measuring acceleration; Measuring deceleration; Measuring shock, i.e. sudden change of acceleration by making use of inertia forces using solid seismic masses
    • G01P15/08—Measuring acceleration; Measuring deceleration; Measuring shock, i.e. sudden change of acceleration by making use of inertia forces using solid seismic masses with conversion into electric or magnetic values

Definitions

  • the present invention describes a system and method for generating a data profile, from the measurement of acceleration data obtained by means of a data acquisition device embedded in a vehicle.
  • the present invention lies in the fields of electrical engineering, data management and the automotive and road implement industry.
  • Document US2017167088 shows a system to determine the quality of a road using devices embedded in vehicles of heavy load used in mining (haul trucks), where sensors are installed on the vehicle's suspension bar and transmitted in real time to a central.
  • US2017167088 suggests a mathematical processing with the identification of the local maximums and minimums of the collected signal and the verification if they are within an acceptable threshold.
  • this system collects data from the vehicle suspension, which may interfere or require more processing to reach the result.
  • US2017167088 is still not able to generate a map of pavement severity degrees, nor does it consider adverse vibrations that the vehicle may suffer that are not related to the quality of the pavement.
  • US2019056224 describes a method to monitor the conditions of a road through an accelerometer embedded in a car.
  • This solution includes sensors installed on the vehicle's chassis, which is subject to interference not caused by road quality.
  • US2019056224 generates a parameter called CoV, which is the ratio between the standard deviation and the mean of the data read and, with this parameter, makes a relationship with an index called HRI, which is a standardized international irregularity index.
  • HRI is a standardized international irregularity index.
  • US2019056224 does not mention the generation of a matrix with the degrees of severity of the path, since it uses the respective HRI index, making it impossible for the user to have access to the history and management data of this application.
  • this solution does not mention the consideration of adverse vibrations that the vehicle may suffer, which are not related to the quality of the pavement, which may impact the result obtained.
  • Document GB2525839 shows a system for collecting data on road irregularities, from equipment installed in the suspension of four-wheel vehicles. This system is focused on identifying holes and bumps, which refer to major impacts. suffered by the vehicle. To measure the severity, GB2525839 computes a sum of four integrals that are related to the data measured on each of the four wheels of the vehicle. In addition to having a great need for on-board processing, GB2525839 does not provide a history of road severities, since it is concerned with superimposing the collected data on top of a map of the region, further increasing the computational complexity of the system. In addition, this solution does not consider adverse vibrations that the vehicle may suffer, which are not related to the quality of the pavement, which may impact the result obtained.
  • Document BR102015020767 shows a system for diagnosing the quality of a road by reading signals provided by various sensors embedded in vehicles.
  • the system uses signals from both dynamic (weight, gear selection, brake force, vehicle speed, acceleration) and static (chassis and suspension properties, wheel configuration and vehicle type) characteristics of the vehicle to be able to estimate road conditions. via.
  • This system is extremely complex, requiring the vehicle to have a plurality of intelligent systems.
  • the state of the art lacks some means to have a greater predictability of the mechanical wear which the vehicle is subject to suffer when traveling a certain route and that allows the driver or company to have a greater understanding of the costs that may be linked to the route that the vehicle travels.
  • the state of the art lacks a system to profile data obtained by a vehicle, in such a way that these data are pre-processed and treated to mitigate or cancel adverse conditions, which the vehicle may suffer, which are not related to the given to be profiled.
  • the present invention solves the problems of the state of the art from a data profiling of at least one object, entity, event or sequence of actions for the use and formation of at least one database that, it also stores the generated profiles, in which the profiled data are obtained through magnitudes read in at least one vehicle.
  • the present invention performs the correlation of the data measured in the vehicle with pre-defined parameters related to the object, entity, event or sequence of actions to carry out the profiling.
  • the present invention uses a data acquisition device (100) embedded in a vehicle, where this acquisition device (100) has a mechanical vibration measurement sensor, being able to collect vehicle vibration data on at least one axis. From the collected data, the acquisition device (100) is able to generate a severity map and identify impacts that the vehicle has been subjected to. For the identification of impacts, the acquisition device (100) has a filtering of the vibration data, in order to identify the vibration data that are in a predetermined condition, thus avoiding the collection and/or processing of data not relating to impact.
  • the acquisition device (100) communicates with at least one data platform comprising at least one database, being able to generate data profiles, based on the collected data and previously defined instructions. Communication between the data acquisition and processing device (100) and the data platform takes place through a network, where the data acquisition and processing device (100) accesses this network through: a dedicated mobile connection; a local connection to a local network installed in a fixed location; or through a network formed by mobile or fixed repeaters distributed throughout a space, not limited to the type of communication carried out.
  • the invention presents a system for generating a data profile, through a data acquisition device (100) embedded in a vehicle, where the acquisition device (100) is positioned at least a region of the vehicle, in which the acquisition device (100) collects at least one vehicle vibration data, in at least one direction, by means of a mechanical vibration measuring element; wherein: the acquisition device (100) comprises a processing unit being provided with a data processing module (103), which operates based on predefined configurations (106).
  • the invention presents a method for generating a data profile, through a data acquisition device (100) embedded in a vehicle, comprising the steps: measurement (101) of at least one data of vehicle vibration by means of the acquisition device (100) positioned in at least one region of the vehicle, the vibration data being measured in at least one direction by means of a mechanical vibration measuring element; and processing the measured vibration data, by means of a processing unit comprised in the acquisition device (100), in which the processing unit carries out a data treatment (103) from predefined configurations (106).
  • the invention presents a road implement comprising at least one data acquisition device (100) positioned in at least one external region of the road implement, where the acquisition device (100) comprises at least one element measuring mechanical vibration in at least three directions, said mechanical vibration measuring element positioned in a region closest to the ground, wherein the acquisition device (100) comprises a processing unit being provided with a data processing module (103), which operates based on predefined configurations (106). Said data processing module (103) being configured to classify acceleration data for a severity map and identify impacts suffered by the road implement.
  • the invention presents a system for assessing pavement quality comprising a data acquisition device (100) embedded in a road implement, said acquisition device (100) positioned in at least one external region of the road implement, where the acquisition device (100) comprises at least one mechanical vibration measuring element in at least three directions, said mechanical vibration measuring element positioned in a region closer to the ground, in which the acquisition device ( 100) comprises a processing unit being provided with a data processing module (103), which operates based on predefined configurations (106). Said data processing module (103) being configured to classify acceleration data for a severity map and identify impacts suffered by the road implement.
  • Figure 1 shows a joint flowchart representing the communication between the data acquisition device (100) and the data platform (200), configured by a testing and analysis station, which parameterize the configuration of the algorithm used in the acquisition device (100).
  • Figure 2 shows a block diagram in detail of an example of the data acquisition device (100), showing its respective components.
  • Figure 3 shows a block diagram of Figure 2 showing in detail the components of the data processing module (103).
  • Figure 4 shows a graph of the signal of the vibration signal after passing through data filters.
  • Figure 5 shows a diagram exemplifying the data platform (200), comprising a data receiver, an operation correlator (210) comprising a first repository (211) and a technique correlator (220) comprising a second repository (221), which perform the correlation and classification of data received to generate a data profile.
  • Figure 6 shows an exemplary severity matrix using arbitrary and demonstrative values on a generation of a severity profile of a road.
  • Figure 7 shows a histogram graph of collected acceleration data, so that the data were sorted and grouped into the respective acceleration ranges. On the ordinate axis, the acceleration ranges (in mG) and on the abscissa, the number of samples collected.
  • Figure 8 shows an exemplified flowchart for the data filter of the acceleration ranges collected for refinement and generation of graphs and severity map.
  • Figure 9 shows two histograms comparing the amplitudes of the originally collected acceleration bands versus the refined bands from the data filter. These data were obtained in tests on a high-speed track.
  • Figure 10 shows another representation of the refined acceleration ranges collected on a high-speed track, being represented in a pie chart.
  • Figure 11 shows two histograms comparing the amplitudes of the originally collected acceleration bands versus the refined bands from the data filter. These data were obtained in tests on an off-road track.
  • Figure 12 shows another representation of the refined acceleration ranges collected on an off-road track, being represented in a pie chart.
  • Figure 13 shows two histograms comparing the amplitudes of the originally collected acceleration bands versus the refined bands from the data filter. These data were obtained in tests on special tracks.
  • Figure 14 shows another representation of the refined acceleration ranges collected on special tracks, being represented in a pie chart.
  • Figure 15 shows a graph with acceleration data collected on an accelerometer axis.
  • Figure 16 shows a graph referring to the result of calculating the moving standard deviation of data from the graph in Figure 15, in a predefined time window.
  • Figure 17 shows the graph of Figure 16 with an exponential moving average plotted, which is calculated on the moving standard deviation data.
  • Figure 18 shows an identification of impacts on the data in the graph in Figure 17.
  • Figure 19 shows a reference axis adopted for the calculations performed in the processing.
  • Figure 20 shows a relationship of the acceleration amplitude data measured in two different axes, the Y axis and the Z axis.
  • Figure 21 shows a result of identifying impacts from processing and filtering considering the influence of the Y and Z axes.
  • Figure 22 shows a comparison between the graph in figure 18 and the graph in figure 21.
  • the invention presents a system for generating a data profile, through a data acquisition device (100) embedded in a vehicle, where the acquisition device (100) is positioned at least a region of the vehicle, in which the acquisition device (100) collects at least one vehicle vibration data, in at least one direction, by means of a mechanical vibration measuring element; wherein: the acquisition device (100) comprises a processing unit being provided with a data processing module (103), which operates based on predefined configurations (106).
  • the generation of a data profile refers to the relationship of information collected in a vehicle with characteristics associated with an entity, object, event or sequence of actions, which they help in the characterization/modeling of these entities, objects, events or sequence of actions to influence decision-making.
  • data profiling refers to characterizing a quality or severity index of a pavement or roadway.
  • “road”, “pavement”, “road” or any synonym have the same purpose, that is, any means on which a vehicle can travel.
  • the generation of data profile refers to the characterization of the health of the vehicle, as it suffers impacts resulting from the severities of the road.
  • the data acquisition and processing device (100) is a hardware embedded in the vehicle, which performs the acquisition and pre-processing functions of the acquired data, subsequently performing the temporal analysis of the information obtained and organizing its transmission to a receiver qualified through a means of communication.
  • the acquisition device (100) which operates independently of actions and commands from the tractor vehicle, comprises a mechanical vibration measuring element, which measures vibration in at least one direction and is used to obtain samples for vibrations in mechanical apparatus of a vehicle.
  • the mechanical vibration measuring element is capable of taking measurements in at least one direction.
  • the present invention can count on a plurality of data acquisition devices (100), these being reading the same data in different positions of the vehicle or reading different types of information from the vehicle.
  • an additional acquisition device (100) is installed on the vehicle for reading loading data, making it possible to measure the amount of cargo the vehicle is carrying.
  • the mechanical vibration measurement element is a three-axis accelerometer, being able to measure and transmit the information of the accelerations in the three orthogonal axes that define space through signals obtained from changing the state of a system spring mass.
  • the vibration measuring element is a microelectromechanical system. In this case, vibration data is read as acceleration data.
  • the vibration measuring element is positioned in a region that has the greatest mechanical oscillation of the vehicle.
  • the mechanical vibration measuring element is positioned on the vehicle in a region closer to the ground in order to make it possible to capture mechanical vibrations/oscillations caused by soil deformations.
  • the vibration measuring element is located at the end of the vehicle axle. In another embodiment, the measurement element is located on a cross member of the vehicle chassis.
  • the same location on the vehicle is used to host a plurality of vibration measurement elements, which increases the redundancy of the vibration measurement at the position of interest.
  • a plurality of locations on the vehicle encompass a plurality of vibration measurement elements for obtaining data in one or more regions of the vehicle.
  • the data processing module (103) of the processing unit comprises at least one classifier (1031) that generates acceleration data to prepare a severity map.
  • Said classifier (1031) classifies the vibration data into predefined acceleration ranges and based on at least one time parameter.
  • the classifier (1031) is responsible for reading the collected vibration data and distributing it in ranges of value amplitudes, being, for example, acceleration amplitudes.
  • the classifier (1031) is able to indicate how long the vibration/acceleration data remained within a given acceleration range.
  • a histogram is generated, which separates the acceleration ranges and indicates how long the data remained in the respective ranges, thus generating a severity map.
  • map is used in the sense of mapping, that is, it is possible to map the severity of a given road by verifying the distributions of acceleration bands and the time at which the data remained in these ranges, which is an indication of whether the path is more or less severe.
  • Said acceleration ranges are predefined to reduce the processing complexity of the hardware embedded in the acquisition device (100). Based on prior knowledge from experiments, a mathematical processing is performed, for example, on the data platform (200), and said acceleration ranges are defined. With this, the classifier (1031) is configured to classify the vibration data within these respective ranges. Still, with this system configuration, it is possible that there is a recalibration of the data processing module (103) and the respective classifier (1031) if necessary.
  • the data processing module (103) comprises at least one identifier (1032) for generating pulse identification data.
  • Said impact identifier is capable of identifying vibration data that are in a predetermined condition. With this, the impact identifier (1032) makes it possible to identify, among the measured vibration data, signals that may indicate an impulse or impact that the vehicle has been subjected to during the journey.
  • the aforementioned impact suffered by the vehicle is due to a hole in the road on which the vehicle travels, and which can cause damage to the vehicle, for example, structural damage or damage to its mechanical components.
  • This type of damage can, to a certain extent, shorten the useful life of the product.
  • cargo vehicles such damage can be transferred to the transported cargo.
  • Said predetermined condition is previously defined and configured in the onboard hardware of the acquisition device (100).
  • the predetermined condition is a decision parameter that allows the identification of an impact, in such a way that the impact identifier (1032) generates an identification data of impulses/impacts when the vibration data is in said predetermined condition.
  • the predetermined condition is at least one threshold defined by a statistical measure related to the vibration data measured in at least one direction.
  • the impact identifier (1032) calculates a function of the vibration data over a time window and defines a threshold. By checking, among the data set of the time window, which vibration data are above this threshold, the impact identifier (1032) generates an impulse data.
  • Said statistical measure/mathematical function can be the mean, a moving average of the data, statistical mode, etc. In this way, the impact identifier (1032) identifies the vibration data that are above said threshold.
  • the impact identifier (1032) has an impact filter that generates proportionalized vibration data by relating vibration data measured in at least two different directions, for example, data from two different accelerometer axes. Based on this, the impact filter considers the influence of vibration in two different directions, being able to identify whether the vibration movements read are related to an anomaly on the road or if they are related to an adverse condition that the vehicle has suffered. For example, the impact filter considers vertical data (up-down movement) and horizontal data (front-back movement) of the vehicle. From this, the impact filter proportionalizes the data based on weights and, thus, is able to identify whether the impact read refers to any adverse condition that the vehicle has suffered.
  • the impact filter is capable of disregarding impacts read referring to the vehicle's start (movement that takes it out of inertia).
  • the impact filter identifies, among the data of the two axes, which one has the greatest amplitude at the same time/sample, and thus adjusts the greatest weight (weighting) to the axis that has the greatest amplitude.
  • the impact filter proportionalizes the data by adjusting the weights of only one of the axes, where the impact filter identifies whether data measured horizontally (front-back) have greater amplitude than data measured vertically (top-bottom). and, if so, increases weight for the horizontal data (front-back).
  • the impact filter reduces the weight of the horizontal data (front-back), so that the vertical data would have a greater influence on the generation of proportionalized data. Additionally, the impact filter can operate and proportionalize the data considering the three directions of the accelerometer, also including the lateral horizontal data of vibrations that the vehicle can suffer.
  • the impact identifier (1032) defines the predetermined condition as being the standard deviation of the proportionalized vibration data that is above a threshold defined by a moving average of the standard deviation of the proportionalized vibration data.
  • the impact identifier (1032) calculates the standard deviation of the proportionalized data within a predefined time window and, in the same time window, calculates an exponential moving average of the calculated standard deviation.
  • the resulting exponential moving average serves as a threshold, such that when the standard deviation of the proportionalized data is above the exponential moving average, the bump identifier considers the read data to be a bump and generates bump ID data.
  • the classifier (1031) and the impact identifier (1032) are used in combination, their operations being as defined in previous embodiments.
  • the system has a data platform (200), communicating with the acquisition device (100).
  • the data platform (200) is provided with a database or repositories (211, 221), which receives the data from the acquisition device (100) and performs the procedure for generating a data profile.
  • the data platform (200) comprises: a receiver of the data emitted by the acquisition device (100); an operation correlator (210), which performs the correlation between the data obtained and a plurality of predefined parameters present in a first repository (211); and a technique correlator (220) that classifies the correlated data, according to an interpretation of the acceleration data for severity map and the pulse identification data, for generating at least one data profile parameter based on the parameters pre-defined and from the classification of correlated data.
  • the data interpretation parameters are previously stored in a second repository (221).
  • the first and second repositories (211 and 221) can be implemented as separate instances within the same database.
  • the data platform (200) correlates the received data with at least one predefined parameter and, with that, generates a data profile.
  • the measured data of the vehicle refer to mechanical vibrations that the vehicle is subjected to when traveling along a certain road
  • the predefined parameter is the predefined route that the vehicle was intended to travel.
  • the operation correlator (210) crosses the mechanical vibration data with the predefined route, in order to generate the mechanical vibration profile of this route.
  • the correlator crosses the mechanical vibration data with the predefined route and with the weight of the load that the vehicle is transporting, in order to generate the profile of this route considering the transported load, in order to verify and profile possible influences of this route on the transported cargo.
  • the operation correlator (210) upon receiving the acceleration data for the severity map and the pulse identification data provided by the acquisition device (100), crosses this data with the route which the vehicle traveled and with the load it was subjected to. With that, the technique correlator (220) classifies these correlated data for the generation of a severity map crossing, for example, the acceleration bands with the detected impulses. This classification results in a matrix capable of indicating the severity index of the pathway, based on predefined classifiers.
  • the data platform (200) is implemented on a local server. In another embodiment, the data platform (200) is implemented on a cloud server.
  • the data platform (200) issues a report to be verified by analysts and technicians responsible for the system. Based on the issued report, analysts and technicians can verify the need to recalibrate or improve some parameter or function of the acquisition device (100) embedded in the vehicle. Thus, during communication with the acquisition device (100), the data platform (200) can send or update the predefined configurations (106), based on inputs from analyzers and technicians and/or from automated systems.
  • the invention presents a method for generating a data profile, through a data acquisition device (100) embedded in a vehicle, comprising the steps: measurement (101) of at least one data of vehicle vibration by means of the acquisition device (100) positioned in at least one region of the vehicle, the vibration data being measured in at least one direction by means of a mechanical vibration measuring element; and processing the measured vibration data, by means of a processing unit comprised in the acquisition device (100), in which the processing unit carries out a data treatment (103) from predefined configurations (106).
  • the data acquisition and processing device (100) measures the data relating to the vibration of a region of a vehicle through a vibration measurement element, and sends the data obtained for the processing unit.
  • the vibration measuring element is an accelerometer capable of measuring the accelerations to which the vehicle is subject in at least one direction from a certain location of the vehicle.
  • the processing unit through the data processing step (103) generates acceleration data for the severity map, where the vibration data is classified (1031) into pre-defined acceleration ranges defined and correlated with at least one time parameter.
  • the collected vibration data are distributed in ranges of value amplitudes, being, for example, acceleration amplitudes.
  • the classification step (1031) it is possible to verify the number of samples, and consequently the time in which the data remained in the respective pre-defined acceleration ranges.
  • an impact identification step (1032) is performed to generate impulse identification data.
  • the processing unit identifies the vibration data that are in a predetermined condition, and this predetermined condition is the decision parameter for identifying impacts.
  • an impact filtering is performed, which generates proportionalized vibration data by relating vibration data in at least two different directions. Based on this, the impact filter considers the influence of vibration in two different directions, being able to identify whether the vibration movements read are related to an anomaly on the road or if they are related to an adverse condition that the vehicle has suffered. For example, the impact filter considers vertical data (up-down movement) and horizontal data (front-back movement) of the vehicle. From this, the impact filter proportionalizes the data based on weights and, thus, is able to identify whether the impact read refers to any adverse condition that the vehicle has suffered.
  • the impact identifier (1032) defines the predetermined condition as being the standard deviation of the proportionalized vibration data that is above a threshold defined by a moving average of the standard deviation of the proportionalized vibration data.
  • the data set (104) with the vibration data for the severity map and the impulse identification data is transmitted (105) to at least one data platform (200).
  • the selected data is sent to the data platform (200), which can be in real time or with the data being sent after the vehicle connects to a fixed local network specific.
  • data visualization is performed in the vehicle itself, so that it is possible to gain access to information from inside the vehicle (onboard).
  • the data platform (200) receives the data selected by the acquisition device (100) and performs the correlation of the selected data with a first existing repository (211) connected to the data platform (200).
  • the correlation performed by the operation correlator (210) associates the data obtained with predefined parameters.
  • the data platform (200) performs the correlation of the data received from the data acquisition and processing device (100) with the existing data in the database that refer to the predefined parameters, which are at least one or a combination of: vehicle identification data; vehicle driver data; load data and the nature of the vehicle's load; stipulated and pre-defined route data; relief data and/or geographical position; climate data.
  • the predefined parameters are at least one or a combination of: vehicle identification data; vehicle driver data; load data and the nature of the vehicle's load; stipulated and pre-defined route data; relief data and/or geographical position; climate data.
  • the predefined parameters are geographic data of the relief and the location where the data acquisition and processing device is located (100). In one embodiment, the predefined parameters are vehicle information such as model and understood load.
  • the data correlated with the predefined parameters are sent to a technique correlator (220), which comprises a second repository (221), and has the function of classifying the correlated data, in order to generate a report and a data profile, which relates pre-defined parameters with vibration data for severity map and impulse identification data.
  • the data profile is a mapping between the geographic location of a road and its irregularity, associated with a severity parameter regarding the possible damage caused to vehicles that transit in the region.
  • the data profile generates a severity matrix, which indicates a correlation between the acceleration ranges and the impacts detected, making it possible to verify the degree of severity regarding the damage that the vehicle may suffer when traveling on that road.
  • the method is applied to at least one sequence of data taken from at least one vibration measurement element obtained while driving the vehicle.
  • the invention presents a road implement comprising at least one data acquisition device (100) positioned in at least one external region of the road implement, where the acquisition device (100) comprises at least one element measuring mechanical vibration in at least three directions, said mechanical vibration measuring element positioned in a region closest to the ground, wherein the acquisition device (100) comprises a processing unit being provided with a data processing module (103), which operates based on predefined settings (106). Said data processing module (103) being configured to classify acceleration data for a severity map and identify impacts suffered by the road implement.
  • the implement features an accelerometer with three orthogonal axes as a vibration measurement element associated with the shaft tip for acquiring vibration data in the directions of interest.
  • the accelerometer is positioned on a lower cross member of the vehicle.
  • the pre-programmed hardware sends the selected data to a transmitter (105) located in the vehicle to establish communication with the data platform (200).
  • the road implement can be provided with an additional acquisition device (100) capable of reading other vehicle information, in addition to the mechanical vibrations read.
  • the road implement comprises an acquisition device (100) capable of reading data and information from the current vehicle charging.
  • this acquisition device (100) has at least one extensometer transducer capable of measuring data on the amount of cargo transported.
  • the communication established between the acquisition device (100) and the data platform (200) is performed through the implementation of a mobile network using known protocols, such as 3G, 4G or 5G.
  • the communication established between the acquisition device (100) and the data platform (200) is performed through a local network, for example, LAN network, which is installed at a fixed location, for for example, an establishment, so that, when the vehicle arrives at this establishment, the communication between the acquisition device (100) and the data platform (200) occurs automatically by known secure means.
  • the measured data is securely stored in the acquisition device (100).
  • the communication established between the acquisition device (100) and the data platform (200) is performed through a local network, for example, LAN network, which is installed at a fixed location, for example, an establishment, so that an operator is responsible for collecting the acquisition device (100) and connecting it to the establishment's local internal network.
  • a local network for example, LAN network
  • the measured data are safely stored in the acquisition device (100) and are sent, through wired or wireless networks, to the data platform (200) present in the path.
  • the communication established between the acquisition device (100) and the data platform (200) is performed through the implementation of an internal network, formed by repeaters found in other data acquisition devices (100) located in other active or passive vehicles.
  • the communication established between the acquisition device (100) and the data platform (200) is carried out through the implementation of an internal network, formed by fixed signal repeaters along paths and roads.
  • the network is formed by antennas distributed along routes and paths that obtain the information sent by the acquisition device (100).
  • the antennas amplify the signal and send it sequentially to the next antenna towards the data platform (200), thus transmitting the information through repetitions of the signal along the path of the antennas.
  • the communication established between the acquisition device (100) and the data platform (200) is performed through the implementation of a satellite network.
  • the communication established between the acquisition device (100) and the data platform (200) is performed in real time through one or more methods mentioned above.
  • the communication established between the acquisition device (100) and the data platform (200) is performed by storing the information in the acquisition device itself (100) selected by the processing unit and sending the information through communication centers present in pre-defined locations.
  • the invention presents a system for assessing pavement quality comprising a data acquisition device (100) embedded in a road implement, said acquisition device (100) positioned in at least one external region of the road implement, where the acquisition device (100) comprises at least one mechanical vibration measuring element in at least three directions, said mechanical vibration measuring element positioned in a region closer to the ground, in which the acquisition device ( 100) comprises a processing unit being provided with a data processing module (103), which operates based on pre-configured configurations. defined (106). Said data processing module (103) being configured to classify acceleration data for a severity map and identify impacts suffered by the road implement.
  • the vibration measurement element in one embodiment, is a three-axis accelerometer capable of performing measurements of mechanical oscillations in at least one region of a vehicle. In one embodiment, this vibration measurement element is positioned on the axle tip or on the lower cross member of the vehicle, as it configures positions susceptible to comparatively greater vibrations in relation to the rest of the vehicle. In one embodiment, a plurality of vibration measurement elements are inserted at a plurality of locations on the vehicle.
  • the generation of the data profile is associated with the quality of a path, referring to its regularity and/or difficulty for the traffic of a vehicle, which is also correlated with the geographic position of the vehicle for carrying out of a geographic mapping of road quality.
  • the generation of the data profile is associated with the quality of the monitored mechanical apparatus, providing information regarding its validity and strengthening estimates of the apparatus' lifetime through the correlation of the data obtained with the existing database on the platform of data (200).
  • the present invention in this example, was applied to a road implement, in such a way that an embedded hardware was installed on the road implement itself, operating as an acquisition device (100) of data, where an accelerometer with three orthogonal axes was installed on the lower beam of the road implement, continuously measuring the vibrations suffered in the axis, caused by the variation/irregularity of the track's soil. From the processing of these data, the system, as schematized in figure 1, operates to generate a road severity profile.
  • the data acquisition device (100) is an embedded hardware that has a processor to perform the collection and mathematical processing on the data collected by the accelerometer.
  • the device (100) can also have a microcontroller for simplified functions.
  • the device (100) preferably receives power from the electrical circuit of the road implement itself, which is supplied by the tractor vehicle.
  • device 100 may rely on a vehicle-independent power source, such as a battery.
  • the acquisition device (100) was designed to perform less complex operations, to reduce the need for embedded processing, energy consumption, network consumption, storage, etc.
  • a simplified schematic of the device (100) can be seen in Figure 2.
  • the device (100) basically has a mechanical vibration measurement component (101), a data cleaning module (102) , a data processing module (103), which generates an output data set (104), and a transmission module (105) which transmits the output data set (104) to the data platform (200).
  • the output data set (104) is formed by acceleration data for the severity map, which is represented with a histogram containing acceleration bands to which the data remained during the route, and impact data, referring to the impacts that the road implement has suffered during the journey.
  • the acceleration data for the severity map and the impact data are generated, respectively, by the classifier (1031) and by the impact identifier (1032), which are modules/steps that occur in the data processing module/stage (103), as can be seen in figure 3.
  • the acceleration data undergo a pre-treatment in the cleaning module (102) to reduce the processing complexity in the treatment module (103).
  • the detected signal passes through a moving average filter and a peak detector, in order to allow a representation with only a part of the original signal, without compromising the accuracy too much.
  • the clean signal can be seen in Figure 4, where the data read is the peak-to-peak signal amplitude. From this cleaning (102), the data is directed to the treatment module (103).
  • the processed data (104) are sent to the data platform (200), shown in figure 5.
  • the transmission (105) of the data to the data platform (200) takes place through from a local LAN of a certain location (e.g. garage).
  • the data exchange only occurs when the vehicle with the road implement is stopped in place and is connected to this known network.
  • the data are stored in an internal memory of the acquisition device (100) until the connection to the network is established.
  • the data platform (200) compiles the received data and directs them to the operation correlator (210), which performs the correlation between the obtained data and a plurality of predefined parameters present in a first repository (211) .
  • the predefined parameters can be one or more of: vehicle identification data; vehicle model data; load data and the nature of the vehicle's load; stipulated and pre-defined route data; relief data and/or geographical position; weather data.
  • the operation correlator (210) can cross the received data with one of these parameters to obtain it as a reference in the generation of the profile. In this example, road implement model data, route data, and load data that the implement was carrying were considered.
  • the operation correlator (210) considers these predefined parameters since these parameters can influence the collected data and, consequently, the decision making.
  • the data correlated with the predefined parameters are sent to the technique correlator (220), which communicates with a second repository (221), and has the function of classifying the correlated data, in order to generate a report and a data profile, which relates the pre-defined parameters with the vibration data for the severity map and impulse identification data.
  • the second repository (221) relies on experimentally obtained insights to assist in classifying the correlated data.
  • This classification in this example, deals with the degree of severity of the pavement, where it can be classified as: “Light”, “Intermediate”, “Severe” or “Very Severe”.
  • the pathway severity profile is generated by the technique correlator (220).
  • An example of a profile can be seen in figure 6, which shows a severity matrix for the pavement over which the vehicle traveled.
  • This analysis provides a correlation between the amount of acceleration spikes (bumps) that were observed in the data and the acceleration ranges (G) in which the acceleration spikes fall.
  • this classification was considered for a given model of road implement and a given load carried by the implement. In case of changing the model, load or other parameters, this classification can also be changed. Additionally, as experiments are carried out, these classifications can be improved.
  • the present invention also enables the generation of a profile on one or more mechanical components of the vehicle, being, in this case - without limitation, a road implement.
  • the acquisition device (100) performs the collection of mechanical vibration data, which the implement is subjected to due to track variations/irregularities; carries out the necessary treatments (103); and transmits (105) the data to the data platform (200).
  • the data platform (200) compiles the classified acceleration data and impact data, directing them to the operation correlator (210), which performs the correlation between the data obtained and a plurality of predefined parameters present in the first repository (211).
  • the predefined parameters can be one or more of: vehicle identification data; vehicle model data; load data and the nature of the vehicle's load; stipulated and pre-defined route data; relief data and/or geographical position; climate data.
  • the second repository (221 ) has insights obtained experimentally regarding the state of health of the vehicle/road implement, for the classification of correlated data. These insights refer to historical data collected on the natural wear and tear of a certain vehicle component as it is used, such as the suspension set, shock absorbers, etc. Based on this, the technique correlator (220) crosses the correlated data with the historical data of said component.
  • the technique correlator (220) crosses the vibration and pulse identification data with the historical data of the suspension set. Considering the pre-defined parameters of model and implement identification, transported load and traveled path, the technique correlator (220) issues a report about the health status profile of the suspension set. In view of this, it is possible to verify whether the suspension set needs immediate maintenance, requires attention or is in good condition - other classifications can be created as required. Said profile is made available to the driver, fleet manager or carrier to report the condition of the component.
  • the transported cargo data are previously stored in the repository (211) or are collected directly in the vehicle through an on-board transducer.
  • the transducer is an extensometer/strain gauge, part of a second acquisition device (100), which reads the amount of load that the road implement has transported or is transporting.
  • These data are stored in a memory embedded in the vehicle, for later transmission to the data platform (200), or transmitted in real time through a network connection with the data platform (200).
  • the classifier (1031) is responsible for reading the collected acceleration data and distributing it in predefined acceleration amplitude ranges. With that, as the acceleration data are being read, the classifier (1031) distributes them in the respective ranges of acceleration amplitudes and, thus, the classifier (1031) is able to indicate the number of samples that each acceleration range contains during a period or route traveled by the vehicle. In this way, the classifier (1031) is able to generate data for the construction of a severity map, since it calculates how long the vehicle was subjected to a given acceleration/vibration.
  • An example of a severity map is through histograms, as shown in figure 7.
  • the predefined acceleration ranges MG
  • the number of samples in the abscissas With this type of information, it is also possible to identify the type of terrain over which the vehicle traveled, given that accelerations vary based on the type of pavement.
  • the respective histogram can be viewed both on the data platform (200) and on a display on the road implement or tractor vehicle.
  • the classification in predefined ranges reduces the processing complexity that the acquisition device (100) needs to perform to generate the severity map, since it does not complex mathematical processing of data classification becomes necessary.
  • These acceleration ranges can be defined experimentally and improved as the data is analyzed. In tests carried out, a sufficient amount of acceleration ranges was defined, which balances data accuracy with low processing complexity.
  • data breaks Jenks Natural Breaks Classification Method
  • data breaks is a data grouping method designed to determine the best organization of values into different classes. This method seeks to reduce the variance within classes and maximize the variance between classes. This process was performed with all the collection logs during the testing period and, after defining the best tracks obtained experimentally, the setup (106) of the acquisition device processing unit (100) was performed.
  • Figure 9 shows a comparison between the acceleration ranges of the original data and the refined data after the data breaks, in a test performed with a road implement on a high-speed track.
  • Figure 10 shows another way of visualizing the refined acceleration ranges, obtained in a test on the high-speed track, represented in a pie chart.
  • Figure 11 shows another comparison between the acceleration ranges of the original and refined data after the data breaks, in test carried out with a road implement on an off-road track.
  • Figure 12 shows another way of visualizing the refined acceleration ranges, obtained in tests on the off-road track, represented in a pie chart.
  • Figure 13 shows another comparison between the acceleration ranges of the original data and those refined after the data breaks, in a test carried out with a road implement on special tracks, which have purposely placed anomalies, such as bumps, ribs, stones of river etc.
  • Figure 14 shows another way of visualizing the acceleration ranges, obtained in tests on special tracks, represented in a pie chart.
  • the treatment module (103) has the impact identifier (1032), which is responsible for identifying acceleration peaks that are related to impacts suffered by the road implement during the journey.
  • the impact identifier is able to identify which data or set of data may represent a true impact suffered by the road implement.
  • the impact identifier (1032) checks whether the read acceleration data is in a predetermined condition.
  • the acceleration data is subjected to pre-processing.
  • the moving standard deviation of the acceleration data is calculated considering a defined time window. This moving standard deviation represents the module of the difference of the points in relation to the mean.
  • a threshold was defined that is relative to the exponential moving average of the standard deviation curve in figure 16. This threshold can be seen in figure 17.
  • the impact identifier (1032) considers that when the standard deviation curve exceeds the exponential moving average, an impact has occurred.
  • This configuration can be seen in Figure 18, where the impulses represent the moments when the standard deviation exceeded the curve of the exponential moving average.
  • the impact identifier (1032) considered a series of impulses, but they were not due to real impacts suffered by the road implement, but to the movement of pulling (moving out of inertia).
  • an impact filter or jerk filter
  • This impact filter considers the influence of acceleration in two different directions, being able to identify whether the vibration movements read are related to an anomaly on the track or if they are related to some adverse condition that the vehicle has suffered (e.g. Sprint).
  • the impact considers acceleration data read in both the Y-axis and the Z-axis. This proportionalizes the acceleration data by creating a weight between the Y-axis and Z-axis acceleration data, generating proportionalized acceleration data.
  • the impact identifier (1032) was configured to generate a proportionalization between the data of the two axes, in order to increase the weight of the Z axis and decrease the weight of the Y axis, when the normalized amplitude of Z is greater than the normalized amplitude of the Y axis; and, likewise, decreasing the Z-axis weight and increasing the Y-axis weight when the Z-normalized amplitude is less than the Y-axis normalized amplitude.
  • the proportionalized data can be, in a non-limiting manner, represented per:
  • V 1 prop n u V t norm + U h ⁇ 7 J norm

Abstract

La présente invention concerne un système et un procédé de génération d'un profil de données à partir de la corrélation entre les paramètres pré-définis avec les données obtenues à l'aide de mesures réalisées dans le véhicule. Plus particulièrement, la présente invention concerne un dispositif d'acquisition de données qui effectue la mesure des données du véhicule et envoie ces données à une plateforme de données, qui comprenant un dispositif de mise en corrélation d'opération et une banque de données contenant des informations sur les paramètres pré-définis. Sur cette base, le dispositif de mise en corrélation met en corrélation les paramètres pré-définis avec les données mesurées en vue de la construction du profil de données. La présente invention trouve une application dans les domaines de l'ingénierie électrique, de la gestion de données et de l'industrie automobile et des véhicules routiers.
PCT/BR2023/050051 2022-02-11 2023-02-13 Système et procédé de génération de profil de données au moyen d'un dispositif embarqué dans un véhicule WO2023150854A1 (fr)

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US9165477B2 (en) * 2013-12-06 2015-10-20 Vehicle Data Science Corporation Systems and methods for building road models, driver models, and vehicle models and making predictions therefrom
US20170167088A1 (en) * 2015-12-15 2017-06-15 Freeport-Mcmoran Inc. Systems and methods of determining road quality
WO2018068048A1 (fr) * 2016-10-07 2018-04-12 Phillips Connect Technologies Llc Système de remorque intelligente
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BR102018067958A2 (pt) * 2018-09-05 2020-03-17 Engebras Tecnologia Ltda. Sistema de monitoramento de tráfego de veículos automotores em vias públicas e método para gerar e transmitir um registro metrológico relativo ao tráfego de veículos automotores
US20200408561A1 (en) * 2019-06-28 2020-12-31 IFP Energies Nouvelles Method of characterizing the condition of a road

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Publication number Priority date Publication date Assignee Title
US9165477B2 (en) * 2013-12-06 2015-10-20 Vehicle Data Science Corporation Systems and methods for building road models, driver models, and vehicle models and making predictions therefrom
US20170167088A1 (en) * 2015-12-15 2017-06-15 Freeport-Mcmoran Inc. Systems and methods of determining road quality
WO2018068048A1 (fr) * 2016-10-07 2018-04-12 Phillips Connect Technologies Llc Système de remorque intelligente
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GB2573738A (en) * 2018-03-27 2019-11-20 Points Protector Ltd Driving monitoring
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