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Robust filtering and prediction using switching models for machine condition monitoring

一种机器、卡尔曼滤波器的技术，应用在仪器、通用控制系统、电气测试/监控等方向

Inactive Publication Date: 2011-11-23
SIEMENS AG
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

During spikes such as those at 521, 522, 523 and steps such as steps 511, 512, the real sensor signal changes drastically in short periods of time, posing a great challenge to the filtering algorithm

Method used

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  • Robust filtering and prediction using switching models for machine condition monitoring
  • Robust filtering and prediction using switching models for machine condition monitoring
  • Robust filtering and prediction using switching models for machine condition monitoring

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Embodiment Construction

[0039] The invention may be embodied in a system for filtering sensor values, which system may be included in a machine monitoring system, or may be a stand-alone system. figure 1 A machine monitoring system 100 according to an exemplary embodiment of the present invention is illustrated. Such as figure 1 As shown, system 100 includes a personal or other computer 110 . Computer 110 may be connected to sensor 171 through wired or wireless network 105 . The system preferably includes additional sensors (not shown) that are likewise connected.

[0040] The sensors 171 are arranged to acquire data representative of characteristics of the machine or system 180 or the environment of the machine. The sensor measures characteristics such as temperature, pressure, humidity, rotational or linear speed, vibration, force, strain, power, voltage, current, resistance, flow rate, proximity, chemical concentration, or any other characteristic. As described above, the sensor 171 measures...

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Abstract

In a machine condition monitoring technique, a sensor reading is filtered using a switching Kalman filter. Kalman filters are created to describe separate modes of the signal, including a steady mode and a non-steady mode. For each new observation of the signal, a new mode is estimated based on the previous mode and state, and a new state is then estimated based on the new mode and the previous mode and state. In the steady mode, evolution covariances of both the observed signal and the rate of change of that signal are low. In the non-steady mode, the evolution covariance of the observed signal is set to a higher value, permitting the observed signal to vary widely, while the evolution covariance of the rate of change of the signal is maintained at a low level.

Description

[0001] priority claim [0002] This application claims priority to pending U.S. Provisional Patent Application Serial No. 61 / 106,701, filed October 20, 2008, and entitled "Robust Filtering and Prediction Using Switching Models for Machine Condition Monitoring," which is hereby incorporated by reference in its entirety middle. technical field [0003] The present invention relates generally to machine condition monitoring for factory automation purposes. More specifically, the present invention relates to techniques for filtering observations in order to estimate true signal values ​​without noise. Background technique [0004] The task of machine condition monitoring is to detect faults as early as possible to avoid further damage to the machine. This is usually done by analyzing data from a set of sensors installed on different parts of the machine to measure temperature, pressure vibrations, etc. When the machine is functioning normally, the sensor data is within the no...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G05B23/02
CPCG05B23/0221
Inventor 袁超
Owner SIEMENS AG
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