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Adaptive sliding window algorithm and interval halving algorithm-based fault detection method

A sliding window and fault detection technology, which is applied in computing, computer components, instruments, etc., can solve problems such as complex algorithms, relying on artificially set thresholds, and difficulty in adapting segment widths to achieve high flexibility, efficiency, and accuracy. , good promotional effect

Active Publication Date: 2016-11-09
BEIJING INSTITUTE OF TECHNOLOGYGY
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Problems solved by technology

However, there are still many problems in the traditional qualitative trend analysis method, such as the difficulty of adapting the width of the extracted segment, the complexity of the algorithm, and the dependence on artificial thresholds, etc.

Method used

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  • Adaptive sliding window algorithm and interval halving algorithm-based fault detection method
  • Adaptive sliding window algorithm and interval halving algorithm-based fault detection method
  • Adaptive sliding window algorithm and interval halving algorithm-based fault detection method

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Embodiment Construction

[0024] The present invention will be described in detail below with reference to the accompanying drawings and examples.

[0025] The invention provides a fault detection method based on an adaptive sliding window algorithm and an interval halving algorithm. The method adopts the interval halving method to extract the recognition trend, and constantly changes the initial point and the end point of the interval window according to the specific conditions during the extraction process. Adaptively change the size of the interval to obtain higher extraction accuracy, and then use the fuzzy trend matching algorithm to match the real-time trend with the characteristic trends of various typical faults in the rule knowledge base to diagnose system faults in real time. Part of the data in the Tennessee-Eastman chemical process is used here. The Tennessee-Eastman process (TEP) is based on the real chemical industry production process by two experts from a chemical company named Eastman i...

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Abstract

The invention discloses an adaptive sliding window algorithm and interval halving algorithm-based fault detection method. The method comprises the steps of extracting an identification trend by adopting an interval halving method; changing an initial point and an ending point of an interval window continuously according to a specific condition in the extraction process; adaptively changing the size of an interval to obtain higher extraction precision; matching a real-time trend with characteristic trends of various typical faults in a rule knowledge base through a fuzzy trend matching algorithm; and diagnosing system faults in real time. According to the method, the accuracy and real-time property of sensor fault identification can be improved.

Description

technical field [0001] The invention belongs to the technical field of intelligent information processing, and in particular relates to a fault detection method based on an adaptive sliding window algorithm and an interval halving algorithm. Background technique [0002] A sensor (English name: transducer / sensor) is a detection device that can feel the measured information, and can transform the sensed information into an electrical signal or other required form of information output according to a certain rule, so as to meet the information requirements. The requirements of transmission, processing, storage, display, recording and control are widely used in various control systems. As a window to understand the process status of the system, the accuracy of its measurement results directly affects the operation of the system. At the same time, the working environment of most sensors is relatively harsh, so they will inevitably fail due to various reasons during use. Once t...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62
CPCG06F18/22
Inventor 邓方刘畅顾晓丹孙健陈杰
Owner BEIJING INSTITUTE OF TECHNOLOGYGY
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