Gas turbine fault prediction method based on correlation analysis
A correlation analysis and gas turbine technology, which is applied in gas turbine engine testing, jet engine testing, neural learning methods, etc. It can solve the problem that the data dimension of gas turbine units is too large to be loaded into memory at one time, and most algorithms are computationally complex and cannot be satisfied. Problems such as online real-time learning of gas turbine operating data
Active Publication Date: 2020-08-28
HEFEI UNIV OF TECH
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Problems solved by technology
[0006](1) The calculation complexity of most of the above algorithms is relatively large, which cannot satisfy the online real-time learning of the operating data of the gas turbine unit;
[0007](2) The data dimension of the gas turbine unit is too large to be loaded into the memory at one time, and new measurement point data may appear continuously. Existing methods cannot handle this situation
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[0057] In this embodiment, a gas turbine failure prediction method based on correlation analysis is applied to a gas turbine system, and monitors the operating status of Z monitoring nodes in the gas turbine system at regular intervals, and keeps recording for a period of time, assuming a total of Monitor m times, so as to obtain the gas turbine operation data set D composed of m monitoring value vectors, denoted as D={sam 1 ,sam 2 ,...,sam v ,...,sam m}, where sam v Indicates the vth monitoring value vector, and Indicates the monitoring value of the i-th monitoring node in the v-th monitoring value vector; 1≤v≤m, 1≤i≤Z; the vector formed by the monitoring value of the i-th monitoring node under m monitoring is defined as X i , Indicates the i-th monitoring node vector X i There are m values, because the i-th monitoring node has been monitored for m times, and there are m monitoring values. The purpose of this gas turbine fault prediction method is to find out the relat...
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The invention discloses a gas turbine fault prediction method based on correlation analysis. The gas turbine fault prediction method comprises the steps that 1, monitoring node vectors to be processedare read in one by one in a flow mode; 2, performing correlation analysis on each currently read monitoring node vector and the monitoring node vector read by the system; 3, performing redundancy analysis on the selected related monitoring nodes; and 4, orienting newly added monitoring nodes, determining causal relationships with other monitoring nodes, repeating the steps 1-4 until the number ofread monitoring node vectors exceeds a limit value, and finally obtaining a corresponding monitoring system causal structure diagram for training a fault prediction model. Therefore, a fault prediction model is obtained, and the fault can be predicted more accurately. According to the gas turbine fault prediction method, a more accurate fault prediction model can be obtained, so that the fault can be predicted more accurately.
Description
technical field [0001] The invention belongs to the field of data mining, in particular to a gas turbine fault prediction method based on correlation analysis. Background technique [0002] At present, the research status of domestic gas turbine condition monitoring and fault diagnosis has made great progress recently, but the technology is still relatively backward, and the application results are few. With the rise of big data technology, how to apply big data related technology to gas turbine condition monitoring and fault diagnosis is a topic worth studying. Gas turbine units continuously generate a large amount of monitoring data during operation. Based on these massive operation monitoring data, it is of great practical significance to carry out research on status analysis, performance monitoring, and fault intelligent diagnosis and prediction of gas turbine units. Through data modeling, real-time health assessment of gas turbine unit status can be performed, status t...
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IPC IPC(8): G06N3/04G06N3/08G01M15/14
CPCG06N3/049G06N3/08G01M15/14
Inventor 杨静方宝富沈安波樊高金江刘峰朱尤杰
Owner HEFEI UNIV OF TECH