Decision or controlled processes are of particular interest for maintenance problems, especially for determining optimal control actions to maximize the reward or minimize the cost of an operating system experiencing failures and when its temporal behavior is described by a Markov process or a semi-Markov process. This presentation includes two parts. In the first part, we introduce the...
We present a novel non-parametric method (MNAT) within materials reliability studies to apply the Time-Temperature Superposition (TTS) principle, enabling prediction of long-term viscoelastic material behavior from short-term accelerated tests—critical for polymers, composites, adhesives, and advanced materials where full-scale durability testing spans decades.
This work presents the TTS R...
Studies on condition-based maintenance optimization typically consider systems with known deterioration processes. In this talk, we make the often more realistic assumption of an unknown deterioration process and we discuss various approaches for determining when to carry out preventive maintenance based on limited condition data. Contrary to many existing studies that consider a certain...
Abstract
Process Analytical Technology (PAT) is a regulatory and scientific framework introduced by the Food and Drug Administration (FDA) to promote innovation and efficiency in pharmaceutical manufacturing by enabling in-line, real-time process monitoring and quality control, consistent with quality by design (QbD) principles, rather than solely through conventional laboratory...
I will review recent work where we take an extrinsic manifold fitting point of view, contrary to a manifold learning (i.e., embedding) approach, to develop an on-line monitoring scheme with a simple geometric interpretation which requires neither decorrelation of process dynamics nor dimensionality reduction. The new monitoring framework for online or ``phase II” SPC monitors deviations from...
Ensuring both the safety and operational continuity of smart factories requires reliable real-time hazard detection and energy-efficient predictive maintenance. This work addresses the joint challenge of guaranteeing ultra-reliable, low-latency communication for safety-critical sensors (Factory Safety Detectors - FSDs) while minimizing energy consumption for a large-scale network of equipment...
The increasing availability of high-frequency telemetry data in electric vehicles (EVs) creates new opportunities for monitoring system health. Within the broader context of Prognostics and Health Management (PHM), these data streams have the potential to facilitate the transition from reactive maintenance to evidence-based condition assessment. However, in real-world industrial settings,...
Chronic Venous Insufficiency (CVI) affecting the lower extremities is common among adults. Compression textile products, such as compression socks, play a crucial role in the treatment and prevention of CVI and other lower-limb disorders [1], [2]. By applying controlled external pressure, these products support venous return and alleviate symptoms such as pain, edema, and venous hypertension...
Functional data analysis methods are increasingly used in Prognostic Health Management (PHM) to model degradation from multi-sensor systems. Multivariate Functional Principal Component Analysis (MFPCA) is effective in this context, but its standard formulation assumes that all units are observed over a common time domain. In practice, operational data often exhibit variable domain lengths due...
Long-term equipment degradation decisively affects production cycles of chemical process industries (CPI), and has a major impact on plant safety, operation and economy. Equipment degradation is caused by underlying phenomena that evolve over time with a rate of change that depends on the operating conditions. To tackle this problem, a Latent Differential Regression Analysis (LDRA) methodology...
The increasing availability of AIS (Automatic Identification System) data in the fishing sector offers new opportunities for monitoring and statistical analysis of maritime activity, but it also poses significant methodological challenges. We present an open problem motivated by the study of fishing vessels operating along the coast of Norway, aiming to promote the development and discussion...
Large scientific infrastructures increasingly depend on availability-driven Operations \& Maintenance (O\&M) models, where every additional hour of uptime translates directly into scientific output. In IFMIF-DONES, this link is especially explicit: higher infrastructure availability enables longer irradiation time and therefore greater throughput and quality of materials data for...
Fusion energy is rapidly transitioning from experimental research to a highly capitalized industrial ambition, with private and public actors targeting near-term deployment of fusion power plants. The cumulative funding in private fusion surged to 13 billion EUR since 2020. The fusion private ecosystem is now larger than ever, with 77 companies and growing. However, beyond the well-known...
The motivation of this work is the development of new methods for selection and combination of multi-source reliability data to obtain useful results within the WPENS project for the IFMIF-DONES facility.
Typically, the component failure rates used in RAMI studies are estimated by the heterogeneous datasets extracted from several sources of different content, structure, and accuracy....
The increasing complexity of fusion facilities demands robust data governance strategies capable of integrating heterogeneous knowledge sources while ensuring semantic consistency, traceability, and regulatory compliance. This talk presents an ontology-based data governance model as the foundational architecture for a Natural Language Query (NLQ) system designed to support maintenance planning...
The increasing use of collaborative robots in warehouse logistics is changing intralogistics systems, improving productivity, but is also creating significant challenges for operator safety, ergonomics, and operational reliability. The introduction of new technologies implies continuous cognitive and physical pressures on operators in modern warehouses, increasing the impact of human fatigue...
The increasing complexity of engineering systems and the growing demand for sustainable energy solutions require robust data-driven approaches for performance evaluation and decision-making. Solar Chimney Power Plants (SCPPs) constitute a promising renewable energy technology; however, their optimal design remains challenging due to the interaction of multiple physical and environmental...
In the Industry 5.0 transition, Humans and Collaborative Robots (Cobots) work together in different operations.. In maintenance ,the role of Human-Robot Collaboration (HRC) is becoming increasingly crucial for achieving sustainable efficiency and human-centered automation. While Collaborative Robot (Cobot) design takes into account worker safety, ergonomics, and process precision, it is...
At CentraleSupélec, research on resilience is conducted by the Laboratoire de Génie Industriel (LGI) and its research team R3 (Risk Resilience Reliability). Today, resilience has become a key issue for all so-called critical infrastructures. Although each sector has its own specific characteristics, the methodological frameworks used to study resilience appear to be very similar. This opens...
FIT(failures in time )-rates are a typical reliability measure for the constant part of the bathtub curve for non-repairable systems. The FIT-rate is the parameter of the exponential distribution in units of 109 hours. It is the inverse of the mean times between failures (MTBF). It is assessed at so-called MTBF-tests. For a serial system, FIT-rates of the individual components are added up....
As time passes, complex industrial systems suffer degradation phenomena that will inevitably lead them to failures. Several degradation models have been proposed to model these phenomena [5]. The most usual ones are based on stochastic processes such as the Wiener process, the Gamma process or the Inverse Gaussian process. In the case of complex systems that must operate without interruptions...
The discretization of the gamma process plays an important role in both theoretical investigations and practical implementations of stochastic modeling. The gamma process is a continuous-time, non-decreasing Lévy process with independent increments and is widely used in applications such as reliability engineering, survival analysis, and degradation modeling.
In practice, however, observed...
Regularization methods such as LASSO, adaptive LASSO, Elastic-Net, and SCAD are widely used for variable selection in statistical modeling. However, these approaches primarily focus on variables with strong effects and often overlook weaker signals, which may lead to biased parameter estimates and reduced predictive performance. To address this limitation, corrected shrinkage strategies have...
This work proposes a framework for analysing reliability and optimizing maintenance in a system subject to multiple degradation processes. Unlike models that assume perfect maintenance, this study incorporates an imperfect maintenance mechanism with diminishing efficiency, where the ability to reduce accumulated degradation decreases with each successive maintenance intervention.
Analytical...
Industrial systems composed of multiple interacting components are often exposed to external events that can simultaneously affect several parts of the system. Such events, commonly referred to as common shocks, introduce strong dependence between component failures and significantly complicate maintenance planning. When multiple components fail at once, the system may experience large...
Degradation modelling based on stochastic processes has become a key tool in reliability analysis and condition-based maintenance of engineering systems. While a large body of literature addresses single-component systems, many practical assets consist of multiple components whose degradation processes are influenced by common environmental or operational factors. In such systems, component...
The verification of a reliability target is one of the last and most expensive steps in the development process of a technical product. It ensures a certain level of product reliability prior to market release and provides a limit for expected warranty costs. Depending on product complexity and diversity as well as production volumes, a sufficient verification of a challenging target may...
Efficient control of spare parts inventory is essential for maintaining the availability of engineering systems operating under progressive degradation. This paper proposes an adaptive decision-support framework that integrates stochastic degradation modeling with data-driven control policies for inventory management. By continuously updating the risk of failure and its implications for future...
Scrap-based steel production plays an essential role in promoting sustainable industrial practices by significantly reducing energy consumption and greenhouse gas emissions compared with conventional ore-based steelmaking. Despite these benefits, maintaining high operational efficiency, reducing the costs associated with recycling operations, and ensuring product quality in scrap-based...
Safety and reliability of industrial equipment in the process industries are substantially influenced by degradation processes such as corrosion, erosion, deposits and blocking of pipes. To that end Risk-Based Inspection and Maintenance (RBIM) methodologies are progressively adopted using prediction models that depict the yearly corrosion rate of piping & equipment groups.
Indeed, piping and...
In binary reliability systems, the minimal cut-sets, path-sets and signature vector [1, 2], provide the basis for computing key reliability measures, such as the survival function and the mean time to failure [1, 3]. However, these quantities are typically obtained by exhaustive evaluation of the structure function over the Boolean state space [1], whose size grows exponentially with the...
The generalised F distribution (GenF) has been called an “umbrella” that covers several conventional distributions. In this work, we compare GenF with the Generalised Extreme Value (GEV) distribution with focus on estimation of return levels. Upper-tail asymptotics of the GenF are examined in relation to GEV. Potential numerical challenges in fitting the GenF are discussed. Data sets of river...
Real-world degradation processes often exhibit three distinct phases, namely a running-in (accommodation) phase, a steady-state (normal) phase, and a catastrophic wear phase. As a consequence, the degradation rate function, here intended as the first derivative of the mean degradation function, is typically bathtub-shaped: it decreases during the first phase, remains approximately constant...
In this work, we introduce a generalized measure of uncertainty, namely the cumulative information $\psi$-measure, in order to provide a unified perspective to the uncertainty framework. Indeed, it is a variability measure which reduces to several well-known information measures for appropriate choices of the function $\psi$. In particular, cumulative versions of Shannon and Tsallis entropies,...
Reliability of large populations of sensors is a major challenge in modern industrial production and applied functional monitoring systems. The massive deployment of low-cost Micro-Electro-Mechanical Systems (MEMS) sensors across several technological domains requires calibration strategies that ensure metrological reliability while remaining feasible at industrial scale. However, traditional...
Abstract
Automatic Differentiation (autodiff or AD) is a technique for computing derivatives that differs from both symbolic and numerical differentiation. It relies on the implementation of the chain rule to functions composed of simpler smooth functions whose Jacobian matrices are known.
Given the implementation of a function composed of smooth operations, Automatic...
Hidden Markov Models (HMMs) are increasingly recognized in reliability engineering as valuable tools for monitoring systems where the true operational state is not directly observable and must be inferred from certain indicators provided by a control system. Accurate estimation of these hidden health states and prediction of failures are crucial for minimizing unexpected downtime and...