Method and device for monitoring the status of a mechanical system of a vehicle using thresholds that vary depending on the operating parameters of the system

Description

[0001] The present invention lies in the field of health monitoring systems for mechanical systems.

[0002] The present invention relates to a method and a device for monitoring the health of a mechanical system equipping a vehicle using variable thresholds according to operating parameters of the system.

[0003] A mechanical system may have several moving parts, for example several rotating parts, such as an input shaft and an output shaft.

[0004] For example, a mechanical system may include one or more bearings to guide the rotation of an input shaft and an output shaft or even one or more intermediate rotating members. A bearing includes, for example, a rolling bearing equipped with one or more rows of rolling elements such as balls, rollers or others.

[0005] A mechanical system can also be equipped with at least one toothed wheel, one pinion, one toothed crown.

[0006] The operation of a mechanical system can be monitored to detect an operating anomaly, or even anticipate the occurrence of a breakdown or malfunction. An aircraft can thus include an on-board monitoring system to monitor the health of a mechanical system in order to increase flight safety.

[0007] Such a monitoring system can, regardless of the mechanical system being monitored, measure and record operational data of the mechanical system, in particular of a vibration nature. This monitoring system can then determine health indicator values ​​based on this operational data and compare them to predefined thresholds to detect a potential anomaly linked to an emerging mechanical defect. The value of each threshold can be obtained by experience or by a statistical analysis of a history of measurements from several similar mechanical systems acquired during testing or validation phases or during the first hours of use of such a mechanical system. The thresholds can, for example, be estimated from a parametric modeling of the health indicator statistics with the assumption that the mechanical system is healthy, namely without anomalies, malfunctions or defects.

[0008] The value of each threshold can be constant over the entire operating range of the mechanical system. Each detection threshold can alternatively have several constant values ​​relating respectively to specific phases of operation of the mechanical system.

[0009] A health indicator may take the form of a vibration indicator evaluated using at least one sensor, this sensor being able to include at least one accelerometer, a tachometer. Such a health indicator may be defined using a signal provided by a single sensor, for example by being equal to the maximum amplitude of a temporal vibration signal provided by an accelerometer or by combining the signals of several sensors. This is for example the case of health monitoring systems designated by the acronym HUMSfor "Health and Usage Monitoring System" in English A health indicator can allow monitoring of a mechanical system as a whole while other health indicators can be specific to a particular type of fault that may appear on a particular part of the mechanical system.

[0010] Documents US 7684936, US 2017 / 0023438, US 8442778, US 8131420, CN 102963533 and US 2011 / 0166799 describe surveillance systems HUMS. Documents US 8442778 and US 2017 / 0023438 are intended in particular for the monitoring of wind turbines. Documents US 8131420, CN 102963533 and US 2011 / 0166799 are dedicated to rotary wing aircraft.

[0011] In particular, document CN 102963533 presents a monitoring system. This monitoring system includes an on-board system for identifying a risk in real time and generating an alert, as well as a remote ground-based system capable of downloading and analyzing the data acquired in flight in order to plan the necessary maintenance operations.

[0012] Document US 2011 / 0166799 describes a monitoring system determining a health indicator whose value is adjusted according to an operating parameter of the monitored mechanical system, and in particular according to a torque exerted on a rotating member. This health indicator can then be compared to a threshold to estimate a risk of a fault occurring.

[0013] Such monitoring systems are interesting, but the usual health indicators are dependent on the operating conditions of the mechanical system. These operating conditions are, for example, characterized by context parameters. These context parameters are, for example, the altitude or forward speed of the aircraft, the torque exerted within a transmission box, the rotation speed of a rotor, etc. This dependence can result in a non-stationarity of the health indicators over the entire operating range of the mechanical system and / or the aircraft. Therefore, these health indicators may only be usable during specific operating regimes.As a result, monitoring the health of a mechanical system may be restricted to certain periods of use, excluding any transient regime in particular, which may delay the detection of a fault, or on the contrary cause the detection of false failures.

[0014] Specific methods applied to health indicators then aim to improve fault detection, in particular by taking into account the operating conditions of the mechanical system.

[0015] For example, EP 2345894 describes a method for indicating a fault propagating in a rotating shaft. After measurements of data corresponding to a health indicator of the shaft and measurements of an input factor, for example a torque exerted on the shaft, a relationship between the health indicator and the input factor is determined. A filtered health indicator is then determined based on this relationship and an effect of the input factor on the health indicator, this effect being estimated using a Kalman filter, recursive least squares estimation and / or a particle filter.

[0016] Document EP 4091945 describes a method for detecting faults in a transmission system of an aircraft. This method proposes to acquire, during several time intervals, a signal relating to the dynamic behavior of the transmission system as well as values ​​of flight parameters of the aircraft. Groups of indicators relating to each time interval are determined from these signals. An estimate of the probability that the values ​​of the indicators of a group of indicators are abnormal compared to a predefined learning condition is calculated for each indicator of the group of indicators, taking into account the measured flight parameters. Health indicators are determined according to this estimate of the probability, then compared with a threshold to identify the presence of a fault.

[0017] WO 2011 / 023596 describes a method and system for monitoring vibrations in a wind turbine. A range of characteristic vibration values ​​is predicted from real-time operating data of an electrical generator of the wind turbine according to rules extracted from a rule set. A real-time comparison of the characteristic operating values ​​of the generator with a calculated threshold is performed to detect and signal a possible fault on the electrical generator.

[0018] Document CN 107218997 describes a method for detecting faults on a hydroelectric group based on the identification of the operating conditions of the hydroelectric group. Historical vibration data is used to estimate a threshold. An operating status signal of the hydroelectric group is acquired in order to determine the operating conditions of the hydroelectric group. An anomaly can thus be sensitively detected based on the historical vibration data and the operating status identified by the status signal when the vibration values ​​exceed the threshold.

[0019] US 2022 / 0163428 discloses a system and method for monitoring the condition of a damper of a gas turbine. Such a gas turbine includes a rotating component, a bearing operatively coupled to the rotating component, and a damper associated with the bearing, as well as sensors and a controller. The controller receives data relating to sensed and / or calculated parameters, and generates a damper-related index based on this data and characteristic of a health condition of the damper. When this index exceeds a predetermined threshold, a notification indicating the health condition of the damper is generated. A type of fault experienced by the damper and a remaining useful life of the damper may also be determined.

[0020] The present invention therefore aims to propose an innovative method and device aimed at monitoring a mechanical system of a vehicle in order to detect as early as possible the appearance of a fault on the mechanical system while limiting the risk of false detection of a fault.

[0021] The present invention relates, for example, to a method for monitoring the health of a mechanical system equipping a vehicle, the mechanical system comprising at least one moving member as well as at least one vibration sensor emitting a vibration signal, the mechanical system or the vehicle comprising at least one context sensor emitting a context signal relating to at least one context parameter, said at least one context parameter being chosen from a list comprising at least one or more functional parameters of the mechanical system, one or more navigation parameters of the vehicle and one or more atmospheric parameters.

[0022] This method is composed of an initial phase of defining a threshold variation model as a function of said at least one context parameter, this initial phase being followed by a phase of operational monitoring of the mechanical system.

[0023] The initial phase includes the following steps: measurements of successive initial vibration values ​​from said at least one vibration sensor and of successive initial context values ​​from said at least one context sensor, determination, using a calculator, of several initial health values ​​of at least one health indicator IN relating to the mechanical system as a function of the initial vibration values, each initial health value being associated with one of the initial context values ​​of said at least one context parameter, definition for each health indicator IN,of a threshold variation model, the threshold variation model being defined as a function of the initial health values ​​of the health indicator IN and initial context values ​​of said at least one context parameter, by partitioning a domain formed by the initial context values ​​of said at least one context parameter into several ranges of values ​​for which the threshold relating to the health indicator IN is statistically constant over each range of values, each range being associated with several of the initial context values, and by determining threshold values ​​with a method using regressions on parameters of quantile distributions conditioned on context parameters and domain decompositions of said at least one context parameter using at least one decision tree.

[0024] The operational monitoring phase includes the following steps during operation of the mechanical system: measurements of successive operational vibration values ​​from said at least one vibration sensor, and of successive operational context values ​​from said at least one context sensor, determination, with the calculator, of an operational health value of at least one health indicator IN based on the vibration operational values, the health operational value of a health indicator IN being associated with one of the operational context values ​​relating to one or more context parameters, determination of the threshold specific to the operational health value of said at least one health indicator INusing the threshold variation model and said at least one associated context operational value, triggering an alert signaling a risk of the presence of a fault on the mechanical system if the operational health value of said at least one health indicator IN is greater than the determined threshold.

[0025] According to this process, one or more health indicators IN are determined during the initial phase from measurements from said at least one vibration sensor, then a threshold variation model is defined for each health indicator IN depending on the values ​​of this health indicator INand measurements from said at least one context sensor on this initial phase using regressions on quantile distribution parameters. To define this variation model, at least one decision tree is used to define one or more ranges of the context parameter(s) for which the threshold relating to the health indicator IN is statistically constant. A threshold relating to a health indicator is considered IN is statistically constant, over a range of contiguous values ​​of a context parameter, if for any division of said range of values ​​into two sub-ranges of contiguous values, the thresholds estimated respectively over the two sub-ranges of values ​​are considered identical according to a statistical test. Such a statistical test may be, for example, a Welch test.

[0026] The term "each" is used for convenience, both in the presence of a single health indicator INthan health indicators IN to facilitate reading.

[0027] The initial phase can thus be a learning period allowing the threshold variation model to be defined for each health indicator. IN of the mechanical system. This initial phase is carried out following the first use of the vehicle or following a significant maintenance operation, for example following the replacement of the mechanical system. During this learning period, the mechanical system is considered to be fault-free. This initial phase thus makes it possible to determine the threshold variation model of the mechanical system considered to be healthy for the vehicle.

[0028] Following this learning period and the definition of the threshold variation model, the health monitoring of the mechanical system is ensured using the threshold variation model during the operational monitoring phase. The method according to the invention thus makes it possible to adapt the threshold value to which a health indicator is compared. IN to the context parameters which makes it possible to improve the detection of a mechanical defect, or of an anomaly linked to an emerging mechanical defect, while reducing the false detection rate.

[0029] The reliability and availability of the mechanical system and of the vehicle equipped with this system are thus improved thanks to the method according to the invention, compared to traditional monitoring methods whose threshold is generally algebraically constant over the entire area of ​​use of the vehicle.

[0030] In addition, the context parameters may include operating parameters of the mechanical system, such as the rotational speed of one or more rotating shafts, the torque(s) at this or these shafts, the temperature of the mechanical system for example.

[0031] The context parameters may include operating parameters of the vehicle, and in particular of an aircraft, equipped with the mechanical system, such as the forward speed of this vehicle, its possible vertical speed, its altitude, the rotation speed of a rotor for example.

[0032] Context parameters may also include atmospheric parameters, such as the temperature and atmospheric pressure outside the mechanical system, for example.

[0033] Thus, the context sensor(s) make it possible to measure context characteristics relating to the operation of the mechanical system, and / or the vehicle as such, as well as relating to the environment, and to emit a temporal context signal, carrying information relating to these context characteristics, to a computer for example. The temporal context signal carries the initial context values ​​during the initial phase of defining a threshold variation model, and the operational context values ​​during the operational monitoring phase of the mechanical system.

[0034] The vibration signal and the context signal can be generated simultaneously, or even synchronously.

[0035] A computer can be present in the vehicle or present on a station separate from the vehicle. The vibration and context signals can be stored in a computer memory or in a memory present in the vehicle and possibly connected to the computer by a wired or wireless connection for example.

[0036] The method according to the invention may comprise one or more of the following characteristics, taken alone or in combination.

[0037] According to one example, the definition of the threshold variation model may be performed using a single decision tree. Alternatively, the definition of the threshold variation model may be performed by aggregating at least two decision trees.

[0038] According to another example compatible with the previous ones, the definition of the threshold variation model can be carried out with a predetermined minimum false alarm rate and a predetermined confidence rate in the minimum false alarm rate. The use of this minimum false alarm rate, corresponding to a probability of issuing false alarms, thus makes it possible to reduce the false detection rate, and consequently, the false alarm issue rate, thus limiting the immobilizations of the mechanical system and the vehicle while no fault is present.

[0039] According to another example compatible with the previous ones, the initial phase can include an analysis step to analyze a sensitivity of each health indicator INto the context parameters and to select influential context parameters. This analysis step is carried out after determining several initial health values ​​of each health indicator IN and before defining a threshold variation model.

[0040] In this way, the context parameters most influential on the variations of each health indicator IN are determined and selected during this analysis. Consequently, only these selected context parameters are taken into account in defining the threshold variation model for each health indicator IN and, subsequently, to determine the variable threshold.

[0041] Alternatively, the initial phase may include a preselection step to select context parameters taken into account by the method from a multitude of context parameters measured by context sensors. This preselection step is carried out before the measurements of the initial vibration and context values.

[0042] In this case, the context parameters most influential on the variations of each health indicator IN are selected prior to carrying out the initial phase, for example by an empirical analysis of measurements on other mechanical systems, for example equivalent or similar. Consequently, only these previously selected context parameters are taken into account in the definition of the threshold variation model for each health indicator IN and, subsequently, to determine the variable threshold.

[0043] According to another example compatible with the previous ones, said initial values ​​of said at least one health indicator IN can be calculated from a decomposition into Fourier coefficients of the successive initial vibrational values ​​modeled with an assumption of cyclostationarity of order one and two of the vibrational signal, which follows a Chi-square law, generalized by a GAMMA distribution.

[0044] The present invention also relates to a computer program comprising instructions which, when the program is executed, lead to the implementation of the various examples of the monitoring method described above. This computer program can be stored for example in a memory of a computer present in the vehicle, or in a memory located in the vehicle and connected to the computer, or even in a memory of a station separate from the vehicle.

[0045] This computer program may be implemented entirely by a computer present in the vehicle or a vehicle computer, or jointly by such a computer or computer present in the vehicle and by a station separate from the vehicle.

[0046] The present invention also relates to a monitoring device configured to monitor a mechanical system equipping a vehicle, the mechanical system comprising at least one moving member, the monitoring device comprising at least one vibration sensor emitting a vibration signal, at least one context sensor emitting at least one context signal relating to at least one context parameter and a computer.

[0047] The monitoring device is configured to implement the various examples of the health monitoring method previously described.

[0048] In addition, the computer may include a single computing unit embedded in the vehicle. Therefore, the entire monitoring process can be carried out in the vehicle, whether the vehicle is stationary or in operation.

[0049] Alternatively, the calculator may comprise a single calculation unit separate from the vehicle, and present for example in a station external to the vehicle. In this case, the step of measuring the initial vibration values ​​and the initial context values ​​is carried out in the vehicle in operation during the initial phase of defining a threshold variation model, then the station receives these initial vibration values ​​and these initial context values, the calculation unit of the station carrying out the steps of determining the initial health values ​​of each health indicator IN,and definition of the threshold variation model. During the operational phase, the step of measuring the vibration operational values ​​and the context operational values ​​is carried out in the vehicle in operation, while the steps of determining the health operational value of each health indicator IN determining the threshold specific to the operational health value of each health indicator IN, and triggering an alert of a risk of the presence of a fault on the mechanical system if the operational health value of at least one health indicator IN is greater than the determined threshold are carried out by the station's calculation unit.

[0050] Alternatively, the calculator may comprise a calculation unit embedded in the vehicle and a calculation unit separate from the vehicle, and present for example in a station external to the vehicle. In this case, the step of measuring the initial vibration values ​​and the initial context values ​​of the initial phase and the step of measuring the operational vibration values ​​and the operational context values ​​of the operational phase are carried out in the vehicle in operation. The other steps of the method may be carried out either in the vehicle in operation or when stopped, or in the station external to the vehicle which receives these initial vibration values ​​and these initial context values ​​as well as these operational vibration values ​​and these operational context values.

[0051] The present invention also relates to a mechanical system comprising at least one moving member and a monitoring device as previously described. This mechanical system may for example be a power transmission box of a vehicle, and of an aircraft in particular.

[0052] The present invention also relates to a vehicle and in particular an aircraft, comprising such a mechanical system.

[0053] The invention and its advantages will appear in more detail in the context of the description which follows with examples given for illustrative purposes with reference to the appended figures which represent: there figure 1 , a schematic side view of an aircraft, the figure 2 , a diagram illustrating a process for monitoring a mechanical system according to the invention, the figure 3 , a diagram illustrating a method of monitoring a mechanical system according to the invention, the Figure 4, a diagram illustrating a histogram of a health indicator conditioned on a context parameter, the Figure 5 , a diagram illustrating a single decision tree associated with two context parameters, the Figure 6 , a diagram illustrating the separation of a data block relating to the values ​​of a health indicator into a space of two context parameters, the Figure 7 , a curve illustrating the variation of the Welch statistic as a function of a context parameter, the Figure 8 , a diagram illustrating a two-dimensional discretization of a data block as a function of two context parameters, the Figure 9 , a diagram illustrating an aggregation of decision trees, the Figure 10 , a diagram illustrating a threshold variation model, and the Figure 11 , a graph representing curves of a variable threshold.

[0054] Elements present in several distinct figures are assigned a single reference.

[0055] There figure 1 represents a vehicle 1, and a rotary wing aircraft of the rotorcraft type in particular. This vehicle 1 comprises a mechanical system 10 provided with one or more moving members 15. These members 15 are for example rotating around an axis AX of rotation.

[0056] The mechanical system 10 may for example comprise an output shaft or an input shaft. The mechanical system 10 may also comprise one or more rotational guide bearings, for example for the rotational guidance of at least one rotating member. A bearing comprises for example a rolling bearing provided with rolling elements.

[0057] The mechanical system 10 may also comprise, for example, at least one toothed wheel, a pinion, a toothed crown, fixed or mobile.

[0058] A mechanical system 10 may be, for example, a gearbox or a power transmission box of the vehicle 1. This mechanical system 10 may be connected, for example, to one or more engines 2, via respectively one or more input shafts and may drive a rotor in rotation via an output shaft, such as for example a main rotor 3 or an auxiliary rotor 4 of a rotorcraft as shown in the figure 1 . According to another example, a mechanical system 10 may be a rotary wing.

[0059] Alternatively, such a mechanical system 10 may, for example, be arranged in a gearbox or a power transmission box of a vehicle 1 or any other mechanical equipment.

[0060] These examples are given merely to illustrate the invention.

[0061] Regardless of its arrangement, the mechanical system 10 also comprises a monitoring device 9 intended to monitor the mechanical system 10 in order to detect and identify the presence of a fault, or a risk of a fault occurring.

[0062] The monitoring device 9 comprises one or more vibration sensors 20, one or more context sensors 25 and a computer 50. The computer 50 can be dedicated to the monitoring device 9 or be shared with other devices of the vehicle 1.

[0063] Each vibration sensor 20 can emit a temporal vibration signal relating to a vibration behavior of the mechanical system 10 as a whole, or to a particular vibration behavior of a member 15 of the mechanical system 10, for example a shaft, a bearing or even a gear. The temporal vibration signal carries successive vibration values ​​relating to the vibrations of the mechanical system 10, according to for example a sampling frequency. The vibration sensor(s) 20 can comprise, for example, an accelerometer, a tachometer or others.

[0064] Each context sensor 25 can emit a temporal context signal relating to a context characteristic linked to the operation of the mechanical system 10 and / or the vehicle 1 as well as to the environment. The temporal context signal carries successive initial context values ​​relating to a context parameter linked to the mechanical system 10, to the vehicle 1 and / or to the environment, according to for example a sampling frequency.

[0065] The context parameters may include operating parameters of the mechanical system 10, such as the rotational speed of one or more rotating shafts, the torque(s) at this or these shafts, the temperature of the mechanical system 10 for example. One or more context sensors 25 may then include different types of sensors associated with each operating parameter of the mechanical system 10, such as for example an accelerometer, a tachometer, a torque meter, or others. One or more context sensors 25 may also include an angular sensor, such as an encoder type sensor, making it possible to measure a variation in the angular position of a rotating member 15 of the mechanical system 10 in order, for example, to synchronize the vibration signals and the context signals with the rotation and the angular positions of this rotating member 15.In the case where vehicle 1 is an aircraft, the context parameters can be managed by an on-board system designated by the acronym . UMS corresponding to the English designation “Usage Monitoring System”.

[0066] The context parameters may include operating parameters of the vehicle 1, such as its forward speed, its vertical speed, its altitude, the rotation speed of a rotor 3, 4 for example. One or more context sensors 25 may then include different types of sensors associated with each operating parameter of the vehicle 1, such as for example an accelerometer, a tachometer, an altimeter, or others.

[0067] The context parameters may also include atmospheric parameters related to atmospheric conditions, such as the temperature outside the mechanical system 1 and the atmospheric pressure, for example. One or more context sensors 25 may then include a thermometer and / or a barometer, for example.

[0068] Furthermore, the calculator 50 may comprise a single calculation unit 51 on board the vehicle 1.

[0069] Alternatively, the calculator 50 may comprise a single calculation unit 55 separate from the vehicle 1, and present for example in a station 40 external to the vehicle 1.

[0070] Alternatively, the calculator 50 may comprise a calculation unit 51 embedded in the vehicle 1 and a calculation unit 55 separate from the vehicle 1, and present for example in a station 40 external to the vehicle 1.

[0071] Each calculation unit 51, 55 may comprise at least one processor and at least one memory 6', at least one integrated circuit, at least one programmable system or at least one logic circuit, these examples not limiting the scope given to the expression "calculation unit". The computer 50 may also be connected to a memory 6', 56 present in the vehicle 1 or in the station 40, by a wired connection or a wireless connection. The computer 50 may also comprise a memory 6'.

[0072] The vibration and context signals can thus be stored in the 6.6' memory after having been transmitted to this 6.6' memory, by a wired link or a wireless link, the vibration and context signals being able to be analog or digital, electrical or optical signals.

[0073] The memory 6,6',56 can also store a computer program comprising instructions which, when the program is executed, lead to the performance of a method for monitoring the health of the mechanical system 10. This method for monitoring the health of the mechanical system 10 is dedicated to monitoring the health, for example vibration, of the mechanical system 10.

[0074] Furthermore, each vibration sensor 20 and each context sensor 25 can continuously emit a vibration signal and a context signal respectively.

[0075] A vibration signal and a context signal may be signals formed by raw measurements emitted respectively by a vibration sensor and a context sensor or by measurements obtained by more or less complex signal processing carried out by a processing unit, for example integrated into the corresponding sensor, from such raw measurements, for example via standard filtering or sampling, or the application of transformations.

[0076] The method for monitoring the health of the mechanical system 10 according to the invention comprises two phases 100, 200 as shown in the diagram of the figure 2 .

[0077] During an initial phase 100, a threshold variation model H is defined, the threshold varying according to the context parameter(s) and being intended to be compared to a health indicator INfor monitoring the health of the mechanical system 10. Then, an operational monitoring phase 200 is executed during which monitoring of the mechanical system 1 is carried out using the threshold determined with the model as a function of the context parameter(s). During the initial phase 100, the mechanical system 10 is considered healthy, and therefore without anomaly or fault.

[0078] The initial phase 100 has several steps as shown in the Figure 3 .

[0079] The initial phase 100 may firstly comprise a preselection step 110 for selecting context parameters taken into account by the method from a multitude of context parameters measured by the context sensor(s) 25. Indeed, a very large number of context parameters, for example more than 1000 context parameters, may be measured on complex systems, and in particular on a vehicle 1 such as an aircraft. In fact, in order to limit the context parameters used by the method according to the invention, only a portion of these measured context parameters are taken into account. This selection may be carried out by feedback on the basis of similar mechanical systems or vehicles, the selected context parameters being the most influential parameters for these similar mechanical systems or vehicles. The selected context parameters may be stored in the memory 6, 6', 56.

[0080] This preselection step 110 is not essential to carrying out the method according to the invention and is therefore optional.

[0081] During a measurement step 120, initial vibration values ​​and successive initial context values ​​originating respectively from the vibration sensor(s) 20 and the context sensor(s) 25 are measured and transmitted to the on-board memory 6,6' to be stored there and / or are transmitted directly to the calculation unit 51.

[0082] Then, during a determination step 130, several initial health values ​​of each health indicator IN are determined in the usual way based on the initial vibration values ​​measured during the measurement step 120, using the calculator 50. The different initial health values ​​of each health indicator IN correspond respectively to different times of the initial phase 100.

[0083] An initial health value of a health indicator IN is for example equal to an initial value from a single vibration sensor 20 or the result of a law involving initial values ​​from several vibration sensors 20 respectively. In the case where the vehicle 1 is an aircraft, these initial health values ​​of health indicators IN are for example determined using a system designated by the acronym HMS corresponding to the English designation “Health Monitoring System”.

[0084] Additionally, each initial health value of a health indicator IN is then associated with the initial context value of the one or more context parameters measured substantially simultaneously with the initial vibrational value(s) used to determine the initial health value of this health indicator IN. For example, an initial health value of a health indicator INis associated with an initial context value of three distinct context parameters.

[0085] This determination step 130 can be carried out by the calculation unit 51 of the calculator 50 which is on board the vehicle 1. Each initial value of a health indicator IN can then be stored in the onboard 6.6' memory. Each initial value of the health indicator(s) IN can thus be calculated during the use of vehicle 1, substantially in real time, or when vehicle 1 stops.

[0086] Alternatively, this determination step 130 can be carried out by the calculation unit 55 of the calculator 50 of the station 40, once the vehicle 1 is stationary, and for example placed on the ground in the case of an aircraft. For this purpose, the initial context values ​​and the initial vibration values ​​are transmitted from the memory 6, 6' present in the vehicle 1 to the calculation unit 55, by a wired link or a wireless link, in the form of analog or digital, electrical or optical signals, for example, or even using a memory card, during a transmission step 135, once the vehicle 1 is stationary. The initial context values ​​and the initial vibration values ​​can then be stored in the memory 56 of the station 40. Each initial value of the health indicator(s) IN can thus be calculated after the use of the vehicle 1, by the calculation unit 55, each initial value of a health indicator INcan be stored in memory 56.

[0087] The initial phase 100 can then comprise, as an alternative or in addition to the preselection step 110, an analysis step 140 to analyze a sensitivity of each health indicator. IN to the context parameters and to select influential context parameters. This analysis step makes it possible to determine and select, among the context parameters used, the context parameters most influential on the variations of each health indicator IN. This analysis step 140 can be carried out by the calculation unit 51 on board the vehicle 1 or by the calculation unit 55 of the calculator 50 of the station 40.

[0088] During a definition step 150, a threshold variation model H is defined for each health indicator IN based on the initial health values ​​of this health indicator IN,considered as characterizing a healthy mechanical system 10, and initial context values ​​of the context parameter(s) previously determined and stored. This definition step 150 requires a significant number of initial health values ​​for each health indicator IN so that this threshold variation model H is reliable and robust. This large number of initial health values ​​must cover a large part of the area of ​​use of vehicle 1 in order to subsequently determine a reliable and safe threshold throughout the area of ​​use of vehicle 1. This large number of initial health values ​​is obtained during a learning period is minimum use of vehicle 1, for example equal to 50 hours, corresponding substantially to the duration of the initial phase 100.

[0089] This definition step 150 can be carried out by the calculation unit 51 of the calculator 50 on board the vehicle 1, with the initial values ​​of the health indicator(s). IN and initial context values ​​stored in memory 6.6'. The initial context values ​​and the initial values ​​of the health indicator(s) IN are transmitted from the memory 6,6' to the computer 50, for example by a wired link or a wireless link, in the form of analog or digital, electrical or optical signals, during a transmission sub-step 155 for example during operation of the vehicle 1 or once the vehicle 1 is stationary. The threshold variation model H can then be stored in the 6.6' memory present in vehicle 1.

[0090] Alternatively, this definition step 150 can be carried out by the calculation unit 55 of the calculator 50 of the station 40, with the initial values ​​of the health indicator(s) IN and initial context values ​​stored in the 6.6' memory of the vehicle 1, after the vehicle 1 has stopped. The initial context values ​​and the initial values ​​of the health indicator(s) IN are in this case transmitted from the memory 6.6' present in the vehicle 1 to the computing unit 55 of the station 40, for example during a transmission sub-step 155. The threshold variation model H can then be stored in memory 56 of station 40.

[0091] Alternatively, this definition step 150 can still be carried out by the calculation unit 55 of the station 40, with the initial values ​​of the health indicator(s) INand initial context values ​​stored in memory 56 of station 40, after stopping vehicle 1. The threshold variation model H can then be stored in memory 56 of station 40.

[0092] The threshold model H is dependent on the context parameters ζ and can be written H ( τ,ζ,γ ) = f -1< ( ζ ), with τ , a probability level of the quantile distribution, corresponding to a requested false alarm rate, γ, a level of uncertainty on the estimated threshold H, corresponding to a level of confidence on this requested false alarm rate, and f -1<(ζ), an inverse cumulative function of a lognormal distribution function of ζ.

[0093] As a reminder, a variable X is said to follow a lognormal distribution if the variable Y = log ( X ) follows a normal law.

[0094] The function f (ζ) can be estimated from a method using decision tree regressions on the quantile distribution parameters via an aggregation of decision trees often referred to in English as “Bagging Decision Tree”.

[0095] A quantile q τ is a value that separates a data set according to a proportion defined by a probability τ . For a set of context parameters ζ, the function f (ζ) is a probability density that characterizes the quantile distribution p ( q τ ( ζ )) depending on the context parameters ζ. The quantile distribution p ( q τ ( ζ )) is defined by the average μ τ ∗ ζ and variance ∑ τ ∗ ζ . The function f (ζ) thus characterizes the relationship between the quantile distributions and the context parameters ζ.

[0096] The function f (ζ) can then be written: f ζ = ρ q τ ζ = LogN μ τ ∗ ζ , ∑ τ ∗ ζ .

[0097] The average μ τ ∗ ζ and variance ∑ τ ∗ ζ can be estimated from the image of the function f (ζ) for a context parameter space ζ of dimension p, p also being the number of context parameters ζ used. Thus, if two distinct context parameters ζ are used, they form a two-dimensional context parameter space.

[0098] The method according to the invention can use statistical modeling of the distribution of the health indicator IN. Furthermore, it is known that health indicators IN estimated from the Fourier coefficients of a vibration signal modeled under the hypothesis of cyclostationarity of order one or two, follow, in the case where the mechanical system 10 does not have a defect, a chi-square law. The generalization of this law is a gamma law Γ two-parameter, one-shape parameter α and a scale parameter β . The statistical distribution of the health indicator IN is thus modeled by a mixture of gamma distributions. Such modeling advantageously allows for the consideration of more complex data distributions and therefore offers greater flexibility. The probability density of such a mixture of gamma distributions for the value x of the health indicator IN can be written: p x = ∑ n = 1 K π n . p x ; α n , β n , with a mixing coefficient π, and where the distribution p ( x; α n ,β n ) is a gamma distribution such that: p x ; α n , β n = 1 β n α n . Γ α n x n − 1 . e − x β n , with Γ(), the Gamma function, and K, the number of gamma distributions.

[0099] The parameters of the mixture model { α k ,β k ,π k} are estimated with an expectation-maximization algorithm known as “Expectation Maximization” while the number of gamma distributionsK is estimated from a Bayesian information criterion called BIC for "Bayesian Information Criterion", with the condition K ≤ N γ Or N γ is the maximum number of components of the gamma distribution to be estimated.

[0100] Once the mixture model is estimated, it is possible to estimate the quantile at a probability level τ , by defining a cumulative distribution F x ( t ) such as: F X t = ∫ 0 t ∑ n = 1 K π n . p x ; α n , β n dx .

[0101] Quantile estimation q τ calculated for a probability level τ amounts to solving the unconstrained optimization problem according to which q τ = argument t | g τ ( t )| , with g τ t = F X t − t = ∫ 0 t ∑ k = 1 K π n . p x ; α n , β n dx − τ .

[0102] As a reminder, the minimum argument ( argmin) of a function represents the value of the variable for which the value of the function concerned reaches its minimum, the value for which the function is minimized being unique.

[0103] First, it is necessary to estimate the parameters of the quantile distribution. First, a parametric modeling of the health indicator IN conditioned on one or more context parameters is carried out by a mixture of Gamma distributions. We then use a method for determining uncertainties on the parameters of the mixture of Gamma distributions by a WLB method called in English "Weighted Likelihood Boostrap". At each sampling according to the WLB method, a quantile value is estimated q τ . This sampling step is carried out p times in order to obtain a sample of quantile value q τ , called Q τ = { q τ 1 , …,q τP} .

[0104] Now the quantity q τ ∼ logN μ τ ∗ Σ τ ∗ is considered as a random variable modeled by a log Normal distribution with parameters: μ τ ∗ , Σ τ ∗ .

[0105] We can then calculate an average: μ τ ∗ = log μ τ 2 Σ τ + μ τ 2 , And μ τ = 1 N b ∑ k = 1 N b q τ k , and a variance: Σ τ ∗ = log Σ τ μ τ 2 + 1 et Σ τ = 1 N b ∑ k = 1 N b q τ k − μ τ 2 .

[0106] There Figure 4 illustrates a histogram 41 of the probability density of a health indicator IN, according to the x value of this health indicator IN. Curves 42,43,44 represent the variations of the Gamma components identified by the mixture model according to the x value of this health indicator IN. Histogram 45 corresponds to the quantile sample estimated and modeled by a Log Normal distribution 46.

[0107] Furthermore, to enable the construction of a regression model via decision trees, the method performs a recursive division of the context parameter space in order to divide it into several ranges for which the threshold relating to the health indicator IN is statistically constant, namely that, for a range of values ​​of a given context parameter, two distinct thresholds resulting from a further division of the range of values ​​of the context parameter into two sub-ranges have, for example, a statistical difference less than a predetermined value. This predetermined value is, for example, equal to the critical value of the Welch statistic. Such a division criterion makes it possible to reduce the dissimilarity of the quantile distributions on the space of context parameters, for example.

[0108] The method according to the invention can, for example, make it possible to express the conditional variability of health indicators. INfrom the variability of quantile distributions, the dissimilarity of quantile distributions on the context parameter space being estimated from a similarity statistic or Welch statistic S j ( k ) between two quantile distributions characterized by their respective means µ τ 1 and µ τ 2 as well as by their respective variances Σ τ 1 and Σ τ 2 according to the relationship: S j k = μ τ 1 k − μ τ 2 k Σ τ 1 k + Σ τ 2 k N b .

[0109] The clues j And k respectively designate the index j of the context parameter ζ j , j varying from 1 to p, and the iteration indicating the index k of route on this context parameter ζ j , N b being the dimension of the quantile sample Q τ . The index k of traversal is an iterator indexed on the vector defined by the context parameter ζ j . Sample means Q τ of quantiles µ τ 1 ( k ) And µ τ 2 ( k ) as well as the variances Σ τ 1 ( k ) And Σ τ 2 ( k ) are estimated from the health indicator IN conditioned on the context parameter ζ j to the course index k .

[0110] A maximum value of the Welch statistic can be determined over the entire context parameter space and can be used to determine the value of the context parameter and the context parameter to be discretized. A discretization coordinate ζ j ∗ k ∗ can then be chosen equal to the maximum value of the Welch statistic following the context parameter ζ j and the traversal index k, such that: ζ j ∗ k ∗ = argmax j , k S j k , with the function argmax(S),giving as result the point(s) for which the function S reaches its maximum value.

[0111] This discretization coordinate ζ j ∗ k ∗ allows to separate a range of values ​​of a context parameter into two sub-ranges. The process of recursive division on the space of context parameters can stop when for example at least one of the following two conditions is verified, allowing to conclude that the threshold relating to the health indicator IN is statistically constant over these two sub-ranges.

[0112] For a first condition, a minimum number of values ​​required to estimate a quantile distribution is reached.

[0113] For a second condition, a hypothesis H 0 , that two means of quantile distributions are no longer statistically different, cannot be rejected. The hypothesis H 0 can be written: µ τ 1 =µ τ 2. This hypothesis H 0 cannot be rejected in particular if the value of the Welch statistic S j ( k ) is less than the critical value estimated from the inverse distribution of the Student law following a probability level α . A low value of the parameter α promotes acceptance of the hypothesis H 0 .

[0114] This estimated critical value is for example equal to t − 1 1 − α 2 , ν , with v, the number of degrees of freedom such that: v = N b − 1 . Σ τ 1 + Σ τ 2 Σ τ 1 2 + Σ τ 2 2 .

[0115] This number v of degrees of freedom conditions the critical value of the Welch statistic and depends on the variance of the quantile sample as well as the dimension of Q τ

[0116] The construction and use of a decision tree 300 is based on the notion of Boolean choice allowing a direction to be taken in the space of context parameters as illustrated in the Figure 5 with an example of a single decision tree 300. The Boolean choices only concern the root node 310 and the decision nodes 320. The root node 310 is the first node of the decision tree 300 from which the decision nodes 320 derive. For a pair of context parameters or a single context parameter, if the condition analyzed at a node 310,320 is true, the left branch is followed. Otherwise, the right branch must be followed. This rule is repeated until a leaf node 330 containing the parameters of the quantile distributions is reached.

[0117] Regardless of the number of decision trees 300, the estimations of the root node 310, decision nodes 320 and leaf nodes 330 follow the following procedure from the dataset D={x,ζ}.

[0118] The example shown on the Figure 5 concerns a single 300 decision tree and a space of two context parameters, Z={ζ 1 ,ζ 2}.

[0119] A minimum number N m of x values ​​of the health indicator IN conditioned on the context parameters ζ as well as a maximum number N γ of gamma distributions for the mixture model must be fixed.

[0120] At the first iteration, k =1, the data block containing the values x of health indicators IN depending on the two context parameters ζ 1 , ζ 2 is split into two data blocks B 1 ,B 2 to the coordinate ζ 1 k = 1 respecting the condition of a minimum number N mof x values ​​of the health indicator IN conditioned on the first context parameter ζ 1 , as shown in the Figure 6 .

[0121] A mixture model of gamma distributions is estimated for each data block B 1 ,B 2 , according to the relationship p ( x ) = ∑ n = 1 K π n . p x α n β n . The averages µ τ 1 and µ τ 2 as well as the variances Σ τ 1 and Σ τ 2 are estimated at a probability level τ . Welch's statistic sj ( k ) is also calculated.

[0122] Then the next iteration, k=2, is performed, the context parameter ζ 1 being incremented ( ζ 1 k = 1 ).

[0123] The process is thus carried out again for the first context parameter ζ 1by successively splitting the data blocks for each iteration until at least one of the two previously mentioned conditions is verified.

[0124] Then the process is also performed for the second context parameter ζ 2 .

[0125] A maximum value of the Welch statistic can be determined over the entire context parameter space, the discretization coordinate ζ j ∗ k ∗ being defined equal to this maximum value S 1 ∗ of Welch's statistics as shown in the Figure 7 .

[0126] The x values ​​of the health indicator IN are then separated, according to a two-dimensional discretization of the health indicator IN for each of the two context parameters ζ 1 ,ζ 2 into two subsets designated for example “data sets” with the value and the context parameter determined previously. The Figure 8represents such a two-dimensional discretization as a function of the context parameters ζ 1 and ζ 2 following the process of constructing a decision tree. The two left and right blocks are separated at the coordinate ζ 1 ∗ .

[0127] A decision node 320 of the decision tree 300 is then created, or even a root node 310 if it is the first node of the tree 300.

[0128] The entire process is then repeated for the creation of each decision node 320 of the decision tree 300. For each branch, this process ends at the creation of a leaf node 330, if the value of the Welch statistic is less than the critical value of the Welch statistic t − 1 1 − α 2 , ν estimated or if the minimum number of values ​​for the health indicator IN is reached in one of the blocks B 1 ,B 2 .

[0129] The procedure for constructing a single 300 decision tree is thus complete when each branch ends with a 330 leaf node. An example using an aggregation of N t decision trees t 1 , ... ,t Nt is represented on the Figure 9 , the procedure for constructing such a set of N t decision trees being similar.

[0130] Several decision trees can then be estimated. For each decision tree, a random draw of data formed by a health indicator IN and a set of context parameters D Nt< = { x,ζ} Nt< is realized. N t corresponds to the number of random draws to produce a set of decision trees T = t 1 , t 2 … t N t .

[0131] The different decision trees thus constructed can then be used during the operational phase 200 by averaging the parameters of the quantile distributions of each decision tree traversed for a value x of the health indicator IN and the associated context parameter values ​​ζ.

[0132] Using the decision tree(s) 300, the function f (ζ) can then be estimated by performing a correlation between the quantile distributions as a function of the context parameters ζ.

[0133] Once the function f (ζ) estimated, it is then possible to predict, for a set of context parameters ζ or a single context parameter ζ, the parameters of the associated quantile distributions.

[0134] For a new set of context parameters ζ or a single context parameter ζ, each decision tree t has a leaf node I(ζ I ,t).

[0135] The predicted quantile distribution over the set of N t decision trees for a set of context parameters ζ is then written: p q τ ζ = f ζ = LogN μ τ ∗ ζ , Σ τ ∗ ζ .

[0136] An average μ τ i ∗ and a variance Σ τ i ∗ of the quantile distribution can be estimated at the leaf node I ( ζ I ,t ) respecting the condition

[0137] The average μ τ ∗ ζ can be written: μ τ ∗ ζ = 1 N t ∑ i = 1 N t s i μ ζ t .

[0138] In particular, the value of the mean of the quantile associated with the leaf node I(ζ I ,t) of the tree t depending on the context parameter(s) ζ is then

[0139] The variance Σ τ i ∗ contains the average of the variances of the N t decision trees and the variance of the estimate of the mean over all generated decision trees such that: ∑ τ ∗ ζ = 1 N t ∑ i = 1 N t s i Σ ζ t + 1 N t ∑ i = 1 N t s i μ ζ t − μ τ ∗ ζ 2 .

[0140] In particular, the value of the quantile variance associated with the leaf node I(ζ I ,t) of the tree t depending on the context parameter(s) ζ is written:

[0141] In this way, a threshold variant model H ( τ,ζ,γ ) as shown in the Figure 10 , can be set for a value x of the health indicator IN depending on two context parameters ζ 1 ,ζ 2 with a probability level τ false alarms equal to 0.9999 and with three different confidence rates, also called uncertainty levels γ , equal respectively to 0.05 (5%), 0.55 (55%) and 0.95 (95%).

[0142] Following the completion of the initial phase 100 and the establishment of the threshold variation model, the operational monitoring phase 200 can be carried out at each use of the vehicle 1 involving the operation of the mechanical system 10, as long as a significant maintenance operation likely to call into question the reliability of the threshold variation model of a mechanical system 10 of the vehicle 1 is not carried out. The operational monitoring phase 200 comprises the following steps.

[0143] During a measurement step 220, successive vibration operational values ​​and context operational values ​​originating respectively from the vibration sensor(s) 20 and the context sensor(s) 25 are measured and transmitted to the memory 6, 6' to be stored there and / or possibly transmitted directly to the computer 50.

[0144] This measurement step 220 is similar to the measurement step 120 of the initial vibration values ​​and the successive initial context values ​​originating respectively from the vibration sensor(s) 20 and the context sensor(s) 25.

[0145] Then, during a determination step 230, several operational health values ​​of each health indicator IN are determined in the usual manner based on the operational vibration values, using the calculator 50. This determination step 230 is similar to the determination step 130 of several initial health values ​​of each health indicator IN, and each operational health value of a health indicator IN is associated with one of the context operational values ​​of one or more context parameters.

[0146] As for the step of determining 130, the step of determining 230 several operational health values ​​of each health indicator IN can be carried out by the computing unit 51 on board the vehicle 1, during use of the vehicle 1 for example, or by the computing unit 55 of the station 40 after stopping the vehicle 1. Each operational value of a health indicator IN can then be stored either in memory 6.6' present in vehicle 1, or in memory 56 of station 40.

[0147] Then during a determination step 250, a threshold specific to the operational health value of each health indicator IN is determined using the threshold variation model and the associated context operational value(s).

[0148] There Figure 11 represents x values ​​of a health indicator INas a function of time t, as well as curves 31,32,33 of variation of the threshold relating to this health indicator IN with a respective level of uncertainty γ equal to 0.05, 0.55 and 0.95 and with a probability level τ of false alarms equal to 0.9999. For comparison, an algebraically constant threshold 35 estimated for example from an inverse cumulative distribution of a Gaussian distribution with a probability level τ =0.9999 is also represented.

[0149] We thus note that the threshold variation curves 31, 32, 33 can be higher or lower than the algebraically constant threshold 35, depending on the time intervals. For example, over a first time interval 37, the algebraically constant threshold 35 is lower than the threshold variation curves 31, 32, 33 and therefore more penalizing in the sense of potentially triggering false alarms.

[0150] Finally, during a triggering step 260, an alert is triggered to indicate to an operator or a driver of the vehicle 1 a risk of the presence of a fault on the mechanical system 10 if the operational health value of each health indicator IN is greater than the determined threshold.

[0151] In this way, the detection of a fault in the mechanical system 10 can be anticipated reliably, efficiently and robustly from the first signs of its appearance, the presence of this fault being able to be detected through the vibrations of this mechanical system 10.

[0152] For example, using one of the threshold variation curves 31,32,33 shown on the Figure 11 thus advantageously avoids the generation of a false alarm for a value x 1 of the health indicator IN.The use of one of the threshold variation curves 31,32,33 also avoids the non-detection of a potential fault for a value x 2 of the health indicator IN.

[0153] Naturally, the present invention is subject to numerous variations in its implementation. Although several embodiments have been described, it is understood that it is not conceivable to exhaustively identify all possible embodiments.

Claims

1. A method for monitoring the health of a mechanical system (10) equipping a vehicle (1), said mechanical system (10) comprising at least one moving member (15) as well as at least one vibration sensor (20) emitting a vibration signal, said mechanical system (10) or said vehicle (1) comprising at least one context sensor (25) emitting a context signal relating to at least one context parameter, said at least one context parameter being chosen from a list comprising at least one or more functional parameters of said mechanical system (10), one or more navigation parameters of said vehicle (1) and one or more atmospheric parameters, said method being composed of an initial phase of defining a threshold variation model as a function of said at least one context parameter followed by a phase of operational monitoring of the mechanical system, said initial phase (100) comprising the following steps: - taking measurements (120) of successive initial vibration values from said at least one vibration sensor (20) and successive initial context values from said at least one context sensor (25), - determining (130), with a calculator (5, 55), several initial health values of at least one health indicator CI relating to said mechanical system (10) as a function of said initial vibration values, each initial health value being associated with one of said initial context values of said at least one context parameter, - defining (150), for each health indicator CI, a threshold variation model, said threshold variation model being defined as a function of said initial health values of said health indicator CI and said initial context values of said at least one context parameter, by partitioning a domain formed by said initial context values of said at least one context parameter into several ranges of values for which said threshold relating to said health indicator CI is statistically constant over each range of values, each range being associated with several of said initial context values, and by determining values of said threshold with a method using regressions on parameters of quantile distributions conditioned on context parameters and domain decompositions of said at least one context parameter using at least one decision tree, said operational monitoring phase (200) comprising the following steps during the operation of the mechanical system (10): - taking measurements (220) of successive operational vibration values from said at least one vibration sensor (20) and successive operational context values from said at least one context sensor (25), - determining (230), with said calculator (5, 55), an operational health value of at least one health indicator CI as a function of said operational vibration values, said operational health value of at least one health indicator CI being associated with one of said operational context values relating to one or more context parameters, - determining (250) said threshold specific to said operational health value of said at least one health indicator CI using said threshold variation model and said at least one associated operational context value, - triggering (260) an alert signalling a risk of presence of a fault in said mechanical system (10) if said operational health value of said at least one health indicator CI is greater than said determined threshold.

2. The method according to claim 1, for which said definition (150) of said threshold variation model is realised by aggregating at least two decision trees.

3. The method according to any one of claims 1 to 2, for which said definition (150) of said threshold variation model is realised with a predetermined minimum rate of false alarms and a predetermined level of confidence in said minimum rate of false alarms.

4. The method according to any one of claims 1 to 3, for which said initial health values of said at least one health indicator CI are calculated from a decomposition, into Fourier coefficients, of said successive initial vibration values modelled with an assumption of first-and second-order cyclostationarity of said vibration signal, which follows a chi-squared distribution, generalised by a gamma distribution.

5. The method according to any one of claims 1 to 4, for which said initial phase (100) comprises an analysis step (140) for analysing a sensitivity of said at least one health indicator CI to said context parameters and for selecting influential context parameters.

6. The method according to any one of claims 1 to 4, for which said initial phase (100) comprises a preselection step (110) for selecting context parameters taken into account by said method from a plurality of context parameters measured by said at least one context sensor (25).

7. The method according to any one of claims 1 to 6, for which said threshold relating to said at least one health indicator CI is considered as being statistically constant over a range of contiguous values of a context parameter if, for any division of said range of values into two sub-ranges of contiguous values, said thresholds estimated respectively over said two sub-ranges of values are considered identical according to a statistical test.

8. A computer program comprising instructions which, when said program is run, cause the method according to any one of claims 1 to 7 to be implemented.

9. A monitoring device (9) configured to monitor a mechanical system (10) equipping a vehicle (1), said mechanical system (10) comprising at least one moving member (15), said monitoring device (9) comprising at least one vibration sensor (20) emitting a vibration signal, at least one context sensor (25) emitting at least one context signal relating to at least one context parameter and said calculator (50), characterised in that said monitoring device (9) is configured for implementing the method according to any one of claims 1 to 7.

10. The monitoring device (9) according to claim 9, for which said calculator (50) comprises a computing unit (51) and a memory (6, 6') on board said vehicle (1).

11. The monitoring device (9) according to any one of claims 9 to 10, for which said calculator (50) comprises a computing unit (56) and a memory (56) separate from said vehicle (1).

12. A mechanical system (10) comprising at least one moving member (15), characterised in that said mechanical system (10) comprises a monitoring device (9) according to any one of claims 9 to 11.

13. An aircraft (1) comprising a mechanical system (10) and a monitoring device (9) according to any one of claims 9 to 11.