Particle Filter-Based Prognostics for an Electrolytic Capacitor Working in Variable Operating Conditions

International audiencePrognostic models should properly take into account the effects of operating conditions on the degradation process and on the signal measurements used for monitoring. In this work, we develop a Particle Filter-based (PF) prognostic model for the estimation of the Remaining Useful Life (RUL) of aluminum electrolytic capacitors used in electrical automotive drives, whose operation is characterized by continuously varying conditions. The capacitor degradation process, which remarkably depends from the temperature experienced by the component, is typically monitored by observing the capacitor Equivalent Series Resistance (ESR). However, the ESR measurement is influenced by the temperature at which the measurement is performed, which changes depending on the operating conditions. To address this problem, we introduce a novel degradation indicator independent from the measurement temperature. Such indicator can, then, be used for the prediction of the capacitor degradation and its RUL. For this, we develop a Particle Filter prognostic model, whose performance is verified on data collected in simulated and experimental degradation tests

Prognostics for an actuator with the combination of support vector regression and particle filter

The accurate prognostics for actuator malfunctions is a challenging task. Developing reliable prognostic methods is vital for providing reasonable preventive maintenance schedules and preventing unexpected failures. Particle filter has been proved to be a traditional approach to deal with actuator prognostic problems. However, the measurement function in the particle filter algorithm cannot be obtained in the prediction process, this paper presents a hybrid framework combining support vector regression (SVR) and particle filter (PF). The SVR output prediction results are employed as the “measurements” for the subsequent PF algorithm. To accomplish the accurate prognostics for actuator fault of civil aircraft, an improved PF based on Kendall correlation coefficient is put forward to solve the problem of particles’ degeneracy. The experimental results are presented, demonstrating that the SVR-PF hybrid approach has satisfactory performance with better prognostics accuracy and higher fault resolution than traditional approaches

Interacting multiple-models, state augmented Particle Filtering for fault diagnostics

International audienceParticle Filtering (PF) is a model-based, filtering technique, which has drawn the attention of the Prognostic and Health Management (PHM) community due to its applicability to nonlinear models with non-additive and non-Gaussian noise. When multiple physical models can describe the evolution of the degradation of a component, the PF approach can be based on Multiple Swarms (MS) of particles, each one evolving according to a different model, from which to select the most accurate a posteriori distribution. However, MS are highly computational demanding due to the large number of particles to simulate. In this work, to tackle the problem we have developed a PF approach based on the introduction of an augmented discrete state identifying the physical model describing the component evolution, which allows to detect the occurrence of abnormal conditions and identifying the degradation mechanism causing it. A crack growth degradation problem has been considered to prove the effectiveness of the proposed method in the detection of the crack initiation and the identification of the occurring degradation mechanism. The comparison of the obtained results with that of a literature MS method and of an empirical statistical test has shown that the proposed method provides both an early detection of the crack initiation, and an accurate and early identification of the degradation mechanism. A reduction of the computational cost is also achieved.

Multiple-Phase Modeling of Degradation Signal for Condition Monitoring and Remaining Useful Life Prediction

Remaining useful life prediction plays an important role in ensuring the safety, availability, and efficiency of various engineering systems. In this paper, we propose a flexible Bayesian multiple-phase modeling approach to characterize degradation signals for prognosis. The priors are specified with a novel stochastic process and the multiple-phase model is formulated to a novel state-space model to facilitate online monitoring and prediction. A particle filtering algorithm with stratified sampling and partial Gibbs resample-move strategy is developed for online model updating and residual life prediction. The advantages of the proposed method are demonstrated through extensive numerical studies and real case studies

A particle filtering and kernel smoothing-based approach for new design component prognostics

International audienceThis work addresses the problem of predicting the Remaining Useful Life (RUL) of components for which a mathematical model describing the component degradation is available, but the values of the model parameters are not known and the observations of degradation trajectories in similar components are unavailable. The proposed approach solves this problem by using a Particle Filtering (PF) technique combined with a Kernel Smoothing (KS) method. This PF-KS method can simultaneously estimate the degradation state and the unknown parameters in the degradation model, while significantly overcoming the problem of particle impoverishment. Based on the updated degradation model (where the unknown parameters are replaced by the estimated ones), the RUL prediction is then performed by simulating future particles evolutions. A numerical application regarding prognostics for Lithium-ion batteries is considered. Various performance indicators measuring precision, accuracy, steadiness and risk of the obtained RUL predictions are computed. The obtained results show that the proposed PF-KS method can provide more satisfactory results than the traditional PF methods

Damage Precursor Based Structural Health Monitoring and Prognostic Framework Using Dynamic Bayesian Network

Structural health monitoring (SHM), as an essential tool to ensure the health integrity of aging structures, mostly focus on monitoring conventional observable damage markers such as fatigue crack size. However, degradation starts and progressively evolves at microstructural levels much earlier than detection of such indicators. This dissertation goes beyond classical approaches and presents a new SHM framework based on evolution of Damage Precursors, when conventional direct damage indicator, such as crack, is unobservable, inaccessible or difficult to measure. Damage precursor is defined in this research as “any detectable variation in material/ physical properties of the component that can be used to infer the evolution of the hidden/ inaccessible/ unmeasurable damage during the degradation”. Accordingly, the degradation process is to be expressed based on progression of damage precursor through time and the damage state assessment would be updated by incorporating multiple different evidences. Therefore, this research proposes a systematic integration approach through Dynamic Bayesian Network (DBN) to include all the evidences and their relationships. The implementation of augmented particle filtering as a stochastic inference method inside DBN enables estimating both model parameters and damage states simultaneously in light of various evidences. Incorporating different sources of information in DBN entails advance techniques to identify and formulate the possible interaction between potentially non-homogenous variables. This research uses the Support Vector Regression (SVR) in order to define generally unknown nonparametric and nonlinear correlation between some of the variables in the DBN structure. Additionally, the particle filtering algorithm is studied more fundamentally in this research and a modified approach called “fully adaptive particle filtering” is proposed with the idea of online updating not only the state process model but also the measurement model. This new approach improves the ability of SHM in real-time diagnostics and prognostics. The framework is successfully applied to damage estimation and prediction in two real-world case studies of 1) crack initiation in a metallic alloy under fatigue and, 2) damage estimation and prognostics in composite materials under fatigue. The proposed framework is intended to be general and comprehensive such that it can be implemented in different applications

Prediction of Infectious Disease outbreaks based on limited information

The last two decades have seen several large-scale epidemics of international impact, including human, animal and plant epidemics. Policy makers face health challenges that require epidemic predictions based on limited information. There is therefore a pressing need to construct models that allow us to frame all available information to predict an emerging outbreak and to control it in a timely manner. The aim of this thesis is to develop an early-warning modelling approach that can predict emerging disease outbreaks. Based on Bayesian techniques ideally suited to combine information from different sources into a single modelling and estimation framework, I developed a suite of approaches to epidemiological data that can deal with data from different sources and of varying quality. The SEIR model, particle filter algorithm and a number of influenza-related datasets were utilised to examine various models and methodologies to predict influenza outbreaks. The data included a combination of consultations and diagnosed influenza-like illness (ILI) cases for five influenza seasons. I showed that for the pandemic season, different proxies lead to similar behaviour of the effective reproduction number. For influenza datasets, there exists a strong relationship between consultations and diagnosed datasets, especially when considering time-dependent models. Individual parameters for different influenza seasons provided similar values, thereby offering an opportunity to utilise such information in future outbreaks. Moreover, my findings showed that when the temperature drops below 14°C, this triggers the first substantial rise in the number of ILI cases, highlighting that temperature data is an important signal to trigger the start of the influenza epidemic. Further probing was carried out among Maltese citizens and estimates on the under-reporting rate of the seasonal influenza were established. Based on these findings, a new epidemiological model and framework were developed, providing accurate real-time forecasts with a clear early warning signal to the influenza outbreak. This research utilised a combination of novel data sources to predict influenza outbreaks. Such information is beneficial for health authorities to plan health strategies and control epidemics

Aplicacion de modelos matematicos para el mantenimiento predictivo

149 p.La presente memoria de tesis presenta una revisión sobre la actividad de investigación aplicada que se ha realizado mediante varios proyectos relacionados con el mantenimiento predictivo asociado a procesos industriales. Uno de los resultados principales es la realización de una herramienta web que permite al operador consultar el tiempo estimado hasta el fallo en un proceso de mecanizado y junto a ello un histórico de datos del sistema. Se han obtenido otros resultados que generan una evolución en el mantenimiento de los sistemas estudiados, lo que reduce el coste y aumenta la productividad de estos. Para ello se han aplicado metodologías híbridas donde el objetivo principal radicaba en la creación de una metodología de mantenimiento predictivo para cada uno de los procesos y en algún caso la posibilidad de generalización de esta a procesos similares