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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

Original features and comprehensive indexes of outer-race defect: (a) RMS; (b) SRA; (c) AAV; (d) Kurtosis factor; (e) ε; (f) ε̄; (g) DSI.
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f15-sensors-12-10109: Original features and comprehensive indexes of outer-race defect: (a) RMS; (b) SRA; (c) AAV; (d) Kurtosis factor; (e) ε; (f) ε̄; (g) DSI.

Mentions: Let fm = 0 Hz, T = 1/fi = 0.061 s to simulate the outer-race defect and let fm = fr = 25 Hz, T = 1/fi = 0.042 s to simulate the inner-race defect. The natural frequency fn = 2,000 Hz. Fifty samples are simulated for each defect. Each simulation sample contains 32,768 points. The initial defects of the outer-race defect and inner-race defect are at about the 40th sample. The original features and comprehensive indexes of outer-race defect are shown in Figure 15. The initial defect is considered as the 41st sample by Kurtosis factor. The randomness of original features is big due to the strong noise in the time-domain waveform. The new initial defect location is determined at the 37th sample by the threshold ε > 0 and earlier than that determined by Kurtosis factor. The failure of outer-race defect appears at the 48th sample by the threshold DSI ≤ 4. The similar rule is seen for the inner-race defect. Kurtosis factor determines the initial defect at 41st sample, while the fuzzy-monitoring coefficient confirms the time at 39th sample. The final failure of inner-race defect is confirmed at 48th sample by DSI. Besides, the parameters ε and ε̄ both have many wild points as result of the randomness of original features. DSI is an excellent index which effectively reflects the irreversible development of the bearing defect.


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)

Original features and comprehensive indexes of outer-race defect: (a) RMS; (b) SRA; (c) AAV; (d) Kurtosis factor; (e) ε; (f) ε̄; (g) DSI.
© Copyright Policy
Related In: Results  -  Collection

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

f15-sensors-12-10109: Original features and comprehensive indexes of outer-race defect: (a) RMS; (b) SRA; (c) AAV; (d) Kurtosis factor; (e) ε; (f) ε̄; (g) DSI.
Mentions: Let fm = 0 Hz, T = 1/fi = 0.061 s to simulate the outer-race defect and let fm = fr = 25 Hz, T = 1/fi = 0.042 s to simulate the inner-race defect. The natural frequency fn = 2,000 Hz. Fifty samples are simulated for each defect. Each simulation sample contains 32,768 points. The initial defects of the outer-race defect and inner-race defect are at about the 40th sample. The original features and comprehensive indexes of outer-race defect are shown in Figure 15. The initial defect is considered as the 41st sample by Kurtosis factor. The randomness of original features is big due to the strong noise in the time-domain waveform. The new initial defect location is determined at the 37th sample by the threshold ε > 0 and earlier than that determined by Kurtosis factor. The failure of outer-race defect appears at the 48th sample by the threshold DSI ≤ 4. The similar rule is seen for the inner-race defect. Kurtosis factor determines the initial defect at 41st sample, while the fuzzy-monitoring coefficient confirms the time at 39th sample. The final failure of inner-race defect is confirmed at 48th sample by DSI. Besides, the parameters ε and ε̄ both have many wild points as result of the randomness of original features. DSI is an excellent index which effectively reflects the irreversible development of the bearing defect.

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