Limits...
A monotonic degradation assessment index of rolling bearings using fuzzy support vector data description and running time.

Shen Z, He Z, Chen X, Sun C, Liu Z - Sensors (Basel) (2012)

Bottom Line: DSI inherits all advantages of ε⁻ and overcomes its disadvantage.A run-to-failure test is carried out to validate the performance of the proposed method.The results show that DSI reflects the growth of the damages with running time perfectly.

View Article: PubMed Central - PubMed

Affiliation: State Key Laboratory for Manufacturing System Engineering, School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an 710049, China. zjshen.2007@stu.xjtu.edu.cn

ABSTRACT
Performance degradation assessment based on condition monitoring plays an important role in ensuring reliable operation of equipment, reducing production downtime and saving maintenance costs, yet performance degradation has strong fuzziness, and the dynamic information is random and fuzzy, making it a challenge how to assess the fuzzy bearing performance degradation. This study proposes a monotonic degradation assessment index of rolling bearings using fuzzy support vector data description (FSVDD) and running time. FSVDD constructs the fuzzy-monitoring coefficient ε⁻ which is sensitive to the initial defect and stably increases as faults develop. Moreover, the parameter ε⁻ describes the accelerating relationships between the damage development and running time. However, the index ε⁻ with an oscillating trend disagrees with the irreversible damage development. The running time is introduced to form a monotonic index, namely damage severity index (DSI). DSI inherits all advantages of ε⁻ and overcomes its disadvantage. A run-to-failure test is carried out to validate the performance of the proposed method. The results show that DSI reflects the growth of the damages with running time perfectly.

No MeSH data available.


Related in: MedlinePlus

Comparisons between ε and ε̄: (a) ε & ε̄ of test 1 failure bearing; (b) ε̄− ε of test 1 failure bearing; (c) ε & ε̄ of test 2 failure bearing; (d) ε̄− ε of test 2 failure bearing; (e) ε & ε̄ of test 3 failure bearing; (f) ε̄− ε of test 3 failure bearing.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC3472819&req=5

f13-sensors-12-10109: Comparisons between ε and ε̄: (a) ε & ε̄ of test 1 failure bearing; (b) ε̄− ε of test 1 failure bearing; (c) ε & ε̄ of test 2 failure bearing; (d) ε̄− ε of test 2 failure bearing; (e) ε & ε̄ of test 3 failure bearing; (f) ε̄− ε of test 3 failure bearing.

Mentions: Thirdly, the fuzzy membership degree si is computed by Equation (17). The fuzzy-monitoring coefficients ε̄ of three bearings are given by FSVDD. The parameter ε̄ and its local enlargement are described in Figure 12. The fuzzy-monitoring coefficient ε̄ is an improvement of the monitoring coefficient ε, and adds the function of defuzzification. The parameters ε̄ and ε have a certain degree as well as lots of differences. The comparisons between ε and ε̄ are carried out as shown in Figure 13. The parameter ε is blue while the parameter ε̄ is green. They are same at the beginning of degradation, and much different with the damage development. The overall trend of ε̄ − ε is accelerated, but the acceleration is not strict and not able to be proved by the second derivative. The fuzzy-monitoring coefficient ε̄ has a similar accelerated trend, which could agree with the relationship of the damage development and running time. On the other hand, the D-value of the neighbor ε̄i gets bigger as the damage increases. The increasing D-value means that the damage severity of the neighbor moments is differentiated easily. However, the fuzzy-monitoring coefficient ε̄ is still oscillating and not consistent with the irreversible damage development.


A monotonic degradation assessment index of rolling bearings using fuzzy support vector data description and running time.

Shen Z, He Z, Chen X, Sun C, Liu Z - Sensors (Basel) (2012)

Comparisons between ε and ε̄: (a) ε & ε̄ of test 1 failure bearing; (b) ε̄− ε of test 1 failure bearing; (c) ε & ε̄ of test 2 failure bearing; (d) ε̄− ε of test 2 failure bearing; (e) ε & ε̄ of test 3 failure bearing; (f) ε̄− ε of test 3 failure bearing.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3472819&req=5

f13-sensors-12-10109: Comparisons between ε and ε̄: (a) ε & ε̄ of test 1 failure bearing; (b) ε̄− ε of test 1 failure bearing; (c) ε & ε̄ of test 2 failure bearing; (d) ε̄− ε of test 2 failure bearing; (e) ε & ε̄ of test 3 failure bearing; (f) ε̄− ε of test 3 failure bearing.
Mentions: Thirdly, the fuzzy membership degree si is computed by Equation (17). The fuzzy-monitoring coefficients ε̄ of three bearings are given by FSVDD. The parameter ε̄ and its local enlargement are described in Figure 12. The fuzzy-monitoring coefficient ε̄ is an improvement of the monitoring coefficient ε, and adds the function of defuzzification. The parameters ε̄ and ε have a certain degree as well as lots of differences. The comparisons between ε and ε̄ are carried out as shown in Figure 13. The parameter ε is blue while the parameter ε̄ is green. They are same at the beginning of degradation, and much different with the damage development. The overall trend of ε̄ − ε is accelerated, but the acceleration is not strict and not able to be proved by the second derivative. The fuzzy-monitoring coefficient ε̄ has a similar accelerated trend, which could agree with the relationship of the damage development and running time. On the other hand, the D-value of the neighbor ε̄i gets bigger as the damage increases. The increasing D-value means that the damage severity of the neighbor moments is differentiated easily. However, the fuzzy-monitoring coefficient ε̄ is still oscillating and not consistent with the irreversible damage development.

Bottom Line: DSI inherits all advantages of ε⁻ and overcomes its disadvantage.A run-to-failure test is carried out to validate the performance of the proposed method.The results show that DSI reflects the growth of the damages with running time perfectly.

View Article: PubMed Central - PubMed

Affiliation: State Key Laboratory for Manufacturing System Engineering, School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an 710049, China. zjshen.2007@stu.xjtu.edu.cn

ABSTRACT
Performance degradation assessment based on condition monitoring plays an important role in ensuring reliable operation of equipment, reducing production downtime and saving maintenance costs, yet performance degradation has strong fuzziness, and the dynamic information is random and fuzzy, making it a challenge how to assess the fuzzy bearing performance degradation. This study proposes a monotonic degradation assessment index of rolling bearings using fuzzy support vector data description (FSVDD) and running time. FSVDD constructs the fuzzy-monitoring coefficient ε⁻ which is sensitive to the initial defect and stably increases as faults develop. Moreover, the parameter ε⁻ describes the accelerating relationships between the damage development and running time. However, the index ε⁻ with an oscillating trend disagrees with the irreversible damage development. The running time is introduced to form a monotonic index, namely damage severity index (DSI). DSI inherits all advantages of ε⁻ and overcomes its disadvantage. A run-to-failure test is carried out to validate the performance of the proposed method. The results show that DSI reflects the growth of the damages with running time perfectly.

No MeSH data available.


Related in: MedlinePlus