A dynamic multi-scale Markov model based methodology for remaining life prediction

Yan, Jihong; Guo, Chaozhong; Wang, Xing

doi:10.1016/j.ymssp.2010.10.018

Cited by 56 publications

(41 citation statements)

References 19 publications

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“…HMM or HSMM is not popularly used in prognostics but is gaining more attention in recent years [27,30,91,96,[99][100][101][102][103][104][105]. The technique was first applied to prognostics by Baruah and Chinnam [93].…”

Section: Hidden Markov Model (Hmm) and Hidden Semi-markov Model (Hsmm)mentioning

confidence: 99%

A review on prognostic techniques for non-stationary and non-linear rotating systems

Kan

Tan

Mathew

2015

Mechanical Systems and Signal Processing

318

153

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a b s t r a c tThe field of prognostics has attracted significant interest from the research community in recent times. Prognostics enables the prediction of failures in machines resulting in benefits to plant operators such as shorter downtimes, higher operation reliability, reduced operations and maintenance cost, and more effective maintenance and logistics planning. Prognostic systems have been successfully deployed for the monitoring of relatively simple rotating machines. However, machines and associated systems today are increasingly complex. As such, there is an urgent need to develop prognostic techniques for such complex systems operating in the real world. This review paper focuses on prognostic techniques that can be applied to rotating machinery operating under nonlinear and non-stationary conditions. The general concept of these techniques, the pros and cons of applying these methods, as well as their applications in the research field are discussed. Finally, the opportunities and challenges in implementing prognostic systems and developing effective techniques for monitoring machines operating under nonstationary and non-linear conditions are also discussed.

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Section: Hidden Markov Model (Hmm) and Hidden Semi-markov Model (Hsmm)mentioning

confidence: 99%

A review on prognostic techniques for non-stationary and non-linear rotating systems

Kan

Tan

Mathew

2015

Mechanical Systems and Signal Processing

318

153

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“…10 [20]. Once parameters in HMMs are determined, RUL prediction is fulfilled by forecasting the progression of health states from the current state (the largest likelihood HMM) to the failure state using transition probability between states and sojourn time in each state (the duration of staying in one state) [255].…”

Section: Hidden Markov Modelsmentioning

confidence: 99%

Cloud-enabled prognosis for manufacturing

et al. 2015

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Advanced manufacturing depends on the timely acquisition, distribution, and utilization of information from machines and processes across spatial boundaries. These activities can improve accuracy and reliability in predicting resource needs and allocation, maintenance scheduling, and remaining service life of equipment. As an emerging infrastructure, cloud computing provides new opportunities to achieve the goals of advanced manufacturing. This paper reviews the historical development of prognosis theories and techniques and projects their future growth enabled by the emerging cloud infrastructure. Techniques for cloud computing are highlighted, as well as the influence of these techniques on the paradigm of cloud-enabled prognosis for manufacturing. Finally, this paper discusses the envisioned architecture and associated challenges of cloud-enabled prognosis for manufacturing

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“…For the purpose of verification, some other methods, which are online data-based SVR prediction, historical data-based SVR prediction, and the Markov model-based prediction algorithm of Ref. [14], are used for comparison. The Figs.…”

Section: The Adaptive Predictionmentioning

confidence: 99%

“…12, 13, 14, and 15, respectively, donate the adaptive prediction map, the prediction map of the prediction model based on real-time data, the prediction map of the prediction model based on historical data, and the prediction map of the algorithm of Ref. [14]. The predicted errors are 0.151, 0.331, 0.352, and 0.384, respectively.…”

Section: The Adaptive Predictionmentioning

confidence: 99%

A SVR-Based Remaining Life Prediction for Rolling Element Bearings

Wang

et al. 2015

J Fail. Anal. and Preven.

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A new approach is proposed to construct a reasonable prediction model for prognostic. The Gaussian mixture model-based health indicator is used for degradation performance and help to determine the threshold of the incipient fault. The support vector regression is joined with least mean square algorithm for the construction of the adaptive prediction model based on the historical data and the online monitoring data. According to the failure threshold, the remaining life can be obtained. Through experimental verification, the results show that the selected health index is able to effectively reflect the degradation of rolling bearings, and the proposed model shows great prediction accuracy in comparison to the common one.

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A dynamic multi-scale Markov model based methodology for remaining life prediction

Cited by 56 publications

References 19 publications

A review on prognostic techniques for non-stationary and non-linear rotating systems

A review on prognostic techniques for non-stationary and non-linear rotating systems

Cloud-enabled prognosis for manufacturing

A SVR-Based Remaining Life Prediction for Rolling Element Bearings

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