Method for diagnosing the operation of an active airflow control system

Abstract

The present invention relates to an active airflow regulating system and a method for diagnosing the operation of an active airflow regulating system for a vehicle, comprising a mechatronic system provided with a housing accommodating a blocking mechanical member, a magnet motor driven by a control circuit and coupled to a movement converter for moving the blocking mechanical member, and an electronic communication line for exchanging information with or receiving commands from a DCU (8) of the vehicle, characterized in that it comprises a series of steps consisting of storing in a memory a numerical sequence S of data obtained by the control circuit inside the mechatronic system during an operating cycle of the active airflow regulating system, applying an algorithmic statistical analysis model to the numerical sequence of data stored in the memory in order to determine singularities, and identifying sporadic errors associated with singularities in order to provide information about the state of the active airflow regulating system.

Description

Detailed description of the invention

[0001] [Technical Field] This invention relates to the field of active airflow control for vehicles, and more particularly to the modification of the air permeability coefficient of a vehicle by the use of electric fins or spoilers, and to thermal control by an active airflow control system that drives a barrier member, such as an electric air shutter on a grille or curtain, for a vehicle, especially the engine, brake system, and more generally any component whose temperature is actively controlled, in particular a powered vehicle equipped with an internal combustion engine or electric or hybrid motor. This barrier member is designed to modify the amount of air passing through the vehicle's radiator and engine compartment and to regulate airflow to manage the efficiency of the vehicle's engine and various main assemblies (combustion engine, electric traction motor and its inverter, gearbox, battery pack, etc.). With the evolution of pollution control standards, engine temperature management has become a major area of ​​development in engine design. Here, engine cooling is optimized to control the temperature of various engine zones while limiting energy consumption.

[0002] The purpose of a thermal management circuit is to maintain various internal components of the engine (such as cylinders and cylinder heads), and, where applicable, peripheral components (such as turbochargers), at their ideal operating temperatures.

[0003] If the engine temperature is too high, the gas inside the cylinder becomes hotter, meaning there is less gas inside the cylinder, increasing the risk of spontaneous combustion (and knocking). On the other hand, if the components deform more, the risk of failure increases.

[0004] On the other hand, when the engine is cold, friction between the piston and cylinder liner is generally higher, resulting in incomplete combustion and consequently significantly higher emissions of pollutants.

[0005] In many modern vehicles, the fins on the grille in front of the radiator are hinged and motor-driven to control the airflow through the radiator. In reality, when a vehicle is traveling at high speed and under low load, high airflow is not necessarily essential for properly cooling the engine.

[0006] Furthermore, the airflow through the radiator disrupts the mainstream airflow around the vehicle, creating air resistance. Blocking the air intake upstream of the radiator with a shutter can also be advantageous. In some cases, the shutter has only two positions (fully open or fully closed).

[0007] Unfortunately, the CO2 emission reduction benefits of these active shutter calendars are lost if the shutters remain locked in the open or closed position, or in an intermediate position, or if they are damaged, for example, in the event of an impact, or if one or more shutters are missing. The precise operation of the active shutter grille affects the vehicle's pollutant emissions. Therefore, it is of paramount importance, and legally required, to ensure the precise operation of the air shutter device, notify the driver of any drop in the vehicle's fuel consumption and pollution levels each time the vehicle is driven, and prompt the driver to quickly move the vehicle to the garage to restore the vehicle's legal emission levels. To achieve this goal, solutions have been provided for checking the general function of the air shutter placement by onboard diagnostic systems. In the case of electric vehicles, thermal control of the motor and battery is also a critical issue, having the same requirements as aerodynamic efficiency. A malfunctioning active airflow control system can disadvantage the vehicle's air permeability and consequently negatively impact the vehicle's range. [Background technology]

[0008] prior art To achieve this objective of detecting malfunctions in the active shutter of a grille, various solutions are known in the prior art.

[0009] German Patent Application Publication No. 102018108162 describes an actuator control method and actuator in which a DC motor drives a system to move a first body relative to a second body, causing the first body to slide along the contact surface of the second body. The principle of this solution is based on current analysis of assessing the behavior of an actuator by providing a region with modified mechanical resistance on the contact surface between a roller shutter and a guide rail.

[0010] This solution has several drawbacks. Firstly, a specific reference zone is required on the adjustment system, which is essential for detecting anomalies using this prior art solution. Anomaly detection is strictly limited to detecting anomalies in this reference zone and does not allow for the detection of general anomalies in mechanical and pneumatic transmission chains at all. This reference zone can also wear out, potentially rendering the signal evaluation inoperable.

[0011] Secondly, it is limited to mechatronics systems using DC motors and cannot be implemented in systems using stepping motors. The use of torque (or DC current) measurement imposes closed-loop motor control, as opposed to stepping motor control, rendering prior art solutions unusable or difficult in this context.

[0012] Thirdly, the signal to be analyzed is a current that reflects torque and is transmitted to the computer. However, the update frequency of this information is only about 10 μs, while the transmission capacity of the multiplexed LIN-type protocol is only about 100 ms, leading to incompatibility and insufficient bandwidth for transmitting the necessary information. Transmitting all the acquired information is not suitable for the very limited bandwidth of the communication network for vehicle control electronics.

[0013] U.S. Patent Application Publication No. 2020300156 describes another anomaly detection system for an engine coolant recirculation system, which includes a grill shutter capable of regulating the airflow circulating from the engine body to the environment coming from outside the vehicle, and stores four trained neural networks obtained by using measured engine coolant temperature as training data to learn weights for four states, including a state in which the grill shutter is closed and the air blown by the fan does not circulate through the air conditioning heater, a state in which the grill shutter is open and the air blown by the fan does not circulate through the air conditioning heater, a state in which the grill shutter is closed and the air blown by the fan circulates through the air conditioning heater, and a state in which the grill shutter is open and the air blown by the fan circulates through the air conditioning heater. Of the four pre-trained neural networks, one corresponding to the current state of the grill shutter and the air circulation state of the blower in the air conditioning heater is used to estimate the engine coolant temperature from the five parameters. Based on the estimated engine coolant temperature value, a malfunction in the engine coolant recirculation system is detected.

[0014] The drawback of this solution is that it provides information on predicting the temperature of the cooling water, and therefore cannot characterize anomalies in the kinematic and pneumatic chains of the shut-off system.

[0015] Furthermore, this solution requires the deployment of four neural networks and therefore involves very complex processing.

[0016] German Patent No. 10201201469 describes an air shutter device that also has an adjustment drive connected to move and transmit force to at least one air shutter for adjusting the at least one air shutter between its predetermined operating positions, the air shutter device also comprising a control device coupled to the adjustment drive in terms of signal transmission, a control device connected to the control device, a storage device coupled in terms of signal transmission, and a data acquisition device that acquires operation data from the air shutter device memory device, wherein a difference in operating positions representing a set of reference operation data is stored in the memory device.

[0017] U.S. Patent No. 213338870 describes another system solution and method for diagnosing mechanical faults affecting an automotive Active Grille Shutter (AGS) system. The method comprises (410) receiving at least one mechanical fault status signal, such as a signal indicating that a specific mechanical fault condition has been detected in the AGS system. The method also comprises (418) receiving a temperature signal indicative of a temperature at a location proximate to a grille shutter of the AGS system, and determining whether to perform extended diagnostics based at least on the mechanical fault condition signal and the temperature signal.

[0018] Disadvantages of the Prior Art Prior art solutions cannot reliably characterize abnormal conditions of mechanical and pneumatic shut-off chains without mechanical modification of the mechanical and pneumatic chains, such as deformation of the shutter or partial damage to the air intake system. Summary of the Invention

[0019] In order to overcome these drawbacks and provide a diagnostic function for the entire system without modifying the shut-off member, air flow faults are characterized using data inside the mechatronic system that moves the shut-off member, or data generated by the shut-off member where applicable. To this end, the invention, in its most general sense, relates to a method for diagnosing the operation of an active air flow adjustment system for a vehicle having the features set forth in claim 1, and to a system having the features set forth in the system claim.

[0020] The method comprises: · a mechanical shut-off member, · a mechatronic system provided with a housing that accommodates a magnet motor driven by a control circuit and coupled to a movement conversion unit for moving the mechanical shut-off member, · an electronic communication line for exchanging information with a vehicle DCU or for receiving commands from a vehicle DCU, said method comprising the following series of steps: · storing, in a memory, a numerical sequence S of data acquired by a control circuit inside the mechatronic system during an operation cycle of the active air flow adjustment system, · applying an algorithmic statistical analysis model to the numerical sequence of data S stored in the memory to determine singularities, · identifying sporadic errors associated with the singularities to provide information relating to the state of the active air flow adjustment system.

[0021] In one variant, the sporadic error identification step is performed by the control circuit, which transmits information representative of the identified sporadic error to the DCU.

[0022] In another variant, the digital data sequence stored in the memory is subjected to: - preprocessing at a first processing frequency by the control circuit, and - Post-processing by the vehicle's DCU / ECU at a second processing frequency lower than the first processing frequency to complete the analysis and identify sporadic errors. The dataset, pre-processed by the control circuit, is sent to the DCU / ECU via the command line.

[0023] In one embodiment, the digital data sequence S stored in memory is -The first type of data, - Including data of at least a second type, which is different from the first type.

[0024] In further embodiments, the method includes an additional step: - Integration of sporadic errors in accordance with commands transmitted by the vehicle's DCU, resulting in an integrated diagnostic status, - Including the transmission of integrated diagnostic status to the vehicle's DCU.

[0025] For the purposes of this patent, "ECU" means an "electronic control unit" that controls specific vehicle functions.

[0026] However, enhanced driving safety, comfort, and infotainment features lead to unsustainable system complexity, with up to several hundred ECUs per vehicle.

[0027] Instead of using multiple ECUs, it is known that a Domain Controller (DCU) is also used for the purpose of centralization. The increase in controllers in the automotive sector is driven particularly by vehicle automation, which requires combining data from different sensors, high-speed processing, and meeting the most stringent safety requirements. It also needs to provide the possibility of continuously developing new features and functionalities throughout the lifespan of the vehicle through over-the-air (OTA) updates.

[0028] Since the nature of the controller depends on the choice of computer architecture of the vehicle in which the present invention is implemented, the present invention shall refer to ECU or DCU without distinction.

[0029] In addition, the statistical analysis algorithm can be provided with supervised, unsupervised, or reinforcement learning sequences.

[0030] Alternatively, statistical analysis algorithms can include clustering selection methods.

[0031] In one variant, the circuit breaker mechanical component lacks a dedicated diagnostic element external to the mechatronics system.

[0032] In the acceptable deformation forms, digital data s i This relates to the operation of magnetic motors in mechatronics systems.

[0033] In the expected alternative form, digital data s i This is the motor phase current measurement value.

[0034] In this case, digital data s i This could also be a measure of the rotor load angle of a magnetic motor.

[0035] In one embodiment, an algorithmic model for statistical analysis to determine specificity is provided, which involves comparing a stored sequence of sampled digital data S with a reference data map.

[0036] In a second embodiment, an algorithmic model for statistical analysis to determine singularity is provided for detecting singularities in a stored sequence of sampled digital data S.

[0037] In the third variant, the algorithmic model for statistical analysis to determine the specificity involves feeding the stored sequence of sampled digital data S to a model obtained by training a neural network from training data corresponding to the nominal operation of the barrier mechanism components.

[0038] In all cases, the sampled digital data S r The reference sequence can be stored for each mechatronics system at the end of the assembly line.

[0039] In this case, the algorithmic model for statistical analysis can compare the sampled digital data sequence with a reference sequence of sampled digital data to detect behavioral drift that may cause degradation of the mechatronic system or verify the normal wear behavior of the system.

[0040] In one embodiment, the stored sequence of sampled digital data S is derived from items of digital data that depend on the mechanical load in the active grid system.

[0041] In this case, the identification of specificities in the stored sequence of sampled digital data S is, • Mass or inertia moving within an active grid system, • and / or mechanical friction in the system, • and / or air load on the system, This may be possible through knowledge of the system temperature and / or other related factors.

[0042] For the purposes of this patent, “singularity” is defined as a point or subset of points in a stored data sequence that differs from a reference sequence corresponding to nominal operation, by comparison with one or more pre-recorded nominal data sequences, or by the difference between the signature of the data sequence and the signature of the nominal data sequence obtained during a pre-learning phase using the same signature calculation function, or as singularity in the sense of Gaussian statistics, or as a point or set of points that differs from the projection of the intermediate curve formed by points that significantly precede the mean of the variation of stored points with respect to the intermediate curve, and is significantly understood as “at least 20% greater than the amplitude of variation in normal or nominal operation.”

[0043] In particular, a distinction is made between data for which a signature (such as torque, current, and position) is required, and information such as temperature stored in the internal memory of the mechatronics system, especially the volatile or non-volatile memory of the controller.

[0044] In one variant, the algorithmic model for statistical analysis to determine the specificity involves feeding the stored sequence of sampled digital data S to a model obtained by training a neural network from training data corresponding to the nominal operation of the barrier mechanism component.

[0045] In certain embodiments, a reference sequence of sampled digital data is stored for each mechatronics system at the end of the assembly line.

[0046] Advantageously, algorithmic models for statistical analysis can compare sampled digital data sequences with a reference sequence of sampled digital data to detect behavioral drift.

[0047] In one embodiment, the stored sequence of sampled digital data S is derived from items of digital data that depend on the mechanical load in the active grid system.

[0048] According to a particular embodiment, the identification of specificity in the stored sequence of sampled digital data S is • Mass or inertia moving within an active grid system, • Mechanical friction in the system, This is made possible by knowledge of the air load on the system and / or other related factors.

[0049] The present invention also relates to an electromechanical active airflow control system (1) for a vehicle, -Shutter mechanism member, - A mechatronics system provided with a housing that contains a magnetic motor (10) which is driven by a control circuit and coupled to a movement conversion unit for moving a circuit breaker mechanical member, - Equipped with electronic communication lines for exchanging information with the vehicle's DCU or for receiving commands from the vehicle's DCU, The method involves a microcontroller or microprocessor executing a computer program stored in its read-only memory. -Sets of digital data acquired by the control circuit inside the mechatronics system (3) during the operating cycle of the active airflow adjustment system i A step of storing a sequence of sampled digital data S, which consists of the above, in memory. - The step of applying an algorithmic statistical analysis model to a sequence of sampled digital data S stored in memory in order to determine the specificity, The present invention relates to a system (1) characterized by controlling a series of steps comprising: identifying sporadic errors associated with specificity, which are information relating to the state of the active airflow adjustment system (1), via a control circuit, and transmitting them to the DCU. [Brief explanation of the drawing]

[0050] The present invention will be better understood by referring to the accompanying drawings and reading the following description of different non-limiting embodiments. [Figure 1] A schematic diagram of an active airflow adjustment system driven by a stepping motor according to the present invention is shown. [Figure 2] A schematic diagram of a modified form of the active airflow adjustment system driven by a sensor-equipped BLDC motor according to the present invention is shown. [Figure 3] A schematic diagram of a modified form of the active airflow adjustment system driven by a sensorless BLDC motor according to the present invention is shown. [Figure 4] This document presents an example of a diagnostic algorithm for fault identification specifically for shutter systems. [Figure 5] Figure 4 shows an example of a learning algorithm for obstacle recognition based on the algorithm presented. [Figure 6] This shows an exploded view of the active shutter air conditioning system. [Figure 7] This shows an exploded view of the active curtain air conditioning system. [Figure 8] Figure 6 shows various examples of malfunctions related to the system. [Figure 9] Figure 6 shows various examples of malfunctions related to the system. [Figure 10] Figure 6 shows various examples of malfunctions related to the system. [Figure 11] Figure 6 shows various examples of malfunctions related to the system. [Figure 12] Figure 6 shows various examples of malfunctions related to the system. [Figure 13] Figure 7 shows various examples of malfunctions related to the system shown. [Figure 14] Figure 7 shows various examples of malfunctions related to the system shown. [Figure 15] Figure 7 shows various examples of malfunctions related to the system shown. [Figure 16]Figure 7 shows various examples of malfunctions related to the system shown. [Figure 17a] This shows a Fresnel diagram for a three-phase motor, which allows for the visualization of the motor's load angle. [Figure 17b] This shows a Fresnel diagram for a three-phase motor, which allows for the visualization of the motor's load angle. [Figure 17c] This shows a Fresnel diagram for a three-phase motor, which allows for the visualization of the motor's load angle. [Figure 17d] This shows a Fresnel diagram for a three-phase motor, which allows for the visualization of the motor's load angle. [Figure 18] This document describes a method for obtaining a series of data acquisitions to identify anomalies based on measurements of power consumed by a motor. [Figure 19] An example of a supervised learning algorithm that can be used as the basis for decision-making for the algorithm presented in Figure 4 is shown. [Modes for Carrying Out the Invention]

[0051] General principles of active airflow control systems Figure 1 shows an overall view of the active airflow adjustment system (1). This adjustment system consists of a mechanical blocking member (2) positioned on the grille of the vehicle that can block the airflow from reaching the radiator, and a mechatronics system (3) that controls the positioning of the mechanical blocking member (2), such as a shutter or roller, and enables modulation of the airflow received by the radiator depending on the state of the mechanical blocking member (2).

[0052] The object of the present invention is to automate the detection and characterization of abnormalities in mechanical and / or pneumatic airflow control elements, particularly shutter disappearance or changes in the geometric shape of a shutter resulting from, for example, impact, even if these abnormalities do not alter the general kinematics of the mechatronic airflow control system. Naturally, the present invention also makes it possible to detect failures such as blockage or seizure in the mechatronic airflow control system.

[0053] The mechatronics system (3) includes a motion transmission chain (20), which is controlled by an electronic control circuit (30). From this point onward, it is driven by a multiphase permanent magnet synchronous motor, referred to as a magnet motor (10), such as a three-phase motor as shown in Figure 1.

[0054] The present invention utilizes signals available on the electronic control circuit (30) of a mechatronics system (3) without requiring the addition of one or more additional sensors on the shut-off mechanical member (2) of the active airflow control system (1). Thus, the mechatronics system (3) according to the present invention can provide diagnostic information when initially incorporated into an active airflow control system (1) that does not have this function. However, if additional sensors are already mounted within the active airflow control system (1), for example on the mechanical shut-off member (2), the present invention can also utilize signals from these sensors, provided that the signals from these sensors are processed by the electronic control circuit (30).

[0055] In the case shown in Figure 1, the magnetic motor (10) is configured to be step-driven. The mechatronics system (3) includes: • An electrical connector (6) that receives power (5) from the vehicle's DC power network, A telecommunication line (7) consisting of commands from the vehicle's Domain Control Unit (DCU) (8) and information generated by the mechatronics system (3) itself and directed to the vehicle's DCU (8), • Electrical protection and filtering modules (9) that comply with applicable electrical and electromagnetic standards, A control circuit (11) equipped with memory, such as a microcontroller, generates vehicle communication, control functions, and drive of the magnetic motor (10) through association with a transistor drive module. A magnetic motor (10) that generates a desired displacement and torque, A multiphase inverter (12) consisting of MOSFET transistors that supply power to the magnetic motor (10), which preferentially provides two-phase or three-phase power, The system is equipped with a motion transmission chain (20) for reducing or converting the motion of the rotor of the magnetic motor (10), the input stage of which is driven by the rotor of the magnetic motor (10), and the output stage (21) which is coupled to a circuit breaker mechanical member (2).

[0056] It is important to note that the control circuit (11), transistor driver module, and multiphase inverter (12) can be incorporated into a specific component (13) known to non-experts by the acronym SOC (System On Chip). Preferably, the data sequence is generated by combining at least two different types of information, which may be derived from, but are not limited to, the following elements: Considering the context of the active airflow control system (1), one (or more) vibration sensors (13) for measuring the vibration level of the moving subassembly, One (or more) temperature sensors (14) that enable the measurement of the internal temperature of the mechatronics system (3) and the estimation of the local temperature of the area of ​​the active airflow control system (1), - One or more position sensors (15) that enable the measurement of the continuous position of the rotor of the magnetic motor (10) or the moving elements within the mechatronics system (3) and the reconstruction of the load angle of the magnetic motor (10), One or more electrical sensors (16) in the multiphase inverter (12) that measure one or more electrical quantities such as voltage or current, and that allow the load angle of the magnet motor (10) to be reconstructed when the inverter is reassembled.

[0057] Combining information from several sensor types can be advantageous when there is strong economic pressure to incorporate components into actuators, such as very basic control electronics or low-sensitivity sensors, where specificity cannot be extracted from information from a single type of sensor. Nevertheless, this specificity can be separated by the cooperation of weak signals and then identified in a sampled sequence of data resulting from the aggregation of data from different types of sources. However, this approach is not limited, and when economic pressure is removed, more complex systems can be assumed, and sporadic errors can be extracted from data sequences presenting information from a single type.

[0058] In the example shown in Figure 1, a typical scenario can be described as follows: The control circuit (11) receives a movement command from the DCU (8) and then generates an appropriate drive sequence for switching the multiphase inverter (12) to generate the movement. The movement command from the DCU (8) can be a whole or partial opening or closing movement, or an absolute position of the stroke to be reached. The present invention involves the control circuit (11) acquiring a data sequence, storing it in memory, analyzing it using digital processing capable of detecting anomalies, and transmitting the sporadic errors associated with these anomalies to the DCU (8) via a communication line (7).

[0059] The data sequence can be acquired temporarily for any movement or in accordance with instructions from the DCU(8) or control circuit(11).

[0060] The control circuit (11) can extract specificities from this stored data sequence using statistical processing of varying complexity, depending on the type of malfunction required and the functional requirements resulting from the desired sensitivity. For example, malfunctions caused by sudden deterioration, such as the failure of a shutter blocking the progress of the blocking mechanical member (2), are much easier to detect than the gradual aging deterioration of the actuator, which leads to slight deterioration of performance close to the minimum required. Therefore, this specificity could be an indication of a wide variety of malfunctions and requires the application of an appropriate processing algorithm to generate sporadic errors from this error for transmission to the DCU (8). “Sporadic errors” means error codes that the DCU (8) can understand and can signal a wide variety of malfunctions the actuator may encounter, such as, but are not limited to, the loss of one of the moving elements, the disconnection of one of the moving elements from the motion transmission chain, the loss of several elements of the blocking mechanical member (2), or damage to part of the motion transmission chain (20).

[0061] As shown in Figure 1, the mechatronics system (3) can incorporate various internal sensors to generate signals used by the control circuit (11). These sensors can be of various types, including vibration sensors (13), temperature sensors (14), pressure sensors, or analog or digital probes that measure the magnetic angle of the rotor. The present invention can also utilize a combination of several signals to generate integrity diagnostics for an active airflow control system.

[0062] Figure 2 shows an alternative implementation of the active airflow control system (1) according to the present invention. This design differs from that shown in Figure 1 in that the magnetic motor (11) is controlled by a sensor-equipped BLDC system, i.e., the magnetic motor (11) is equipped with direct control or FOC closed-loop control using direct measurement of its rotor position. For this purpose, one or more sensors (15) are provided to inform a microcontroller of the precise position of the rotor of the permanent magnet motor (11), which itself generates a desired displacement and torque.

[0063] Figure 3 shows a modified form of the active airflow adjustment system (1) according to the present invention. This design differs from that shown in Figure 2 in that the magnet motor (11) is a sensorless BLDC, meaning that the rotor position information of the magnet motor (11) no longer comes from a position sensor (15), but is estimated from the electrical quantities measured at the terminals of the phase supply line of the magnet motor (11) by a measuring device (17) for phase current and / or voltage, which may be equipped with one or more voltage and / or current sensors.

[0064] Naturally, these are not the only ways to implement mechatronics systems, and those skilled in the art can easily imagine other alternative forms for the design and control of magnetic motors, as well as for the contents of internal sensors in mechatronics systems used to detect malfunctions.

[0065] Various types of defects are illustrated by several examples, various means of detecting these defects are presented, and several solutions for extracting anomalies from the information provided by the detection means are also presented. Finally, several algorithmic implementations for converting detected anomalies into tangible error signals are provided. Those skilled in the art can use some or all of these examples and replace any missing components with solutions known in the art; therefore, the examples described are for illustrative purposes only and do not limit the scope of the invention.

[0066] Detailed explanation of variations of the defect detection algorithm An example of an algorithm (200) describing fault diagnosis for an active airflow regulating system (1) in which the blocking mechanical components are a set of shutters is represented by the flowchart given in Figure 4. Following the successful completion of a single or periodic learning phase (100) as described in Figure 5, the algorithm can be triggered upon receipt of a request from the DCU (8) or can be started automatically when the actuator is activated by the DCU (8).

[0067] An optional upstream step (120) of this algorithm is to obtain authorization from DCU(8) to perform a system state diagnosis in the case of an on-demand diagnosis, and if this fails, the diagnosis is permanently initiated during any movement performed by the mechatronics system.

[0068] The first step (210) is to initiate the movement phase of the breakaway mechanical member (2) generated by the mechatronics system (3). During this movement phase, step (211) is to initiate a series of data s by the control circuit (11). i The objective is to obtain the stored s. Data storage is obtained with respect to some or all of the movement of the blocking member, and in either case, ends in step (212) triggered by the end of the movement. The microcontroller then uses a dedicated algorithm to detect specificities, which may be similar to those of a machine learning or deep learning model, to obtain the stored s i Analyze the data series (213). Add the data series s i The analysis then proceeds to a step (214) for classifying the information obtained, which results in four possible outcomes. a. Since no specificity was detected, the system was classified as “nominal”. This is followed by step (220) in which the mechatronics system (3) generates an operating code for the DCU (8), which in step (221) is associated with the transmission of the confidence level of the previously performed classification (214) to the DCU (8). b. The system was classified as a “system with one degraded shutter” because a specificity corresponding to degradation equivalent to a single active shutter failure was detected. This is followed by step (230), in which the mechatronics system (3) generates a sporadic error code for the DCU(8), which in step (232) is associated with the transmission of the confidence level of the classification performed earlier in step (214) and the transmission in step (233) of the environmental context in which the diagnostics in steps (210) to (214) were performed. c. The system was classified as a “system with significant degradation” because an anomaly corresponding to degradation equivalent to the failure of several active shutters was detected. This is followed by step (231) in which the mechatronics system (3) generates sporadic error codes for the DCU(8), which are then associated in step (232) with the transmission of the confidence level of the classification performed earlier in step (214) and the transmission in step (233) of the environmental context in which the diagnostics in steps (210) to (214) were performed. d. The classification resulting from the analysis of the data series performed in step (214) did not allow for convergence to information with a sufficient level of confidence to deliver to DCU(8). This is followed by step (240), in which the mechatronics system (3) generates a code corresponding to the "unavailable" state of the diagnostic classification and transmits it to DCU(8), which in step (241) is associated with the transmission to DCU(8) of the environmental context in which the diagnostics in steps (210) to (214) could not be performed and converged.

[0069] Step (250) is common to all classifications providing tangible information and, depending on the confidence level generated in the previous step, if it is above a pre-set threshold, e.g., 99.99%, the user is directed to step (260). In step (260), an integrated error code or integrated function code is generated and transferred by the mechatronics system (3) to the DCU (8) for use in supplying the OBD strategy. Alternatively, if the confidence level falls below a pre-set threshold, step (250) is followed by step (270), where the mechatronics system recommends a strategy for integrating sporadic errors, essentially consisting of the movement of one or more partial or whole actuators, into the DCU.

[0070] In step (300), if step (241) has already been performed, the mechatronics system (3) summarizes the update of the analysis of the stored data series (i.e., a dedicated algorithm or model) so that it may be able to classify in the future data sequences that the current statistical analysis has not been able to converge.

[0071] In step (300), if step (270) has already been performed, the mechatronics system (3) begins summarizing the update of the analysis of the stored data sequences (i.e., the dedicated algorithm or model) so that it can in the future classify the data sequence having the highest confidence level compared to the data sequence just calculated.

[0072] Next, steps (260) and (300) lead to step (280), which corresponds to the termination of the algorithm for defect identification.

[0073] It should be noted that the integration strategy may be handled by the algorithms for defect identification presented herein, or may be an alternative algorithm potentially specific to the integration code transferred to DCU(8), which may be proposed by the system or imposed by DCU(8). DCU(8) may have priority responsibility for the strategy adopted to integrate the information (i.e., sporadic errors or well-functioning sequential codes).

[0074] The defect identification algorithms described herein are for illustrative purposes only and should not be considered to limit the scope of the invention. Those skilled in the art can easily imagine similar but different alternatives that satisfy the essential technical requirement of notifying the DCU(8) of deviations from nominal operation by error code.

[0075] As an example, a sequence (300) illustrating backpropagation applied to a learning model is represented by the flowchart shown in Figure 5. In step (301), if it becomes clear that the previous analysis of the data sequence diverged instead of converging toward the correct classification of the system among the given options given in Figure 4, an internal algorithm analysis step (302) is performed by the control circuit (11) (or by the vehicle DCU (8)) to propose an update to the model (i.e., algorithm) used to perform the analysis of the data sequence. In step (303), if an update can be proposed to classify this data sequence while ensuring the same level of performance as the previous sample classification, in step (307) a formal update request can be sent to the vehicle DCU, and if approved, in step (308) it can be stored and activated in the MCU. However, if an updated solution cannot be identified in steps (302) and (303), the sequence is completed in step (304) by storing the environmental context in which the diagnosis was performed in the non-volatile memory of the control circuit (11) (or vehicle DCU (8)) so that the limitations of the current solution can be identified later.

[0076] In step (301), if it is found that a previous analysis of the data sequence has produced a classification with a confidence level of less than 99.99%, then in step (305), an internal algorithm analysis loop is started by the control circuit (11) (or by the vehicle's DCU (8)) to propose an update to the model (i.e., the algorithm model) used to perform the analysis of the data sequence. In step (306), if it is possible to propose an update that produces a classification with a confidence level of more than 99.99% while guaranteeing the same confidence level as the previous sample classification, then in step (307), a formal update request can be sent to the vehicle's DCU (8), and if approved, it can be stored and activated in the control circuit (11) in step (308). If an update solution cannot be proposed in step (306), the sequence ends.

[0077] Different types of malfunctions Figure 6 shows an example of an active airflow control system (1), in which the blocking mechanical component (2) consists of a set of shutters. This active airflow control system is positioned in the car's grille in front of the radiator and includes a mechatronics system (3) that drives a shaft (4) through the output gear of the mechatronics system (3). This shaft (4) controls the positioning of the right shutter (23-25) and the left shutter (26-28) via a transmission (19, 29).

[0078] Figure 7 shows another example of an active airflow control system (1), in which a blocking mechanical member (2) consists of a frame (31) that can be closed by a curtain (32), the curtain (32) can be wrapped around a shaft (4) that passes through the output gear of a mechatronics system (3).

[0079] Figures 8 to 16 illustrate different types of malfunctions associated with the active airflow control system (1) in a non-limiting manner. Figures 8 to 12 illustrate malfunctions of the mechanical blocking member (2) fitted to the shutter as shown in Figure 6, and Figures 13 to 16 illustrate malfunctions of the mechanical blocking member (2) fitted to the curtain as shown in Figure 7. These figures represent a system with an active grid in which the shutter rotation shaft is positioned horizontally, but the present invention is also extended to any other orientation of the shutter rotation shaft.

[0080] Figure 8 shows a situation in which one of the central shutters is missing due to impact or damage to its pivot pin. It then replaces a permanent opening (34) through which air can flow, regardless of the orientation directed by the actuator. However, the missing shutter (24) results in a change of force acting on the mechatronics system (3) during the orientation change, and the signatures of these forces can be detected by processing electrical signals measured directly at the power and control ports of the mechatronics system (3). In the example described in Figure 8, the central shutter is missing, but the present invention is not limited to this case, and different signatures can be observed even if the end shutters are missing, or if some shutters are missing, and the signatures also vary depending on the combination of missing shutters.

[0081] Figures 9 to 11 show another malfunction of the active shutter airflow adjustment system in which all shutters (26, 27, 28) are present, but one or more of them are no longer driven by the motion transmission mechanism (36). This may be due to, for example, damage to the connecting rod (37) as shown in Figure 9, or damage to the shutter drive bracket (38) as shown in Figure 10, or damage to the transmission shaft (39) as seen in Figure 11.

[0082] This malfunction alters the forces acting on the mechatronics system (3) during orientation changes. The signatures of these forces can be detected by processing electrical signals measured directly at the power and control ports of the mechatronics system (3).

[0083] Figure 12 illustrates another malfunction scenario in which shutter (24) is no longer driven, preventing the opening or closing of adjacent shutters (23, 25). This behavior can occur when the drive shaft of a shutter fails, and is associated with a shift in the stroke of the other shutters. This type of failure can result in a one-time overtightness required to disengage the contacting shutters, or a serious overtightness resulting from the system being completely locked in a position outside the stopper. It should be noted that seizure of the drive shafts of one or all dampers can also lead to a one-time or overall increase in the torque required for the opening or closing stroke.

[0084] Figures 13 to 15 illustrate other malfunction scenarios of the active curtain airflow adjustment system where elements of the motion transmission mechanism have failed. This can be caused, for example, by damage to the drive shaft (4) as shown in Figure 13, damage to the drive cable (33) as shown in Figure 14, or a tear (40) in the curtain (32) as shown in Figure 15.

[0085] This changes the force acting on the mechatronics system (3) during the orientation change. The signatures of these forces are detected by processing electrical signals measured directly at the power and control ports of the mechatronics system (3).

[0086] Figure 16 shows another malfunction where the curtain (32) frame (31) is deformed or even damaged. This type of damage (35) can result in a one-time overtightness required to move the curtain, or a serious overtightness due to complete closure of the system. It should be noted that seizure of the drive shafts of one or all dampers can also lead to a one-time or overall increase in the torque required for the opening or closing stroke by the mechatronics system (3).

[0087] Detailed description of the first specificity measurement modification form In one embodiment, detection of operational specificity is based on sampling and analysis of load angles.

[0088] The load angle corresponds to the magnetic angular displacement of the rotor with respect to the angular position that cancels out the magnetic torque between the rotor and stator. A common way to represent this is to use a Fresnel diagram, as shown in Figure 17 by subfigures a, b, c, and d, where the magnetic states of the rotor and stator are represented by vectors. In this diagram representing a three-phase machine, vectors U, V, and W are the magnetic states corresponding to each motor phase supplied with the same voltage.

[0089] In this representation, the load angle (52) corresponds to the angle between the stator vector (50) and rotor vector (51) of the magnetic field. The resulting torque at the rotor level, generated by the power supply at the stator level, varies from zero torque when this angle is equal to 0°, as shown in Figure 17a, to a maximum when the vector is 90°, as shown in Figure 17d. The torque represented by the torque vector (53) is directly proportional to the sine of the load angle (52) and the supply current. Figures 17b and 17c show intermediate situations where the load angle is between these values ​​to visualize the expansion of the torque vector between load angles of 0° and 90°.

[0090] Assuming there is no load on the rotor, the load angle (52) is 0°, and the stator vector and rotor vector are collinear. The actual angular position of the rotor is the same as the command position.

[0091] When a force is applied to the rotor, such as brake torque, load, or drive torque, the load angle (52) increases and is no longer equal to 0°. If this load angle (52) exceeds 90°, the torque decreases, which can lead to a loss of synchronization between the rotor and the stator magnetic field, known as rotor stall.

[0092] Therefore, measuring the load angle (52) requires measuring the rotor angular position.

[0093] In its broadest sense, the present invention is compatible with all types of synchronous motor control, but more specifically, this solution is dedicated to stepping motor control. Various solutions are available for measuring rotor position, and the use of dedicated sensors such as analog magnetic sensing probes is preferred, but even then, they have the drawback of not having very high resolution and may show their limitations when measuring very small fluctuations in the load angle (52). In actuators with high mechanical reduction, such as those used in the present invention, the reduction chain reduces the perceptible effect of fluctuations in the load on the driven member on the driving motor by a factor equal to the reduction. Also, due to this reduction, the rotor speed is much greater than the speed of the driven member by a factor equal to the reduction. Therefore, calculating the rotor angle at each step or each microstep requires very large, and therefore expensive, computational resources and often does not fit the target price for these applications. Thus, instead of measuring the load angle by comparing the stator angle with the absolute rotor angle reconstructed from two signals from an analog quadrature probe, it is proposed to improve angular resolution by significantly reducing the number of load angle measurements during full rotor rotation.

[0094] A first solution continues to use an analog probe to measure the magnetic field of the sensor magnet, but does not require reconstruction of the absolute rotor angle, which is a source of inaccuracy and consumes computational resources. It is then desirable to measure only the zero crossings of the probe, which are most accurate because they are locations where magnetic field variation is at maximum. In order to correctly identify these zero crossings, it is necessary to measure and store the maximum and minimum values of the probe for each magnetic period, and the zero crossing is then reconstructed as an average between the maximum and minimum values of the preceding step. This ensures insensitivity to changes in magnetization amplitude, whether slow irreversible variations due to aging or short-term reversible variations, for example due to temperature variations. Therefore, measuring the load angle involves ·triggering a counter n for steps or microsteps s , ·stopping the counter when a zero crossing Z of the analog probe is measured, preferentially using the formula where V M and V m are respectively the maximum and minimum values of rotation n-1,

[0095] [Formula] , c , storing the maximum and minimum values measured by the analog probe for rotation n , ·calculating the load angle L s using the formula where N is the number of steps or microsteps in a mechanical period

[0096] [Formula] , A .

[0097] The second solution is to replace the analog probe with a digital probe. This solution results in slightly less accuracy because it does not allow for adjusting the transition threshold for each magnetic period according to the measurements obtained during the previous period, but it offers both financial savings and reduced computational resources. By incorporating a digital probe, it is possible to use a microcontroller without analog inputs, and it is no longer necessary to reconstruct the rotor angle by calculation. The method for measuring the load angle is the same as in the previous solution, and • Trigger a step counter or microstep counter when the digital probe's zero crossover is measured. • Stop the counter when the digital probe's zero crossover is measured. ·formula

[0098]

number

[0099] In either solution, the electrical period L A,1t The load angle measurements over a certain period can be averaged according to the following formula:

[0100]

number

[0101] For greater accuracy, motion can be analyzed over multiple electrical cycles, or even over one or more rotations of the gearbox output shaft. In this case, if more weight is given to the most recently measured sample, it is possible to use a sliding average of the load angles over N samples, such as a weighted sliding average.

[0102]

number

[0103] Detailed explanation of the second variant of specificity measurement In one embodiment, detection of operational specificity is based on sampling and analysis of electrical signals measured at the terminals of an RC filter in series with the power supply of a mechatronics system (3), also known as an actuator, the mechatronics system (3) is coupled with so-called self-rectifying control, known to those skilled in the art as "BLDC control," and comprises a multiphase motor supported by an electronic module incorporated into the mechatronics system (3).

[0104] Figure 18 shows a simplified electrical circuit of a three-phase driver using six transistors Q1-Q6 according to the present invention, where Q1 and Q4 (Q2 and Q5, Q3 and Q6 respectively) drive the current through phase C (phases B and A respectively). Resistor (60) is a sampling resistor used to measure the sum of the currents flowing through each phase of the magnet motor (10) via an RC filter (63) consisting of resistor (61) and capacitor (62). The output of the RC filter (63) is acquired as a voltage by an analog-to-digital converter and is sequentially converted and preprocessed into information by, for example, a microcontroller.

[0105] A brief explanation of alternative measurements for specificity Another parameter of interest to measure for fault detection is the number of steps required for a motor driven in stepping mode to move from one stop position to another. This type of size is particularly useful for detecting blocking faults because it can significantly reduce the effective stroke. In a more detailed example, this parameter can also be used to identify a missing shutter.

[0106] The measurement of specificity is not limited to functional quantities in electric motors, such as current measurement or load angle measurement as presented above, but can easily be obtained from dedicated sensors inside the actuator. One example is a pressure sensor or temperature sensor whose fluctuations will be proportional to the incoming airflow. Therefore, it is quite conceivable that different scenarios be developed to make the measurements of these sensors sensitive to the aforementioned problems.

[0107] Explanation of an example of a specificity recognition algorithm For example, one means of anomaly detection compatible with current measurement, as illustrated in Figure 18, involves identifying values ​​in a stored data series that exceed the expected value by a certain number of standard deviations. This is described here using a form developed for current measurement, but can also be used for other types of measurement data. Anomaly detection is achieved by digitally processing the expansion of a physical value consisting of the sum of the currents I of the N phases of a multiphase motor, measured in a resistor (60). During nominal operation in synchronous mode, the current has a stable value and the rotor of the geared motor exhibits regular and symptomatic behavior; however, when the geared motor is exposed to an abnormal or faulty active airflow control system (1), this value expands toward a different, more irregular value.

[0108] The variation in current I measured across resistor (60) is processed by sampling, for example, using an analog-to-digital converter of a microcontroller, and the standard deviation σ of the current amplitude values ​​measured across resistor (60) over N samples is calculated over a sliding or fixed time window that is less than or equal to the duration of the movement associated with acquiring the information.

[0109] Characterization of the operating mode and detection of faults are performed by analyzing the variation in this standard deviation σ, and in some cases supplemented by analyzing the expansion of the median over N samples of current measured at the terminals of R1. - A stable standard deviation σ below the threshold measured during nominal operation corresponds to problem-free operation. - A significant increase in the standard deviation and / or the median current measured at the terminal of R1, exceeding a threshold, indicates burnout of one or more shutters and / or their drive mechanisms, and therefore fluid control drift. - An increasing standard deviation exceeding a threshold and / or a significant decrease in the median current measured at the terminal of R1 corresponds to loss in one or more shutters, sticking in the open position, or partial failure of one or more shutters, and thus to drift in the fluid control. - For example, value N 2* σ 2 This is compared with the total phase current value I. This digital processing distinguishes the normal operation region from the abnormal operation region, and the value N 2* σ 2 It becomes possible to set a standard deviation threshold E, and if it exceeds the standard deviation threshold E, the microcontroller or ASIC determines that the geared motor is driving a faulty gate mechanism. Several other types of algorithmic statistical analysis models can also be used in combination.

[0110] Description of variations of the specificity recognition algorithm In one embodiment, the singularity is identified using a learning algorithm, which can be trained to identify the type of error encountered, such as a missing shutter or a damaged shutter that blocks the system, but it can also identify the location of the fault, for example, a missing shutter at one end or in the middle.

[0111] If the driven component consists of a set of shutters, the fault detection algorithm can benefit from modifications to the driven component to improve detection. Therefore, it is conceivable to provide a specific signature for each shutter that can be faithfully distinguished by the algorithm. This could be a binary code with a point resistance element added to each shutter, as described in U.S. Patent Application No. 9810138(B2), where the resistance element is detectable in the form of a spot overtight torque generated by the actuator, and the point resistance elements are carefully positioned so that each induces overtight torque at different moments in the opening or closing stroke. Thus, the absence of a shutter is indicated by the absence of a friction peak during the overall movement of the driven component. It should be noted that such a signature could be measured, for example, by a very simple algorithm that detects a current peak consumed by the actuator exceeding a certain threshold at a given position to acknowledge the presence of each shutter.

[0112] If the actuators are a set of shutters or curtains, the signature can be more nuanced by leveraging the power of deep learning to provide each actuator shutter with a specific surface area, specific mass, or specific profile. This could lead to the measurement of highly specific signatures, for example, by linking to the system's inertia, its pneumatics, or even obtaining vibrations specific to each shutter.

[0113] Description of other variations of the singularity recognition algorithm It should be noted that there are numerous possible variations of the specificity identification algorithm that conform to the present invention, each with its own advantages and disadvantages. For example, some are more robust but require a specific sequence of operation of a device to be driven to measure the fault, others must be performed in a stationary state, others must be in motion, and still others require additional external data such as vehicle speed, wind speed, or temperature, so the selected solution, or set of selected solutions, is defined by the specification. Some examples of identification algorithms are given below.

[0114] According to one variant of the present invention, the specificity identification algorithm is based on the measurement of hydrodynamic torque fluctuations. When a shutter is missing or a curtain is torn, the pressure on the entire blackout system decreases, and as a result, the torque required for the actuator to open or close decreases. Hydrodynamic torque T d This can be expressed as a function of the actuator stroke x according to the following equation:

[0115]

number

[0116] The algorithm can then directly utilize measured values ​​of torque, current, or load angle as a sequence of sampled digital data to identify the anomaly, or, to improve accuracy, acquire this data along the actuator stroke to identify the anomaly W(x0,x1) on the workpiece supplied by the actuator during the movement between x0 and x1 according to the following equation.

[0117]

number

[0118] Next, the measured workpiece can be simply compared to a baseline curve corresponding to a healthy actuator. It should be noted that this method consumes very few computational resources but is highly dependent on wind speed and vehicle speed. However, if the available computational power allows for the implementation of a more intelligent algorithm, several specific actuator movements can be performed to estimate the relative velocity of the air impacting the blackout device.

[0119] According to another variant of the present invention, if the blackout system is a set of shutters, the specificity identification algorithm can be based on the measurement of system stiffness.

[0120] According to another variant of the present invention, the specificity identification algorithm may be based on the measurement of the system's inertia. When the AGS actuator is controlled such that the angular velocity of the actuator rotor increases linearly between two instants, the rotor acceleration is constant. By performing an open cycle with the vehicle stationary, faults such as a missing shutter can be detected by measuring the variation in inertia during this cycle compared to a reference value stored in the system. Alternatively, this change in inertia may be measured from readings of torque, current, or load angle, but these examples are not limiting to the present invention.

[0121] Detailed explanation of variations of the learning algorithm In one embodiment, an algorithm describing fault detection and diagnosis requires an initial supervised learning phase in which a set number of cycles are supplied while the system is in nominal (or functional) mode only. An example of such a supervised learning algorithm is shown in the flowchart of Figure 19.

[0122] The classification system is interested in s iThis indicates the number of training cycles X required to provide results with optimal confidence over the entire period using sampled acquisitions of the data series. The confidence level is a variable that increases as a function of X, growing rapidly as a function of X, and then changing only slightly beyond a certain value of X. Thus, for example, with a limited number of training cycles of 10 or 20, depending on the sensitivity of the algorithm, it is possible to obtain a confidence level close to 100% by seeking a desired high confidence level above 99%, where 100% means total certainty and 0% means total uncertainty.

[0123] The learning algorithm (100) is triggered each time the actuator is set to a motion state by the control circuit (11), which is called step (110), followed by step (111) which checks that the initial learning phase is complete, which can be done by reading a memory register. If the learning phase has already been successfully completed, the learning algorithm terminates and triggers the fault detection algorithm (200).

[0124] On the other hand, if the learning phase is not deemed complete, in step (112), the mechatronics system (3) for the DCU (8) generates a "non-operating" state in the fault detection system. Then, the mechatronics system (3) initiates movement. Step (113) of the learning algorithm involves the control circuit (11) sampling and acquiring data of interest and storing this data in the internal memory of the mechatronics system (3). Step (113) is completed when the end of actuator movement is detected. The learning algorithm then triggers a statistical analysis of the data stored for this learning cycle in step (113) in step (114). The next step (115) involves the control circuit (11) checking the accuracy of the movement performed by the actuator and the correct environmental conditions (i.e., the correct context), leading to a decision on whether to retain the data from the current cycle as reference data for the learning algorithm. If retained, this data is stored as reference data in the memory register in the final step (116). The reference data storage preferentially includes contextual data from stored cycles, such as information on temperature, vehicle speed, and air pressure in the shutter, which may come from external sensors, and the associated information may then be generated by the mechatronics system (3) to the DCU (8). If the check concludes that the data is inappropriate, the data is not stored, and the cycle is not considered a reference cycle, which can then be generated by the mechatronics system (3) to notify the DCU (8).

[0125] Embodiment in which data processing is shared between the ECU and DCU(8) In the above description, it is generally assumed that all algorithmic processing is performed solely through the control circuit (11), and that the control circuit (11) then transmits information characterizing the nature of any malfunctions detected in the airflow chain, regardless of whether or not mechanical faults are present.

[0126] However, it is also possible to divide the algorithmic processing between the control circuit (11) and the vehicle's DCU (8), the vehicle's DCU (8) generally has much higher computing capacity, and other vehicle data can be optionally considered to achieve a more complete integration of vehicle diagnostics. However, this embodiment must take into account the fact that the transmission bus between the equipment and the vehicle's DCU has limited bandwidth and raw sensor data cannot be transmitted directly to the vehicle's DCU. The “distributed” embodiment of the present invention then consists of high-frequency preprocessing of raw data in the control circuit (11) for computing a preprocessed dataset, which is then transmitted from the control circuit (11) to the vehicle's DCU (8) using, for example, the LIN protocol at a frequency that fits the bandwidth of the communication line (7), and the vehicle's DCU (8) performs post-processing at a second frequency lower than the first frequency to enable the DCU (8) to complete the analysis and identify sporadic errors using these datasets transmitted at low frequencies, and then enables the DCU (8) to characterize aerodynamic and / or mechanical failures of the airflow control system.

Claims

1. A method for diagnosing the operation of an active airflow control system (1) for a vehicle, - Circuit breaker mechanical member (2), - A mechatronics system (3) is provided with a housing that accommodates a magnetic motor (10) coupled to a movement conversion unit for moving the aforementioned circuit breaker mechanical member (2), and a control circuit (11) for driving the magnetic motor (10), - Includes an electronic communication line (7) for exchanging information with the vehicle's DCU (8) or for receiving commands from the vehicle's DCU (8), The method described above is - A step of storing in memory a numerical sequence S of data acquired by the control circuit (11) inside the mechatronics system (3) during the operation cycle of the active airflow adjustment system (1), - The step of applying an algorithmic statistical analysis model to the numerical sequence of data S stored in the memory in order to determine the specificity, A method for diagnosing the operation of an active airflow control system (1), comprising a series of steps including: - identifying sporadic errors associated with the specificity in order to provide information regarding the state of the active airflow control system (1).

2. A method for diagnosing the operation of an active airflow control system (1) according to claim 1, characterized in that the step of identifying the sporadic errors is performed by the control circuit (11), and the control circuit (11) transmits information representing the identified sporadic errors to the DCU.

3. The sequence of digital data stored in the memory is - Preprocessing at a first processing frequency by the control circuit (11), and - The subject of post-processing by the vehicle's DCU / ECU (8) at a second processing frequency lower than the first processing frequency, in order to complete the analysis and identify sporadic errors. A method for diagnosing the operation of an active airflow control system (1) according to claim 1, characterized in that a dataset preprocessed by the control circuit (11) is transmitted to the DCU / ECU (8) via a command line (7).

4. The sequence of digital data S stored in memory is a. Data of the first type, b. A method for diagnosing the operation of the active airflow control system (1) according to claim 1, comprising at least a second type of data different from the first type.

5. The above method, as an additional step, a. Integration of the sporadic errors in accordance with the commands transmitted by the vehicle's DCU(8), resulting in an integrated diagnostic status, b. A method for diagnosing the operation of the active airflow control system (1) according to claim 1, comprising transmitting the integrated diagnostic status to the vehicle's DCU (8).

6. A method for diagnosing the operation of the active airflow adjustment system (1) according to claim 1, characterized in that one of the aforementioned data is a measured value of the motor phase current.

7. A method for diagnosing the operation of an active airflow adjustment system (1) according to claim 1, characterized in that one of the aforementioned data is a measured value of the load angle of the rotor of the magnetic motor (10).

8. A method for diagnosing the operation of an active airflow control system (1) according to claim 1, characterized in that a learning sequence is provided to a statistical analysis algorithm.

9. A method for diagnosing the operation of the active airflow control system (1) according to claim 1, characterized in that a cluster selection method is provided for the statistical analysis algorithm.

10. A method for diagnosing the operation of an active airflow adjustment system (1) according to claim 1, characterized in that the shutoff mechanical member (2) lacks a dedicated diagnostic element located outside the mechatronics system (3).

11. A method for diagnosing the operation of an active airflow adjustment system (1) according to claim 1, characterized in that one of the aforementioned data relates to the operation of the magnetic motor (10) of the mechatronics system (3).

12. A method for diagnosing the operation of an active airflow control system (1) according to claim 1, characterized in that the algorithm statistical analysis model for determining specificity is provided with a comparison between a stored sequence of sampled digital data S and a reference data map Sr.

13. A method for diagnosing the operation of an active airflow control system (1) according to claim 1, characterized in that the algorithm statistical analysis model for determining the singularity is provided with the detection of at least one singularity in a stored sequence of sampled digital data S.

14. A method for diagnosing the operation of an active airflow adjustment system (1) according to claim 1, characterized in that the algorithmic statistical analysis model for determining the specificity provides a stored sequence of sampled digital data S to a model obtained by training a neural network from training data corresponding to the expected operation of the shutoff mechanical member (2).

15. A method for diagnosing the operation of an active airflow control system (1) according to claim 1, characterized in that a reference sequence of sampled digital data Sr is stored for each mechatronics system (3) at the end of the system (1) or the assembly line of the system (1) on the vehicle.

16. A method for diagnosing the operation of an active airflow control system (1) according to claim 15, characterized in that the algorithmic statistical analysis model can compare a sampled digital data sequence with a reference sequence of the sampled digital data Sr in order to detect behavioral drift that can cause deterioration of the mechatronics system or verify normal wear behavior of the system.

17. A method for diagnosing the operation of an active airflow control system (1) according to claim 1, characterized in that the stored sequence of sampled digital data S is derived from digital data dependent on the mechanical load in the active grid system.

18. Identifying the specificity in the stored sequence of the sampled digital data S is - Mass or inertia moving within the active grid system, - and / or mechanical friction in the said system, - and / or the air load on the said system, A method for diagnosing the operation of the active airflow control system (1) according to claim 17, characterized in that it is made possible by knowledge of the and / or system temperature.

19. An electromechanical active airflow control system (1) for a vehicle, - Circuit breaker mechanical member (2), - A mechatronics system (3) is provided with a housing that accommodates a magnetic motor (10) coupled to a movement conversion unit for moving the aforementioned circuit breaker mechanical member (2), and a control circuit (11) for driving the magnetic motor (10), - Includes an electronic communication line (7) for exchanging information with the vehicle's DCU (8) or for receiving commands from the vehicle's DCU (8), The electromechanical active airflow control system (1) is characterized in that the control circuit (11) includes a microcontroller or microprocessor executing a computer program stored in its read-only memory to control the steps of the method according to any one of claims 1 to 18.

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