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WO2012037429A2 - Prédiction de défaillance et maintenance - Google Patents

Prédiction de défaillance et maintenance Download PDF

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Publication number
WO2012037429A2
WO2012037429A2 PCT/US2011/051873 US2011051873W WO2012037429A2 WO 2012037429 A2 WO2012037429 A2 WO 2012037429A2 US 2011051873 W US2011051873 W US 2011051873W WO 2012037429 A2 WO2012037429 A2 WO 2012037429A2
Authority
WO
WIPO (PCT)
Prior art keywords
rules
rule
refining
ranking
message set
Prior art date
Legal status (The legal status 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 status listed.)
Ceased
Application number
PCT/US2011/051873
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English (en)
Other versions
WO2012037429A3 (fr
Inventor
Dmitriy Fradkin
Fabian Moerchen
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Siemens Corp
Original Assignee
Siemens Corp
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 Siemens Corp filed Critical Siemens Corp
Priority to EP11761763.9A priority Critical patent/EP2616976A4/fr
Publication of WO2012037429A2 publication Critical patent/WO2012037429A2/fr
Publication of WO2012037429A3 publication Critical patent/WO2012037429A3/fr
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

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Classifications

    • 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
    • G05B23/0283—Predictive maintenance, e.g. involving the monitoring of a system and, based on the monitoring results, taking decisions on the maintenance schedule of the monitored system; Estimating remaining useful life [RUL]
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling

Definitions

  • the present disclosure relates to work cycles, and more particularly to a failure prediction and maintenance in a work cycle.
  • a method of failure prediction in a work cycle includes generating a plurality of rules for predicting failure, evaluating the plurality of rules for predictability with respect to at least one of a machine and a message set generated by the machine and a sensor generating sensor data, ranking the plurality of rules based on the evaluation, and refining at least one rule of the plurality of rules having a threshold ranking.
  • a system for performing a method of failure prediction in a work cycle includes a processor configured to predict failure in a work cycle, the processor generating a plurality of rules for predicting failure, evaluating the plurality of rules for predictability with respect to at least one of the machine and the message set generated by the machine and a sensor generating sensor data, ranking the plurality of rules based on the evaluation, and refining at least one rule of the plurality of rules having a threshold ranking, and a memory configured to store the plurality of rules.
  • FIG. 1 is a flow diagram showing a method for failure prediction according to an embodiment of the present disclosure
  • FIG. 2 is a flow diagram showing a method of data sequence processing according to an embodiment of the present disclosure
  • FIG. 3A is a flow diagram showing a method of pattern evaluation according to an embodiment of the present disclosure
  • FIG. 3B is a process diagram showing a method of pattern evaluation according to an embodiment of the present disclosure.
  • FIG. 4 is a diagram of a system for failure prediction and maintenance in a work cycle according to an embodiment of the present disclosure.
  • patterns indicative of an upcoming failure or a need for intervention may be identified. For example, sensor measurements showing sudden significant changes may be indicate a failure or need for recalibration. Apart from sensor data, many systems also generate a log messages.
  • Occurrences of particular messages, patterns of several messages or changes in frequency of messages and patterns can be indicative of upcoming failures.
  • exemplary implementations use genetic programming with a framework for representing sequences of heterogeneous objects using a wide range of predicates and patterns.
  • Heterogeneous data e.g., such as log data, sensor measurements, images, etc.
  • Genetic programming is used to find patterns predictive for particular types of equipment failures or other events of interest.
  • predictive patterns may be found over long streams of data, without explicit negative examples, combining multiple types of information, and can evaluate them over complete history.
  • Embodiments of the present disclosure may be implemented in a work flow environment comprising a computer system generating logs, a sensor system taking measurements and passing the measurements to a computer system for generating messages, a system generating state data, etc.
  • Embodiments of the present disclosure are applicable to messages or sensors apart from one another.
  • An exemplary machine embodying a method according to the present disclosure may include one or more sensors, wherein the sensors generate measurements processed by machine to generate messages.
  • An exemplary work flow environment may include a machine such as a process control system, sensor system (e.g., a Computer Tomography (CT) or Magnetic Resonance
  • a process control system e.g., a Computer Tomography (CT) or Magnetic Resonance
  • sensor system e.g., a Computer Tomography (CT) or Magnetic Resonance
  • MRI Imaging
  • a machine such as a remote interface control system (e.g., oil & gas, water and wastewater, power generation and distribution, and transportation), a heating control system (e.g., thermoforming or baking process), door control system, condition monitoring system, and electrical charging component.
  • a remote interface control system e.g., oil & gas, water and wastewater, power generation and distribution, and transportation
  • a heating control system e.g., thermoforming or baking process
  • door control system e.g., condition monitoring system, and electrical charging component.
  • Components of a work flow environment that may function as sensors may include audio and video input devices, data loggers, controllers, monitors, input/output (I/O) devices, switching devices, protection equipment, motor starters, load feeders, position switches, commanding/signaling devices, transformers, power supplies, etc.
  • I/O input/output
  • a failure prediction method takes heterogeneous data and positive cases in a long stream of data as input and searches for patterns that are predictive, by themselves, and for patterns whose frequency changes are predictive.
  • a framework is deployed based on genetic programming for finding predictive patterns from data sequences.
  • the framework may directly mine predictive patterns, without generating complete sets of frequent patterns.
  • the framework may perform evaluation of patterns on complete data history, without the need for labeled negative examples.
  • the framework may find predictive over- or under-represented patterns.
  • the framework may further find and incorporate constraints on supplemental information, such as text or numeric data accompanying the sequence.
  • An event sequence over a set of events E is a sequence of pairs (t t ;s ⁇ of event sets (consisting of elements of E) and time stamps t The ordering is based on time.
  • the length of the event sequence is n .
  • a sequence database, SDB , of size N is a collection of N such event sequences.
  • a pattern P has support (P) - s in an SDB D if D contains s distinct event sequences that match P .
  • a pattern is frequent if and only if its support is no less than a predefined minimum support value t , i.e. support (P) > t .
  • An exemplary implementation may use genetic programming with a rule language tailored to preventive maintenance to automatically find, evaluate, and improve patterns that predict events of interest in historical data gathered from equipment.
  • the rule language covers various conditions. These conditions may include conditions based on sensor data (e.g., violating thresholds, detected drifts, increase or decrease in noise), conditions based on message data (e.g., matching keywords, regular expressions, parameters violating thresholds), conditions based on other data (e.g., features extracted from image data), constraints on the duration between different conditions, and constraints on the frequency of conditions or partial orders of conditions. Conditions may be combined into a partial order expressing (partial) temporal order. Linear and non-linear combinations of conditions and partial orders of conditions may be used as well.
  • a pattern is an antecedent part of a rule.
  • a rule [A before C] predicts failure within 5 minutes
  • [A before C] is a pattern predicting a failure F.
  • this difference is unimportant.
  • it is important because a method may include multiple rules from a single pattern, and they may have different performance.
  • the terms rules and patterns are interchangeable.
  • initial rules may be generated (101).
  • the initial rules may be evaluated for their predictability with respect to a specific problem (102).
  • An evaluation of the initial rules may be performed by comparing a frequency prior to failures with frequency overall in order to rate the initial rules.
  • Rules may be kept according to the ratings (103) and refined to generate more complex rules (104).
  • the refinement may include combining rules, extending rules by additional conditions, and changing parameters of rules (such as thresholds). In this manner the system can
  • the method may evaluate the refined rules at each cycle to check for a target performance (105), and in the case where insufficient improvement is determined that method may end.
  • a user can review patterns and guide the system by supplying initial patterns or changing suggested patterns based on domain knowledge where the system may create new, variants of the pattern.
  • a data sequence may be defined as a sequence ( ⁇ ; , ⁇ ) comprising objects o ; occurring at time t t (201).
  • the time t can be the same for multiple objects.
  • each object o i contains a number of fields f j , which may be referred to as o v . This is a general setting that can accommodate many applications.
  • More complicated concepts may be handled by appropriate pre-processing (202). For example, if it is known that under particular circumstances a string field contains a numeric value that could be useful, such values may be extracted into separate fields as pre-processing steps. Such a situation may arise in an application where objects correspond to log messages with a fixed set of fields, and where message text frequently has a pre-specific format with a particular set of parameters, depending on the type of the message. In such a case it is straightforward to extract these parameters into separate fields. For example, in a system where messages have templates, such as 'Component %1 has parameter %2 set at %3', %1, %2, %3 are placeholders for the actual values that may be injected at runtime. The placeholders can be strings, or numbers. If the template is known in advance, messages may be matched to it, and values of all parameters may be determined. Otherwise, similar messages may be grouped and pieces where they differ may be identified.
  • the messages differ in several places: X vs Y, Temperature vs Pressure, and 30 vs 120. It may be assumed that these are parameters, since the other parts of these messages, e.g., "has parameter” and "set at” are aligned.
  • a method may group together messages by string similarity and identify parts where they differ, and treat the values as parameters.
  • Another example of a pre-processing is an extraction of trends from sensor data. If a particular object has a numeric property, a new numeric field may be created corresponding to the change in the value of the original field. This will allow for the use of patterns over the original variable and over its rate of change.
  • the framework defines predicates over images, large texts, and other data structures
  • a data sequence may be converted into an event sequence by representing objects with the indices of functions that take value 1 for these objects (204).
  • each object may be represented as an item-set, and the object sequence as a sequence of item-sets, e.g., an event sequence.
  • patterns may be described based on a single object and patterns may be described over multiple objects. This is achieved by allowing patterns to be combined via different functions:
  • patterns that can be expressed using the framework can include combinations of conditions based on sensor data (e.g., violating thresholds, detected drifts, increase or decrease in noise), conditions based on message data (e.g., matching keywords, regular expressions), and conditions based on other data (e.g., features extracted from image data).
  • the patterns that can be expressed may further include combinations of conditions in a partial order expressing (partial) temporal order, constraints on the duration of a pattern or time between different conditions, and constraints on the frequency of conditions or partial orders of conditions.
  • pattern evaluation given a historical database and a pattern, a predictive quality of the pattern may be evaluated.
  • a failure or an Event of Interest at time T (301).
  • a period of time before a failure or event of interest as [- ⁇ ,-i 2 ] > with > t 2 as alert window.
  • a pattern is considered to be predictive for a particular EOI if it occurs in the alert window, and is considered a false positive if it occurs outside of an alert window or a late window (303).
  • Patterns occurring during the late window are not counted as either true or false positives.
  • the quality of a pattern is a measure (or measures) of fraction of pattern occurrences in the alert window, as well as fraction of an EOI that have a pattern in their alert windows.
  • Standard predictive measures such as sensitivity (e.g., fraction of cases of interest identified) and specificity (e.g., fraction of true alerts) can thus be determined and used to drive the optimization.
  • patterns both at object level (e.g., functions b ⁇ ) and at the event sequence level, may be found or determined within the genetic programming framework, which tunes patterns both at the level of objects (e.g., by modifying individual low-level function 6 ), and on the global level, by changing the structure of whole pattern.
  • An initial population of object-level patterns may be randomly generated using basic predicates (301).
  • An alternative option for generating initial population could include mining of frequent sequential or partial order patterns in the alert windows.
  • a population is then evaluated with respect to predictiveness measures (302), and a new population is generated based on the results (303). Patterns that have higher scores produce more children (whether in the form of exact copies, mutations, or cross-over or combinations with other patterns).
  • Methods of producing a new individual may be characterized as refinements, generalizations, combinations and cross-overs. In the case of refinement a new Boolean function may be added to a particular node in the pattern, or is replaced by a more specific one (e.g., LessThanOrEqual for numbers or Contains for strings could be replaced by Equals).
  • a parameter is replaced by a more specific one.
  • a numeric parameter in LessThan may be replaced by a smaller one, or a string parameter in Contains may be extended, or Duration time parameter could be shorted or Frequency parameter increased.
  • a pattern may be wrapped within a new high-level function, such Duration or Frequency.
  • a Boolean function may be removed from a particular node in the pattern, or is replaced by a more general one or a parameter may be replaced by a less specific one.
  • patterns may be combined using high-level functions described above.
  • patterns can exchange some of their groups/nodes.
  • a number of heuristics can be used to guide the process. For example, if a particular pattern has high specificity but low sensitivity, a generalization might be more preferable, while in the reverse situation generalization could be useful.
  • Embodiments described herein are applicable in various domains including performance monitoring and failure or EOI prediction for medical scanners, power equipment (e.g., turbines, plants), trains, car and airplanes, business processes, and patients.
  • power equipment e.g., turbines, plants
  • trains e.g., cars
  • car and airplanes e.g., trains
  • business processes e.g., business processes, and patients.
  • the following is an exemplary case of an evolutionary method for generating and refining rules in a system.
  • the exemplary system comprises 2 sensors recording
  • sensorl and sensor2 output numeric values (assuming only positive values for this example) and 3 types of messages, with templates.
  • the exemplary message templates are:
  • %1 is out of tune. (%1 can be XAxisCamera, YAxisCamera, Motor)
  • Message2 Restarting module %1.
  • Message3 Starting operation %1.
  • N N positive examples of failure
  • a user may want to trigger alerts when a failure (e.g., stress cracks forming in a piece of equipment) may be expected within 7 days, assuming that alerts on the last day are late.
  • a failure e.g., stress cracks forming in a piece of equipment
  • An initial set of rule(s) may be defined using predicates, for example:
  • rule3 Message 1 occurs
  • rule4 Message2 occurs.
  • rule7 Message 1 occurs twice in 5 minutes
  • rule8 Message2 occurs OR sensor 1> 10
  • the new set of rules may be evaluated. For example:
  • rule9 Message 1 occurs and text contains 'Camera' (generalization)
  • rulelO Message2 occurs AND sensorl>15 within 5 minutes
  • top rules are stored according to a target criteria.
  • top rules may be defined to have a precision above 50% and recall greater than 10%).
  • a software application program is tangibly embodied on a non-transitory computer-readable storage medium, such as a program storage device or computer-readable storage medium, with an executable program stored thereon.
  • the application program may be uploaded to, and executed by, a machine comprising any suitable architecture.
  • a computer system (block 401) for performing failure prediction in a work cycle includes, inter alia, a CPU (block 402), a memory (block 403) and an input/output (I/O) interface (block 404).
  • the computer system (block 401) is generally coupled through the I/O interface (block 204) to a display (block 405) and various input devices (block 406) such as a mouse, keyboard, medical scanners, power equipment, etc.
  • the display (block 405) may be implemented to display the rules, e.g., as the rules evolve during evaluation, ranking and refinement or as an output set of rules.
  • the support circuits can include circuits such as cache, power supplies, clock circuits, and a communications bus.
  • the memory (block 403) can include random access memory (RAM), read only memory (ROM), disk drive, tape drive, etc., or a combination thereof.
  • the present invention can be implemented as a module (block 407) of the CPU or a routine stored in memory (block 403) and executed by the CPU (block 402) to process input data (block 408).
  • the data may include image information from a camera, which may be stored to memory (block 403)
  • the computer system (block 401) is a general purpose computer system that becomes a specific purpose computer system when executing the routine of the present disclosure.
  • the computer platform (block 401) also includes an operating system and micro instruction code.
  • the various processes and functions described herein may either be part of the micro instruction code or part of the application program (or a combination thereof) which is executed via the operating system.
  • various other peripheral devices may be connected to the computer platform such as an additional data storage device and a printing device.

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Abstract

La présente invention concerne un procédé de prédiction de défaillance dans un cycle de travail, comprenant les étapes consistant à générer une pluralité de règles permettant de prédire une défaillance (101), évaluer la pluralité de règles pour la prévisibilité par rapport à une machine et/ou à un ensemble de messages généré par la machine et/ou à un capteur générant des données de détection (102), classer la pluralité de règles en fonction de l'évaluation (103), et préciser une ou plusieurs règles de la pluralité de règles présentant un classement seuil (104).
PCT/US2011/051873 2010-09-16 2011-09-16 Prédiction de défaillance et maintenance Ceased WO2012037429A2 (fr)

Priority Applications (1)

Application Number Priority Date Filing Date Title
EP11761763.9A EP2616976A4 (fr) 2010-09-16 2011-09-16 Prédiction de défaillance et maintenance

Applications Claiming Priority (2)

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US38340210P 2010-09-16 2010-09-16
US61/383,402 2010-09-16

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WO2012037429A2 true WO2012037429A2 (fr) 2012-03-22
WO2012037429A3 WO2012037429A3 (fr) 2012-07-19

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Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2014209700A1 (fr) * 2013-05-13 2014-12-31 Abb Technology Ag Surveillance conditionnelle de systèmes industriels
CN107655668A (zh) * 2017-09-20 2018-02-02 上海振华重工(集团)股份有限公司 港口机械的故障分析数据的采集方法
WO2019179765A1 (fr) * 2018-03-23 2019-09-26 Koninklijke Philips N.V. Procédé d'auto-correction pour l'annotation d'un groupe de données à l'aide d'un mécanisme de rétroaction
CN112149868A (zh) * 2020-08-20 2020-12-29 汉威科技集团股份有限公司 用于燃气使用习惯和安全分析的智能诊断方法
US11150630B2 (en) 2017-10-19 2021-10-19 International Business Machines Corporation Predictive maintenance utilizing supervised sequence rule mining
EP3933589A1 (fr) 2020-06-30 2022-01-05 Roche Diagnostics GmbH Système de réaction de chaîne d'événements

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CN116883007B (zh) * 2023-07-26 2025-06-10 长安汽车金融有限公司 一种催收动作推荐方法、系统、电子设备及存储介质

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Cited By (13)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2014209700A1 (fr) * 2013-05-13 2014-12-31 Abb Technology Ag Surveillance conditionnelle de systèmes industriels
CN107655668A (zh) * 2017-09-20 2018-02-02 上海振华重工(集团)股份有限公司 港口机械的故障分析数据的采集方法
CN107655668B (zh) * 2017-09-20 2019-10-29 上海振华重工(集团)股份有限公司 港口机械的故障分析数据的采集方法
US11150630B2 (en) 2017-10-19 2021-10-19 International Business Machines Corporation Predictive maintenance utilizing supervised sequence rule mining
US11150631B2 (en) 2017-10-19 2021-10-19 International Business Machines Corporation Predictive maintenance utilizing supervised sequence rule mining
US11967421B2 (en) 2018-03-23 2024-04-23 Koninklijke Philips N.V. Self-correcting method for annotation of data pool using feedback mechanism
WO2019179765A1 (fr) * 2018-03-23 2019-09-26 Koninklijke Philips N.V. Procédé d'auto-correction pour l'annotation d'un groupe de données à l'aide d'un mécanisme de rétroaction
CN111886653A (zh) * 2018-03-23 2020-11-03 皇家飞利浦有限公司 使用反馈机制对数据工具的注释的自校正方法
US20200365262A1 (en) * 2018-03-23 2020-11-19 Koninklijke Philips N.V. Self-correcting method for annotation of data pool using feedback mechanism
CN111886653B (zh) * 2018-03-23 2025-03-25 皇家飞利浦有限公司 使用反馈机制对数据工具的注释的自校正方法
EP3933589A1 (fr) 2020-06-30 2022-01-05 Roche Diagnostics GmbH Système de réaction de chaîne d'événements
WO2022002861A1 (fr) 2020-06-30 2022-01-06 F. Hoffmann-La Roche Ag Système de réaction en chaîne d'événements
CN112149868A (zh) * 2020-08-20 2020-12-29 汉威科技集团股份有限公司 用于燃气使用习惯和安全分析的智能诊断方法

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