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Cluster anomaly detection method of combustion gas turbine

A gas turbine and anomaly detection technology, applied in the direction of measuring devices, instruments, etc., can solve problems such as excessive information volume, limited fault monitoring level, and insufficient early warning technology

Inactive Publication Date: 2012-07-25
HARBIN INST OF TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0004] In order to solve the problems that the existing gas turbine sensors collect a large number of data, the amount of information is excessively large, the early warning technology is insufficient, the level of fault monitoring is simple and limited, and the degree of misjudgment is high, a gas turbine cluster anomaly detection method is provided.

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  • Cluster anomaly detection method of combustion gas turbine
  • Cluster anomaly detection method of combustion gas turbine
  • Cluster anomaly detection method of combustion gas turbine

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specific Embodiment approach 1

[0019] Specific implementation mode one: as figure 1 As shown, the specific process of the gas turbine cluster anomaly detection method described in this embodiment is:

[0020] Step 1. Obtain monitoring data and input parameters: Obtain G monitoring data at different times from the monitoring software of the gas turbine, and use P below k Indicates the monitoring value at the kth moment, where 1≤k≤G; and input parameters: weight vector The number of iterations LT, the update parameter λ, and the abnormal reference ratio β;

[0021] Step 2, extracting the feature quantities that characterize the characteristics of each different moment: select the feature quantity PF from the monitoring data k (l), where 1≤l≤29, which corresponds to the values ​​of the following 29 measuring points: gearbox vibration, generator DEX vibration, generator DEY vibration, generator EEX vibration, generator EEY vibration, gas generator speed , Generator total actual power, 3# bearing Y vibration...

specific Embodiment approach 2

[0030] Specific implementation mode two: as figure 1 As shown, the clustering anomaly detection method described in this embodiment is characterized in that:

[0031] In step 4, the cluster center sample to which each sample belongs is obtained from the G samples fc k = arg f max { a ( k , f ) + r ( k , f ) } The specific method is:

[0032] Step 41: Obtain the similarity between the bth sample and the gth sample Sim ( b , g ) = - Dist ( PF ...

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Abstract

The invention provides a cluster anomaly detection method of a combustion gas turbine, which solves the problems of insufficient early warming, simple and limited fault monitoring level and high misjudgment degree, caused by that the conventional combustion gas turbine has the defects of large quantity of data collected by a sensor and excessively large information amount. The cluster anomaly detection method disclosed by the invention has special processes as follows: obtaining monitoring data and input parameters; extracting characteristic quantities for representing characteristics at different moments; establishing an overall distance matrix for measuring similarities among sample points at the different moments; obtaining a cluster center sample which each sample belongs to from G samples; sorting the samples with the same cluster center as one type, so as to obtain sets of clusters; and ordering the quantity of cluster results, so as to obtain a set of abnormal samples. The cluster anomaly detection method disclosed by the invention has a small calculation resource demand, and low time and space costs. The cluster anomaly detection method is used for finally representing abnormal data points by rapidly clustering data based on a linear clustering manner, and has very strong accountability.

Description

technical field [0001] The invention relates to a cluster abnormality detection method of a gas turbine. Background technique [0002] As an important giant power machine, the gas turbine has the characteristics of compact structure, stable operation, high thermal efficiency, etc., and its application range is becoming wider and wider. In reality, the safe and reliable work of gas turbines is highly required. Under the daily working conditions of gas turbines, analyzing and monitoring the health of the units and analyzing and detecting various abnormal situations that may occur can avoid or facilitate timely handling of gas turbines. Major failure of the machine. [0003] At present, all gas turbine manufacturers have installed more sensors on the turbines to monitor the working status of the turbines. The data information (such as gas turbine speed, inlet and outlet temperature, etc.) monitored and recorded is of great significance and use value to the operation guarantee...

Claims

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Application Information

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IPC IPC(8): G01D21/02
Inventor 贺惠新林志荣于达仁
Owner HARBIN INST OF TECH
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