Model-based prognosis using an explicit degradation model and Inverse FORM for uncertainty propagation

International audienceIn this paper, an analytical method issued from the field of reliability analysis is used for prognosis. The inverse first-order reliability method (Inverse FORM) is an uncertainty propagation method that can be adapted to remaining useful life (RUL) calculation. An extended Kalman filter (EKF) is first applied to estimate the current degradation state of the system, then the Inverse FORM allows to compute the probability density function (pdf) of the RUL. In the proposed Inverse FORM methodology, an analytical or numerical solution to the differential equation that describes the evolution of the system degradation is required to calculate the RUL model. In this work, the method is applied to a Paris fatigue crack growth model, and then compared to filter-based methods such as EKF and particle filter using performance evaluation metrics (precision, accuracy and timeliness). The main advantage of the Inverse FORM is its ability to compute the pdf of the RUL at a lower computational cost

Remaining Useful Life Prediction and Uncertainty Quantification of Proton Exchange Membrane Fuel Cell Under Variable Load

International audienceAlthough, the proton exchange membrane fuel cell is a promising clean and efficient energy converter that can be used to power an entire building in electricity and heat in a combined manner, it suffers from a limited lifespan due to degradation mechanisms. As a consequence, in the past years, researches have been conducted to estimate the state of health and now the remaining useful life (RUL) in order to extend the life of such devices. However, the developed methods are unable to perform prognostics with an online uncertainty quantification due to the computational cost. This paper aims at tackling this issue by proposing an observer-based prognostic algorithm. An extended Kalman filter estimates the actual state of health and the dynamic of the degradation with the associated uncertainty. An inverse first-order reliability method is used to extrapolate the state of health until a threshold is reached, for which the RUL is given with a 90% confidence interval. The global method is validated using a simulation model built from degradation data. Finally, the algorithm is tested on a dataset coming from a long-term experimental test on an eight-cell fuel cell stack subjected to a variable power profile

Converter based electrochemical impedance spectroscopy for fuel cell stacks

Fuel cells are important devices in a hydrogen-based chain of energy conversion. They have distinctive advantages over batteries with their higher energy density and faster refueling speed, which make them attractive in stationary power supplies and heavy-duty vehicles. However, the high cost and low durability associated with modern fuel cells are still hindering their wider commercialization. Besides developing more reliable and lower cost materials and advanced assemblies of cells and stacks, a practical and effective diagnostic tool is highly needed for fuel cells to identify any abnormal internal conditions and assist with maintenance scheduling or application of on-board mitigating schemes. Conventionally, linear instruments were used for fuel cell EIS, however, limited to single cells or short stacks only as a laboratory testing method. With recent developments, EIS enabled by switching power converters are capable of being applied to a high-power stack directly. This approach has the potential for practical field applications such as a servicing tool for fuel cell manufacturers or an on-board diagnostic tool of a moving vehicle. Previous works on converter based EIS have made a few different attempts at conceptually realizing this solution while several significant issues were not well recognized and resolved yet. As such, this thesis explores further on this topic to address the flexibility of EIS perturbation generation, the perturbation frequency range, and the linkage between fuel cell EIS requirements and the converter design to push for its readiness for practical implementations. Several new solutions are proposed and discussed in detail, including a total software approach for existing high-power converters to enable wide-frequency-range EIS, a redesign of the main dc/dc converter enabling wide-frequency-range perturbations, and a separate auxiliary converter as a standalone module for EIS operation. A detailed analysis of oscillations brought by converter based EIS in powertrains is also presented

Advanced data-driven methods for prognostics and life extension of assets using condition monitoring and sensor data.

A considerable number of engineering assets are fast reaching and operating beyond their orignal design lives. This is the case across various industrial sectors, including oil and gas, wind energy, nuclear energy, etc. Another interesting evolution is the on-going advancement in cyber-physical systems (CPS), where assets within an industrial plant are now interconnected. Consequently, conventional ways of progressing engineering assets beyond their original design lives would need to change. This is the fundamental research gap that this PhD sets out to address. Due to the complexity of CPS assets, modelling their failure cannot be simplistically or analytically achieved as was the case with older assets. This research is a completely novel attempt at using advanced analytics techniques to address the core aspects of asset life extension (LE). The obvious challenge in a system with several pieces of disparate equipment under condition monitoring is how to identify those that need attention and prioritise them. To address this gap, a technique which combined machine learning algorithms and practices from reliability-centered maintenance was developed, along with the use of a novel health condition index called the potential failure interval factor (PFIF). The PFIF was shown to be a good indicator of asset health states, thus enabling the categorisation of equipment as “healthy”, “good ” or “soon-to-fail”. LE strategies were then devoted to the vulnerable group labelled “good – monitor” and “soon-to-fail”. Furthermore, a class of artificial intelligence (AI) algorithms known as Bayesian Neural Networks (BNNs) were used in predicting the remaining useful life (RUL) for the vulnerable assets. The novelty in this was the implicit modelling of the aleatoric and epistemic uncertainties in the RUL prediction, thus yielding interpretable predictions that were useful for LE decision-making. An advanced analytics approach to LE decision-making was then proposed, with the novelty of implementing LE as an on-going series of activities, similar to operation and maintenance (O&M). LE strategies would therefore be implemented at the system, sub-system or component level, meshing seamlessly with O&M, albeit with the clear goal of extending the useful life of the overall asset. The research findings buttress the need for a paradigm shift, from conventional ways of implementing LE in the form of a project at the end of design life, to a more systematic approach based on advanced analytics.Shafiee, Mahmood (Associate)PhD in Energy and Powe