Nonparametric time series modelling for industrial prognostics and health management

Mosallam, Ahmed; Medjaher, Kamal; Zerhouni, Noureddine

doi:10.1007/s00170-013-5065-z

Cited by 46 publications

(25 citation statements)

References 34 publications

(34 reference statements)

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“…Mosallam et al [34], as detailed in Table 2. For instance, vibration is known to accelerate deterioration, but it is unclear whether this is caused by sudden peaks (i. e., minima or maxima), frequent changes (i. e., standard deviation), or a constantly high tremor (i. e., average).…”

Section: Traditional Machine Learningmentioning

confidence: 99%

Forecasting remaining useful life: Interpretable deep learning approach via variational Bayesian inferences

Kraus

Feuerriegel

2019

Decision Support Systems

103

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Predicting the remaining useful life of machinery, infrastructure, or other equipment can facilitate preemptive maintenance decisions, whereby a failure is prevented through timely repair or replacement. This allows for a better decision support by considering the anticipated time-to-failure and thus promises to reduce costs. Here a common baseline may be derived by fitting a probability density function to past lifetimes and then utilizing the (conditional) expected remaining useful life as a prognostic. This approach finds widespread use in practice because of its high explanatory power.A more accurate alternative is promised by machine learning, where forecasts incorporate deterioration processes and environmental variables through sensor data. However, machine learning largely functions as a black-box method and its forecasts thus forfeit most of the desired interpretability. As our primary contribution, we propose a structured-effect neural network for predicting the remaining useful life which combines the favorable properties of both approaches: its key innovation is that it offers both a high accountability and the flexibility of deep learning. The parameters are estimated via variational Bayesian inferences. The different approaches are compared based on the actual timeto-failure for aircraft engines. This demonstrates the performance and superior interpretability of our method, while we finally discuss implications for decision support.

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Section: Traditional Machine Learningmentioning

confidence: 99%

Forecasting remaining useful life: Interpretable deep learning approach via variational Bayesian inferences

Kraus

Feuerriegel

2019

Decision Support Systems

103

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“…Pronostia is an experimental platform designed and realized at the Automatic Control and Micro-Mechatronic Systems (AS2M) Department of Franche-Comté Electronics, Mechanics, Thermal Processing, Optics-Science and Technology (FEMTO-ST) Institute (http://www.femto-st.fr/) (Besançon, France), with the aim of collecting real data related to accelerated degradation of bearings. Such data are used to validate methods for bearing condition assessment, diagnostic and prognostic [19,[51][52][53][54][55][56][57][58][59].…”

Section: Data Descriptionmentioning

confidence: 99%

Hidden Semi-Markov Models for Predictive Maintenance

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Dimiccoli

et al. 2015

Mathematical Problems in Engineering

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Realistic predictive maintenance approaches are essential for condition monitoring and predictive maintenance of industrial machines. In this work, we propose Hidden Semi-Markov Models (HSMMs) with (i) no constraints on the state duration density function and (ii) being applied to continuous or discrete observation. To deal with such a type of HSMM, we also propose modifications to the learning, inference, and prediction algorithms. Finally, automatic model selection has been made possible using the Akaike Information Criterion. This paper describes the theoretical formalization of the model as well as several experiments performed on simulated and real data with the aim of methodology validation. In all performed experiments, the model is able to correctly estimate the current state and to effectively predict the time to a predefined event with a low overall average absolute error. As a consequence, its applicability to real world settings can be beneficial, especially where in real time the Remaining Useful Lifetime (RUL) of the machine is calculated.

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“…The existing PHM methods can be grouped into three different categories: modelbased [6], data-driven [7,8] and hybrid approaches [9,10]. Model-based approaches attempt to incorporate physical models of the system into the estimation of the RUL.…”

Section: Introductionmentioning

confidence: 99%

A neural network-evolutionary computational framework for remaining useful life estimation of mechanical systems

2019

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This paper presents a framework for estimating the remaining useful life (RUL) of mechanical systems. The framework consists of a multi-layer perceptron and an evolutionary algorithm for optimizing the data-related parameters. The framework makes use of a strided time window to estimate the RUL for mechanical components. Tuning the data-related parameters can become a very time consuming task. The framework presented here automatically reshapes the data such that the efficiency of the model is increased. Furthermore, the complexity of the model is kept low, e.g. neural networks with few hidden layers and few neurons at each layer. Having simple models has several advantages like short training times and the capacity of being in environments with limited computational resources such as embedded systems. The proposed method is evaluated on the publicly available C-MAPSS dataset [1], its accuracy is compared against other state-of-the art methods for the same dataset.

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Nonparametric time series modelling for industrial prognostics and health management

Cited by 46 publications

References 34 publications

Forecasting remaining useful life: Interpretable deep learning approach via variational Bayesian inferences

Forecasting remaining useful life: Interpretable deep learning approach via variational Bayesian inferences

Hidden Semi-Markov Models for Predictive Maintenance

A neural network-evolutionary computational framework for remaining useful life estimation of mechanical systems

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