Distance-based analysis of dynamical systems reconstructed from vibrations for bearing diagnostics

Ng, Selina S. Y.; Cabrera, Javier; Tse, Peter W.; Chen, Allison H.; Tsui, Kwok Leung

doi:10.1007/s11071-014-1857-4

Cited by 11 publications

(2 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As the most common machine elements, rolling bearings are playing an exceptionally vital role in almost all kinds of rotating machinery. 1,2 Rolling bearings are as such used in industries at length due to their relatively lower price and operational ease. The manoeuvre of rotating machinery, just like an aeroengine, is entirely dependent upon the health state of rolling bearings which accounts for almost 45%–55% of these equipment failures.…”

Section: Introductionmentioning

confidence: 99%

Multi-feature entropy distance approach with vibration and acoustic emission signals for process feature recognition of rolling element bearing faults

Fei

Choy

Bai

et al. 2017

Structural Health Monitoring

View full text Add to dashboard Cite

To accurately reveal rolling bearing operating status, multi-feature entropy distance method was proposed for the process character analysis and diagnosis of rolling bearing faults by the integration of four information entropies in time domain, frequency domain and time–frequency domain and two kinds of signals including vibration signals and acoustic emission signals. The multi-feature entropy distance method was investigated and the basic thought of rolling bearing fault diagnosis with multi-feature entropy distance method was given. Through rotor simulation test rig, the vibration and acoustic emission signals of six rolling bearing faults (ball fault, inner race fault, outer race fault, inner ball faults, inner–outer faults and normal) are gained under different rotational speeds. In the view of the multi-feature entropy distance method, the process diagnosis of rolling bearing faults was implemented. The analytical results show that multi-feature entropy distance fully reflects the process feature of rolling bearing faults with the change of rotating speed; the multi-feature entropy distance with vibration and acoustic emission signals better reports signal features than single type of signal (vibration or acoustic emission signal) in rolling bearing fault diagnosis; the proposed multi-feature entropy distance method holds high diagnostic precision and strong robustness (anti-noise capacity). This study provides a novel and useful methodology for the process feature extraction and fault diagnosis of rolling element bearings and other rotating machinery.

show abstract

Section: Introductionmentioning

confidence: 99%

Multi-feature entropy distance approach with vibration and acoustic emission signals for process feature recognition of rolling element bearing faults

Fei

Choy

Bai

et al. 2017

Structural Health Monitoring

View full text Add to dashboard Cite

show abstract

“…Recently, a novel earth mover distance-based bearing performance degradation assessment method [ 24 ] was proposed by one of the co-authors to describe bearing deterioration over time. Even though the aforementioned method is effective in evaluating the current health condition of bearings, it has some limitations, illustrated as follows.…”

Section: Introductionmentioning

confidence: 99%

State Space Formulation of Nonlinear Vibration Responses Collected from a Dynamic Rotor-Bearing System: An Extension of Bearing Diagnostics to Bearing Prognostics

Tse

Wang

2017

Sensors

View full text Add to dashboard Cite

Bearings are widely used in various industries to support rotating shafts. Their failures accelerate failures of other adjacent components and may cause unexpected machine breakdowns. In recent years, nonlinear vibration responses collected from a dynamic rotor-bearing system have been widely analyzed for bearing diagnostics. Numerous methods have been proposed to identify different bearing faults. However, these methods are unable to predict the future health conditions of bearings. To extend bearing diagnostics to bearing prognostics, this paper reports the design of a state space formulation of nonlinear vibration responses collected from a dynamic rotor-bearing system in order to intelligently predict bearing remaining useful life (RUL). Firstly, analyses of nonlinear vibration responses were conducted to construct a bearing health indicator (BHI) so as to assess the current bearing health condition. Secondly, a state space model of the BHI was developed to mathematically track the health evolution of the BHI. Thirdly, unscented particle filtering was used to predict bearing RUL. Lastly, a new bearing acceleration life testing setup was designed to collect natural bearing degradation data, which were used to validate the effectiveness of the proposed bearing prognostic method. Results show that the prediction accuracy of the proposed bearing prognostic method is promising and the proposed bearing prognostic method is able to reflect future bearing health conditions.

show abstract

Using Dynamics to Analyse Time Series

Lunel

2017

Patterns of Dynamics

View full text Add to dashboard Cite

Distance-based analysis of dynamical systems reconstructed from vibrations for bearing diagnostics

Cited by 11 publications

References 29 publications

Multi-feature entropy distance approach with vibration and acoustic emission signals for process feature recognition of rolling element bearing faults

Multi-feature entropy distance approach with vibration and acoustic emission signals for process feature recognition of rolling element bearing faults

State Space Formulation of Nonlinear Vibration Responses Collected from a Dynamic Rotor-Bearing System: An Extension of Bearing Diagnostics to Bearing Prognostics

Using Dynamics to Analyse Time Series

Contact Info

Product

Resources

About