Unscented Kalman Filtering for Prognostics Under Varying Operational and Environmental Conditions

Prognostics gained a lot of research attention over the last decade, not the least due to the rise of data-driven prediction models. Also hybrid approaches are being developed that combine physics-based and data-driven models for better performance. However, limited attention is given to prognostics for varying operational and environmental conditions. In fact, varying operational and environmental conditions can significantly influence the remaining useful life of assets. A powerful hybrid tool for prognostics is Bayesian filtering, where a physical degradation model is updated based on realtime data. Although these types of filters are widely studied for prognostics, application for assets in varying conditions is rarely considered in literature. In this paper, it is proposed to apply an unscented Kalman filter for prognostics under varying operational conditions. Four scenarios are described in which a distinction is made between the level in which real-time and future loads are known and between short-term and long-term prognostics. The method is demonstrated on an artificial crack growth case study with frequently changing stress ranges in two different stress profiles. After this specific case, the generic application of the method is discussed. A positioning diagram is presented, indicating in which situations the proposed filter is useful and feasible. It is demonstrated that incorporation of physical knowledge can lead to highly accurate prognostics due to a degradation model in which uncertainty in model parameters is reduced. It is also demonstrated that in case of limited physical knowledge, data can compensate for missing physics to yield reasonable predictions.

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Aircraft Engines Remaining Useful Life Prediction with an Improved Online Sequential Extreme Learning Machine

Applied Sciences ◽

10.3390/app10031062 ◽

2020 ◽

Vol 10 (3) ◽

pp. 1062 ◽

Cited By ~ 1

Author(s):

Tarek Berghout ◽

Leïla-Hayet Mouss ◽

Ouahab Kadri ◽

Lotfi Saïdi ◽

Mohamed Benbouzid

Keyword(s):

Extreme Learning Machine ◽

Life Prediction ◽

Remaining Useful Life ◽

Data Driven ◽

Selection Strategy ◽

Mapping Technique ◽

Model Parameters ◽

Aircraft Engines ◽

Useful Life ◽

Learning Machine

The efficient data investigation for fast and accurate remaining useful life prediction of aircraft engines can be considered as a very important task for maintenance operations. In this context, the key issue is how an appropriate investigation can be conducted for the extraction of important information from data-driven sequences in high dimensional space in order to guarantee a reliable conclusion. In this paper, a new data-driven learning scheme based on an online sequential extreme learning machine algorithm is proposed for remaining useful life prediction. Firstly, a new feature mapping technique based on stacked autoencoders is proposed to enhance features representations through an accurate reconstruction. In addition, to attempt into addressing dynamic programming based on environmental feedback, a new dynamic forgetting function based on the temporal difference of recursive learning is introduced to enhance dynamic tracking ability of newly coming data. Moreover, a new updated selection strategy was developed in order to discard the unwanted data sequences and to ensure the convergence of the training model parameters to their appropriate values. The proposed approach is validated on the C-MAPSS dataset where experimental results confirm that it yields satisfactory accuracy and efficiency of the prediction model compared to other existing methods.

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Adaptive Degradation Prognostic Reasoning by Particle Filter with a Neural Network Degradation Model for Turbofan Jet Engine

Data ◽

10.3390/data3040049 ◽

2018 ◽

Vol 3 (4) ◽

pp. 49 ◽

Cited By ~ 3

Author(s):

Faisal Khan ◽

Omer Eker ◽

Atif Khan ◽

Wasim Orfali

Keyword(s):

Neural Network ◽

Particle Filter ◽

Health Management ◽

Cost Effective ◽

Remaining Useful Life ◽

Operating Conditions ◽

Data Driven ◽

Prediction Errors ◽

Degradation Model ◽

Jet Engine

In the aerospace industry, every minute of downtime because of equipment failure impacts operations significantly. Therefore, efficient maintenance, repair and overhaul processes to aid maximum equipment availability are essential. However, scheduled maintenance is costly and does not track the degradation of the equipment which could result in unexpected failure of the equipment. Prognostic Health Management (PHM) provides techniques to monitor the precise degradation of the equipment along with cost-effective reliability. This article presents an adaptive data-driven prognostics reasoning approach. An engineering case study of Turbofan Jet Engine has been used to demonstrate the prognostic reasoning approach. The emphasis of this article is on an adaptive data-driven degradation model and how to improve the remaining useful life (RUL) prediction performance in condition monitoring of a Turbofan Jet Engine. The RUL prediction results show low prediction errors regardless of operating conditions, which contrasts with a conventional data-driven model (a non-parameterised Neural Network model) where prediction errors increase as operating conditions deviate from the nominal condition. In this article, the Neural Network has been used to build the Nominal model and Particle Filter has been used to track the present degradation along with degradation parameter.

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Failure Prognosis Based on Relevant Measurements Identification and Data-Driven Trend-Modeling: Application to a Fuel Cell System

Processes ◽

10.3390/pr9020328 ◽

2021 ◽

Vol 9 (2) ◽

pp. 328

Author(s):

Mohand Djeziri ◽

Oussama Djedidi ◽

Samir Benmoussa ◽

Marc Bendahan ◽

Jean-Luc Seguin

Keyword(s):

Fuel Cells ◽

Estimation Error ◽

Clean Energy ◽

Degradation Process ◽

Cell System ◽

Remaining Useful Life ◽

Operating Conditions ◽

Data Driven ◽

Physical Knowledge ◽

Failure Prognosis

Fuel cells are key elements in the transition to clean energy thanks to their neutral carbon footprint, as well as their great capacity for the generation of electrical energy by oxidizing hydrogen. However, these cells operate under straining conditions of temperature and humidity that favor degradation processes. Furthermore, the presence of hydrogen—a highly flammable gas—renders the assessment of their degradations and failures crucial to the safety of their use. This paper deals with the combination of physical knowledge and data analysis for the identification of health indices (HIs) that carry information on the degradation process of fuel cells. Then, a failure prognosis method is achieved through the trend modeling of the identified HI using a data-driven and updatable state model. Finally, the remaining useful life is predicted through the calculation of the times of crossing of the predicted HI and the failure threshold. The trend model is updated when the estimation error between the predicted and measured values of the HI surpasses a predefined threshold to guarantee the adaptation of the prediction to changes in the operating conditions of the system. The effectiveness of the proposed approach is demonstrated by evaluating the obtained experimental results with prognosis performance analysis techniques.

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New Adaptive Prognostics Approach Based on Hybrid Feature Selection with Application to Point Machine Monitoring

Annual Conference of the PHM Society ◽

10.36001/phmconf.2018.v10i1.500 ◽

2018 ◽

Vol 10 (1) ◽

Author(s):

Vepa Atamuradov ◽

Kamal Medjaher ◽

Pierre Dersin ◽

Noureddine Zerhouni ◽

Fatih Camci

Keyword(s):

Feature Selection ◽

Feature Selection Method ◽

Remaining Useful Life ◽

Parameter Estimates ◽

Model Parameters ◽

Weighted Likelihood ◽

Degradation Model ◽

Machine Monitoring ◽

Useful Life ◽

Weighted Combination

This paper proposes a new adaptive prognostics approach consisting of hybrid feature selection and remaining-useful-life (RUL) estimation steps for railway point machines. In step-1, different time-domain based features are extracted and the best ones are selected by the hybrid feature selection method. Then, a degradation model is fitted to each of the selected features and the parameters are estimated. In step-2, the RUL of the component is predicted by using the proposed adaptive prognostics approach. The adaptive prognostics is based on the weighted likelihood combination of the estimated model parameters. The model parameters each of which estimated by curve fitting are used in the calculation of the likelihood probability weights. Then, an adaptive degradation model is built by using the weighted combination of the model parameter estimates and the component RUL is estimated. The proposed approach is validated on in-field point machine sliding-chair degradation and the results are discussed.

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Remaining useful life prediction of cylinder liner based on nonlinear degradation model

Eksploatacja i Niezawodnosc - Maintenance and Reliability ◽

10.17531/ein.2022.1.8 ◽

2021 ◽

Vol 24 (1) ◽

pp. 62-69

Author(s):

Jianxiong Kang ◽

Yanjun Lu ◽

Bin Zhao ◽

Hongbo Luo ◽

Jiacheng Meng ◽

...

Keyword(s):

Estimation Method ◽

Cylinder Liner ◽

Likelihood Estimation ◽

Remaining Useful Life ◽

Model Parameters ◽

Operational Environment ◽

Unknown Parameters ◽

Degradation Model ◽

Proposed Model ◽

Useful Life

In order to effectively monitor the wear and predict the life of cylinder liner, a nonlinear degradation model with multi-source uncertainty based on Wiener process is established to evaluate the remaining useful life (RUL) of cylinder liner wear. Due to complex service performance of cylinder liner, the uncertainty of operational environment and working conditions of cylinder liner wear are considered into the model by a random function. The probability density function (PDF) formula of RUL is derived, and the maximum likelihood estimation method is adopted to estimate the unknown parameters of PDF. Considering the evaluated parameters as the initial values, the model parameters are updated adaptively, and an adaptive PDF is obtained. Furthermore, the proposed model is compared with two classical degradation models. The results show that the proposed model has a good performance for predicting the life, and the error is within 5%. The method can provide a reference for condition monitoring of cylinder liner wear.

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Remaining Useful Life Estimation of Bearings Using Data-Driven Ridge Regression

Applied Sciences ◽

10.3390/app10248977 ◽

2020 ◽

Vol 10 (24) ◽

pp. 8977

Author(s):

Pangun Park ◽

Mingyu Jung ◽

Piergiuseppe Di Marco

Keyword(s):

Time Series ◽

Ridge Regression ◽

Sliding Window ◽

Remaining Useful Life ◽

Data Driven ◽

Series Data ◽

Degradation Model ◽

Time Frequency ◽

Model Based ◽

Useful Life

Predicting the remaining useful life (RUL) of mechanical bearings is a challenging industrial task since RUL can differ even for the same equipment due to many uncertainties such as operating condition, model inaccuracy, and sensory noise in various industrial applications. This paper proposes the RUL prediction method combining analytical model-based and data-driven approaches to forecast when a failure will occur based on the time series data of bearings. Feature importance ranking and principal component analysis construct a reliable and predictable health indicator from various statistical time, frequency, and time–frequency domain features of the observed signal. The adaptive sliding window method then optimizes the parameters of the degradation model based on the ridge regression of the time series sequence with the sliding window. The proposed adaptive scheme provides significant performance improvement in terms of the RUL estimation accuracy and robustness against the possible errors of the degradation model compared to the traditional Bayesian approaches.

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A probabilistic approach to remaining useful life prediction of rolling element bearings

Structural Health Monitoring ◽

10.1177/1475921718758517 ◽

2018 ◽

Vol 18 (2) ◽

pp. 466-485 ◽

Cited By ~ 7

Author(s):

Guru Prakash ◽

Sriram Narasimhan ◽

Mahesh D. Pandey

Keyword(s):

Fault Detection ◽

Change Point ◽

Probabilistic Approach ◽

Remaining Useful Life ◽

Model Parameters ◽

Rolling Element Bearings ◽

Two Phase ◽

Degradation Model ◽

Rolling Element ◽

Surrogate Measures

In this article, we present a probabilistic approach for fault detection and prognosis of rolling element bearings based on a two-phase degradation model. One of the main issues in dealing with bearing degradation is that the degradation mechanism is unobservable and can only be inferred through appropriate surrogate measures obtained from indirect sensory measurements. Furthermore, the stochastic nature of the degradation path renders fault detection and estimating the end-of-life characteristics from such data extremely challenging. When such components are a part of a larger system, the exact degradation path depends on both the operating and loading conditions, which means that the most effective condition monitoring approach should estimate the degradation model parameters under operational conditions, and not solely from isolated component testing or historical information. Motivated by these challenges, a two-phase degradation model using surrogate measures of degradation from vibration measurements is proposed and a Bayesian approach is used to estimate the model parameters. The underlying methodology involves using priors from historical data, while the posterior calculations are undertaken using surrogate measures obtained from a monitored unit combined with the aforesaid priors. The problem of fault detection is posed as a change point location problem. This allows the prior knowledge obtained from the past failures to be integrated for maintenance planning of a currently working unit in a systematic way. The correlation between the degradation rate and the time of occurrence of the change point, an often overlooked aspect in prognosis, is also considered in here. A numerical example and a case study are presented to illustrate the overall methodology and the results obtained using this approach.

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Remaining Useful Life Prediction of MOSFETs via the Takagi–Sugeno Framework

Energies ◽

10.3390/en14082135 ◽

2021 ◽

Vol 14 (8) ◽

pp. 2135

Author(s):

Marcin Witczak ◽

Marcin Mrugalski ◽

Bogdan Lipiec

Keyword(s):

Historical Data ◽

Field Effect Transistors ◽

Failure Process ◽

Practical Method ◽

Remaining Useful Life ◽

Data Driven ◽

Data Repository ◽

Uncertainty Interval ◽

Useful Life ◽

Takagi Sugeno

The paper presents a new method of predicting the remaining useful life of technical devices. The proposed soft computing approach bridges the gap between analytical and data-driven health prognostic approaches. Whilst the former ones are based on the classical exponential shape of degradation, the latter ones learn the degradation behavior from the observed historical data. As a result of the proposed fusion, a practical method for calculating components’ remaining useful life is proposed. Contrarily to the approaches presented in the literature, the proposed ensemble of analytical and data-driven approaches forms the uncertainty interval containing an expected remaining useful life. In particular, a Takagi–Sugeno multiple models-based framework is used as a data-driven approach while an exponential curve fitting on-line approach serves as an analytical one. Unlike conventional data-driven methods, the proposed approach is designed on the basis of the historical data that apart from learning is also applied to support the diagnostic decisions. Finally, the entire scheme is used to predict power Metal Oxide Field Effect Transistors’ (MOSFETs) health status. The status of the currently operating MOSFET is determined taking into consideration the knowledge obtained from the preceding MOSFETs, which went through the run-to-failure process. Finally, the proposed approach is validated with the application of real data obtained from the NASA Ames Prognostics Data Repository.

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Remaining Useful Life Prediction of Broken Rotor Bar Based on Data-Driven and Degradation Model

Applied Sciences ◽

10.3390/app11167175 ◽

2021 ◽

Vol 11 (16) ◽

pp. 7175

Author(s):

Islem Bejaoui ◽

Dario Bruneo ◽

Maria Gabriella Xibilia

Keyword(s):

Principal Component Analysis ◽

Induction Motor ◽

Life Prediction ◽

Principal Component ◽

Component Analysis ◽

Remaining Useful Life ◽

Degradation Model ◽

Broken Rotor Bar ◽

Industrial Sectors ◽

Useful Life

Rotating machines such as induction motors are crucial parts of most industrial systems. The prognostic health management of induction motor rotors plays an essential role in increasing electrical machine reliability and safety, especially in critical industrial sectors. This paper presents a new approach for rotating machine fault prognosis under broken rotor bar failure, which involves the modeling of the failure mechanism, the health indicator construction, and the remaining useful life prediction. This approach combines signal processing techniques, inherent metrics, and principal component analysis to monitor the induction motor. Time- and frequency-domains features allowing for tracking the degradation trend of motor critical components that are extracted from torque, stator current, and speed signals. The most meaningful features are selected using inherent metrics, while two health indicators representing the degradation process of the broken rotor bar are constructed by applying the principal component analysis. The estimation of the remaining useful life is then obtained using the degradation model. The performance of the prediction results is evaluated using several criteria of prediction accuracy. A set of synthetic data collected from a degraded Simulink model of the rotor through simulations is used to validate the proposed approach. Experimental results show that using the developed prognostic methodology is a powerful strategy to improve the prognostic of induction motor degradation.

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Particle Filtering Based Remaining Useful Life Prediction for Electromagnetic Coil Insulation

Sensors ◽

10.3390/s21020473 ◽

2021 ◽

Vol 21 (2) ◽

pp. 473

Author(s):

Haifeng Guo ◽

Aidong Xu ◽

Kai Wang ◽

Yue Sun ◽

Xiaojia Han ◽

...

Keyword(s):

Particle Filtering ◽

Remaining Useful Life ◽

Electrical Parameters ◽

Degradation Model ◽

Accelerated Degradation ◽

Insulation Failure ◽

Electromagnetic Coils ◽

Degradation Monitoring ◽

Insulation Degradation ◽

Useful Life

Electromagnetic coils are one of the key components of many systems. Their insulation failure can have severe effects on the systems in which coils are used. This paper focuses on insulation degradation monitoring and remaining useful life (RUL) prediction of electromagnetic coils. First, insulation degradation characteristics are extracted from coil high-frequency electrical parameters. Second, health indicator is defined based on insulation degradation characteristics to indicate the health degree of coil insulation. Finally, an insulation degradation model is constructed, and coil insulation RUL prediction is performed by particle filtering. Thermal accelerated degradation experiments are performed to validate the RUL prediction performance. The proposed method presents opportunities for predictive maintenance of systems that incorporate coils.

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