Estimation of the Remaining Useful Life by using Wavelet Packet Decomposition and HMMs

Diego, Tobon-Mejia,; Medjaher, Kamal; Zerhouni, Noureddine; Tripot, Gérard

doi:10.1109/aero.2011.5747561

Cited by 31 publications

(18 citation statements)

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“…where the quantity ( ), which takes into account the duration dependent forward variable ( ), is calculated through (17). The reader is referred to Rabiner's work [13] for the interpretation on the observation parameters reestimation formulas.…”

Section: Mathematical Problems In Engineeringmentioning

confidence: 99%

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Hidden Semi-Markov Models for Predictive Maintenance

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Lemeire

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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Section: Mathematical Problems In Engineeringmentioning

confidence: 99%

“…To overcome this drawback, a modified version of HMM, which takes into account an estimate of the duration in each state, has been proposed in the works of Tobon-Mejia et al [16][17][18][19]. Thanks to the explicit state sojourn time modeling, it has been shown that it is possible to effectively estimate the RUL for industrial equipment.…”

Section: Introductionmentioning

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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“…4-(a). Note that this classification is not totally binary as it may exist prognostic methods based on the fusion of more than one approach [19], see the In model-based prognostic, the physical component or system and its degradation phenomenon are represented by a set of mathematical laws. The obtained behavioral model is then used to predict the future evolution of the degradation [20,21].…”

Section: Prognostic Approachesmentioning

confidence: 99%

CNC machine tool's wear diagnostic and prognostic by using dynamic Bayesian networks

Diego

Medjaher

Zerhouni

2012

Mechanical Systems and Signal Processing

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199

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The failure of critical components in industrial systems may have negative consequences on the availability, the productivity, the security and the environment. To avoid such situations, the health condition of the physical system, and particularly of its critical components, can be constantly assessed by using the monitoring data to perform on-line system diagnostics and prognostics. The present paper is a contribution on the assessment of the health condition of a Computer Numerical Control (CNC) tool machine and the estimation of its Remaining Useful Life (RUL). The proposed method relies on two main phases: an off-line phase and an on-line phase. During the first phase, the raw data provided by the sensors are processed to extract reliable features. These latter are used as inputs of learning algorithms in order to generate the models that represent the wear's behavior of the cutting tool. Then, in the second phase, which is an assessment one, the constructed models are exploited to identify the tool's current health state, predict its RUL and the associated confidence bounds. The proposed method is applied on a benchmark of condition monitoring data gathered during several cuts of a CNC tool. Simulation results are obtained and discussed at the end of the paper.

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“…With the aim of investigating the case in which a failure condition is unknown during the training phase, we consider bearing monitoring data taken from the NASA benchmark repository [42], which have been used in several other works [37,[43][44][45]. As the available data set does not contain any explicit information about the bearing conditions, we use the detection time of the failure state as a measure of model performance.…”

Section: Real Datamentioning

confidence: 99%

“…It has to be noted that frequency [35] (i.e., Fourier transform) or time-frequency [36,37] (i.e., wavelet transform) domain features could be used as well.…”

Section: Feature Extraction and Dimensionality Reductionmentioning

confidence: 99%

Online Adaptive Bearings Condition Assessment Using Continuous Hidden Markov Models

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Sahli

2014

Advances in Mechanical Engineering

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Condition assessment (CA) of bearings can be performed by using left-right hidden Markov models, because of the monotonically increasing pattern of the bearings degradation. Classical CA approaches assume that all possible system states are fixed and known a priori. The training of the system is performed offline at once with data from all of the system states. These assumptions significantly impede condition assessment applications in case that all the possible states of the system are not known in advance, or changes in environmental or operative conditions occur during the tool's usage. To overcome these limitations, we propose combining left-right continuous HMMs (CHMM) with a change point detection algorithm for (i) estimating, from historical observations, the initial number of the CHMM states and the initial guess of its model parameters and (ii) updating the state space as well as the model parameters during monitoring. Moreover, to deal with multidimensional sensor measurements, we propose using kernel principal component analysis for dimensionality reduction. Qualitative and quantitative evaluations of the proposed methodology have been performed using both simulated and real data from the NASA benchmark repository. Compared to state of the art techniques, the proposed methodology results in (i) an improvement of the HMM training phase in terms of iterations number; (ii) the detection of unknown states at an early stage; and (iii) an effective change of the CHMM's structure to represent the degradation processes more accurately in presence of unknown conditions. IntroductionMachines and equipments breakdown in industrial environments can have significant impact in the profitability of a business. Maintaining the engineering assets in operating conditions is an industrial and economical requirement. Nowadays optimization strategies for maintenance are an important part of research for companies [1,2], since, next to energy costs, maintenance spending due to equipment or machine failure can be the largest part of the operational budget [3].Corrective maintenance [1,4], which is the system maintenance strategy subject of this work, is based on condition monitoring and is performed after a degradation modality emerges in a system, with the goal of preventing the system from failing. A fundamental requirement of this strategy is the detection of the abnormal behavior, that will lead to the system failure, at an early stage in order to give enough time to perform the repair operations.Industrial equipments and their components are often characterized by a very complex structure. For this reason the usage of mathematical models for condition monitoring can represent an arduous task. On the other hand, industrial machines are usually equipped with monitoring devices (sensors placed in critical parts of the system under study) which are able to record and collect large amount of measurements. Given the availability of these huge amounts of data, the methodology proposed in this paper will focus on datadriven mo...

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Estimation of the Remaining Useful Life by using Wavelet Packet Decomposition and HMMs

Cited by 31 publications

References 31 publications

Hidden Semi-Markov Models for Predictive Maintenance

Hidden Semi-Markov Models for Predictive Maintenance

CNC machine tool's wear diagnostic and prognostic by using dynamic Bayesian networks

Online Adaptive Bearings Condition Assessment Using Continuous Hidden Markov Models

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