WO2019062833A1 - Système et procédé de diagnostic intelligent - Google Patents

Système et procédé de diagnostic intelligent Download PDF

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Publication number
WO2019062833A1
WO2019062833A1 PCT/CN2018/108250 CN2018108250W WO2019062833A1 WO 2019062833 A1 WO2019062833 A1 WO 2019062833A1 CN 2018108250 W CN2018108250 W CN 2018108250W WO 2019062833 A1 WO2019062833 A1 WO 2019062833A1
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WO
WIPO (PCT)
Prior art keywords
board
data
main
fault
prediction
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PCT/CN2018/108250
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English (en)
Chinese (zh)
Inventor
聂仕华
叶浩
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上海微电子装备（集团）股份有限公司
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Publication of WO2019062833A1 publication Critical patent/WO2019062833A1/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
    • G05B23/0208—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterized by the configuration of the monitoring system
    • G05B23/0213—Modular or universal configuration of the monitoring system, e.g. monitoring system having modules that may be combined to build monitoring program; monitoring system that can be applied to legacy systems; adaptable monitoring system; using different communication protocols
    • 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

Definitions

  • the common method used by the industry to solve the above problems is to increase various sensing devices, and to detect the temperature, humidity, pressure, current and other factors inside the device in real time. Because the occurrence of faults has the characteristics of “randomness” and “accidentality”, The detected data needs to be stored in real time, which greatly increases the storage time and increases the storage space, that is, the space complexity and time complexity will increase linearly, even exponentially; and when the failure occurs, often only The data of the first few seconds or the first few minutes before the failure occurs, a large amount of data is garbage or redundant data, which not only takes up a lot of hardware space, but also increases the hardware cost, and also greatly increases the high frequency I/O operation of the computer. The running time affects the overall performance of the device.
  • the present invention provides an intelligent diagnostic system and method for realizing fault pre-judging and fault pre-processing, accelerating production progress, and improving equipment yield, in view of the problems existing in the prior art.
  • the main system includes a main system main control board, a main pivot board, and a plurality of main data boards, and the main hub board is connected to a sensor corresponding to the main system, and is configured to receive measurement data of the sensor, and Sending to each of the main data boards of the main system for calculation;
  • a diagnostic prediction board connected to the main system main control board and the sub-system main control board, for periodically acquiring intermediate operation data of the main data board or the slave data board and receiving according to The intermediate running data performs fault prediction, and feeds back the predicted result to the main system main control board or the sub-system main control board.
  • the main system further includes a data bus and a control bus connected to the main system main control board, the main pivot board, and the main data board;
  • the sub-system further includes a data bus and a control bus connected to the system main control board, the slave hub board, and the slave data board.
  • the diagnostic prediction board uses a host computer or a PowerPC board.
  • the diagnostic prediction board includes a fault prediction module, a database, and a fault receiving and processing module.
  • diagnostic prediction board is further connected to the main hub card and the slave hub card through a HSSL fiber transmission bus and a serial port connection bus.
  • the present invention also provides a diagnostic method using the intelligent diagnostic system as described above, comprising the steps of:
  • S1 the main hub board and the slave board obtain real-time detection data of the corresponding sensor, and send the detection data to the main data board and calculate from the data board;
  • the diagnosis prediction board performs fault prediction according to the received intermediate operation data, and feeds back the prediction result to the main system main control board or the sub-system main control board.
  • the intermediate operation data is periodically acquired by the main hub card and the slave hub card, and the intermediate operation data is transmitted to the diagnosis prediction board.
  • the intermediate operation data is periodically acquired by the fault diagnosis board, fault prediction is performed, and the prediction information and the intermediate operation data are transmitted to the diagnosis prediction board.
  • the fault prediction includes the following steps:
  • the fault diagnosis board periodically acquires the intermediate running data according to the time parameter and the interest data in the configuration file, and puts the intermediate running data into the memory buffer;
  • the fault diagnosis board performs real-time monitoring on the acquired intermediate operation data, and determines whether the data value is in a security range set by the configuration file;
  • step S3 further includes: after the diagnosis prediction board receives the fault information, first sending an instruction to suspend all the sub-system actions, and processing the fault, determining the fault mechanism, whether to run the action retry, if allowed , then send a "retry" command to the sub-system participating in the action, retry the action, if the retry fails the same, report it to the server; if not, send a "system error” message to the server, waiting for the manual Intervention.
  • step S3 includes the following steps:
  • the diagnostic prediction board is configured according to sampling time and data of interest in the configuration file
  • the diagnostic prediction board samples the data of the fault diagnosis board every n servo cycles and stores it in a database, where n is a natural number and is set by a configuration file;
  • the fault prediction module in the diagnostic prediction board comprehensively processes the data sampled in this time and the historical data in the database, fits the data change curve, and searches for a corresponding rule in the database to obtain fault prediction information;
  • the data variation curve is fitted by a method of least squares or averaging trend.
  • the sub-system 200 includes a system main control board 1, a slave hub board 6 and a plurality of data board 3, and the system main control board 1, the slave board 6 and the data board 3
  • the connected data bus 4 and the control bus 5 are connected to the sensor corresponding to the sub-system 200 for receiving the measurement data of the sensor and transmitting the data to the data card 3 of the sub-system 200. Calculation.
  • the configurability of time and data can be configured according to the time parameter of the configuration file DTS.cfg and the data of interest. If it is a motion subsystem, the data can take the voltage or current data of the motor; if it is a lighting subsystem, The data can take parameters such as laser light intensity and laser dose; if it is an environmental subsystem, the data can take parameters such as temperature, pressure and humidity. Of course, it is not limited to the above parameters, and the specific parameters are defined by the actual scene or the engineer.
  • the initialization time is read from the configuration file DTS.cfg. This parameter can also be set in real time by the user interface. Since the probability of failure is often the highest at initialization and machine startup, the time at this time The interval is as small as possible, and can be set to 1 servo cycle; when the device is stable, it can be adjusted in real time according to the needs or actual conditions;
  • the security range is set by the configuration file DTS.cfg; as shown in Figure 4, the security range can be set to three states: health, fault, and Sub-health; health status corresponds to data within a safe range; sub-health status corresponds to data within m% of the safety threshold; fault status corresponds to data exceeding m% above the safety threshold, where m is a real number, by configuration file set up;
  • the fault diagnosis board 13 reports a warning message to the fault prediction board 300 through the serial connection bus 9. And feedback to the drive components in the system main control board 1 in real time, and make corresponding adjustments to avoid deterioration of operating conditions;
  • the system is defined as the fault state at this time, and the fault diagnosis board 13 first encodes the fault type, and reports the fault information to the serial connection bus 9.
  • the fault prediction board 300 processes and directly feeds back to the driving component in the system main control board 1 to perform an initialization operation to avoid the fault waiting state, and facilitates the upper layer to send a “Retry” or other request, where m is a real number and is configured by a configuration file. set up.
  • the fault receiving and processing module 12 in the diagnostic prediction board 300 first sends an event to suspend all the sub-systems 200, processes the fault, determines the fault mechanism, and operates the action.
  • Retry if allowed, send a "Retry" command to the sub-system 200 participating in the action, retry the action; if not, send a "system error" to the server, waiting for manual intervention.
  • the main hub card 2 and the detection data of the sensor are acquired in real time from the hub card 6, and the detection data is sent to the data board 3 for calculation; the data running of the data board 3 in the middle of the data processing may be Write to its external memory or DPRAM according to configuration requirements.
  • the fault diagnosis board 13 periodically acquires the intermediate operation data of the data board 3, performs fault prediction, and transmits the predicted information and the intermediate operation data to the diagnostic prediction board 300.
  • the fault prediction includes the following steps:
  • the fault diagnosis board 13 periodically captures the intermediate running data of the data board 3 according to the time parameter and the interest data in the configuration file DTS.cfg, and may also actively acquire and put into the memory buffer.
  • the data of interest can take the voltage or current data of the motor; if it is a lighting subsystem, the data can take parameters such as laser intensity and laser dose; if it is an environmental subsystem, then the sense Interest data can take parameters such as temperature, pressure, and humidity.
  • the above parameters are examples and are not limited to the above parameters. The specific parameters are defined by the actual scenario or by the engineer.
  • the fault diagnosis board 300 performs real-time monitoring on the acquired intermediate operation data to determine whether the data value is in the security range set by the configuration file; the security range is set by the configuration file DTS.cfg; the security range can be set to three Status: health, fault, and sub-health; health status corresponds to data within a safe range; sub-health status corresponds to data within m% of the safety threshold; and fault status corresponds to data above the safety threshold m%, where m Real number, set by configuration file;
  • the diagnosis prediction board 300 performs fault prediction according to the received intermediate operation data, and feeds back the prediction result to the system main control board 1. Includes the following steps:
  • the fault diagnosis board 13 is added, and the distributed fault diagnosis and processing model is adopted to implement pre-detection and online processing of faults in the main system 100 and the sub-system 200, thereby further improving.
  • the efficiency is improved.

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  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Engineering & Computer Science (AREA)
  • Automation & Control Theory (AREA)
  • Testing And Monitoring For Control Systems (AREA)

Abstract

L'invention concerne un système et un procédé de diagnostic intelligent. Le système comprend un système principal (100), un sous-système (200), et une carte de prédiction de diagnostic (300). Le système principal (100) comprend une carte de contrôle maître (1) du système principal (100), une carte de concentrateur principal (2), et plusieurs cartes de données principales (3). La carte de concentrateur principal (2) est connectée à un capteur correspondant au système principal (100), et sert à recevoir des données de mesure du capteur et à envoyer les données de mesure aux cartes de données principales (3) du système principal (100) à des fins de calcul. Le sous-système (200) comprend une carte de contrôle maître (1) du sous-système (200), une carte de sous-concentrateur (6), et plusieurs cartes de sous-données (3). La carte de sous-concentrateur (6) est connectée à un capteur correspondant au sous-système (200), et sert à recevoir des données de mesure du capteur et à envoyer les données de mesure aux cartes de sous-données (3) du sous-système (200) à des fins de calcul. La carte de prédiction de diagnostic (300) est connectée à la carte de contrôle maître (1) du système principal (100) et à la carte de contrôle maître (1) du sous-système (200), et sert à obtenir régulièrement des données opérationnelles intermédiaires des cartes de données principales (3) ou des cartes de sous-données (3), et à mettre en œuvre une prédiction de défaut en fonction des données opérationnelles intermédiaires reçues, et à renvoyer le résultat de prédiction à la carte de contrôle maître (1) du système principal (100) ou à la carte de contrôle maître (1) du sous-système (200). Des opérations de mémoire en temps réel peuvent être mises en œuvre sur des données mesurées par les capteurs, et des opérations d'E/S de fréquence chronophages sur des données en lot peuvent être évitées.
PCT/CN2018/108250 2017-09-28 2018-09-28 Système et procédé de diagnostic intelligent WO2019062833A1 (fr)

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Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113051137A (zh) * 2021-04-22 2021-06-29 北京计算机技术及应用研究所 一种可扩展的服务器远程健康管理系统设计方法
CN113655773A (zh) * 2021-07-19 2021-11-16 东风汽车集团股份有限公司 一种车机系统通信串口压力测试系统及方法
CN116880400A (zh) * 2023-07-27 2023-10-13 小黑(广州)智能科技有限公司 一种智能生产流程管理系统

Families Citing this family (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112838944B (zh) * 2020-07-29 2022-08-12 中兴通讯股份有限公司 诊断及管理、规则确定及部署方法、分布式设备、介质
CN114726712B (zh) * 2022-03-31 2022-11-08 湖南宇诺辰电子科技有限公司 一种加固板卡的控制方法和系统

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR20130039692A (ko) * 2011-10-12 2013-04-22 쥬키 가부시키가이샤 전자부품 실장장치, 전자부품 실장시스템 및 전자부품 실장방법
CN104977921A (zh) * 2014-04-07 2015-10-14 韩华泰科株式会社 用于自动监视装置故障的系统和方法

Family Cites Families (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101393543A (zh) * 2007-09-18 2009-03-25 西门子公司 一种故障分析和诊断的方法及系统
CN102662788A (zh) * 2012-04-28 2012-09-12 浪潮电子信息产业股份有限公司 一种计算机系统故障诊断决策及处理方法
CN103389723B (zh) * 2012-05-11 2015-09-09 北汽福田汽车股份有限公司 电机控制器的故障检测系统及方法
CN103197231B (zh) * 2013-04-03 2014-12-31 湖南大学 用于模拟电路故障诊断和预测的fpga装置
CN107054410B (zh) * 2017-04-01 2019-06-11 广州地铁集团有限公司 道岔转辙机的智能诊断系统及诊断方法

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR20130039692A (ko) * 2011-10-12 2013-04-22 쥬키 가부시키가이샤 전자부품 실장장치, 전자부품 실장시스템 및 전자부품 실장방법
CN104977921A (zh) * 2014-04-07 2015-10-14 韩华泰科株式会社 用于自动监视装置故障的系统和方法

Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113051137A (zh) * 2021-04-22 2021-06-29 北京计算机技术及应用研究所 一种可扩展的服务器远程健康管理系统设计方法
CN113051137B (zh) * 2021-04-22 2024-03-26 北京计算机技术及应用研究所 一种可扩展的服务器远程健康管理系统设计方法
CN113655773A (zh) * 2021-07-19 2021-11-16 东风汽车集团股份有限公司 一种车机系统通信串口压力测试系统及方法
CN113655773B (zh) * 2021-07-19 2023-02-28 东风汽车集团股份有限公司 一种车机系统通信串口压力测试系统及方法
CN116880400A (zh) * 2023-07-27 2023-10-13 小黑(广州)智能科技有限公司 一种智能生产流程管理系统
CN116880400B (zh) * 2023-07-27 2023-12-22 小黑(广州)智能科技有限公司 一种智能生产流程管理系统

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