Parameter estimation and remaining useful life prediction of lubricating oil with HMM

Du, Ying; Wu, Tonghai; Makiš, Viliam

doi:10.1016/j.wear.2016.11.047

Cited by 43 publications

(40 citation statements)

References 14 publications

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“…The PSST performance condition is separated into three states: health, alarm and failure. 5 The feature matrix of PC matrix has different state subspaces between health state and alarm state. 11 Therefore, we used the main angle of basis vector between subspace of health state (S 0 ) and the subspace of alarm state (S t ) to describe the PSST performance condition.…”

Section: Construction Of Wear Degradation Indexmentioning

confidence: 99%

“…Using the data from oil spectral analysis, Zhang et al 4 have predicted the RUL of PSST based on multi-output least squares support vector regression (LS-SVR) method. Du et al 5 have built the healthy state monitoring model of lubrication oil based on hidden Markov model (HMM), and the real-time monitoring of mechanical transmission system has been realized. Liu et al 6 have been established the degradation failure model based on Wiener process.…”

Section: Introductionmentioning

confidence: 99%

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Remaining useful life prediction for power-shift steering transmission based on fusion of multiple oil spectra

Yan

Zheng

2018

Advances in Mechanical Engineering

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Remaining useful life prediction is a critical issue to fault diagnosis and health management of power-shift steering transmission. Power-shift steering transmission wear, which leads to the increase of wear particles and severe wear afterwards, is a slow degradation process, which can be monitored by oil spectral analysis, but the actual degree of the power-shift steering transmission degradation is often difficult to evaluate. The main purpose of this article is to provide a more accurate remaining useful life prediction methodology for power-shift steering transmission compared to relying solely on an individual spectral oil data. Our methodology includes multiple degradation data fusion, degradation index construction, degradation modelling and remaining useful life estimation procedures. First, the robust kernel principal component analysis is used to reduce the data dimension, and the state space model is utilized to construct the wear degradation index. Then, the Wiener process-based degradation model is established based on the constructed degradation index, and the explicit formulas for several important quantities for remaining useful life estimation such as the probability density function and cumulative distribution function are derived. Finally, a case study is presented to demonstrate the applicability of the proposed methodology. The results show that the proposed remaining useful life prediction methodology can objectively describe the power-shift steering transmission degradation law, and the predicted remaining useful life has been extended as 65 Mh (38.2%) compared with specified maintenance interval. This will reduce the maintenance times of power-shift steering transmission life cycle and finally save the maintenance costs.

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Section: Construction Of Wear Degradation Indexmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Remaining useful life prediction for power-shift steering transmission based on fusion of multiple oil spectra

Yan

Zheng

2018

Advances in Mechanical Engineering

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“…where D represents the equidistant sampling time. The Kolmogorov backward differential equation 33 is solved by the aforementioned conversion rate matrix equation (5), and the following probability transfer matrix is obtained where the transition probabilities P ij (t) ¼ P(…”

Section: Crf and Mrl Functions For Rl Predictionmentioning

confidence: 99%

Model parameter estimation and residual life span prediction of pneumatic diaphragm pump based on hidden Markov model in intelligent spraying

Yan

Chen

et al. 2019

International Journal of Advanced Robotic Systems

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Pneumatic diaphragm pump is an important part in intelligent spraying. When pneumatic diaphragm pump does not work normally, the entire intelligent spraying product line will be malfunctioned. To maintain and manage pneumatic diaphragm pump effectively, the grade analysis of the health status of pneumatic diaphragm pump is generally used according to its working state. Due to the effects of condition monitoring and random faults, some observable health predictions are often inaccurate. There are very few papers dealing with the health monitoring of pneumatic diaphragm pump and their estimation of residual life span. In this article, a method with vector autoregressive model and continuous-time hidden Markov model was proposed to analyze and evaluate the life span of pneumatic diaphragm pump based on the estimation error of the health condition and the cumulative deterioration of pneumatic diaphragm pump. It is modeled through a continuous-time Markov chain with three states, which includes unobservable healthy state 0, unobservable warning state 1, and observable fault state 2. The expectation–maximization algorithm is used to estimate the model parameters of the fitted hidden Markov. Through the posterior probability of pneumatic diaphragm pump in warning state 1, the derived conditional reliability function and mean residual life span formula can be calculated to evaluate the residual life span of pneumatic diaphragm pump. The results showed that the method can effectively predict the residual life span of pneumatic diaphragm pump, illustrate the effectiveness of the model, and improve the accuracy of the health status rating.

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“…The use of the RF approach is however limited due to modeling complexity and computational load. Du et al [30] effectively use Vector autoregression and Kalman filter for time series modeling of wear debris concentration data. The residual data obtained from Kalman filter, then work as an input to the Hidden Markov Model for state prediction.…”

Section: Introductionmentioning

confidence: 99%

Lubricating Oil Remaining Useful Life Prediction Using Multi-Output Gaussian Process Regression

Tanwar

Raghavan

2020

IEEE Access

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Lubricant condition monitoring (LCM) is a preferred condition monitoring (CM) technology for fault diagnosis and prognosis owing to its ability to derive a wide range of information from the system (machine/equipment) state and lubricant state. Given the importance of LCM for maintenance decision support, an accurate and reliable remaining useful life (RUL) prediction framework is necessary. The LCM health information in the form of degradation trends is therefore evaluated using numerous statistical, model-based, and artificial intelligence approaches by various researchers. A multitude of factors widely affects the degradation trends viz. operating conditions, environmental variations, oil replenishments, oil loss, chemical breakdown, etc. These factors increase the complexity of the time-series degradation trends making RUL prediction intractable using several of the standard statistical approaches. Therefore, limited research is available on lubricating oil RUL prediction with these influential factors accounted for. Focusing on the complexity of the degradation trend with oil replenishment effects (ORE), we propose the use of the Gaussian process regression (GPR) model for RUL prediction in this study. The model has an advantage over other data-driven approaches as it is a non-parametric Bayesian method. To exploit prior information and historical data collected, the approach is extended to multi-output GPR (MO-GPR) which effectively defines the correlations between historical degradation trends for similar lubrication systems with the current degradation pattern of a system being monitored in real-time. Three different oil replenishment strategies are considered under MO-GPR to demonstrate the applicability and flexibility of this method.

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Parameter estimation and remaining useful life prediction of lubricating oil with HMM

Cited by 43 publications

References 14 publications

Remaining useful life prediction for power-shift steering transmission based on fusion of multiple oil spectra

Remaining useful life prediction for power-shift steering transmission based on fusion of multiple oil spectra

Model parameter estimation and residual life span prediction of pneumatic diaphragm pump based on hidden Markov model in intelligent spraying

Lubricating Oil Remaining Useful Life Prediction Using Multi-Output Gaussian Process Regression

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