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Generation of failure models for embedded analytics and diagnostic/prognostic reasoning

A fault model, fault technology, applied in general control systems, control/regulation systems, electrical testing/monitoring, etc., to solve problems such as trouble, long-term hiding, damage, etc.

Active Publication Date: 2019-05-21
SIEMENS AG
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

On the other hand, the cause may remain hidden for a long time and lead to costly consequential damage in terms of downtime, repair costs, etc.
For operators and maintenance personnel it can be cumbersome, time consuming and difficult to determine which alarms are critical, difficult to diagnose the cause of an apparent misoperation, what causes what effect, which alarm is the root cause

Method used

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  • Generation of failure models for embedded analytics and diagnostic/prognostic reasoning
  • Generation of failure models for embedded analytics and diagnostic/prognostic reasoning
  • Generation of failure models for embedded analytics and diagnostic/prognostic reasoning

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Embodiment Construction

[0018] This document describes systems, methods, and apparatus that generally relate to building and using fault models for embedded analytics and diagnostic / predictive reasoning. The techniques described herein enable embedded devices with flexible embedded analytics capabilities to convert imprecise and incomplete data (from sensors, maintenance, etc.) its behavior. These techniques provide a systematic approach to defining fault knowledge, fault models, and the formalization of the basic architecture, concepts, and tasks required for fault modeling. Furthermore, fault models enable efficient reasoning with uncertain information at the device level and generate knowledge from data. Failure models also allow the utilization of numerical models that can be used to generate physically-based concrete data to generate parameters, thresholds, and even the raw (synthetic) data needed to learn the components used by the solution.

[0019] For complex systems, the actual data flow ...

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Abstract

A computer-implemented method for detecting faults and events related to a system includes receiving sensor data from a plurality of sensors associated with the system. A hierarchical failure model ofthe system is constructed using (i) the sensor data, (ii) fault detector data, (iii) prior knowledge about system variables and states, and (iii) one or more statistical descriptions of the system. The failure model comprises a plurality of diagnostic variables related to the system and their relationships. Probabilistic reasoning is performed for diagnostic or prognostic purposes on the system using the failure model to derive knowledge related to potential or actual system failures.

Description

technical field [0001] The present invention is primarily concerned with improving the resilience of control systems with respect to the construction and use of fault models for embedded analysis and diagnostic / predictive reasoning. The disclosed technology can be applied, for example, to various automated production environments using industrial controllers such as programmable logic controllers (PLCs) and distributed control systems (DCS). Background technique [0002] In traditional industrial automation systems, alarm messages are used as an important means of preventing subsequent damage, for monitoring deviations from the healthy operation of machines and systems, and for guiding maintenance activities. Alarms can be triggered at all levels of an automation system or machine data acquisition and supervisory system, from field devices to Supervisory Control and Data Acquisition (SCADA) and Manufacturing Execution System (MES) levels, and even back-end levels (e.g. via ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G05B23/02
CPCG05B23/0243G05B23/0278G05B2219/49181G05B19/0428G06N7/01G05B2223/02G05B23/0248G05B23/0254G06N5/022
Inventor 贾斯蒂尼安·罗斯卡斯特芬·兰帕特尔
Owner SIEMENS AG
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