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Device fault mode identification method based on improved CS-LSSVM

A pattern recognition and equipment failure technology, applied in character and pattern recognition, computational models, biological models, etc., can solve problems such as difficulty in obtaining optimal solutions, lack of vitality of algorithms, etc., to achieve excellent search effect and high degree of fit , the effect of improving the recognition accuracy

Inactive Publication Date: 2017-02-08
JIANGSU UNIV OF SCI & TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, the standard CS algorithm is fixed in the search step size and recognition rate, which makes the algorithm lack of vitality and difficult to obtain the optimal solution, so it needs to be improved

Method used

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  • Device fault mode identification method based on improved CS-LSSVM
  • Device fault mode identification method based on improved CS-LSSVM
  • Device fault mode identification method based on improved CS-LSSVM

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0082] In this example, in the MATLAB environment, the optimization capability of the improved cuckoo algorithm is tested and compared with other commonly used optimization algorithms. The simulation parameters are set as follows:

[0083] Number of nests N=20;

[0084] Iterative algebra M=200;

[0085] The maximum bird's nest elimination probability Pmax = 0.75, the minimum bird's nest elimination probability Pmin = 0.1;

[0086] The maximum value of the search step size is Smax=0.1, and the minimum value is Smin=0.01;

[0087] The upper bound of the bird's nest position is Ub=[100, 1000], and the lower bound Lb=[0.1, 0.01].

[0088] After simulation, figure 2 is a parameter optimization graph using a genetic (GA) algorithm; image 3 is the parameter optimization graph using the particle swarm optimization (PSO) algorithm; Figure 4 is the parameter optimization graph using the standard cuckoo search (CS) algorithm; Figure 5 It is a parameter optimization graph using ...

Embodiment 2

[0091] In this embodiment, a certain MTU diesel engine is taken as an example to identify and simulate the failure mode.

[0092] The vibration signal of the diesel engine is selected, and the KPCA method is used to reduce the dimension. Get 8 feature parameters as follows:

[0093] peak-to-peak x p-p ;Average of absolute values kurtosis β; wave index s f ; Peak index measurement C f ; Impulse index I f ; Margin indicator CL f .

[0094] data normalization;

[0095] Set the failure mode label as follows:

[0096] 1 - normal situation;

[0097] 2- The oil outlet valve is worn;

[0098] 3- More oil supply;

[0099] 4- The fuel supply advance angle is late;

[0100] 5- The fuel supply advance angle is early;

[0101] 6- Needle valve stuck;

[0102] 7- Needle valve worn.

[0103] Divide 7 groups of vibration signals under different working conditions, each group is divided into 10 samples, a total of 70 samples;

[0104]Each sample has 8 data, a total of 560 data;...

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Abstract

The present invention discloses a device fault mode identification method based on an improved CS-LSSVM. The method comprises the following steps: 1, collecting the monitoring data in the normal condition and the abnormal condition, and performing preprocessing; 2, initializing the Cuckoo search algorithm parameters; 3, building an optimized objective function; 4, updating the bird's nest position through a Levee flight mode; 5, updating the optimized objective function; 6, updating the bird's nest position according to the obsolescence probability; 7, calculating the optimal bird's nest position of the iteration; 8, determining whether the optimal bird's nest position of the iteration reaches the maximum iteration algebra or not, if the iteration does not reach the maximum iteration algebra, returning back to the step 4, and if the iteration reaches the maximum iteration algebra, outputting the optimal bird's nest position; and 9, obtaining the LSSVM optimal penalty factors and the optimal kernel function parameters, and employing the LSSVM to perform fault mode identification of the test sample. The device fault mode identification method based on the improved CS-LSSVM is better in the rate of convergence and the precision of the LSSVM parameter optimization, can obtain globally optimal solution and can be better suitable for the identification of the LSSVM for the device fault mode.

Description

technical field [0001] The invention relates to a device failure mode identification method, which belongs to the technical field of failure mode identification. Background technique [0002] With the continuous improvement of economic efficiency requirements of modern enterprises, the requirements for equipment stability are also getting higher and higher. Once the equipment fails, it may lead to the shutdown of the factory, or produce other safety accidents, posing a serious threat to life and property. In view of the complexity of modern equipment and noise interference, it is difficult to model and analyze actual faults. Therefore, timely and accurate identification of equipment failure modes, that is, equipment failure mode identification, can provide technical support for the maintenance of faulty equipment. [0003] In recent years, people have begun to use the LSSVM classifier to classify the failure modes of equipment. LSSVM, also known as least squares support ve...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06N3/00
CPCG06N3/006G06F18/2111
Inventor 杨奕飞谈敏佳何祖军朱海洋苏贞吴艳艳冯静
Owner JIANGSU UNIV OF SCI & TECH
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