Abnormity detection and fault diagnosis algorithm of gas turbine based on sensor associated network

A fault diagnosis algorithm and technology of gas turbines, applied in instruments, calculations, electrical digital data processing, etc., can solve problems such as threats to production safety, difficulty in knowledge representation, and description of the complete state of the system to achieve accurate anomaly detection and fault diagnosis Effect

Active Publication Date: 2016-12-21
中国船舶重工集团公司第七〇三研究所
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Problems solved by technology

When the system fails, the effect of the failure will spread along the direction of information transmission, and cause the status of many sensors to appear abnormal one after another and alarm
When the source of the fault cannot be confirmed, it is difficult for the operator to formulate a reasonable solution, which leads to further...

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  • Abnormity detection and fault diagnosis algorithm of gas turbine based on sensor associated network

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Embodiment Construction

[0017] refer to figure 1 , a gas turbine anomaly detection and fault diagnosis algorithm based on sensor association network, the steps of the diagnosis algorithm are as follows:

[0018] Step 1: Signal preprocessing, divide the time series of gas turbine operation data measured by sensor measuring points into continuous cycle type, trend type signal and discrete command signal, and use corresponding feature extraction methods for different types of signals to obtain Accurately characterize the behavior of signals;

[0019] Step 2: Evaluate the correlation between any two sensor measuring points in the preprocessed measuring point set, and use the obtained correlation index and measuring point set to construct a sensor association network model of the gas turbine;

[0020] Step 3: Perform hierarchical clustering on the obtained gas turbine sensor association network model, and use the form of matrix to represent the sensor association network model after hierarchical clusteri...

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Abstract

The invention provides an abnormity detection and fault diagnosis algorithm of a gas turbine based on sensor associated network and relates to the technical field of gas turbine fault diagnosis. The diagnosis algorithm comprises following steps: step 1. signal processing; step 2: evaluating the correlation between any two sensor test points in a pretreated test point set; step 3. hierarchically clustering an obtained gas turbine sensor associated network model; step 4. evaluating the structure distribution of the residual matrix corresponding to the smallest granularity sub-class by using the information entropy index; step 5. calculating information entropy of all sub-classes in all layers and calculating super-class information entropy index of sub-classes whose information entropy indexes are transfinite. The abnormity detection and fault diagnosis algorithm of gas turbine based on sensor associated network is used achieving abnormity detection on the complete operation status of large gas turbines, especially suitable for the case where sensor test point information is very rich.

Description

Technical field: [0001] The invention relates to the technical field of gas turbine fault diagnosis, in particular to a gas turbine anomaly detection and fault diagnosis algorithm based on a sensor association network. Background technique: [0002] Complex industrial systems such as large gas turbines have complex structures and functions, harsh working environments and changing working conditions, and are fault-prone systems, and are usually composed of multi-level subsystems and auxiliary systems in structure. Under the action of material flow, the transmission process of energy flow and information flow guided by material flow is realized, and the monitoring of its operating status can be realized through hundreds, thousands or even tens of thousands of sensors installed on the structure. When the system fails, the effect of the failure will spread along the direction of information transmission, and cause the status of many sensors to appear abnormal one after another a...

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Application Information

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IPC IPC(8): G06F19/00
CPCG16Z99/00
Inventor 徐志强王伟影崔宝唐瑞
Owner 中国船舶重工集团公司第七〇三研究所
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