Modelling and measurement of wear particle flow in a dual oil filter system for condition monitoring

Henneberg, Morten; Eriksen, René Lynge; Fich, Jens

doi:10.1016/j.wear.2016.05.017

Cited by 9 publications

(5 citation statements)

References 17 publications

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“…The quasi-stationary phase provided information on the dominant wear mode, whereas wear particle quantity governed the transition from the quasi-stationary phase to the breakdown phase [7]. In another attempt, three operational states based on flow in dual filters were proposed by Henneberg et al [8]. Mohamed et al [9] that predicted the remaining useful life of lubricating oil, considering the first passage time approach for degradation trajectory and continuous-time Markov chain for the dynamic operating environment.…”

Section: Lubricating Condition Monitoring (Lcm)mentioning

confidence: 99%

Multistate Diagnosis and Prognosis of Lubricating Oil Degradation Using Sticky Hierarchical Dirichlet Process–Hidden Markov Model Framework

2021

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In this study, we present a state-based diagnostic and prognostic methodology for lubricating oil degradation based on a nonparametric Bayesian approach, i.e., sticky hierarchical Dirichlet process–hidden Markov model (HDP-HMM). An accurate health state-space assessment for diagnostics and prognostics has always been unobservable and hypothetical in the past. The lubrication condition monitoring (LCM) data is generally segregated as “healthy or unhealthy”, representing a binary state-based perspective to the problem. This two-state performance-based formulation poses limitations to the precision and accuracy of the diagnosis and prognosis for real data wherein there may be multiple states of discrete performance that are characteristic of the system functionality. In particular, the reversible and nonlinear time-series trends of degradation data increase the complexity of state-based modeling. We propose a multistate diagnostic and prognostic framework for LCM data in the wear-out phase (i.e., the unhealthy portion of degradation data), accounting for irregular oil replenishment and oil change effects (i.e., nonlinearity in the degradation signal). The LCM data is simulated for an elementary mechanical system with four components. The sticky HDP sets the prior for the HMM parameters. The unsupervised learning over infinite observations and emission reveals four discrete health states and helps estimate the associated state transition probabilities. The inferred state sequence provides information relating to the state dynamics, which provides further guidance to maintenance decision making. The decision making is further backed by prognostics based on the conditional reliability function and mean residual life estimation.

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Section: Lubricating Condition Monitoring (Lcm)mentioning

confidence: 99%

Multistate Diagnosis and Prognosis of Lubricating Oil Degradation Using Sticky Hierarchical Dirichlet Process–Hidden Markov Model Framework

2021

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“…In actual online inductive wear debris monitoring, the problems faced mainly include: interference (mainly including electromagnetic interference, vibration and temperature), wear debris depositing, and impurities [11–14].…”

Section: Difficulties and Shortage Of Online Inductive Wear Debris mentioning

confidence: 99%

Review on online inductive wear debris monitoring technology

Chen

Wang

et al. 2019

J. eng.

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“…The solved problem of numerical analysis of electromagnetic characteristics analysis includes the positive problem and inverse problem. The positive problem of numerical analysis in inductive wear debris monitoring is to reveal the electromagnetic characteristics overtime and space distribution in electromagnetic fields [10–16].…”

Section: Solved Problem Of Numerical Analysis Of Electromagnetic Chmentioning

confidence: 99%

Review on numerical analysis of electromagnetic characteristics for ferromagnetic wear debris

Chen

Wang

et al. 2019

J. eng.

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Modelling and measurement of wear particle flow in a dual oil filter system for condition monitoring

Cited by 9 publications

References 17 publications

Multistate Diagnosis and Prognosis of Lubricating Oil Degradation Using Sticky Hierarchical Dirichlet Process–Hidden Markov Model Framework

Multistate Diagnosis and Prognosis of Lubricating Oil Degradation Using Sticky Hierarchical Dirichlet Process–Hidden Markov Model Framework

Review on online inductive wear debris monitoring technology

Review on numerical analysis of electromagnetic characteristics for ferromagnetic wear debris

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