Title of Invention: Failure detection and locating system and engine containing the same
Abstract: A system for detecting and locating failures that occur in a complex system includes elements (CAP, MODVAL, MODDP, MODFD) for detecting and locating a failure affecting at least one sub-system of the complex system. The system also includes an element (MODPD) for making a decision on any action to be taken in the event of such detection. A rocket engine can be monitored with this system.
Detailed Description: Part general_description_paragraphs: The invention lies in the field of failure detection.
It more particularly concerns a system capable of diagnosing a failure in a complex system and of taking a suitable step in relation to this diagnosis.
The invention can be used for example to monitor a complex system such as a rocket engine when it is being developed on a test installation.
In the current state of the art, it is usual to monitor a complex system by monitoring the functioning of a plurality of components of this system taken individually, and to take a decision e.g. stopping of the system, when a certain number of components are deemed to have a fault. These techniques use a so-called <<majority voting system>> or <<voting logic>> approach.
The voting logic approach can be perfected for the monitoring of highly complex systems in which malfunctions of very different types regarding either their cause or their level of seriousness are likely to occur.
Part SUBJECT AND SUMMARY OF THE INVENTION: The invention proposes a system for detecting and locating failures in a complex system, comprising:

    
    
        at least one sensor for delivering a signal representing the functioning of a component of the said complex system;
        at least one module for validating the proper functioning of this sensor capable of analysing this signal and of delivering:
        
            at least one validated signal associated with a so-called <<first order>> confidence level; or
            a message representing a failure of at least one sensor;
        
        
        a failure detection module capable of detecting a failure affecting at least one sub-system or component from the validated signal and, if this is the case, of generating at least one raw failure message comprising raw information on the failure and a so-called <<second order>> confidence level;
        a data merging module which, at least from one raw failure message or from the message representing a sensor failure, is capable of generating a consolidated failure message comprising consolidated information on a failure affecting at least one sub-system of the complex system and a so-called <<third order>> confidence level; and
        a decision-making module capable of obtaining the said consolidated failure message and of deciding on at least one action to be taken in relation to :
        
            the consolidated information on a failure;
            the said third order confidence level; and
            a level of seriousness of the said failure.
        
        
    
    


Contrary to majority voting systems, the data merging module of the system according to the invention combines the raw failure messages together taking into account the <<confidence level>> parameters.
The raw failure messages associated with their own confidence levels that are input into the data merging module derive from the different failure detection modules associated with different components, sub-systems or the system as a whole.
The system of the invention is therefore advantageously capable of determining a precise diagnosis of the failure by combining and fine-tuning the data derived from the different sensors or failure detection modules, for confirmation, invalidation or moderation thereof via a data merging operation.
Contrary to majority voting systems, the system for detecting and locating failures according to the invention is able to adapt the action to be taken in relation to the seriousness of the failure and the reliability of knowledge of the failure.
In one particular embodiment, the decision-taking module of the failure detecting and locating system of the invention comprises means for obtaining the level of seriousness and identifying the action to be taken, this action being stored in memory in a database.
This database may be formed for example by a database derived from failure mode, effects and critical analysis (FMECA).
The action to be taken in the event of detection of a failure may consist of stopping the complex system or of acting on one or more of the components thereof, these being determined in relation to the failure, and more particularly may entail de-activating, activating or re-activating a component, modifying its operating point or sending a request to a regulating system.
The action to be taken may also consist of waiting for at least one other consolidated failure message emitted by the data merging module to confirm whether an action should or should not be taken.
This characteristic advantageously allows a reduction in the number of undue stoppages and action only to be taken when the confidence level of the failure is sufficient, the character of sufficiency being considered in relation to the level of seriousness of the failure.
In one embodiment, the decision-making module of the failure detection system of the invention uses a fuzzy logic technique. With this characteristic it is advantageously possible to introduce the notion of doubt and indecision as to the action to be taken.
Fuzzy logic techniques are generally used in applications of target-identification type or sensor selection type; but it is noteworthy that the use of fuzzy logic leading to a decision entailing physical action on a component of a complex system, this decision taking into account an index of confidence regarding a failure and its criticality, is not known in the state of the art.
The invention also concerns a rocket engine comprising a failure detection system such as mentioned above.
The invention is of particular interest in the development of a rocket engine on a test installation, in that it allows:

    
    
        a reduction in the number of undue stoppages of tests, and action to be taken in relation to the pair: failure seriousness/reliability level of knowledge of the failure;
        action to be taken in relation to the type of failure;
        waiting for confirmation of the failure, if needed, before taking action;
        easy scalability using detection modules defined modular fashion in relation to the architecture of an engine; and
        consideration to be given to the notion of doubt as to the action to be taken.
    
    


Part DETAILED DESCRIPTION OF EMBODIMENTS OF THE INVENTION: FIG. 1 illustrates a failure detection system conforming to one particular embodiment of the invention.
This system allows the detection of failures of a complex system comprising one or more sub-systems, each of these sub-systems comprising one or more components.
The failure detection system according to the invention comprises a certain number of sensors CAP each of these sensors being capable of delivering a signal SCAP representing the functioning of at least one component COMP of the complex system.
In the example shown FIG. 1, two components COMP are illustrated, each one associated with a sensor CAP capable of delivering a signal SCAP representing a component with which it is associated.
The signals SCAP delivered by the sensors CAP may be low frequency signals for example; this is notably the case when these sensors measure movement, a flow rate, pressure, temperature or velocity.
The signals SCAP may also be high frequency signals, in particular when the sensors with which they are associated measure vibrations or pressures likely to undergo rapid variations.
The detection system of the invention comprises a validation module MODVAL capable of validating the proper functioning of each of the sensors CAP.
In the example of embodiment described here, the validation module MODVAL comprises:

    
    
        a reference model 21;
        a bias detection module 22; and
        a noise detection module 23.
    
    


In the embodiment described here the validation module MODVAL detects failures affecting the sensors CAP delivering low frequency signals:

    
    
        either by detecting a bias in the signal SCAP using redundancies of this signal and/or outputs of the reference model 21;
        or by detecting noises in the signal SCAP, zero noise indicating a sensor CAP out of service and excessive noise indicating a problem of electrical nature.
    
    


In the embodiment described here, the validation module MODVAL detects failures affecting the sensors delivering high frequency signals SCAP by detecting non-symmetry in this signal.
According to the invention, the validation module MODVAL, for each measurement, delivers a validated signal SIGVAL associated with a first order confidence level NC1 if the sensor is operational, or a message MSGCAP representing a sensor failure.
The messages MSGCAP representing a sensor failure are transmitted to a data merging module MODFD of which a description will be given later.
The validated signals SIGVAL and the first order confidence levels NC1 associated therewith are transmitted to a failure detection module MODDP.
The failure detection module MODDP is capable of detecting failures affecting a component, the complex system as a whole or a sub-system of this complex system.
The algorithms used for failure detection can be of various types and may in particular make use of functions for threshold calculation, identification, calculation of residuals, filtering, comparison with physical or behavioural models . . . .
The functioning of a failure detection module MODDP will be specifically described with reference to FIG. 2 in the particular context of detecting engine failures.
The failure detection module MODDP, when detecting a failure affecting a component, a sub-system or the system as a whole, is capable of generating a raw failure message MSGPB comprising raw information INFPB on this failure and a second order confidence level NC2.
The failure detection system conforming to the invention also comprises a data merging module MODFD capable of receiving:

    
    
        firstly, messages MSGCAP representing a sensor failure; and
        secondly, raw failure messages MSGPB.
    
    


The purpose of the data merging module MODFD is in particular to confront and integrate multiple raw information items INFPB received in the raw failure messages MSGPB and in the sensor failure messages MSGCAP so as to reduce uncertainty on the resulting information.
The data merging module MODFD therefore generates consolidated failure messages MSGPC comprising consolidated information INFPC on a failure affecting the system as a whole or a sub-system of the complex system or a component and a third order confidence level.
The consolidated failure messages MSGPC are sent by the data merging module MODFD to a decision-making module MODPD.
The decision-making module MODPD is capable of making a decision on the action to be taken in relation to the consolidated information INFPC on the failure, of third order confidence level, obtained for this failure and to the level of seriousness of this failure included in the database BC.
In the example in FIG. 1, the decision-making module MODPD comprises access means to a database BC formed of a database derived from analysis of failures which may affect the complex system and their associated criticality.
FIG. 2 illustrates an example of a consolidated failure message MSGPC which can be used in a failure detection system according to the invention.
This message comprises five fields, namely:

    
    
        a DATE field comprising the date of the failure;
        an IDSS field comprising an identifier of the sub-system affected by the failure or, when applicable, an identifier of the system if the failure affects the system as a whole;
        an identifier IDCOMP of the component affected by the failure;
        the third order confidence level NC3 obtained by the data merging module MODFD; and
        the identifier IDFAILURE of this failure.
    
    


The data in the first, second and third fields of the consolidated failure message MSGPC form consolidated information INFPC on the failure in the meaning of the invention.
The failure message MSG can be sent in parallel towards a terminal for rapid interpretation of the results.
In the example of embodiment described here the raw failure messages MSGPB have an identical format to the consolidated failure message MSG except that in their fourth fields they comprise a second order confidence level NC2.
FIG. 3 illustrates a recorded database BC.
This recording comprises four fields, namely:

    
    
        a field IDSS comprising the identifier of a sub-system;
        a field IDFAILURE comprising the identifier of a failure;
        a field NG comprising a level of seriousness of this failure; and
        a field IDACTION comprising the identifier of a predefined action to be taken in relation to the said failure.
    
    


FIG. 4 illustrates a rocket engine conforming to the invention.
This engine comprises two propellant tanks referenced 131 and 132 each one upstream of a pump 111, 112. The flow rate of each of these propellants can be regulated by a valve 121, 122. In the embodiment described here this engine comprises:

    
    
        a first sensor CAP1 capable of measuring the vibrations V111 or the acceleration of this pump 111 and of delivering a signal SCAP1 representing these vibrations; and
        a second sensor CAP2 capable of measuring the pressure P111 at the output of the pump 111 and of delivering a signal SCAP2 representing this pressure.
    
    


These signals SCAP1 and SCAP2 are illustrated in FIGS. 5A and 6A.
It will be assumed with reference to these Figures, that the blade of a turbine starts to degrade at a time T1 and this degradation leads to loss of the blade at a time T2, the blade operating normally between times 0 and T1.
Therefore, time T1 can be considered to be the rupture initiation time of the blade.
It will be noted in these diagrams that the pressure P111 at the output of the pump 111 drops slightly between times T1 and T2 then drops abruptly on and after time T2 corresponding to loss of the blade.
This engine comprises a failure detection and diagnosis system 105 conforming to the invention.
According to the invention, the signals SCAP1 and SCAP2 are processed by a validation module MODVAL.
Therefore, according to the invention this validation module delivers two validated signals SIGVAL1 and SIGVAL2 illustrated in FIGS. 5B and 6B, each one associated with a first order confidence level respectively NC11, NC12 illustrated in FIGS. 5B and 6B as dotted lines.
In this example, the first order confidence level NC11 of sensor CAP1 remains high i.e. it is 0.95.
On the other hand, the first order confidence level of sensor CAP2 starts to decrease slightly at time T2 then much more abruptly until it reaches a value of substantially 0.45.
In the example described here, there is a drop in the first order confidence level NC12 of sensor CAP2 but nevertheless it is considered in this example that the confidence level of CAP2 remains sufficient to measure the pressure at the output of the pump 111.
Therefore, in this example, the validation module MODVAL does not generate a sensor failure message MSGCAP.
In the example of embodiment described here, the signal SIGVAL1 corresponds to SCAP1 but the signal SIGVAL2 is obtained from the signal SCAP2 by eliminating measuring noise.
The signals SIGVAL1 and SIGVAL2 are received with the first order confidence levels NC11 and NC12 by a failure detection module MODDP.
In the example of embodiment described here, this failure detection module MODDP is capable of detecting a failure affecting an engine turbine (this turbine being a sub-system in the meaning of the invention).
For this purpose, this module uses two algorithms that are able:

    
    
        firstly, to analyse the vibrations of a turbine; and
        secondly, to analyse the performance of a turbine.
    
    


In the embodiment described here the failure detection module MODDP, at times T3 and T4 respectively illustrated in FIG. 7, detects:

    
    
        a first malfunction (<<high turbine vibration level>>) with a second order confidence level NC21 of 0.9; and
        a second malfunction (<<low turbine yield>>) with a second order confidence level NC22 of 0.45.
    
    


These data items form raw information in the meaning of the invention.
They are sent to a data merging module MODFD, in raw failure messages MSGPB1 and MSGPB2 at times T3 and T4.
According to the invention, the data merging module MODFD, from the raw failure messages MSGPB1 and MSGPB2 received at times T3 and T4, generates one or more consolidated failure messages.
In the example of embodiment described here, it will be assumed that the data merging module MODFD merges the raw information contained in the raw messages MSGPB1 and MSGPB2 so as respectively to generate at times T3 and T4:

    
    
        a first consolidated failure message MSGPC1 identical to the failure message MSGPB1; and
        a second consolidated failure message obtained by merging the raw information contained in the two raw failure messages MSGPB1 and MSGPB2.

The consolidated failure message MSGPC2 is illustrated in FIG. 8.

    
    


In this example, it will be assumed that the data merging module is capable of detecting the loss of a blade of the turbo-pump 111, with a third order confidence level NC32 of 0.7, the date of the loss of the blade being estimated at time T4.
The consolidated failure message MSGPC1 and MSGPC2 are sent to a decision-making module MODPD at times T3 and T4 respectively.
According to the invention, the decision-making module MODPD, on the basis of the two consolidated messages MSGPC1 and MSGPC2, considers whether action must be taken.
In the example of embodiment described here, on receipt of the first consolidated message MSGPC1, the decision-making module MODPD decides to wait until receipt of another consolidated message before initiating physical action on a member of the engine.
On the other hand, on receipt of the second consolidated message MSGPC2, the decision-making module MODPD takes two actions, namely:

    
    
        the emitting of a failure message; and
        the triggering of a thrust-reducing command to offset the failure.
    
    


This second action is materialized in FIG. 4 by the arrow referenced ACTREQUESTED; it is performed in this example by a control system which in this example acts on the valve 121 regulating the flow rates of the propellant tanks 131 and 132.
In the example of embodiment described here, the decision-making module MODPD uses a fuzzy logic technique known to persons skilled in the art and which is detailed below.
According to the invention, the decision made regarding the action to be taken (or not taken) is made in relation to the third level confidence level NC3 and to the level of seriousness NG of the detected failure.
In the embodiment described here, these variables NC3, NG vary over the interval [0; 1].
To illustrate an example of embodiment of the invention, it is considered that the decision-making module MODPD uses the two tables TNC3, TNG whose set-inclusion functions are characterized by the following tables and represented in the form of trapezoids in FIGS. 10A and 10B:







TABLE 







TNC3








Third order confidence



level (NC3)
Value





Scarcely reliable (PF)
NC3 = [0; 0.15; 0.35] Y = [1; 1; 0]


Fairly reliable (MF)
NC3 = [0.15; 0.35; 0.4; 0.6] Y = [0; 1; 1; 0]


Reliable (F)
NC3 = [0.4; 0.6; 0.65; 0.85] Y = [0; 1; 1; 0]


Very reliable (TF)
NC3 = [0.65; 0.85; 1] Y = [0; 1; 1]
















TABLE 







TNG








Level of seriousness (NG)
Value





Not very serious (PG)
NG = [0; 0.15; 0.35] Y = [1; 1; 0]


Fairly serious (MG)
NG = [0.15; 0.35; 0.4; 0.6] Y = [0; 1; 1; 0]


Serious (G)
NG = [0.4; 0.6; 0.65; 0.85] Y = [0; 1; 1; 0]


Very serious (TG)
NG = [0.65; 0.85; 1] Y = [0; 1; 1]









The output of the decision-making module MODPD decides on whether or not to set up corrective action in relation to the diagnosed failure. In the example of embodiment described here, this <<Decision>> variable varies within the same domain as the input data NC3, NG; the set-inclusion function conforms to the breakdown given in following Table TDEC:







TABLE 







TDEC










Decision (DEC)
Value







No action (PA)
DEC = [0; 0.26; 0.4] Y = [1; 1; 0]



Doubt (D)
DEC = [0.26; 0.4; 0.62; 0.7] Y = [0; 1; 1; 0]



Action (A)
DEC = [0.62; 0.7; 1] Y = [0; 1; 1]










The three tables TNC3, TNG et TDEC are stored in memory by the decision-making module MODPD.
Table TREG below summarizes the preceding rules:







TABLE 







TREG










NC3















Decision

PF
MF
F
TF


















NG
PG
PA
PA
PA
PA




MG
PA
D
A
A




G
D
A
A
A




TG
D
A
A
A










In the example of embodiment described here, each event within the doubt interval is associated with a value TMAX of maximum residence time in this status; if this value is reached, an order for system stoppage is given for safety reasons.


{'bibliographic_information': "{'country': 'US', 'doc-number': '13581076', 'kind': 'B2', 'date': '20110217', 'extension': '281', 'invention_title': 'Failure detection and locating system and engine containing the same'}", 'source_file': './grant/2014/ipg141111.xml', 'source_data': 'USPTO-Grant'}