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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

Results of SVDD for tests 1–3 failure bearing: (a) ε of test 1 failure bearing during the whole life; (b) local enlargement of ε for test 1 failure bearing; (c) ε of test 2 failure bearing during the whole life; (d) local enlargement of ε for test 2 failure bearing; (e) ε of test 3 failure bearing during the whole life; (f) local enlargement of ε for test 3 failure bearing.
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f8-sensors-12-10109: Results of SVDD for tests 1–3 failure bearing: (a) ε of test 1 failure bearing during the whole life; (b) local enlargement of ε for test 1 failure bearing; (c) ε of test 2 failure bearing during the whole life; (d) local enlargement of ε for test 2 failure bearing; (e) ε of test 3 failure bearing during the whole life; (f) local enlargement of ε for test 3 failure bearing.

Mentions: Secondly, the vector xi is constructed by the four original features and imported into SVDD to compute the monitoring coefficient ε. Figure 8 provides the parameter ε and its local enlargement of three bearings.


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)

Results of SVDD for tests 1–3 failure bearing: (a) ε of test 1 failure bearing during the whole life; (b) local enlargement of ε for test 1 failure bearing; (c) ε of test 2 failure bearing during the whole life; (d) local enlargement of ε for test 2 failure bearing; (e) ε of test 3 failure bearing during the whole life; (f) local enlargement of ε for test 3 failure bearing.
© Copyright Policy
Related In: Results  -  Collection

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

f8-sensors-12-10109: Results of SVDD for tests 1–3 failure bearing: (a) ε of test 1 failure bearing during the whole life; (b) local enlargement of ε for test 1 failure bearing; (c) ε of test 2 failure bearing during the whole life; (d) local enlargement of ε for test 2 failure bearing; (e) ε of test 3 failure bearing during the whole life; (f) local enlargement of ε for test 3 failure bearing.
Mentions: Secondly, the vector xi is constructed by the four original features and imported into SVDD to compute the monitoring coefficient ε. Figure 8 provides the parameter ε and its local enlargement of three bearings.

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