Sensor fault diagnosis method and system of first-order follow-up system based on dynamic trend

A sensor fault and diagnosis method technology, applied in the field of sensor fault diagnosis of first-order servo control system, sensor fault diagnosis system, real-time diagnosis of sensor fault of first-order servo system, can solve fault estimation, fault separation, sensor fault monitoring, etc. problem, achieve strong real-time performance, meet research needs, and avoid creating inaccurate effects

Inactive Publication Date: 2019-08-30
CHINA JILIANG UNIV
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Problems solved by technology

[0003] Purpose of the invention: In order to solve the common problems of sensor fault monitoring, fault estimation and fault separation in the first-order servo control system, a real-time diagnosis method for sensor faults of the first-order servo system based on dynamic trend is proposed

Method used

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  • Sensor fault diagnosis method and system of first-order follow-up system based on dynamic trend
  • Sensor fault diagnosis method and system of first-order follow-up system based on dynamic trend
  • Sensor fault diagnosis method and system of first-order follow-up system based on dynamic trend

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Embodiment Construction

[0073] The following takes an ultrasonic sensor of a certain order follow-up liquid level control system as an example to introduce the specific implementation steps of the online fault diagnosis method provided by the present invention.

[0074] Combine figure 2 As shown in the process, the steps are as follows:

[0075] 1. In the experiment, the final expected liquid level is set to 10cm, the equipment running time in a single experiment is set to 1000s, and the sampling period is T s =0.5s.

[0076] The follow-up system is set to input a ramp signal with a slope of 0.01cm / s, and a signal with an initial value of zero is input at zero time. Select the faults of different strengths within the range of 20% of the set value at the time of 500s, and analyze the law of liquid level changes under different fault conditions.

[0077] 2. Set t=25s as the data window size, then the data point n of each window is:

[0078] n=t / T=50

[0079] There are N=2001 data in an experiment, and there are...

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Abstract

The invention provides a sensor fault diagnosis method and system of a first-order follow-up system based on a dynamic trend, and relates to a sensor fault diagnosis method of a first-order follow-upcontrol system. The method includes steps: realizing switching between a multiplicative fault module, a non-fault module and an additive fault module through simulation of a mechanical switch, and acquiring experiment data; establishing fault monitoring, fault estimation and fault separation static models based on analysis of normal and fault system dynamic characteristics, and proposing an onlineapplication diagnosis model calibration method and a diagnosis process; and verifying the validity of the fault diagnosis method and high precision of the diagnosis according to online simulation ofa "complex process system innovation experiment platform". The method can be widely applied to sensor online fault diagnosis of the first-order follow-up control system.

Description

Technical field [0001] The invention relates to a sensor fault diagnosis system and a method thereof, in particular to a general first-order follow-up control system sensor fault diagnosis method and a system thereof, and a real-time diagnosis method for the first-order follow-up system sensor fault based on dynamic trends is proposed, which belongs to data driven field. Background technique [0002] In recent years, fault diagnosis technology has been greatly developed and is widely used in different fields. The existing fault diagnosis technology can be divided into three categories. The third type of method is based on the data-driven method and does not rely on the analytical model of the control object. It uses real-time data to process and analyze the fault types online, such as wavelet analysis, Kalman filtering and Hilbert transform; or summarize based on a large number of known samples Fault features are used for reasoning, such as neural networks, Bayesian theory, gen...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G05B23/02
CPCG05B23/0278
Inventor 那文波高宇李明
Owner CHINA JILIANG UNIV
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