WO2017105181A1 - Système et procédé de prédiction de défaillances dans des équipements répartis à distance - Google Patents

Système et procédé de prédiction de défaillances dans des équipements répartis à distance Download PDF

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Publication number
WO2017105181A1
WO2017105181A1 PCT/MX2015/000186 MX2015000186W WO2017105181A1 WO 2017105181 A1 WO2017105181 A1 WO 2017105181A1 MX 2015000186 W MX2015000186 W MX 2015000186W WO 2017105181 A1 WO2017105181 A1 WO 2017105181A1
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WO
WIPO (PCT)
Prior art keywords
data
processing system
machine
equipment
remotely distributed
Prior art date
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Ceased
Application number
PCT/MX2015/000186
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English (en)
Spanish (es)
Inventor
José Antonio DIAZ QUINTANAR
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Individual
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Individual
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Priority to PCT/MX2015/000186 priority Critical patent/WO2017105181A1/fr
Publication of WO2017105181A1 publication Critical patent/WO2017105181A1/fr
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

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Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01D—MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR
    • G01D3/00—Indicating or recording apparatus with provision for the special purposes referred to in the subgroups
    • G01D3/08—Indicating or recording apparatus with provision for the special purposes referred to in the subgroups with provision for safeguarding the apparatus, e.g. against abnormal operation, against breakdown
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks

Definitions

  • the technical field of the present invention is electrical, since it is a system that measures and detects any anomaly by means of sensors installed in the equipment and communicates the data to a remote system that generates statistics and fault prediction reports.
  • the present invention is about a system that monitors sensors placed in key parts of the equipment installed remotely distributed, said system collects the data of the sensors with what generates the durability statistics of each piece, estimating the useful life of each one even by regions with a different climate that could impact them, and then generate reports for their due attention anticipating failures due to the approach to that final point of life.
  • FIGURES Figure 1 is a block diagram showing the scheme of the system.
  • the Failure Prediction System in Remotely Distributed Equipment is composed of:
  • the sensors (1) installed in key points of the equipment, consisting of measuring temperature and relative humidity, voltage or current, detection of voltage, current and continuity, and obtaining the global positioning coordinates (GPS).
  • GPS global positioning coordinates
  • Data capture module (2) which obtains the readings of the sensors and detectors and records them in its internal memory (7) to send them to a remote data processing system.
  • Mobile data device (3) that receives the service reports for predictive and corrective maintenance and serves as a communications link in places where there is no access to a data network.
  • the operation of the Failure Prediction System on Remotely Distributed Equipment works by recording the dates, hours, minutes and seconds each time a component of a machine is activated by detecting the voltage or applied current or continuity when it comes to switches, and keeps a periodic record of the existing ambient temperature and the temperature of those heat sensitive components.
  • This register is frequency adjustable according to the needs of each machine application and is sent to a data processing system (5) that has a neural network (6) that classifies the information generated by regions of temperature and relative humidity , by type of machine monitored, by type of sensor, by type of service, by version or age of machine, by level of predominant voltage in the power supply, by frequency of use of the machine, by temperature reached by the monitored components, by time of operation of each component, by technical personnel that attends its maintenance and by point of sale or service where each machine operates.
  • the Failure Prediction Method in Remotely Distributed Equipment consists of the following steps:
  • the system operator adjusts the operating parameters of the system, such as:
  • the data capture module (2) you have installed obtains a reading of each component and the GPS module by sending an initial status report to the processing system (5).
  • the processing system (5) starts the registration for the new machine monitored in its database.
  • the processing system (5) feeds the data to the neural network (6).
  • the neural network (6) updates its outputs and reclassifies the information by emptying it into the database.
  • the neural network (6) detects that a value is out of the expected, it generates an alarm message for registration in the database (8).
  • the processing system (5) sends the alarm message to the mobile data terminal (3) that comes with the technical personnel assigned to the machine.
  • the piece is delivered to the technical staff that will attend
  • the data capture module (2) When the technical personnel check the machine, the data capture module (2) is detected by a wireless signal by the mobile data terminal (3) of the technical personnel, thus initiating the data exchange indicating the pieces that will be changed. Additionally when the place where the machine does not have access to a data network, the data capture module (2) uses said temporary connection to empty the information to the mobile data terminal (3) which at the time Access a data network will send them to the processing system (5).
  • the data capture module (2) updates its status and sends the change of parts record to the processing system (5).
  • the processing system (5) generates a report of the activity indicating the response time of the personnel, durability of each updated component when it was due to a fault, parts that were changed and the calculation of the cost of the service.
  • the processing system opens a new record in the database feeding the neuron network! (6) for update.

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  • Physics & Mathematics (AREA)
  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Data Mining & Analysis (AREA)
  • Molecular Biology (AREA)
  • Biophysics (AREA)
  • Computational Linguistics (AREA)
  • Artificial Intelligence (AREA)
  • Evolutionary Computation (AREA)
  • General Health & Medical Sciences (AREA)
  • Biomedical Technology (AREA)
  • Computing Systems (AREA)
  • General Engineering & Computer Science (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Mathematical Physics (AREA)
  • Software Systems (AREA)
  • Health & Medical Sciences (AREA)
  • Selective Calling Equipment (AREA)

Abstract

La présente invention concerne un système et un procédé de prédiction de défaillances dans des équipements répartis à distances, ledit système étant composé des capteurs installés dans des points clés des équipements fonctionnant dans des lieux à distance qui mesurent la température et l'humidité relative, la tension ou le courant, la détection de tension, courant et continuité, et obtiennent les coordonnées de positionnement global (GPS), un module de capture de données qui permet d'obtenir les lectures des capteurs et détecteurs, un système de traitement de données à distance qui recueille les données, un réseau neuronal et un dispositif de données mobile ; le système enregistrant les données des capteurs avec le module de capture de données installé dans chaque machine à distance et les envoie au système de traitement de données qui utilise le réseau neuronal pour classifier les informations afin d'établir des statistiques des défaillances et de prédire le temps de vie de chaque composant générant des rapports d'attention au moyen du terminal de données mobile que le personnel technique détient. Cette invention a pour objet de fournir un outil qui, dans des cas où plusieurs machines fonctionnent à distance, permet de diminuer ou également d'éliminer les temps hors fonctionnement des équipements ou au moins de diminuer les temps de réponse à des rapports de défaillance, d'optimiser les inventaires de réfactions et d'éviter la perte de ressources et de temps du personnel technique, lorsque celui-ci s'occupe d'un rapport.
PCT/MX2015/000186 2015-12-14 2015-12-14 Système et procédé de prédiction de défaillances dans des équipements répartis à distance Ceased WO2017105181A1 (fr)

Priority Applications (1)

Application Number Priority Date Filing Date Title
PCT/MX2015/000186 WO2017105181A1 (fr) 2015-12-14 2015-12-14 Système et procédé de prédiction de défaillances dans des équipements répartis à distance

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
PCT/MX2015/000186 WO2017105181A1 (fr) 2015-12-14 2015-12-14 Système et procédé de prédiction de défaillances dans des équipements répartis à distance

Publications (1)

Publication Number Publication Date
WO2017105181A1 true WO2017105181A1 (fr) 2017-06-22

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WO (1) WO2017105181A1 (fr)

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107909116A (zh) * 2017-12-07 2018-04-13 无锡小天鹅股份有限公司 洗衣机故障识别方法及装置
CN113065733A (zh) * 2020-12-15 2021-07-02 江苏苏星资产管理有限公司 一种基于人工智能的电气资产管理方法
CN114641741A (zh) * 2019-11-07 2022-06-17 Abb瑞士股份有限公司 基于机器学习算法进行温度估计的转换器故障行为预测

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5566092A (en) * 1993-12-30 1996-10-15 Caterpillar Inc. Machine fault diagnostics system and method
US20010001851A1 (en) * 1998-09-15 2001-05-24 Piety Kenneth R. Database wizard
US20020059320A1 (en) * 2000-10-12 2002-05-16 Masatake Tamaru Work machine management system
US20050081410A1 (en) * 2003-08-26 2005-04-21 Ken Furem System and method for distributed reporting of machine performance
US7308322B1 (en) * 1998-09-29 2007-12-11 Rockwell Automation Technologies, Inc. Motorized system integrated control and diagnostics using vibration, pressure, temperature, speed, and/or current analysis

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5566092A (en) * 1993-12-30 1996-10-15 Caterpillar Inc. Machine fault diagnostics system and method
US20010001851A1 (en) * 1998-09-15 2001-05-24 Piety Kenneth R. Database wizard
US7308322B1 (en) * 1998-09-29 2007-12-11 Rockwell Automation Technologies, Inc. Motorized system integrated control and diagnostics using vibration, pressure, temperature, speed, and/or current analysis
US20020059320A1 (en) * 2000-10-12 2002-05-16 Masatake Tamaru Work machine management system
US20050081410A1 (en) * 2003-08-26 2005-04-21 Ken Furem System and method for distributed reporting of machine performance

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107909116A (zh) * 2017-12-07 2018-04-13 无锡小天鹅股份有限公司 洗衣机故障识别方法及装置
CN114641741A (zh) * 2019-11-07 2022-06-17 Abb瑞士股份有限公司 基于机器学习算法进行温度估计的转换器故障行为预测
CN113065733A (zh) * 2020-12-15 2021-07-02 江苏苏星资产管理有限公司 一种基于人工智能的电气资产管理方法
CN113065733B (zh) * 2020-12-15 2024-04-30 江苏苏星资产管理有限公司 一种基于人工智能的电气资产管理方法

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