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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

The Time-domain waveforms and Hilbert spectrums of test 2 failure bearing: (a) Time-domain waveform on 4,000 min; (b) Hilbert spectrum on 4,000 min; (c) Time-domain waveform on 10,710 min; (d) Hilbert spectrum on 10,710 min; (e) Time-domain waveform on 11,400 min; (f) Hilbert spectrum on 11,400 min.
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f10-sensors-12-10109: The Time-domain waveforms and Hilbert spectrums of test 2 failure bearing: (a) Time-domain waveform on 4,000 min; (b) Hilbert spectrum on 4,000 min; (c) Time-domain waveform on 10,710 min; (d) Hilbert spectrum on 10,710 min; (e) Time-domain waveform on 11,400 min; (f) Hilbert spectrum on 11,400 min.

Mentions: The left subgraphs are the time domain waveforms at different moments, and the right subgraghs are their corresponding Hilbert spectra. The moment of 4,000 min is the normal stage for the test 1 failure bearing, and the fault characteristic frequencies are not seen in Figure 9(b). A characteristic frequency of 234.7 Hz close to the inner-race defect frequency appears in Figure 9(d), and gradually increases along with the damage development in Figure 9(f,h). Therefore, the moment of 9,860 min could be more suitable as the initial defect time for the test 1 failure bearing. The two initial defect times of test 2 failure bearing are equivalent. The rolling element defect frequency and its second harmonic appear at 10,710 min in Figure 10(d). The contrast experiment of test 3 failure bearing provides the same results as that of test 1 failure bearing. The time determined by εi > 0, 3,040 min, is more suitable as the degradation beginning time. However, the monitoring coefficient ε is not the ideal indicator. First of all, the parameter ε is oscillating, while the actual damages are irreversible. Then the monitoring coefficient ε increases slowly with time, which is not able to reflect the accelerated relationship between the damage development and the running time perfectly. That could be as a result of the fuzziness of the damage quantitative. SVDD might need an improvement to deal with the fuzzy 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)

The Time-domain waveforms and Hilbert spectrums of test 2 failure bearing: (a) Time-domain waveform on 4,000 min; (b) Hilbert spectrum on 4,000 min; (c) Time-domain waveform on 10,710 min; (d) Hilbert spectrum on 10,710 min; (e) Time-domain waveform on 11,400 min; (f) Hilbert spectrum on 11,400 min.
© Copyright Policy
Related In: Results  -  Collection

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

f10-sensors-12-10109: The Time-domain waveforms and Hilbert spectrums of test 2 failure bearing: (a) Time-domain waveform on 4,000 min; (b) Hilbert spectrum on 4,000 min; (c) Time-domain waveform on 10,710 min; (d) Hilbert spectrum on 10,710 min; (e) Time-domain waveform on 11,400 min; (f) Hilbert spectrum on 11,400 min.
Mentions: The left subgraphs are the time domain waveforms at different moments, and the right subgraghs are their corresponding Hilbert spectra. The moment of 4,000 min is the normal stage for the test 1 failure bearing, and the fault characteristic frequencies are not seen in Figure 9(b). A characteristic frequency of 234.7 Hz close to the inner-race defect frequency appears in Figure 9(d), and gradually increases along with the damage development in Figure 9(f,h). Therefore, the moment of 9,860 min could be more suitable as the initial defect time for the test 1 failure bearing. The two initial defect times of test 2 failure bearing are equivalent. The rolling element defect frequency and its second harmonic appear at 10,710 min in Figure 10(d). The contrast experiment of test 3 failure bearing provides the same results as that of test 1 failure bearing. The time determined by εi > 0, 3,040 min, is more suitable as the degradation beginning time. However, the monitoring coefficient ε is not the ideal indicator. First of all, the parameter ε is oscillating, while the actual damages are irreversible. Then the monitoring coefficient ε increases slowly with time, which is not able to reflect the accelerated relationship between the damage development and the running time perfectly. That could be as a result of the fuzziness of the damage quantitative. SVDD might need an improvement to deal with the fuzzy 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