WO2014043667A1 - Approche basée sur la logique pour le diagnostic de comportement d'un système - Google Patents

Approche basée sur la logique pour le diagnostic de comportement d'un système Download PDF

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Publication number
WO2014043667A1
WO2014043667A1 PCT/US2013/060070 US2013060070W WO2014043667A1 WO 2014043667 A1 WO2014043667 A1 WO 2014043667A1 US 2013060070 W US2013060070 W US 2013060070W WO 2014043667 A1 WO2014043667 A1 WO 2014043667A1
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WIPO (PCT)
Prior art keywords
model
dependency
assessment
sensor data
abnormal
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PCT/US2013/060070
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English (en)
Inventor
Yunsong MENG
Dan G. Tecuci
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Siemens Corporation
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Filing date
Publication date
Application filed by Siemens Corporation filed Critical Siemens Corporation
Priority to EP13773462.0A priority Critical patent/EP2895927A1/fr
Priority to KR1020157009632A priority patent/KR20150058343A/ko
Priority to JP2015532135A priority patent/JP2015534179A/ja
Priority to CN201380055495.1A priority patent/CN104756028A/zh
Publication of WO2014043667A1 publication Critical patent/WO2014043667A1/fr

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    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B23/00—Testing or monitoring of control systems or parts thereof
    • G05B23/02—Electric testing or monitoring
    • G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
    • G05B23/0218—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults
    • G05B23/0243—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults model based detection method, e.g. first-principles knowledge model
    • G05B23/0245—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults model based detection method, e.g. first-principles knowledge model based on a qualitative model, e.g. rule based; if-then decisions
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B23/00—Testing or monitoring of control systems or parts thereof
    • G05B23/02—Electric testing or monitoring
    • G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
    • G05B23/0218—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults
    • G05B23/0243—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults model based detection method, e.g. first-principles knowledge model
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B23/00—Testing or monitoring of control systems or parts thereof
    • G05B23/02—Electric testing or monitoring
    • G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
    • G05B23/0259—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterized by the response to fault detection
    • G05B23/0275—Fault isolation and identification, e.g. classify fault; estimate cause or root of failure
    • G05B23/0278—Qualitative, e.g. if-then rules; Fuzzy logic; Lookup tables; Symptomatic search; FMEA

Definitions

  • the present disclosure relates to system behavior diagnosis and, more specifically, to a logic-based approach for system behavior diagnosis.
  • a method for assessing system status includes receiving user-provided information pertaining to operation of a system.
  • a dependency model of the system is constructed based on the received information.
  • a logical model of the system is constructed and combined with the dependency model when it is determined that the user-provided information is sufficient to construct the logical model.
  • Sensor data from sensors installed within the system is monitored.
  • the combined model is applied to the sensor data when the combined model is available and the dependency model is applied to the sensor data when the combined model is not available.
  • a set of abnormal system components is determined from the application of the combined model/dependency model to the sensor data.
  • An assessment of system status is determined based on the set of abnormal system components.
  • the user-provided information pertaining to operation of a system under assessment may include expert knowledge relating to fault dependency of one or more components of the system or relating to a manner in which the one or more components of the system fail.
  • the user-provided information pertaining to operation of a system under assessment may be encoded in Answer Set Programming (ASP) formalism either by the user or automatically based on user-input.
  • ASP Answer Set Programming
  • the constructed dependency model and/or the combined model may be expressed in Answer Set Programming (ASP) formalism.
  • ASP Answer Set Programming
  • the constructed dependency model might only describe how failures propagate through the system under assessment.
  • the constructed logical model may describe complex functional
  • Determining whether the received user-provided information is sufficient to construct the logical model of the system under assessment may include constructing the logical model and attempting to apply the constructed logical model to the monitored sensor data and determining whether a meaningful result is obtained.
  • the combining of the logical model and the dependency model may be performed using an Answer Set Programming (ASP) solver.
  • ASP Answer Set Programming
  • Applying the combined model or the dependency model to the sensor data may be performed by an Answer Set Programming (ASP) solver.
  • ASP Answer Set Programming
  • Providing the assessment of system status based on the determined set of one or more abnormal system components may include prioritizing minimal solutions.
  • Providing the assessment of system status based on the determined set of one or more abnormal system components may include prioritizing solutions supported by a greatest number of sensors.
  • a method for assessing system status includes receiving a dependency model of a system under assessment and a logical model of the system under assessment. Sensor data is monitored from one or more sensors installed within the system under assessment. The logical model is combined with the dependency model and the combined model is applied to the sensor data using an Answer Set Programming (ASP) solver. A set of one or more abnormal system components is determined from the application of the combined model to the sensor data. An assessment of system status is provided based on the determined set of one or more abnormal system components.
  • ASP Answer Set Programming
  • Providing the assessment of system status based on the determined set of one or more abnormal system components may include prioritizing minimal solutions.
  • Providing the assessment of system status based on the determined set of one or more abnormal system components may include prioritizing solutions supported by a greatest number of sensors.
  • a computer system includes a processor and a non-transitory, tangible, program storage medium, readable by the computer system, embodying a program of instructions executable by the processor to perform method steps for assessing system status.
  • the method includes receiving user-provided information pertaining to operation of a system under assessment.
  • a dependency model of the system under assessment is constructed based on the received user-provided information. It is determined whether the received user-provided information is sufficient to construct a logical model of the system under assessment.
  • a logical model of the system under assessment is constructed and the logical model is combined with the dependency model when it is determined that the user-provided information is sufficient.
  • Sensor data from one or more sensors installed within the system under assessment is monitored.
  • the combined model is applied to the sensor data when the combined model is available and the dependency model is applied to the sensor data when the combined model is not available.
  • a set of one or more abnormal system components is determined from the application of the combined model/dependency model to the sensor data.
  • An assessment of system status is provided based on the determined set of one or more abnormal system components.
  • the constructed dependency model might only describe how failures propagate through the system under assessment.
  • the constructed logical model may describe complex functional
  • the combining of the logical model and the dependency model may be performed using an Answer Set Programming (ASP) solver.
  • ASP Answer Set Programming
  • the applying of the combined model or the dependency model to the sensor data may be performed by an Answer Set Programming (ASP) solver.
  • ASP Answer Set Programming
  • Providing the assessment of system status based on the determined set of one or more abnormal system components may include prioritizing minimal solutions or prioritizing solutions supported by a greatest number of sensors.
  • FIG. 1 is a schematic diagram illustrating a Water Tank Problem (WTP) for explaining exemplary embodiments of the present invention
  • FIG. 2 is a diagram illustrating simplified oil lubricating system upon which exemplary embodiments of the present invention may be applied to;
  • FIG. 3 is a set of statements representing a dependency model based on the system of FIG. 2 in accordance with exemplary embodiments of the present invention
  • FIG. 4 is a flow chart illustrating a hybrid approach for system diagnosis in accordance with exemplary embodiments of the present invention
  • FIG. 5A is a diagram illustrating an overall architecture for system diagnosis in accordance with exemplary embodiments of the present invention.
  • FIG. 5B is a schematic diagram illustrating an architecture for the monitoring and diagnosis system in accordance with exemplary embodiments of the present invention.
  • FIG. 6 shows an example of a computer system capable of implementing the method and apparatus according to embodiments of the present disclosure.
  • fault dependency model One approach for performing diagnosis based on sensor data, utilizing expert knowledge, is the fault dependency model. This approach is based on the realization that some components require the proper operation of other components in order to operate properly themselves. As single failure may present as multiple abnormal sensor readings, the fault dependency model attempts to pinpoint the root cause of the abnormal readings based on a prior knowledge of what sorts of malfunctions are likely to cause other abnormal sensor readings.
  • FIG. 1 is a schematic diagram illustrating the WTP.
  • a left tank 10 and a right tank 11.
  • a single tap 12 is free to move between each tank so as to fill both tanks with water.
  • Each tank has a hole at the bottom that loses water.
  • the left tank 10 loses water at a rate of v1 while the right tank 11 loses water at a rate of v2.
  • the tap 12 fills either tank at a rate w.
  • a command called“switch” may be used to move the tap to change tanks.
  • Sensor x1 is a Boolean sensor monitoring the state of the left tank 10 while sensor x2 is a Boolean sensor monitoring the state of the right tank 11. Sensors x1 and x2 register a 1 when the water level in the tank is increasing and they register a 0 when the water level is decreasing.
  • Nonmonotonic reasoning may be used to examine the state of x1 and x2 together.
  • the fact that x2 is decreasing and not increasing may indicate that the left tank 11 has failed, for example, due to a leak.
  • x1 is increasing and not decreasing
  • it may be determined that it is the tap 12 that is malfunctioning.
  • One example of nonmonotonic reasoning is the Answer Set Programming (ASP) formalism.
  • ASP Answer Set Programming
  • Exemplary embodiments seek to apply ASP to various monitoring and diagnostic systems to deduce a cause of sensor abnormalities where the particular problem is not directly observable.
  • ASP may be used as a unifying language for integrating other reasoning approaches.
  • Exemplary embodiments of the present invention may utilize two additional approaches for reasoning diagnosis.
  • the first such approach may be called the fault dependency model.
  • the understanding that a single malfunction can cause other sensors to read abnormally is used to trace back a sequence of abnormal sensor readings to an actual cause of failure.
  • we start with the expert understanding that a failure in the tap may lead to an abnormal state of the left tank, which may lead to an abnormal reading for x1.
  • a failure in the tap may lead to an abnormal state of the right tank, which may lead to an abnormal reading for x2.
  • This knowledge may be expressed accordingly:
  • This dependency model may be interpreted to mean that when x1 is observed to be abnormal, then tank 1 and/or the tap could be faulty and similarly, when x2 is observed to be abnormal, then tank 2 and/or the tap could be faulty.
  • the following dependency matrix may be used to describe this situation:
  • the fault dependency model in order to generate the fault dependency model, the only expert knowledge required is how faults propagate between components. Such knowledge can be automatically obtained from system design documents such as CAD/CAM. Other information, such as the functional perspective of the system, may be ignored. For this reason, the fault dependency model may be simple to construct and use for diagnostic purposes, however, this approach may have relatively low resolution compared to other reasoning approaches. Thus, for a given number of sensors, the fault dependency model might not be able to pinpoint the exact cause of the problem as narrowly as other approaches.
  • Exemplary embodiments of the present invention may also use another approach for diagnostic reasoning.
  • This other approach may be referred to as the logical models approach.
  • complex functional interdependencies between system components may be expressed as logical formulas. While this approach may require additional expert knowledge, a higher diagnostic resolution may be achieved, which may mean that for a given number of sensors, a cause of the problem may be more narrowly pinpointed.
  • each command is associated with a state variable that describes the system state. For example, for the command fill1, the state“filling1” will be true if the tap is filling tank 1 and the state“filling1” will be false otherwise.
  • the expert knowledge of the WTP example may be captured in the following two rules, which may also utilize the modeling language of ASP: inStatus(filling1) ⁇ value(fill1,1), not ab(tap) (1) not inStatus(filling1) ⁇ value(fill1,0), not ab(tap) (2)
  • the right-hand side of the rule is to be interpreted as conjunctions, meaning that for the left hand side to hold, all conditions on the right should hold as well.
  • rule (1) says that if the value of the command fill1 is 1 (to fill left tank) and the tap is not in a faulty condition, then the system should be in a state that the left tank is being filled.
  • Rule (2) holds similarly that if the value of the command fill1 is 0 (not to fill the left tank) and the tap is not in a faulty condition, then the system should be in a state that the left tank is not being filled.
  • Logical equations may also be used to specify the relationships between sensor readings and the state variables, for example:
  • rule (3) says that if the system is not in the state of filling tank1 (left tank) and tank1 is not faulty, then x1 should not detect an increase.
  • rule (4) similarly holds that if the system is not in the state of filling tank1 (left tank) and tank2 is not faulty, then x2 should detect an increase.
  • the sensor readings are:
  • the logical models approach while being more complex, may provide a more detailed explanation as to the cause of a potential failure.
  • the dependency models approach is simpler but provides less detail as to the cause of a potential failure.
  • Exemplary embodiments of the present invention combine the logical models approach with the dependency models approach, using the modeling language of ASP, to provide a hybrid modeling approach that can be both simple, where needed, and provide high resolution, where available.
  • exemplary embodiments give preference to those solutions that are minimal, for example, preference is given to the simplest possible explanations that can account for the observed sensor readings.
  • preference is given to those solutions with the greatest amount of supporting evidence, for example, the minimal solution with the greatest number of supporting sensor observations.
  • the nonmonotonic formalism of Answer Set Programming may be used as the computational framework as this formalism utilizes nonmonotonic reasoning and is well equipped to deal with dynamic domains. This approach may be integrated with existing monitoring and diagnosis systems that use such sensor data.
  • Exemplary embodiments of the present invention are described herein with respect to a simplified lubricating oil system.
  • the simplified lubricating oil system is offered as an example system to which exemplary embodiments of the present invention may be applied, and it is to be understood that exemplary embodiments of the present invention may be applied to any system being monitored.
  • Exemplary embodiments of the present invention may accordingly rely solely on dependency models where a complete model of the complex system is not available or has otherwise not been provided.
  • FIG. 2 is a diagram illustrating simplified oil lubricating system upon which exemplary embodiments of the present invention may be applied to.
  • Three pumps “ACPMP1” 201,“ACPMP2” 202, and“DCPMP” 203 are connected to the lubricating oil reservoir 200.
  • the two AC pumps 201 and 202 may form a
  • a cooler 204 On the AC line, a cooler 204, a filter 205 and a pressure regulator valve 206 are connected in sequence.
  • Four indicator sensors are provided. They include: psvac 211, which indicates the pressure in the reservoir 200, dpsw 212 and ps 213, which indicate whether oil flows in the two lines, and LOPS 214, which indicates the oil pressure at the terminal.
  • FIG. 3 is a set of statements representing a dependency model based on the system of FIG. 2 in accordance with exemplary embodiments of the present invention.
  • the illustrated dependency model of FIG. 3 can be obtained directly from the diagram of FIG. 2.
  • the dependency model of FIG. 3 shows the chain of dependency for each object in the system illustrated in FIG. 2.
  • an abnormal sensor reading detected in lops (214) may be caused by a problem in the lops (214), a problem in the regulatorValve (206), a problem in the filter (205), a problem in the cooler (204), a problem in acpmp1 (201), or a problem in res (200).
  • an abnormal sensor reading detected in lops (214) may be caused by a problem in the lops (214), a problem in the regulatorValve (206), a problem in the filter (205), a problem in the cooler (204), a problem in acpmp2 (202), or a problem in res (200).
  • an abnormal sensor reading detected in dpsw (212) may be caused by a problem in the dpsw (212), a problem in the regulatorValve (206), a problem in the filter (205), a problem in the cooler (204), a problem in acpmp1 (201), or a problem in res (200).
  • an abnormal sensor reading detected in dpsw (212) may be caused by a problem in the dpsw (212), a problem in the regulatorValve (206), a problem in the filter (205), a problem in the cooler (204), a problem in acpmp2 (202), or a problem in res (200).
  • an abnormal sensor reading detected in lops (214) may be caused by a problem in the lops (214), a problem in the dcpmp (203), or a problem in res (200).
  • an abnormal sensor reading detected in ps may be caused by a problem in the ps (213), a problem in the dcpmp (203), or a problem in res (200).
  • an abnormal sensor reading detected in psvac may be caused by a problem in the psvac (211), or a problem in res (200).
  • This hierarchical relationship of abnormal sensor readings and possible causes is summarized in the dependency matrix provided below.
  • the columns represent the abnormal sensor readings and the rows represent the possible causes.
  • a value of“1” indicates that the abnormal sensor reading in the column may be caused by a failure or other malfunction in the row while a value of “0” indicates that the abnormal sensor reading in the column may not be cause by a failure or malfunction in the row:
  • the simplified lubricating oil system example is not fully diagnosable using the existing indicator sensors. For example, if both lops and dpsw are detected to be faulty, it may not be possible to distinguish which component is faulty based on dependency information alone. In such a case, any one or more components could be faulty.
  • the sensor readings would be: value(acPmp1Dmd,1), value(acPmp2Dmd,0),
  • the dependency model approach is easy to obtain and involves simple reasoning but may provide relatively low resolution, which may mean that for a given number of sensors, a malfunction cannot be narrowed down as well as when using the logical model approach.
  • the logical model approach may provide the higher resolution, which may mean that for the given number of sensors, a malfunction can be narrowed down to a smaller subset of potential problems.
  • the logical model approach may require additional user input to establish. For example, more expert data may be required to build a complete set of logical statements for understanding the system.
  • FIG. 4 is a flow chart illustrating a hybrid approach for system diagnosis in accordance with exemplary embodiments of the present invention.
  • user information may be obtained (Step S41).
  • the user information may be expert knowledge about the system being monitored and may pertain to the proper operation of the system and the known ways in which problems may be observed.
  • This user information may be provided, for example, via a user interface that prompts the user for the desired information.
  • Obtaining the user information may also include formatting the user-provided input into a format that is unambiguous and convenient.
  • Step S42 It may then be determined whether the obtained user-provided information is sufficient to establish a logical model (Step S42). This determination may be made either manually, by putting the question to the user, or automatically by analyzing the sufficiency of the information.
  • a logical model may be constructed in either event and the sufficiency of the constructed logical model may be determined by whether the logical model is able to provide a meaningful result.
  • the sufficiency of the logical model may be assessed by running simulations or by examining the interdependence of the set of logical statements to see how many different combinations of possible sensor values can be explained by the model.
  • Step S42 If the obtained user-provided information is sufficient to establish a logical model (Yes, Step S42) then the logical model may be generated from the available information (Step S43). If the obtained user-provided information is not sufficient to establish a logical model (No, Step S42) then a dependency model may be generated from the available information (Step S44a). Additionally, in the event that the logical model is built, the dependency model may still be built (Step S44b). At any point thereafter, additional information about the system may be obtained from a user or otherwise learned by monitoring operation of the system and when additional information is obtained, the logical model may be expanded, or created if it had not previously been created due to lack of sufficient information.
  • the dependency information may then be encoded using an Answer Set Programming (ASP) framework (Step 45a/b).
  • ASP Answer Set Programming
  • connectTo(dcPmpDmd,res) For dependencies between the components and indicators, the following may be used: associate(dcPmp,ps).
  • the transitive closure of connection can be computed using the following ASP rules:
  • the two models may be combined and executed using ASP solvers (Step S46). Where no logical model has been built, the dependency model may still be executed using the ASP solver (Step S47).
  • the ASP solver computation may be a particularly efficient way to determine the cause of abnormal sensor readings or to otherwise diagnose a potential problem.
  • the ASP encodings of the model rules may be written either manually or automatically.
  • the knowledge of ASP may be hidden from the end users and ASP encodings may be automatically generated using the knowledge that users input in a familiar form.
  • Visual modeling tools may be used for end users to specify information such as: components, command, indicators, and state variables;
  • association of components with commands and indicators dependency between components; definition of state variables using sensors readings; constraints; and/or expert rules.
  • Step S48 sensor data from a network of sensors installed within a system may be analyzed using the models to determine when maintenance and/or remedial action should be taken or to otherwise render a diagnosis as to the condition of the system.
  • Step S46 when the logical and dependency models are combined (Step S46), the results of the
  • dependency model may be used to enhance the diagnostic results of the logical model. Accordingly, previous diagnostic efforts performed using the dependency model may be carried over to the logical model approach.
  • Step S49 Preference may include rejecting potential results that are not preferential or otherwise ordering the results for display in accordance with preference.
  • FIG. 5A is a diagram illustrating an overall architecture for system diagnosis in accordance with exemplary embodiments of the present invention.
  • Sensor data 501 from the system may be fed into the a monitoring system 503, for example, periodically, such as daily, or continuously, to identify any abnormal behavior.
  • the data may be validated 502 prior to being monitored. Data validation may be performed to ensure that the data being monitored is meaningful.
  • the monitor system 503 will return a warning and activate the diagnostic system with all sensor readings and the identified faulty sensors.
  • the diagnostic engine 504 will generate a set of possible explanations using the reasoning explained in the previous sections and therefore render a component diagnosis 505.
  • FIG. 5B is a schematic diagram illustrating an architecture for the monitoring and diagnosis system in accordance with exemplary embodiments of the present invention.
  • This architecture may be coherent with ISO 13374 (Condition Monitory and Diagnostics of Machines).
  • the architecture may include ISO 13374 layers for data acquisition 506, data manipulation 507, state detection 508, health assessment 509, prognostic assessment 510, and advisory generation 511.
  • Exemplary embodiments of the present invention may additionally utilize prognostic assessment.
  • Reasoning about dynamicity may be the basis for prognosis and planning.
  • Dynamicity may play a role in effective diagnosis as well.
  • rules (1) and (2) provided above may be modified as the following dynamic rules:
  • the diagnosis system may be able to reason about dynamicity and, as a result, may be able to provide prognosis and planning services.
  • exemplary embodiments of the present invention may extend the diagnostic framework with the ability to reason about actions and changes. For example, returning to the WTP discussed above, if it is asked what would happen if a command value(switch,1) is given in the current state, the system would be able to predict that value(x1,1,2), value(x2,0,2) since the tap is still stuck. If the knowledge about fixing the tap is encoded and the system is asked to restore itself to normal status, then a repair plan would be generated.
  • Exemplary embodiments of the present invention may additionally utilize a higher level action language to help end users to encode the knowledge about dynamicity. For example, instead of writing the rule (14), the users can encode using a more natural language approach. Rule (14) will be generated automatically therefrom.
  • Exemplary embodiments of the present invention may also enhance the system with ontology knowledge to increase diagnostic accuracy. For example, consider the Lubricating Oil System example. Instead of describing faults uniformly in terms of abnormality of components, exemplary embodiments of the present invention may specify the fault types and their relations using the ontology that an abnormal sensor reading can be indicative of a general fault or a functional fault.
  • exemplary embodiments of the present invention give functional faults greater priority when computing a minimal diagnosis. This can be written in the following ASP rules:
  • the system may be able to conclude that ⁇ functionalFault(regulatorValve) ⁇ .
  • Exemplary embodiments of the present invention may extend the diagnostic system by adding query interfaces to ontologies. Such an interface may help the users to better encode their knowledge and use it in bolstering the diagnosis of the system.
  • exemplary embodiments of the present invention compute minimal diagnosis in order to prioritize the minimal solutions.
  • embodiments of the present invention may use meta-programming techniques to reduce computation of subset minimal diagnosis to the computation of answer sets of a different program.
  • exemplary embodiments of the present invention may extend this approach to cover arbitrary program under the stable model semantics.
  • ASP solvers may then be used to solve the minimal diagnostic problem in the specific domain.
  • FIG. 6 shows an example of a computer system which may implement a method and system of the present disclosure.
  • the system and method of the present disclosure may be implemented in the form of a software application running on a computer system, for example, a mainframe, personal computer (PC), handheld computer, server, etc.
  • the software application may be stored on a recording media locally accessible by the computer system and accessible via a hard wired or wireless connection to a network, for example, a local area network, or the Internet.
  • the computer system referred to generally as system 1000 may include, for example, a central processing unit (CPU) 1001, random access memory (RAM) 1004, a printer interface 1010, a display unit 1011, a local area network (LAN) data transmission controller 1005, a LAN interface 1006, a network controller 1003, an internal bus 1002, and one or more input devices 1009, for example, a keyboard, mouse etc.
  • the system 1000 may be connected to a data storage device, for example, a hard disk, 1008 via a link 1007.
  • aspects of the present invention may be embodied as a system, method or computer program product.
  • aspects of the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment (including firmware, resident software, micro-code, etc.) or an embodiment combining software and hardware aspects that may all generally be referred to herein as a“circuit,”“module” or “system.”
  • aspects of the present invention may take the form of a computer program product embodied in one or more computer readable medium(s) having computer readable program code embodied thereon.
  • the computer readable medium may be a computer readable signal medium or a computer readable storage medium.
  • a computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing.
  • a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
  • a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof.
  • a computer readable signal medium may be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
  • Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
  • Computer program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages.
  • the program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server.
  • the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider).
  • LAN local area network
  • WAN wide area network
  • Internet Service Provider for example, AT&T, MCI, Sprint, EarthLink, MSN, GTE, etc.
  • These computer program instructions may also be stored in a computer readable medium that can direct a computer, other programmable data processing apparatus, or other devices to function in a particular manner, such that the instructions stored in the computer readable medium produce an article of
  • manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.
  • the computer program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks.
  • each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s).
  • the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved.

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  • Automation & Control Theory (AREA)
  • Fuzzy Systems (AREA)
  • Mathematical Physics (AREA)
  • Quality & Reliability (AREA)
  • Testing And Monitoring For Control Systems (AREA)

Abstract

L'invention concerne un procédé destiné à évaluer le statut d'un système, qui contient la réception d'informations fournies par l'utilisateur, relatives à l'opération d'un système (S41). Un modèle de dépendance du système est construit sur la base des informations reçues (S43a/b). Un modèle logique du système est construit (S43) et combiné avec le modèle de dépendance (S46) lorsqu'il est déterminé que les informations fournies par l'utilisateur sont suffisantes pour construire le modèle logique (S42). Des données de capteur, provenant de capteurs installés dans le système, sont surveillées, le modèle combiné est appliqué aux données de capteur, lorsque le modèle combiné est disponible et que le modèle de dépendance est appliqué aux données de capteur lorsque le modèle combiné n'est pas disponible (S48). Un ensemble de composantes de système anormales est déterminé, à partir de l'application du modèle de dépendance/modèle combiné sur les données de capteur et une évaluation de l'état du système est déterminée sur la base de l'ensemble de composantes anormales du système.
PCT/US2013/060070 2012-09-17 2013-09-17 Approche basée sur la logique pour le diagnostic de comportement d'un système WO2014043667A1 (fr)

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EP13773462.0A EP2895927A1 (fr) 2012-09-17 2013-09-17 Approche basée sur la logique pour le diagnostic de comportement d'un système
KR1020157009632A KR20150058343A (ko) 2012-09-17 2013-09-17 시스템 행동 진단을 위한 논리 기반 접근법
JP2015532135A JP2015534179A (ja) 2012-09-17 2013-09-17 システム挙動診断のための論理ベースアプローチ
CN201380055495.1A CN104756028A (zh) 2012-09-17 2013-09-17 用于系统行为诊断的基于逻辑的方法

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CN104756028A (zh) 2015-07-01
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KR20150058343A (ko) 2015-05-28

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