Method for diagnosing the operation of an active airflow regulation system - Patents.com

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] The present invention relates to the field of active airflow conditioning of vehicles, in particular the modification of the air permeability coefficient of the vehicle by using motorized fins or spoilers, and to the thermal control of vehicles, in particular the engine, the braking system, and more generally any component whose temperature is actively regulated, in particular a powered vehicle with an internal combustion engine or an electric or hybrid motor, by an active airflow conditioning system that drives a blocking element, for example a motorized air shutter on a grill or curtain. The blocking element is designed to modify the amount of air passing through the radiator and the engine compartment of the vehicle, and to regulate the airflow in order to manage the efficiency of the engine and the various main assemblies of the vehicle (combustion engine, electric traction motor and its inverter, gearbox, battery pack, etc.). With the evolution of pollution prevention standards, engine temperature management has become a major area of ​​development in engine design. Here, engine cooling is optimized to control the temperature of the various engine zones while limiting energy consumption.

[0002] The purpose of the thermal management circuit is to maintain the various internal components of the engine (cylinder, cylinder head, etc.), and where applicable, peripheral components (eg, turbocharger), at their ideal operating temperatures.

[0003] If the engine temperature is too high, the gases in the cylinders will be hotter, which means there will be less gas inside the cylinders, meaning there will be a greater risk of self-ignition (and knocking), while components will deform more and there will be a greater risk of failure.

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

[0005] In many modern vehicles, the fins of the grille in front of the radiator are hinged and motor-driven to control the airflow through the radiator. In fact, when the vehicle is running at high speed and low load, high airflow is not necessarily essential to adequately cool the engine.

[0006] Furthermore, the air flowing through the radiator disrupts the main flow around the vehicle and creates air resistance. It can also be advantageous to block the air intake upstream of the radiator with a shutter. In some cases, the shutter only has 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 during an impact, or if one or more shutters are missing. The correct operation of the active shutter grille affects the pollutant emissions of the vehicle. It is therefore of paramount importance, and legally required, to ensure the correct operation of the air shutter device and to inform the driver of any deterioration of the vehicle's fuel consumption and pollution levels every time the vehicle is driven, and to prompt the driver to go quickly to the garage to restore the vehicle's legal emission levels. To achieve this goal, solutions have been provided to check the general functioning of the air shutter arrangement by means of an on-board diagnostic system. In the case of electric vehicles, the thermal control of the motor and the battery is also a key issue, having the same requirements as the aerodynamic efficiency. A faulty active airflow regulation system can penalize the air transmission of the vehicle and thus the vehicle's range. [Background technology]

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

[0009] DE 102018108162 A1 describes an actuator control method and an actuator, in which a DC motor drives a system to move a first body relative to a second body, the first body sliding along a contact surface of the second body. The principle of this solution is based on a current analysis that assesses the behavior of the actuator by providing an area of ​​modified mechanical resistance on the contact surface between the roller shutter and the guide rail.

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

[0011] Secondly, it is limited to mechatronic systems using DC motors and cannot be implemented in systems using stepper motors: the use of torque (or DC current) measurement imposes closed-loop motor control as opposed to stepper motor control, making prior art solutions unusable or difficult to use in this situation.

[0012] Thirdly, the signal to be analysed is the current reflecting the torque and is transmitted to a computer. However, the update frequency of this information is of the order of 10 μs, whereas the transmission capacity of multiplexed LIN-type protocols is of the order of 100 ms, leading to incompatibilities and insufficient bandwidth to transmit the required information. The transmission of all the acquired information is not adapted to the very limited bandwidth of the communication network for the vehicle control electronics.

[0013] US Patent Application Publication No. 2020300156 describes another anomaly detection system for an engine coolant recirculation system, which is equipped with a grille shutter that can adjust the circulating airflow coming from outside the vehicle to the environment of the engine body, and four trained neural networks are stored, which are obtained by using at least five parameters consisting of the engine coolant temperature at engine start, the amount of air taken into the engine, the amount of fuel injected into the engine, the outside air temperature, and the vehicle speed as input parameters of the neural network, and by using the measured values ​​of the engine coolant temperature as training data to learn weights for four states including a state in which the grille shutter is closed and the air blown by the blower does not circulate through the air conditioning heater, a state in which the grille shutter is open and the air blown by the blower does not circulate through the air conditioning heater, a state in which the grille shutter is closed and the air blown by the blower circulates through the air conditioning heater, and a state in which the grille shutter is open and the air blown by the blower circulates through the air conditioning heater. Among the four trained neural networks, one of the trained neural networks corresponding to the current state of the grille shutter and the circulation state of the air blower in the air conditioning heater is used to estimate the engine coolant temperature from the five parameters. An engine coolant recirculation system fault is detected based on the estimated engine coolant temperature value.

[0014] The drawback of this solution is that it provides information on the prediction of the cooling water temperature, which does not allow to characterize anomalies in the kinematic and pneumatic chains of the shutoff system.

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

[0016] German Patent No. 10201201469 describes an air shutter device also having an adjustment drive connected to move and transmit force to at least one air shutter in order to adjust the at least one air shutter between its predefined operating positions, the air shutter device also having a control device coupled to the adjustment drive in terms of signal transmission, a storage device coupled to the control device in terms of signal transmission, and a data acquisition device for capturing operating data from the air shutter device memory device, and differences in operating positions representing a set of reference operating data are stored in the memory device.

[0017] U.S. Patent No. 2,133,38870 describes another system solution and method for diagnosing mechanical faults affecting an Active Grille Shutter (AGS) system in a motor vehicle. The method involves receiving (410) at least one mechanical fault status signal, such as a signal indicating that a particular mechanical fault condition has been detected in the AGS system. The method also involves receiving (418) a temperature signal indicative of a temperature at a location proximate the grille shutter of the AGS system, and determining whether to perform extended diagnostics based on at least the mechanical fault condition signal and the temperature signal.

[0018] Shortcomings of the prior art Prior art solutions cannot reliably characterize abnormalities in the condition of the mechanical and pneumatic shutoff chains without mechanical modifications of the mechanical and pneumatic chains, such as deformation of the shutters or partial breakage of the air inlet system. Summary of the Invention

[0019] To overcome these drawbacks and provide a diagnostic capability for the entire system without modifying the blocking member, data within the mechatronic system that moves the blocking member or, where applicable, data generated by the blocking member is used to characterize airflow malfunctions. To this end, the present invention in its most general sense relates to a method for diagnosing the operation of an active airflow conditioning system for a vehicle having the features recited in claim 1, and to a system having the features recited in the system claim.

[0020] The method is: -Shutoff mechanical components; a mechatronics system including a housing for accommodating a magnet motor driven by a control circuit and coupled to a movement converter for moving a cutoff mechanical member; an electronic communication line for exchanging information with the vehicle's DCU or receiving commands from the vehicle's DCU; The method comprises: storing in a memory a numerical sequence S of data acquired by a control circuit within the mechatronic system during an operating cycle of the active air flow regulation system; applying an algorithmic statistical analysis model to the numerical sequence of data S stored in the memory in order to determine the singularity; identifying sporadic errors associated with an anomaly to provide information regarding the status of the active airflow regulation system;

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

[0022] In another variation, the digital data sequence stored in the memory is - pre-processing at a first processing frequency by a 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 data set preprocessed by the control circuit is sent to the DCU / ECU via a command line.

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

[0024] In a further embodiment, the method includes the additional step of: - integration of sporadic errors according to instructions sent by the vehicle's DCU, resulting in an integrated diagnostic status; -Transmitting integrated diagnostic status to the vehicle's DCU.

[0025] For purposes of this patent, "ECU" means an "electronic control unit" that controls a particular vehicle function.

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

[0027] The use of domain controllers (DCUs) is also known for the purpose of centralization via DCUs rather than using multiple ECUs. The increase in controllers in the automotive sector is driven in particular by vehicle automation, which must combine data from different sensors, require fast processing and meet the most stringent safety requirements. It must also offer the possibility to continuously develop new features and functionalities over the life of the vehicle via Over The Air (OTA) updates.

[0028] Because the nature of the controller will depend on the choice of computer architecture of the vehicle in which the invention is implemented, the present invention will refer to it interchangeably as an ECU or DCU.

[0029] Additionally, statistical analysis algorithms can be provided with supervised, unsupervised, or reinforcement learning sequences.

[0030] Alternatively, the statistical analysis algorithm may comprise a clustering selection method.

[0031] In one variant, the shutoff mechanical member lacks a dedicated diagnostic element external to the mechatronic system.

[0032] In an acceptable variation, the digital data s i relates to the operation of magnet motors in mechatronic systems.

[0033] In a contemplated alternative, digital data s i are the motor phase current measurements.

[0034] In this case, the digital data s i may be a measure of the rotor load angle of the magnet motor.

[0035] In one embodiment, an algorithmic model for statistical analysis to determine specificity is provided by a comparison between a stored sequence of sampled digital data S and a reference data map.

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

[0037] In a third variant, the algorithmic model for the statistical analysis to determine the specificity consists in subjecting 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 cut-off machine member.

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

[0039] In this case, an algorithmic model for statistical analysis can compare the sampled digital data sequence with a reference sequence of sampled digital data to detect behavioral drifts 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 an item of digital data that is dependent on the mechanical load in the active grid system.

[0041] In this case, the identification of anomalies in a stored sequence of sampled digital data S can be done by: Mass or inertia moving within the active grid system, and / or mechanical friction in the system, and / or the air load on the system; and / or system temperature.

[0042] For the purposes of this patent, "specificity" is defined as a point or a subset of points in the 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 preliminary learning phase obtained by the same signature calculation function, or as a specificity in the sense of Gaussian statistics, or a point or a set of points that differs from the projection of the intermediate curve formed by points that significantly precede the average of the variations of the stored points with respect to the intermediate curve, and is meaningfully understood as "exceeding the amplitude of the variations in normal or nominal operation by at least 20%".

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

[0044] In one variant, the algorithmic model for the statistical analysis to determine the specificity consists in subjecting 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 cut-off machine element.

[0045] In a particular embodiment, a reference sequence of sampled digital data is stored for each mechatronic system at the end of the assembly line.

[0046] Advantageously, the algorithmic model for statistical analysis can compare the sampled digital data sequence to 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 an item of digital data that is dependent on the mechanical load in the active grid system.

[0048] According to a particular embodiment, the identification of peculiarities in the stored sequence of sampled digital data S comprises: Mass or inertia moving within the active grid system, and / or mechanical friction in the system, and / or the air load on the system.

[0049] The present invention also provides an electromechanical active airflow regulation system (1) for a vehicle, comprising: - a blocking mechanical component; - a mechatronics system provided with a housing containing a magnet motor (10) driven by a control circuit and coupled to a movement converter for moving a cut-off mechanical member; - an electronic communication line for exchanging information with the vehicle's DCU or for receiving commands from the vehicle's DCU, The method comprises the steps of: a microcontroller or microprocessor executing a computer program stored in its read-only memory; - a set of digital data acquired by a control circuit within the mechatronic system (3) during an operating cycle of the active air flow regulation system; i storing in memory a sequence of sampled digital data S consisting of: - applying an algorithmic statistical analysis model to the sequence of sampled digital data S stored in a memory to determine singularity; - controlling a series of steps consisting of: - identifying sporadic errors associated with specificities, which are information regarding the status of the active airflow adjustment system (1), and transmitting the information to the DCU by a control circuit. [Brief description of the drawings]

[0050] The invention will be better understood from reading the following description of different non-limiting embodiments, with reference to the attached drawings, in which: [Figure 1] FIG. 2 shows a schematic diagram of an active airflow regulation system driven by a stepper motor according to the present invention. [Diagram 2] 1 shows a schematic diagram of a variant of an active airflow regulation system driven by a sensored BLDC motor according to the present invention. [Diagram 3] FIG. 2 shows a schematic diagram of a variant of an active airflow regulation system driven by a sensorless BLDC motor according to the present invention. [Figure 4] 1 shows an example of a diagnostic algorithm for fault identification specific to a shutter system. [Diagram 5] FIG. 4 shows an example of a learning algorithm for fault recognition based on the presented algorithm. [Figure 6] An exploded view of the active shutter air conditioning system is shown. [Figure 7] An exploded view of the active curtain air conditioning system is shown. [Figure 8] 7 shows various examples of malfunctions associated with the system shown in FIG. 6. [Figure 9] 7 shows various examples of malfunctions associated with the system shown in FIG. 6. [Figure 10] 7 shows various examples of malfunctions associated with the system shown in FIG. 6. [Figure 11] 7 shows various examples of malfunctions associated with the system shown in FIG. 6. [Figure 12] 7 shows various examples of malfunctions associated with the system shown in FIG. 6. [Figure 13] 8 shows various examples of malfunctions associated with the system shown in FIG. 7. [Figure 14] 8 shows various examples of malfunctions associated with the system shown in FIG. 7. [Figure 15] 8 shows various examples of malfunctions associated with the system shown in FIG. 7. [Figure 16]8 shows various examples of malfunctions associated with the system shown in FIG. 7. [Figure 17a] FIG. 2 shows a Fresnel diagram of a three-phase motor which enables the load angle of the motor to be visualized. [Figure 17b] FIG. 2 shows a Fresnel diagram of a three-phase motor which enables the load angle of the motor to be visualized. [Figure 17c] FIG. 2 shows a Fresnel diagram of a three-phase motor which enables the load angle of the motor to be visualized. [Figure 17d] FIG. 2 shows a Fresnel diagram of a three-phase motor which enables the load angle of the motor to be visualized. [Figure 18] A method is presented for obtaining a series of data acquisitions for identifying anomalies based on measurements of power consumed by a motor. [Figure 19] An example of a supervised learning algorithm that can be used as a decision-making basis for the algorithm presented in Figure 4 is shown.

[0051] General principles of active airflow regulation systems Figure 1 shows a general view of an active airflow conditioning system (1), which consists of a mechanical blocking element (2) positioned on the grille of the vehicle and capable of blocking the airflow reaching the radiator, and is equipped with a mechatronic system (3) that controls the positioning of the mechanical blocking element (2), such as a shutter or roller, and allows the modulation of the airflow received by the radiator depending on the state of the mechanical blocking element (2).

[0052] The object of the invention is to automate the detection and characterization of anomalies in mechanical and / or pneumatic airflow regulating elements, in particular the disappearance of shutters or changes in the geometry of the shutters, resulting for example from impacts, even if these anomalies do not change the general kinematics of the mechatronic airflow regulating system. Naturally, the invention also makes it possible to detect faults such as blockages or seizures in mechatronic airflow regulating systems.

[0053] The mechatronic system (3) comprises a motion transmission chain (20) driven by a polyphase permanent magnet synchronous motor, hereinafter referred to as magnet motor (10), e.g. a three-phase motor as shown in FIG. 1, controlled by an electronic control circuit (30).

[0054] Since the present invention utilizes signals available on the electronic control circuitry (30) of the mechatronic system (3) without the need to add one or more additional sensors on the mechanical blocking member (2) of the active airflow regulating system (1), the mechatronic system (3) according to the present invention is able to provide diagnostic information when installed in an active airflow regulating system (1) that does not initially have this capability. However, when additional sensors are already implemented in the active airflow regulating system (1), for example on the mechanical blocking member (2), the present invention can also use signals from these sensors, provided that the signals from these sensors are processed by the electronic control circuitry (30).

[0055] In the case shown in FIG. 1, a magnet motor (10) is configured to be step-driven. The mechatronics system (3) includes: an electrical connector (6) for receiving power (5) from the vehicle's DC power network; an electrical communication line (7) carrying commands from the vehicle's Domain Control Unit (DCU) (8) and information generated by the mechatronic system (3) itself and directed to the vehicle's DCU (8); - an electrical protection and filtering module (9) complying with the applicable electrical and electromagnetic standards; A control circuit (11) with memory, such as a microcontroller, that generates vehicle communication, control functions, and drive for the magnet motor (10) in association with a transistor drive module; A magnet motor (10) for generating the desired displacement and torque; a multi-phase inverter (12), preferentially two or three phases, consisting of MOSFET transistors supplying power to the magnet motor (10); A motion transmission chain (20) for reducing or transforming the motion of the rotor of the magnet motor (10), the input stage of which is driven by the rotor of the magnet motor (10) and the output stage (21) of which is coupled to the cut-off mechanical member (2).

[0056] It is important to note that the control circuit (11), the transistor driver module and the multi-phase inverter (12) can be integrated into a specific component (13) known to the layman by the acronym SOC (System On Chip). Preferably, the data sequence is generated by combining at least two different types of information, which can be derived from, but are not limited to, the following elements: one (or more) vibration sensors (13) for measuring the vibration level of the moving subassembly, taking into account the context of the active airflow regulation system (1); one (or more) temperature sensors (14) that measure the internal temperature of the mechatronic system (3) and allow for inferring the local temperature in the area of ​​the active airflow regulation system (1), one (or more) position sensors (15) that measure the continuous position of the rotor of the magnet motor (10) or of a moving element in the mechatronic system (3) and make it possible to reconstruct the load angle of the magnet motor (10), One (or more) electrical sensors (16) that measure one or more electrical quantities, such as voltage or current, in the multi-phase inverter (12) and that, when recombined, allow the load angle of the magnet motor (10) to be reconstructed.

[0057] Combining information from several sensor types can be advantageous when there is strong economic pressure on components built into the actuator, such as very basic control electronics or low sensitivity sensors, which do not allow extracting peculiarities from information from a single type of sensor. Nevertheless, this peculiarity can be isolated by the cooperation of weak signals and then identified in a sequence of sampled data resulting from the aggregation of data from different types of sources. However, this approach is not limiting and, once the economic pressure is removed, more complex systems can be envisaged, allowing the extraction of sporadic errors from a data sequence presenting a single type of information.

[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 the appropriate drive sequence for the switches of the polyphase inverter (12) to generate a movement. The movement command from the DCU (8) can be a full or partial opening or closing movement, or an absolute position of the stroke to be reached. The invention consists in taking a data sequence by the control circuit (11), storing it in a memory, analysing it using a digital process capable of detecting anomalies and transmitting sporadic errors associated with this anomaly to the DCU (8) via the communication line (7).

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

[0060] The control circuit (11) can extract singularities from this sequence of stored data using statistical processing of various complexity depending on the functional requirements resulting from the type of fault sought and the desired sensitivity. For example, a fault caused by a sudden deterioration, such as a break in a shutter that blocks the progress of the cutoff mechanical member (2), is much easier to detect than a gradual aging of the actuator that leads to a slight degradation of performance close to the required minimum. This singularity can therefore be a symptom of a wide variety of faults and requires the application of appropriate processing algorithms to generate a sporadic error from this error for transmission to the DCU (8). By "sporadic error" we mean an error code that the DCU (8) can understand and that can signal a wide variety of faults encountered by the actuator, such as, but 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 some elements of the cutoff mechanical member (2) or the breakage of a part of the motion transmission chain (20).

[0061] As shown in Figure 1, the mechatronic 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 an integrity diagnostic of the active airflow conditioning system.

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

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

[0064] Of course, these are not the only ways to implement a mechatronic system, and those skilled in the art can easily imagine other alternatives for the design and control of the magnet motors, as well as for the contents of the mechatronic system's internal sensors used to detect faults.

[0065] Different types of defects are illustrated by several examples, different means of detecting these defects are also presented, as well as several solutions for extracting idiosyncrasies from the information provided by the detection means. Finally, several algorithm implementations are provided for converting the detected idiosyncrasies into a tangible error signal. The described examples are for illustrative purposes only and do not limit the scope of the invention in any way, since a person skilled in the art may use some or all of these examples to replace missing components with solutions known in the art.

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

[0067] An optional step (120) upstream of this algorithm consists in obtaining authorization from the DCU (8) to perform a diagnosis of the system state in case of on-demand diagnosis, failing which the diagnosis is permanently activated during any movement performed by the mechatronic system.

[0068] The first step (210) consists in initiating a movement phase of the cutoff mechanical member (2) generated by the mechatronic system (3). During this movement phase, a step (211) comprises the step of generating a series of data s i The storage of data may relate to part or all of the blocking member's movement, and in either case ends with a step (212) triggered by the end of the movement. The microcontroller then uses a dedicated algorithm to detect peculiarities, which may be similar to a machine learning or deep learning model, to obtain the stored s. i Analyze the data series (213). i This is followed by a step (214) of classifying the information resulting from the analysis of (a) which results in four possible outcomes: a. No singularity was detected and the system was classified as "nominal". This is followed by a step (220) in which the mechatronics system (3) generates an operating code for the DCU (8), which is associated in step (221) with the transmission to the DCU (8) of the confidence level of the previously performed classification (214). b. A singularity corresponding to a degradation equivalent to the failure of a single active shutter has been detected, so the system has been classified as "system with one degraded shutter". This is followed by a step (230) in which a sporadic error code is generated by the mechatronics system (3) for the DCU (8), which code is associated in step (232) with the transmission of the confidence level of the classification previously performed in step (214) and with the transmission in step (233) of the environmental context in which the diagnosis of steps (210) to (214) was performed. c. Since peculiarities corresponding to degradation corresponding to the failure of some active shutters have been detected, the system has been classified as a "system with significant degradation". This is followed by a step (231) in which a sporadic error code is generated by the mechatronics system (3) for the DCU (8), which code is associated in step (232) with the transmission of the confidence level of the classification previously performed in step (214) and with the transmission in step (233) of the environmental context in which the diagnosis of steps (210) to (214) was performed. d. The classification resulting from the analysis of the data series carried out in step (214) did not make it possible to converge on information with a sufficient level of confidence to be able to deliver the information to the DCU (8). This is followed by a step (240) in which a code corresponding to the "unavailable" state of the diagnostic classification is generated by the mechatronic system (3) and transferred to the DCU (8), this code being associated in a step (241) with the transmission to the DCU (8) of the environmental context in which the diagnoses of steps (210) to (214) were carried out and could not converge.

[0069] Step (250), common to all categories providing tangible information, leads the user to step (260) if the confidence level is equal to or greater than a preset threshold, for example 99.99%, depending on the confidence level generated in the previous step. In step (260), a consolidated error code or a consolidated function code is generated and can be transferred by the mechatronics system (3) to the DCU (8) and used to feed the OBD strategy. Alternatively, if the confidence level is below the preset threshold, step (250) is followed by step (270), in which the mechatronics system recommends a strategy for consolidating sporadic errors, essentially consisting of one or more partial or total actuator movements, in the DCU.

[0070] In step (300), if step (241) has already been performed, the mechatronic system (3) summarizes updates to the analysis of the stored data series (i.e. the dedicated algorithm or model) in order to be able to classify in the future data sequences for which the current statistical analysis has not allowed to converge.

[0071] In step (300), if step (270) has already been performed, the mechatronic system (3) starts summarizing the updates of the analysis of the stored data series (i.e. the dedicated algorithm or model) so as to be able to classify in the future the data sequence that has the highest confidence level compared to the data sequence just calculated.

[0072] Steps (260) and (300) then lead to step (280), which corresponds to the end of the algorithm for fault identification.

[0073] Note that the consolidation strategy may be handled by the algorithm for fault identification presented here, or it may be an alternative algorithm suggested by the system or imposed by the DCU (8), potentially specific to the consolidation code transferred to the DCU (8). The DCU (8) may have priority responsibility for the strategy adopted to consolidate information (i.e. sporadic errors or well-behaved sequential codes).

[0074] The fault identification algorithms described herein are for illustrative purposes only and should not be considered as limiting the scope of the present invention in any way. Those skilled in the art can easily imagine similar but different alternatives that meet the essential technical requirement of informing the DCU (8) of deviations from nominal operation by means of an error code.

[0075] As an example, a sequence (300) illustrating backpropagation applied to a learning model is represented by the flow chart shown in Figure 5. If, in step (301), it turns out that the previous analysis of the data sequence has diverged instead of converging towards 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 of the model (i.e. algorithm) used to perform the analysis of the data sequence. If, in step (303), an update can be proposed to classify this sequence of data while guaranteeing the same level of performance as the classification of the previous sample, in step (307), a formal update request can be sent to the vehicle DCU and, if approved, can be stored and activated in the MCU in step (308). However, if an updated solution cannot be identified in steps (302) and (303), then in step (304) the environmental context in which the diagnosis was performed is stored in non-volatile memory of the control circuit (11) (or vehicle DCU (8)) so that limitations of the current solution can be identified later, completing the sequence with this step.

[0076] If in step (301) it is found that the previous analysis of the data sequence produced a classification with a confidence level of less than 99.99%, then in step (305) an inner algorithm analysis loop is initiated by the control circuit (11) (or by the vehicle's DCU (8)) to propose an update to the model (i.e. the algorithmic model) used to perform the analysis of the data sequence. If in step (306) an update can be proposed to produce a classification with a confidence level of more than 99.99%, while ensuring the same confidence level o as the classification of the previous sample, then in step (307) a formal update request can be sent to the vehicle DCU (8) and, if approved, can be stored and activated in the control circuit (11) in step (308). If in step (306) no update solution could be proposed, the sequence ends.

[0077] Different types of defects Figure 6 shows an example of an active airflow regulation system (1), in which the mechanical blocking member (2) consists of a set of shutters. The active airflow regulation system comprises a mechatronic system (3) positioned in the car grille in front of the radiator and driving a shaft (4) passing through an output gear of the mechatronic system (3). The shaft (4) controls the positioning of right shutters (23-25) and left shutters (26-28) via a transmission (19, 29).

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

[0079] Figures 8 to 16 show, in a non-limiting manner, different types of failures associated with an active airflow regulation system (1). Figures 8 to 12 show a failure of a mechanical blocking member (2) that fits into a shutter as shown in Figure 6, and Figures 13 to 16 show a failure of a mechanical blocking member (2) that fits into a curtain as shown in Figure 7. These figures represent a system with an active grid where the shutter rotating shaft is positioned horizontally, but the invention extends to any other orientation of the shutter rotating shaft.

[0080] Figure 8 shows the situation where one of the central shutters is missing due to impact or breakage of its pivot pin. It is then replaced by a permanent opening (34) through which air can flow, regardless of the orientation commanded by the actuator. However, the missing shutter (24) will cause the forces on the mechatronic system (3) to change during the orientation change, and the signature of these forces can be detected by processing the electrical signals measured directly at the power and control ports of the mechatronic system (3). In the example illustrated in Figure 8, the central shutter is missing, but the invention is not limited to this case and different signatures can be observed if an end shutter is missing or even if several shutters are missing, and the signature also changes with the combination of missing shutters.

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

[0082] This fault changes the forces applied to the mechatronic system (3) during the orientation change, and the signature of these forces can be detected by processing electrical signals measured directly at the power and control ports of the mechatronic system (3).

[0083] Figure 12 shows another fault situation where a shutter (24) is no longer driven and prevents the opening or closing of adjacent shutters (23, 25). This behavior can occur when a shutter's drive shaft breaks, accompanied by a shift in the stroke of the other shutters. This type of break can result in a one-off excess torque required to disengage the contacting shutter, or a severe excess torque due to the system being completely locked in a position outside the stall. It should be noted that seizure of the drive shafts of one or all dampers can also lead to a one-off or overall increase in the torque required for the opening or closing stroke.

[0084] Figures 13-15 show another fault condition of the active curtain airflow regulation system where an element of the motion transmission mechanism has failed. This can be caused, for example, by a broken drive shaft (4) as shown in Figure 13, a broken 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 forces applied to the mechatronic system (3) during the orientation change, and the signatures of these forces are detected by processing electrical signals measured directly at the power and control ports of the mechatronic system (3).

[0086] Figure 16 shows another fault situation where the frame (31) of the curtain (32) is deformed or even broken. This type of break (35) can result in a one-off excess torque required to move the curtain, or a severe excess torque due to a complete blockage of the system. It should be noted that a seizure of the drive shaft of one or all dampers can also lead to a one-off or total increase in the torque required for an opening or closing stroke by the mechatronic system (3).

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

[0088] The load angle corresponds to the magnetic angular displacement of the rotor relative to the angular position that cancels the magnetic torque between the rotor and the stator. A common way of representing this is to use a Fresnel diagram, as shown in Figure 17 by sub-diagrams a, b, c and d, where the magnetic states of the rotor and stator are represented by vectors. In this diagram, which represents a three-phase machine, the vectors U, V, 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 the 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 FIG. 17a, to a maximum when the vector is at 90°, as shown in FIG. 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, in order to visualize the evolution of the torque vector between load angles 0° and 90°.

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

[0091] When a force is applied to the rotor, such as a braking torque, a load, or a drive torque, the load angle (52) increases and is no longer equal to 0°. If this load angle (52) exceeds 90°, the torque decreases and 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 invention is compatible with all types of synchronous motor control, but this solution is more specifically dedicated to stepper motor control. Various solutions are available for measuring the rotor position, and the use of dedicated sensors such as analog magnetic sensing probes is preferred, but they nevertheless have the drawback of not being very high resolution and may show their limitations when measuring very small variations in the load angle (52). In actuators with high mechanical reduction such as those used in the invention, the reduction chain reduces the perceptible influence on the drive motor of the variations in the load experienced by the driven member 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, which often do not meet the target price of these applications. Therefore, 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 drastically reduce the number of load angle measurements during a complete rotor revolution to improve the angle resolution.

[0094] A first solution is to continue to use an analog probe to measure the magnetic field of the sensor magnet, but without the need to reconstruct the absolute rotor angle, which is a source of inaccuracies and consumes computational resources. It is then desired to measure only the zero crossing of the probe, which is the most accurate since it is where the magnetic field variations are maximum. In order to correctly identify this zero crossing, the maximum and minimum values ​​of the probe must be measured and stored for each magnetic period, and the zero crossing is then reconstructed as the average value between the maximum and minimum values ​​of the previous step. This ensures insensitivity to changes in the magnetization amplitude, such as slow irreversible variations due to aging, or short-term reversible variations due to, for example, temperature fluctuations. Measuring the load angle is therefore Step or microstep counter s Triggering the ·V M and V m are the maximum and minimum values ​​of rotation n-1, respectively.

[0095]

number

[0096]

number

[0046]

[0097] The second solution is to replace the analogue probe with a digital probe. This solution does not make it possible to adjust the transition threshold for each magnetic cycle according to the measurements obtained during the previous cycle, so the obtained accuracy is slightly reduced, but it brings both monetary savings and a reduction in the required computational resources. By incorporating a digital probe, it is possible to use a microcontroller without analogue inputs and it is no longer necessary to reconstruct the rotor angle by calculation. The method of measuring the load angle is also similar to the previous solution, and Triggering a step or microstep counter when a zero crossing of a digital probe is measured Stopping the counter when the zero crossing of the digital probe is measured; ·formula

[0098]

number

[0099] For both solutions, the electrical period L A,1t The load angle measurements over can be averaged according to the following formula:

[0100]

number

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

[0102]

number

[0103] Detailed Description of the Second Specificity Measurement Variation In one embodiment, the detection of the operational peculiarities is based on sampling and analysis of the electrical signal measured at the terminals of an RC filter in series with the power supply of a mechatronic system (3), also known as an actuator, comprising a polyphase motor coupled with a so-called self-commutated control, known to those skilled in the art as "BLDC control", and carried by an electronic module integrated in the mechatronic system (3).

[0104] 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, respectively, and Q3 and Q6) drive the current through phase C (phase B and phase A, respectively). Resistor (60) is a sampling resistor used to measure the sum of the currents through each phase of the magnet motor (10) through an RC filter (63) consisting of resistor (61) and capacitor (62). The output of the RC filter (63) is taken as a voltage by an analog-to-digital converter and is continuously converted and preprocessed into information, for example, by a microcontroller.

[0105] A brief description of alternative measures of specificity Another parameter that may be of interest to measure in order to detect faults is the number of steps it takes 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, since it can significantly reduce the effective stroke. In a detailed example, this parameter can also be used to identify missing shutters.

[0106] The singularity measurements are not limited to functional quantities in the electric motor, such as the current measurements or the load angle measurements presented above, but could easily come from dedicated sensors inside the actuator. An example would be a pressure sensor or a temperature sensor whose variations would be proportional to the incoming airflow. It would therefore be well conceivable to develop different scenarios to make the measurements of these sensors sensitive to the aforementioned disturbances.

[0107] Description of an example of a uniqueness identification algorithm For example, one means of anomaly detection, compatible with current measurements as illustrated in the description of FIG. 18, is to identify values ​​in the stored data series that exceed the expected value by more than a few standard deviations. This is described here using the format developed for current measurements, but can also be used for other types of measurement data. Anomaly detection is achieved by digitally processing the evolution of a physical value consisting of the sum of the currents I of the N phases of the polyphase motor, measured at resistor (60). During nominal operation in synchronous mode, the current has a stable value and the rotor of the geared motor is regular and free of symptomatic behavior, but when the geared motor is exposed to an abnormal or faulty active airflow regulation system (1), this value evolves towards a different and more irregular value.

[0108] The processing of the variations in the current I measured across the resistor (60) is sampled, for example using an analog-to-digital converter in a microcontroller, and the standard deviation σ of the amplitude values ​​of the current measured across the 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 the acquisition of the information.

[0109] Characterization of the operating modes and detection of faults is carried out by analyzing the variation of this standard deviation σ, possibly complemented by an analysis of the evolution of the median over N samples of the current measured at the terminals of R1. - A stable standard deviation σ below the threshold measured during nominal operation corresponds to problem-free operation - An increasing standard deviation above a threshold and / or a significant increase in the median current measured at the terminals of R1 indicates seizure of one or more shutters and / or their drive mechanisms and therefore fluid control drift. -An increasing standard deviation above a threshold and / or a significant decrease in the median current measured at the terminals of R1 corresponds to the loss of one or more shutters, sticking in the open position, or partial destruction of one or more shutters and thus a drift in the fluid control. - As an example, the value N 2* σ 2 is compared with the total phase current value I. This digital processing distinguishes areas of normal operation from areas of abnormal operation and 2* σ 2 A standard deviation threshold E can be set for φ, above which the microcontroller or ASIC determines that the geared motor is driving a faulty gating mechanism. Several other types of algorithmic statistical analysis model combinations can also be used.

[0110] Description of variations of the uniqueness identification algorithm In one embodiment, the anomalies are identified using a learning algorithm that can be trained to identify the type of error encountered, such as a missing shutter or a broken shutter blocking the system, but can also identify the location of the fault, for example a missing shutter at one end or in the middle.

[0111] In the case where the driven member consists of a set of shutters, the fault detection algorithm can benefit from a modification of the driven member to improve the detection. It is therefore envisaged to provide for each shutter a specific signature that can be faithfully distinguished by the algorithm. This can be a binary code, as in US patent application 9810138(B2), with the addition of point resistance elements to each shutter, detectable in the form of spot excess torques generated by the actuator, the point resistance elements being carefully positioned so that each one induces an excess torque at a different moment of the opening or closing stroke. The absence of a shutter is then indicated by the absence of friction peaks during the overall movement of the driven member. It should be noted that such a signature can be measured, for example, by a very simple algorithm that detects current peaks consumed by the actuator that exceed a certain threshold at a given position to acknowledge the presence of each shutter.

[0112] If the actuator is a set of shutters or curtains, the signature can be more subtle, harnessing the power of deep learning by providing each actuator shutter with a specific surface area, a specific mass, or a specific profile. This can lead to the measurement of very specific signatures, for example linked to the inertia of the system, linked to its pneumatic pressure, or even by obtaining a unique vibration for each shutter.

[0113] Description of other variations of the uniqueness identification algorithm Note that there are many possible variations of specificity identification algorithms that are compatible with the present invention, each with its advantages and disadvantages. For example, some are more robust but require a specific sequence of device actuations to be activated to measure the fault, others must be performed in a stationary state, others in motion, and still others require the addition of external data such as vehicle speed, wind speed or temperature, so that 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 invention, the singularity identification algorithm is based on the measurement of hydrodynamic torque variations. A missing shutter or a torn curtain reduces the pressure on the entire blackout system, which in turn reduces the torque required by the actuator to perform the opening or closing movement. The hydrodynamic torque T d 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 the torque, current, or load angle measurements as a sequence of sampled digital data to identify the anomalies, or to increase its accuracy, take this data along the actuator stroke and calculate ex according to the following formula: 0 and x 1 The singularity W(x 0 ,x 1) can be identified.

[0117]

number

[0118] The measured work can then simply be compared with a reference curve corresponding to a healthy actuator. It should be noted that this method consumes very few computational resources, but is highly dependent on the wind speed and the vehicle speed. However, if the available computational power allows to implement more intelligent algorithms, some specific actuator movements can be made to estimate the relative speed of the air impinging on the blackout device.

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

[0120] According to another variant of the invention, the singularity identification algorithm can be based on a measurement of the inertia of the system. If the AGS actuator is controlled so that the angular velocity of the actuator rotor increases linearly between two instants, the rotor acceleration is constant. By carrying out an opening cycle with the vehicle stationary, a fault such as a missing shutter can be detected by measuring the variation of the inertia during this cycle, compared to a reference value stored in the system. Alternatively, this change in inertia can be measured from torque, current or load angle readings, but these examples do not limit the invention.

[0121] Detailed description of the variations of the learning algorithm In one embodiment, the algorithm describing the fault detection diagnosis requires an initial supervised learning phase in which a set number of cycles are provided with the system in nominal (or functional) mode only. An example of such a supervised learning algorithm is shown in the flow chart of FIG.

[0122] Classification systems of interest i Denotes the number of learning cycles X required to provide results with an optimal confidence rate over the entire period using sampled acquisition of the data series. The confidence rate 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, with a limited number of learning cycles, for example 10 or 20 depending on the sensitivity of the algorithm, it is possible to obtain a confidence rate close to 100%, where 100% means total certainty and 0% means total uncertainty, by seeking a desired high confidence level of more than 99%.

[0123] The learning algorithm (100) is triggered every time the actuator is put into motion by the control circuit (11), referred to as step (110), and is followed by step (111) of checking 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 ends and triggers the fault detection algorithm (200).

[0124] On the other hand, if it is not determined that the learning phase is completed, in step (112), a "non-operating" state of the fault detection system is generated by the mechatronic system (3) for the DCU (8). A movement is then initiated by the mechatronic system (3). Step (113) of the learning algorithm consists in a sampled acquisition of data of interest by the control circuit (11) and in storing these data in an internal memory of the mechatronic system (3). Step (113) is completed when the end of the actuator movement is detected. The learning algorithm then triggers, in step (114), a statistical analysis of the data stored for this learning cycle in step (113). The next step (115) consists in a check by the control circuit (11) of the correctness of the movements performed by the actuators and of the correct environmental conditions (i.e. the correct context), leading to a decision whether to retain the data from the current cycle as reference data for the learning algorithm. If so, these data are stored as reference data in a memory register in a final step (116). The storage of reference data preferentially includes contextual data from the stored cycle, such as information on temperature, vehicle speed, air pressure at the shutters, etc., which may come from external sensors, and 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, so that the cycle is not considered a reference cycle, and a notification may be generated by the mechatronics system (3) to inform the DCU (8).

[0125] An embodiment in which data processing is shared between the ECU and the DCU (8) In the above description, it has been generally assumed that all algorithmic processing is performed solely via the control circuit (11), which then transmits information characterizing the nature of any faults detected in the air flow chain, whether or not there is a mechanical fault.

[0126] However, it is also possible to split the algorithm processing between the control circuit (11) and the vehicle's DCU (8), which generally has a much higher computational capacity and can also optionally take into account other vehicle data in order to achieve a more complete integration of the vehicle diagnostics. However, this embodiment must take into account the fact that the transmission BUS between the equipment and the vehicle's DCU has a limited bandwidth and the raw sensor data cannot be sent directly to the vehicle's DCU. A "distributed" embodiment of the invention then consists of a high-frequency pre-processing of the raw data in the control circuit (11) to calculate a pre-processed data set, which is then transmitted from the control circuit (11) to the vehicle's DCU (8) at a frequency that matches the bandwidth of the communication line (7), for example using the LIN protocol, and the vehicle's DCU (8) uses these data sets transmitted at a low frequency to perform post-processing at a second frequency lower than the first frequency in order to complete the analysis and to be able to identify sporadic errors, which then allows the DCU (8) to characterize the pneumatic and / or mechanical faults of the airflow regulation system.

Claims

1. 1. A method for diagnosing the operation of an active airflow conditioning system (1) for a vehicle, comprising: - a cut-off mechanical element (2), a mechatronic system (3) provided with a housing containing a magnet motor (10) driven by a control circuit (11) and coupled to a movement converter for moving said cut-off mechanical member (2); an electronic communication line (7) for exchanging information with a DCU (8) of said vehicle or for receiving commands from said DCU (8) of said vehicle, The method further comprising: - storing in a memory a sequence S of values ​​of data obtained by said control circuit (11) inside said mechatronic system (3) during an operating cycle of said active air flow regulation system (1); - applying an algorithmic statistical analysis model to said numerical sequence of data S stored in said memory in order to determine singularity; - identifying sporadic errors associated with said peculiarities in order to provide information regarding the status of said active airflow conditioning system (1),

2. 2. The method for diagnosing the operation of an active airflow regulating system (1) as claimed in claim 1, characterized in that the step of identifying the sporadic error is performed by the control circuit (11), the control circuit (11) transmitting information representative of the identified sporadic error to the DCU.

3. The sequence of digital data stored in the memory comprises: - pre-processing at a first processing frequency by said control circuit (11), and - subject to post-processing by the DCU / ECU (8) of the vehicle at a second processing frequency lower than the first processing frequency in order to complete an analysis and identify sporadic errors; 2. The method for diagnosing the operation of an active airflow conditioning system (1) according to claim 1, characterized in that the data set preprocessed by the control circuit (11) is transmitted to the DCU / ECU (8) via a command line (7).

4. A sequence of digital data S stored in a memory is a. a first type of data; and b. at least a second type of data different from said first type.

5. The method further comprises the additional step of: a. Consolidating the sporadic errors according to commands sent by the vehicle's DCU (8) resulting in a consolidated diagnostic status; and b. transmitting said integrated diagnostic status to a DCU (8) of said vehicle.

6. A method for diagnosing the operation of an active airflow regulating system (1) according to claim 1 or 5, characterized in that one of said data is a measurement of the motor phase current.

7. 6. A method for diagnosing the operation of an active airflow conditioning system (1) according to claim 1 or 5, characterized in that one of the data is a measurement of the load angle of the rotor of the magnet motor (10).

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

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

10. 2. A method for diagnosing the operation of an active airflow regulation system (1) according to claim 1, characterized in that the shutoff mechanical member (2) is devoid of any diagnostic-dedicated elements external to the mechatronic system (3).

11. 6. A method for diagnosing the operation of an active airflow conditioning system (1) according to claim 1 or 5, characterized in that one of the data relates to the operation of the magnet motor (10) of the mechatronic system (3).

12. The algorithmic model of statistical analysis for determining specificity includes a stored sequence of sampled digital data S and a reference data map S. ｒ 2. A method for diagnosing the operation of an active airflow regulating system (1) according to claim 1, characterized in that a comparison is provided between:

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

14. 2. A method for diagnosing the operation of an active airflow conditioning system (1) according to claim 1, characterized in that the algorithmic model for the statistical analysis to determine specificity consists in subjecting the 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 cut-off mechanical member (2).

15. Sampled digital data S ｒ A method for diagnosing the operation of an active airflow conditioning system (1) according to any one of claims 1 to 14, characterized in that a reference sequence of is stored for each mechatronic system (3) at the end of the assembly line of the system (1) or of the system (1) on the vehicle.

16. The algorithmic model for the statistical analysis performs a statistical analysis of the sampled digital data sequence to detect behavior drifts that may cause degradation of the mechatronic system or may verify normal wear behavior of the system. ｒ 16. A method for diagnosing the operation of an active airflow regulating system (1) according to claim 15, characterized in that the measured sequence of the active airflow regulating system (1) can be compared with a reference sequence of:

17. 2. A method for diagnosing the operation of an active airflow regulating system (1) as claimed in claim 1, characterized in that the stored sequence of sampled digital data S is derived from digital data dependent on mechanical loads in the active grid system.

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

19. An electromechanical active airflow regulation system (1) for a vehicle, comprising: - a cut-off mechanical element (2), a mechatronic system (3) provided with a housing containing a magnet motor (10) driven by a control circuit (11) and coupled to a movement converter for moving said cut-off mechanical member (2); an electronic communication line (7) for exchanging information with a DCU (8) of said vehicle or for receiving commands from said DCU (8) of said vehicle, The electromechanical active airflow regulating system (1), characterized in that the method comprises a microcontroller or microprocessor executing a computer program stored in its read-only memory to control a series of steps corresponding to claims 1 to 18.

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