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AR130698A1 - ANTICIPATE THE CAUSE OF ABNORMAL OPERATION IN INDUSTRIAL MACHINES - Google Patents

ANTICIPATE THE CAUSE OF ABNORMAL OPERATION IN INDUSTRIAL MACHINES

Info

Publication number
AR130698A1
AR130698A1 ARP230102676A ARP230102676A AR130698A1 AR 130698 A1 AR130698 A1 AR 130698A1 AR P230102676 A ARP230102676 A AR P230102676A AR P230102676 A ARP230102676 A AR P230102676A AR 130698 A1 AR130698 A1 AR 130698A1
Authority
AR
Argentina
Prior art keywords
future
industrial machine
computer
operating mode
time series
Prior art date
Application number
ARP230102676A
Other languages
Spanish (es)
Inventor
Cdric Schockaert
Original Assignee
Wurth Paul Sa
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 Wurth Paul Sa filed Critical Wurth Paul Sa
Publication of AR130698A1 publication Critical patent/AR130698A1/en

Links

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
    • G05B23/0218—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
    • G05B23/0224—Process history based detection method, e.g. whereby history implies the availability of large amounts of data
    • G05B23/024—Quantitative history assessment, e.g. mathematical relationships between available data; Functions therefor; Principal component analysis [PCA]; Partial least square [PLS]; Statistical classifiers, e.g. Bayesian networks, linear regression or correlation analysis; Neural networks
    • 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]

Landscapes

  • Physics & Mathematics (AREA)
  • Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Automation & Control Theory (AREA)
  • Artificial Intelligence (AREA)
  • Evolutionary Computation (AREA)
  • Mathematical Physics (AREA)
  • Testing And Monitoring For Control Systems (AREA)
  • Numerical Control (AREA)

Abstract

Una computadora identifica una transición de estado de parámetro con una ocurrencia pronosticada en el futuro, en donde la transición de estado de parámetro es una transición crítica debida a un aumento pronosticado en la probabilidad de que el modo de operación de una máquina industrial cambien en el futuro, en donde el modo de operación es un estado técnico de la máquina. La computadora procesa una serie de tiempo multivariada operacional ({{X}}_op) procesando una serie de tiempo multivariada operacional ({{X}}_op) que representa la operación de la máquina industrial en particular (101) durante un intervalo de tiempo de operación específico (T_op) que está en curso en el presente (t_actual). La computadora proporciona una serie de tiempo multivariada futura ({X}_ft) que representa la operación prevista de la máquina industrial en particular (101) durante un intervalo de tiempo de predicción en particular (T_ft) que llega al futuro. La computadora anticipa (423) la transición de estado de parámetro (11a, 31) si se cumplen las dos condiciones siguientes (i) se prevé que por lo menos un parámetro en particular tenga un valor que será distinto de un valor de referencia en por lo menos un segmento de desviación; (ii) se prevé que la probabilidad de cambio en el modo de operación de la máquina industrial aumente.A computer identifies a parameter state transition with a predicted occurrence in the future, wherein the parameter state transition is a critical transition due to a predicted increase in the probability that the operating mode of an industrial machine will change in the future, wherein the operating mode is a technical state of the machine. The computer processes an operational multivariate time series ({{X}}_op) by processing an operational multivariate time series ({{X}}_op) representing the operation of the particular industrial machine (101) during a specific operation time interval (T_op) that is ongoing at present (t_current). The computer provides a future multivariate time series ({X}_ft) representing the predicted operation of the particular industrial machine (101) during a particular prediction time interval (T_ft) reaching into the future. The computer anticipates (423) the parameter state transition (11a, 31) if both of the following conditions are met (i) at least one particular parameter is expected to have a value that will be different from a reference value in at least one deviation segment; (ii) the probability of change in the operating mode of the industrial machine is expected to increase.

ARP230102676A 2022-10-05 2023-10-05 ANTICIPATE THE CAUSE OF ABNORMAL OPERATION IN INDUSTRIAL MACHINES AR130698A1 (en)

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
LU502876A LU502876B1 (en) 2022-10-05 2022-10-05 Anticipating the cause of abnormal operation in industrial machines

Publications (1)

Publication Number Publication Date
AR130698A1 true AR130698A1 (en) 2025-01-08

Family

ID=83688642

Family Applications (1)

Application Number Title Priority Date Filing Date
ARP230102676A AR130698A1 (en) 2022-10-05 2023-10-05 ANTICIPATE THE CAUSE OF ABNORMAL OPERATION IN INDUSTRIAL MACHINES

Country Status (4)

Country Link
AR (1) AR130698A1 (en)
LU (1) LU502876B1 (en)
TW (1) TW202433209A (en)
WO (1) WO2024074516A1 (en)

Family Cites Families (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US11232371B2 (en) * 2017-10-19 2022-01-25 Uptake Technologies, Inc. Computer system and method for detecting anomalies in multivariate data
US11157782B2 (en) * 2017-11-16 2021-10-26 International Business Machines Corporation Anomaly detection in multidimensional time series data
EP3696619A1 (en) * 2019-02-15 2020-08-19 Basf Se Determining operating conditions in chemical production plants
WO2021160260A1 (en) * 2020-02-12 2021-08-19 Swiss Reinsurance Company Ltd. Digital platform using cyber-physical twin structures providing an evolving digital representation of a risk-related real world asset for quantifying risk measurements, and method thereof
AU2021257589A1 (en) * 2020-04-16 2022-09-22 Abb Schweiz Ag Method for an intelligent alarm management in industrial processes

Also Published As

Publication number Publication date
WO2024074516A1 (en) 2024-04-11
TW202433209A (en) 2024-08-16
LU502876B1 (en) 2024-04-08

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