WO2020026256A1 - Entité d'inspection et procédé mis en œuvre dans cette dernière permettant la gestion d'un défaut de sortie d'un système - Google Patents

Entité d'inspection et procédé mis en œuvre dans cette dernière permettant la gestion d'un défaut de sortie d'un système Download PDF

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Publication number
WO2020026256A1
WO2020026256A1 PCT/IN2018/050504 IN2018050504W WO2020026256A1 WO 2020026256 A1 WO2020026256 A1 WO 2020026256A1 IN 2018050504 W IN2018050504 W IN 2018050504W WO 2020026256 A1 WO2020026256 A1 WO 2020026256A1
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WO
WIPO (PCT)
Prior art keywords
fault
input
output
time profile
time
Prior art date
Application number
PCT/IN2018/050504
Other languages
English (en)
Inventor
Perepu SATHEESH KUMAR
Anusha Pradeep MUJUMDAR
Chakri PADALA
Original Assignee
Telefonaktiebolaget Lm Ericsson (Publ)
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Telefonaktiebolaget Lm Ericsson (Publ) filed Critical Telefonaktiebolaget Lm Ericsson (Publ)
Priority to PCT/IN2018/050504 priority Critical patent/WO2020026256A1/fr
Publication of WO2020026256A1 publication Critical patent/WO2020026256A1/fr

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Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computing arrangements using knowledge-based models
    • G06N5/02—Knowledge representation; Symbolic representation
    • G06N5/022—Knowledge engineering; Knowledge acquisition
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F11/00—Error detection; Error correction; Monitoring
    • G06F11/07—Responding to the occurrence of a fault, e.g. fault tolerance
    • G06F11/0703—Error or fault processing not based on redundancy, i.e. by taking additional measures to deal with the error or fault not making use of redundancy in operation, in hardware, or in data representation
    • G06F11/0751—Error or fault detection not based on redundancy
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F11/00—Error detection; Error correction; Monitoring
    • G06F11/07—Responding to the occurrence of a fault, e.g. fault tolerance
    • G06F11/0703—Error or fault processing not based on redundancy, i.e. by taking additional measures to deal with the error or fault not making use of redundancy in operation, in hardware, or in data representation
    • G06F11/079—Root cause analysis, i.e. error or fault diagnosis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/004—Artificial life, i.e. computing arrangements simulating life
    • G06N3/008—Artificial life, i.e. computing arrangements simulating life based on physical entities controlled by simulated intelligence so as to replicate intelligent life forms, e.g. based on robots replicating pets or humans in their appearance or behaviour

Definitions

  • the fault in the output may be classified into two categories depending on the cause: (i) fault in the output occurred because of its own variable (because of a faulty sensor, for example)
  • An object of embodiments herein is to provide a mechanism for improving the handling of an output fault in an output of a system.
  • the object is achieved by providing a system operable with multiple inputs and multiple outputs.
  • the system comprises the above inspection entity for handling an output fault in a first output of the system.
  • Fig. 1 is a schematic overview depicting a new system according to embodiments herein;
  • Fig. 5b is a schematic depicting a time profile of a fault in an output according to embodiments herein;
  • Fig. 6 is a block diagram depicting an inspection entity according to embodiments herein.
  • the embodiments herein disclose determining a time profile of an input fault associated with a time profile of the output fault. Thanks to the time profile of the fault in the input, it is enabled to take the dynamic information of the system into account and take a necessary corrective action with respect to the specific fault in the input.
  • FIG. 1 illustrates one example of a configuration of a system 100 for handling a fault in an output signal, numerous other configurations may also be used to implement embodiments of the present disclosure.
  • Examples of the system 100 may also include a vacuum robot, and a wearable device, e.g. a watch, a wristband, glasses, contact lenses, e-textiles and smart fabrics, a headband, a beany and cap, jewellery such as rings, bracelets, and hearing aid-like devices that are designed to look like earrings.
  • a wearable device e.g. a watch, a wristband, glasses, contact lenses, e-textiles and smart fabrics, a headband, a beany and cap, jewellery such as rings, bracelets, and hearing aid-like devices that are designed to look like earrings.
  • An inspection entity 110 which may be located internally in or externally to the system 100 is for handling an output fault in a first output of the system 100, particularly determining what fault in which input impacted the output fault.
  • the inspection entity 110 When the inspection entity 110 is located externally to the system 100, it may communicate with the system 100 with any wired or wireless communication technology.
  • the inspection entity 110 may identify the output fault in the first output.
  • the inspection entity 110 has the knowledge of the first input which has impacted the output fault, however is still not aware of what an exact fault in the first input is, and a time instant when the exact fault in the first input occurred. In order to obtain the above information, one or more of the following actions will be performed.
  • the inspection entity 110 determines, based on a dictionary, a time profile of the input fault associated with a time profile of the output fault.
  • the respective time profile of the input and output fault indicates how the respective input and output fault changes over time.
  • the dictionary comprises a mapping between the time profile of the input fault and the time profile of the output fault. Constructing of the dictionary will be discussed later. By knowing the time profile of the output fault, it is possible to arrive at the time profile of the input fault.
  • the inspection entity 110 may determine, based on the determined time profile of the input fault, a time instant indicating when the input fault impacting the output fault occurred.
  • the time instant indicating when the input fault impacting the output fault occurred may also be called the associated time instant in short, or the position of the input fault,
  • the associated time instant or the position of the input fault may comprise one or more time instants.
  • the position of the input fault comprises more than one time instant, it may comprise a starting time instant and an ending time instant.
  • the inspection entity 110 may compare a whole time profile of the input fault with the time profile of the input fault in the dictionary, the latter normally has a shorter time length, e.g., 5 seconds, than the former.
  • a part that is most correlated with the time profile of the input fault in the dictionary will be determined as the position of the input fault. Accordingly, the starting time instant and the ending time instant of the input fault respectively correspond to the beginning and the ending of the most correlated part of the whole time profile of the input fault.
  • Determining the associated time instant brings technical benefit of knowing an exact cause of the output fault can be inferred since both the time instance and specific input fault have been identified.
  • the inspection entity 110 may send the time profile of the fault in the first input and the associated time instant, to the system 100 or other device, so that proper corrective action to the specific fault in the input can be performed.
  • the inspection entity 110 may construct the plurality of adjacency matrices at different time instants based on the multiple inputs and multiple outputs under the test.
  • the embodiments herein enable to construct the 3D graph only once to obtain the mappings in the dictionary.
  • the embodiments herein are computationally less complex when compared with the conventional method, as the construction 3D graph is performed only once to obtain the mappings in the dictionary. Hence, necessary corrective actions can be done based on the dictionary without constructing the 3D graph again. An easy solution is therefore achieved herein.
  • the dictionary enables about 50% reduction in time to identify the fault in an input, due to the mapping helps to reduce the time spent in identifying the fault in an input, type of fault in the input etc.
  • an adjacency matrix is a 2D graph depicting correlations between vertices.
  • a 3D graph is able to reflect dynamic information of the system 100.
  • the 3D graph refers to a plurality of the adjacency matrices constructed at different time instants.
  • the nodes A-B of the network form the vertices of the directed adjacency matrix AJ. Edges of the directed adjacency matrix indicate connections between nodes.
  • the rows correspond to sources and the columns correspond to destinations.
  • the adjacency matrix can be understood as follows. For example, if the first element is‘G, this means the node A is directly affecting the node ⁇ ’. If it is zero it means the nodes are not connected. It should be noted that the value will be 1 even when there exists a connection at any time i.e. irrespective of dynamics of the system.
  • the plurality of adjacency matrices would be stacked as shown in Fig. 4b.
  • the plurality of adjacency matrices at different time instants form a 3-dimensional array, therefore it is referred to as 3D graph.
  • the adjacency matrix will be complex.
  • the adjacency matrix is constructed by filling the elements of matrix with either conditioned correlation or conditioned mutual information. The usage of these metrics gives the relation between their variables exactly conditioned on other variables.
  • the multiple inputs and multiple output of the system 100 are the vertices of the 3D graph.
  • the following embodiments will be described in context of two inputs i lt i 2 and two outputs o 1 , o 2 , however the skilled person will appreciate that the embodiments herein are also applied to any number of input and outputs.
  • the inspection entity 110 may obtain multiple inputs and multiple outputs of the system 100 under a test at this time instant.
  • the inspection entity 110 may compute a metric (either conditional correlation or conditional mutual information) between the input and the output.
  • the adjacency matrices A 0 - A 2 are symmetric matrices, however they are not necessary symmetric, depending on a design of the system.
  • the 3D graph may be constructed by stacking the constructed adjacency matrices as shown in Fig. 4b.
  • a delay e.g., in second, transferring information from an input to an output. In other words, how long time it will take that the input i 1 impacts the output o x ;
  • magnification factor/weight indicating the input i x amplifies by 0.5 times in construction of the output o 1 ;
  • magnification factor/weight indicating the input i 2 amplifies by 0.25 times in construction of the output o 1 ;
  • k 2 an amplification factor, which varies along with a value of the time instant k, so it may also be called a dynamic amplification factor in terms of time instance, e.g., seconds.
  • the dynamic amplification factor indicates that the input i 2 amplifies by k 2 times in construction of the output o 2 ;
  • any fault in input i 1 may affect the output o 1 and fault in input t 2 may propagate to the outputs o 1 and o 2 .
  • the input may be correlated with each other.
  • the inspection entity 110 may construct the dictionary based on the plurality of adjacency matrices, particularly, based on the computed metrics.
  • the dictionary indicates a mapping of time profiles between an output o 1 and an input i 1 .
  • the time profile of the output o 1 is in a sinusoidal shape, with a time length, i.e., a time window, 10 seconds.
  • a frequency of the output (signal) o 1 is 0.1 Flz with amplitude 1 unit.
  • the time profile of the Input i x is also in a sinusoidal shape, with a time length, i.e., a time window, 5 seconds.
  • the inspection entity 110 may obtain multiple inputs and multiple outputs of the system 100 and try to identify the time profile of the fault in the input i 1 given the fault in the output o 1 , and the associated time instant as follows.
  • the inspection entity 110 may determine, based on a dictionary, the time profile of the fault in the input ⁇ which is corresponding to the time profile of the fault in the output o 1 . The inspection entity 110 may then determine a time instant when the input fault impacting the output fault occurred.
  • Robots Case Study In any robot, there may be many inputs and outputs. These outputs facilitate the functions of the robot such as navigation, and performing designed tasks etc.
  • the inputs these robots take may be actuator commands such as voltage supplied to a motor etc. Any fault in the voltage to the motor may result in a fault at the output of the robot. Such faults may result in the robot displaying undesirable behaviour.
  • a fault in the voltage given to the motor of one leg may result in improper navigation of the robot.
  • improper navigation is an example of a fault in output
  • the faults in the voltage and in the camera are examples of faults in input.
  • identifying the root source of the fault occurring during navigation requires the identification of the fault in the input from which the fault is propagated.
  • the faults in the different inputs may impact the output differently.
  • Fig. 6 is a block diagram depicting the inspection entity 110 according to embodiments herein for handling an output fault in a first output of a system 100 which is operable with multiple inputs and multiple outputs.
  • the inspection entity 110 may comprise processing circuitry 601, e.g. one or more processors, configured to perform the methods herein.
  • the inspection entity 110 may comprise an obtaining module 610, e.g. a receiver or transceiver.
  • the inspection entity 110, the processing circuitry 601, and/or the obtaining module 610 is configured to obtain the multiple inputs and multiple outputs of the system 100.
  • the inspection entity 110, the processing circuitry 601, and/or the fault detection and attribution module 611 may be configured to identify the output fault; and to attribute the output fault to the first input.
  • the inspection entity 110 may comprise a first determining module 612.
  • the inspection entity 110, the processing circuitry 601, and/or the first determining module 612 is configured to determine, based on a dictionary, a time profile of an input fault associated with a time profile of an output fault.
  • the input fault is in a first input.
  • the respective time profile of the input and output fault indicates how the respective input and output fault changes over time.
  • the dictionary comprises a mapping between the time profile of the input fault and the time profile of the output fault.
  • the inspection entity 110 may comprise a second determining module 613.
  • the inspection entity 110, the processing circuitry 601, and/or the second determining module 613 is configured to determine, based on the determined time profile of the input fault, a time instant indicating when the input fault impacting the output fault occurred.
  • the inspection entity 110, the processing circuitry 601, and/or the obtaining module 610 may be configured to further obtain multiple inputs and multiple outputs of the system 100 under a test.
  • the inspection entity 110 may comprise a second constructing module 615.
  • the inspection entity 110, the processing circuitry 601, and/or the second constructing module 615 may be configured to construct the dictionary based on a plurality of adjacency matrices, wherein each adjacency matrix specifies metrics between the multiple inputs and multiple outputs at a specific time instant.
  • the inspection entity 110 may comprise a second constructing module 615.
  • the inspection entity 110, the processing circuitry 601, and/or the second constructing module 615 may be configured to determine the metrics in each adjacency matrix, and to derive, based on the metrics in each adjacency matrix, an algebraic expression defining the mapping between a time profile of each input fault and a time profile of each output fault.
  • the methods according to the embodiments described herein for the inspection entity 110 are respectively implemented by means of e.g. a computer program or a computer program product 605, comprising instructions, i.e., software code portions, which, when executed on at least one processor, cause the at least one processor to carry out the actions described herein, as performed by the inspection entity 110.
  • the computer program product 605 may be stored on a computer-readable storage medium 606, e.g. a disc, universal serial bus (USB) stick or similar.
  • the computer-readable storage medium 606, having stored thereon the computer program product 605, may comprise the instructions which, when executed on at least one processor, cause the at least one processor to carry out the actions described herein, as performed by the inspection entity 110.
  • the computer-readable storage medium may be a non-transitory computer-readable storage medium.
  • ASIC application-specific integrated circuit
  • Several of the functions may be implemented on a processor shared with other functional components of a radio network node, for example.

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  • Engineering & Computer Science (AREA)
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  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Data Mining & Analysis (AREA)
  • Evolutionary Computation (AREA)
  • Computational Linguistics (AREA)
  • Computing Systems (AREA)
  • General Physics & Mathematics (AREA)
  • Mathematical Physics (AREA)
  • Software Systems (AREA)
  • Artificial Intelligence (AREA)
  • Test And Diagnosis Of Digital Computers (AREA)

Abstract

L'invention concerne une entité d'inspection (110) et un procédé mis en œuvre dans cette dernière permettant de gérer un défaut de sortie dans une première sortie d'un système (100) pouvant fonctionner avec des entrées multiples et des sorties multiples. Le procédé comprend : l'obtention (S210) des entrées multiples et des sorties multiples du système (100); la détermination (S240), en fonction d'un dictionnaire, d'un profil temporel d'un défaut d'entrée associé à un profil temporel du défaut de sortie, le défaut d'entrée étant situé dans une première entrée, le profil temporel respectif du défaut d'entrée et de sortie indiquant la manière dont les défauts d'entrée et de sortie respectifs changent dans le temps, le dictionnaire comprenant une mise en correspondance entre le profil temporel du défaut d'entrée et le profil temporel du défaut de sortie; et la détermination (S250), en fonction du profil temporel déterminé du défaut d'entrée, d'un instant correspondant au moment où le défaut d'entrée impactant le défaut de sortie s'est produit.
PCT/IN2018/050504 2018-08-02 2018-08-02 Entité d'inspection et procédé mis en œuvre dans cette dernière permettant la gestion d'un défaut de sortie d'un système WO2020026256A1 (fr)

Priority Applications (1)

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PCT/IN2018/050504 WO2020026256A1 (fr) 2018-08-02 2018-08-02 Entité d'inspection et procédé mis en œuvre dans cette dernière permettant la gestion d'un défaut de sortie d'un système

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PCT/IN2018/050504 WO2020026256A1 (fr) 2018-08-02 2018-08-02 Entité d'inspection et procédé mis en œuvre dans cette dernière permettant la gestion d'un défaut de sortie d'un système

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Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
GB2372892A (en) * 2001-02-28 2002-09-04 Ntl Group Ltd Adaptive fault detection and localisation in television distribution networks using digital signal processing
US9565689B2 (en) * 2013-10-23 2017-02-07 Texas Instruments Incorporated Near-optimal QoS-based resource allocation for hybrid-medium communication networks
EP3285170A1 (fr) * 2016-08-09 2018-02-21 Fujitsu Limited Système, programme et procédé de gestion de tâche de profiling d'application

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
GB2372892A (en) * 2001-02-28 2002-09-04 Ntl Group Ltd Adaptive fault detection and localisation in television distribution networks using digital signal processing
US9565689B2 (en) * 2013-10-23 2017-02-07 Texas Instruments Incorporated Near-optimal QoS-based resource allocation for hybrid-medium communication networks
EP3285170A1 (fr) * 2016-08-09 2018-02-21 Fujitsu Limited Système, programme et procédé de gestion de tâche de profiling d'application

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