Life Analysis of Fan Spindle Bearing based on Grey Markov Algorithm

doi:10.23940/IJPE.19.02.P5.397405

Abstract

Abstract:

In order to improve the reliability and economic benefit of fan operation, a state estimation and residual life prediction method of fan spindle bearing based on grey Markov chain is proposed. Firstly, a grey distribution model of bearing wear conditionofthe wind turbine main shaft is established. Then, using the residual error correction (Markov prediction analysis method) and division of bearing wear state grade, which determines the state level interval limits, the main shaft bearing wear state transition probability is calculated and the state transition matrix of the Markov process is constructed. Finally, this method is used to calculate the life of the main shaft bearing of the wind turbine.

Key words: spindle bearing, residual life, Markov, gray model

Han Zhang, Jianxin Wu, Yiqing Qiu, and Qiang Chen. Life Analysis of Fan Spindle Bearing based on Grey Markov Algorithm [J]. Int J Performability Eng, 2019, 15(2): 397-405.

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Wear state	State interval	State
A	(0~0.2)	Normal
B	(0.2~0.4)	Light
C	(0.4~0.7)	Moderate
D	(0.7~1)	Severe
E	>1	Need replace

Number	Degenerate quantity	Number	Degenerate quantity
1	0.01	8	0.222
2	0.023	9	0.224
3	0.046	10	0.287
4	0.066	11	0.234
5	0.092	12	0.333
6	0.137	13	0.352
7	0.171	14	0.396

Check time	Wearamount	Wearstate
0	0.014	A
1T	0.022	A
2T	0.045	B
3T	0.061	B
4T	0.093	B
5T	0.134	B
6T	0.168	B
7T	0.192	B

Inspecttime	A	B	C	D	E	Abrasionstate
T	0.702	0.2952	0.0020	0.0006	0	A
2T	0.4958	0.4406	0.0648	0.0017	0.0004	B
3T	0.3497	0.4942	0.1354	0.0243	0.0011	B
...	...	...	...	...	...	...
9T	0.0425	0.2643	0.2194	0.1833	0.2973	E

state	A	B	C	D	E
The numberof Steps	9	6	4	2	0
Residual life${{T}_{r}}$	9T	6T	4T	2T	0