Condition-based maintenance of multi-component systems with degradation state-rate interactions

This paper presents an approach to optimise condition-based maintenance (CBM) of multi-component systems where the state of certain components could affect the rate of degradation of other components, i.e., state-rate degradation interactions. We present a real example of an industrial cold box in a petrochemical plant, where data collected on fouling of its tubes show that the extent of fouling of one tube affects the rate of fouling of other tubes due to overloading. A regression model is used to characterise the state-rate degradation interactions for this example. Further, we optimise the condition-based maintenance policy for this system using simulated annealing. The outcomes of the case study demonstrate that modelling degradation interactions between components in the system can have significant positive impact on CBM policy of the system. The paper therefore tackles a problem that has not been addressed in the literature, paving way for further developments in this important area of research with practical applications.This is the author accepted manuscript. The final version is available from Elsevier via http://dx.doi.org/10.1016/j.ress.2015.11.01

Digital Manufacturing on a Shoestring - Low cost digital solutions for SMEs

This paper introduces the Digital Manufacturing on a Shoestring programme as a whole and demonstrates the way in which it addresses the need for low cost digital solutions for SME Manufacturers. It discusses challenges associated with integrating low cost technologies into industrial solutions and the style of IT architectures best suited for integrating these solutions into industrial environments.Engineering and Physical Sciences Research Council [grant number EPSRC: EP/R032777/1

Robust Condition-Based Operations & Maintenance: Synergizing Multi-Asset Degradation Rate Interactions and Operations-Induced Degradation

Effective operations and maintenance (O&M) in modern production systems hinges on careful orchestration of economic and degradation dependencies across a multitude of assets. While the economic dependencies are well studied, degradation dependencies and their impact on system operations remain an open challenge. To address this challenge, we model condition-based production and maintenance decisions for multi-asset systems with degradation interactions. There is a rich literature on condition-based O&M policies for single-asset systems. These models fail to represent modern systems composed of multiple interacting assets. We are providing the first O&M model to optimize O&M in multi-asset systems with embedded decision-dependent degradation interactions. We formulate robust optimization models that inherently capture degradation and failure risks by embedding degradation signals via a set of constraints, and building condition-based uncertainty sets to model probable degradation scenarios. We offer multiple reformulations and a solution algorithm to ensure computational scalability. Performance of the proposed O&M model is evaluated through extensive experiments, where the degradation is either emulated or taken from vibration-based readings from a rotating machinery system. The proposed model provides significant improvements in terms of operation, maintenance, and reliability metrics. Due to a myriad of dependencies across assets and decisions, it is often difficult to translate asset-level failure predictions to system-level O&M decisions. This challenge puts a significant barrier to the return on investment in condition monitoring and smart maintenance systems. Our approach offers a seamless integration of data-driven failure modeling and mathematical programming to bridge the gap across predictive and prescriptive models

On fault propagation in deterioration of multi-component systems

In extant literature, deterioration dependence among components can be modelled as inherent dependence and induced dependence. We find that the two types of dependence may co-exist and interact with each other in one multi-component system. We refer to this phenomenon as fault propagation. In practice, a fault induced by the malfunction of a non-critical component may further propagate through the dependence amongst critical components. Such fault propagation scenario happens in industrial assets or systems (bridge deck, and heat exchanging system). In this paper, a multi-layered vector-valued continuous-time Markov chain is developed to capture the characteristics of fault propagation. To obtain the mathematical tractability, we derive a partitioning rule to aggregate states with the same characteristics while keeping the overall aging behaviour of the multi-component system. Although the detailed information of components is masked by aggregated states, lumpability is attainable with the partitioning rule. It means that the aggregated process is stochastically equivalent to the original one and retains the Markov property. We apply this model on a heat exchanging system in oil refinery company. The results show that fault propagation has a more significant impact on the system's lifetime comparing with inherent dependence and induced dependence

The stochastic opportunistic replacement problem, part III: improved bounding procedures

We consider the problem to find a schedule for component replacement in a multi-component system, whose components possess stochastic lives and economic dependencies, such that the expected costs for maintenance during a pre-defined time period are minimized. The problem was considered in Patriksson et al. (Ann Oper Res 224:51–75, 2015), in which a two-stage approximation of the problem was optimized through decomposition (denoted the optimization policy). The current paper improves the effectiveness of the decomposition approach by establishing a tighter bound on the value of the recourse function (i.e., the second stage in the approximation). A general lower bound on the expected maintenance cost is also established. Numerical experiments with 100 simulation scenarios for each of four test instances show that the tighter bound yields a decomposition generating fewer optimality cuts. They also illustrate the quality of the lower bound. Contrary to results presented earlier, an age-based policy performs on par with the optimization policy, although most simple policies perform worse than the optimization policy

Condition-based maintenance of multi-component systems with degradation state-rate interactions

This paper presents an approach to optimise condition-based maintenance (CBM) of multi-component systems where the state of certain components could affect the rate of degradation of other components, i.e., state-rate degradation interactions. We present a real example of an industrial cold box in a petrochemical plant, where data collected on fouling of its tubes show that the extent of fouling of one tube affects the rate of fouling of other tubes due to overloading. A regression model is used to characterise the state-rate degradation interactions for this example. Further, we optimise the condition-based maintenance policy for this system using simulated annealing. The outcomes of the case study demonstrate that modelling degradation interactions between components in the system can have significant positive impact on CBM policy of the system. The paper therefore tackles a problem that has not been addressed in the literature, paving way for further developments in this important area of research with practical applications

Développement d'une trousse d'intervention pour une prise en charge de la maintenance en milieu de faible maturité

Contexte : L’évolution rapide de l’environnement concurrentiel et le manque de ressources nécessaires aux activités de maintenance obligent les entreprises à adapter leurs stratégies de gestion d’actifs aux besoins réels. La mise en place d’un programme de maintenance efficace et efficient est un moyen d’atteindre cette compétitivité souhaitée. Cependant, la mise en oeuvre d’un tel programme de maintenance n’est souvent pas simple en raison de l’absence d’approche structurée d’aide à la décision. Les modèles de maturité des capacités présentent une telle approche structurée et se révèlent très utiles pour mesurer les différents aspects des pratiques en vigueur dans une organisation. Objectifs : Le document propose un outil d’aide à la décision pour soutenir les entreprises dans leurs activités de maintenance et améliorer leurs maturités. Méthodologie : La méthodologie en trois étapes adoptée dans cette approche soutient que les entreprises pourraient atteindre des niveaux élevés de maturité en concentrant leurs ressources sur les matériels critiques. Son adoption offre une vision claire des enjeux internes et externes inhérents aux entreprises, mais aussi une connaissance du matériel critique afin de porter des actions dont le bénéfice est garanti. La démarche permet également d’analyser le niveau de maturité atteint par les entreprises et fournit une structure d’actions évolutives. Résultats : Il ressort de ce travail, une trousse d’intervention composée de trois principaux modules développés en mode agile sur le logiciel tableur Excel de la suite Microsoft Office. L’outil a été déployé pour application dans le Laboratoire de recherche en Imagerie et Orthopédie (LIO) à l’École de technologie supérieure de Montréal. L’analyse pré et post application démontre une meilleure compréhension de l’importance des actifs et une vision structurée des processus de maintenance à appliquer. Originalité : Ce document présente une méthode d’alignement des besoins réels d’une organisation pour la satisfaction des segments de clientèle à son portefeuille d’actifs. Il est possible d’y observer les différentes connexions depuis la clientèle de l’organisation à son parc d’équipements. Le document devrait être utile à la fois aux chercheurs et aux professionnels de la maintenance intéressés par l’utilisation de nouvelles méthodes de gestion de la maintenance intégrant certaines exigences de la norme ISO 55000 sur la gestion d’actifs

Towards system-level prognostics : Modeling, uncertainty propagation and system remaining useful life prediction

Prognostics is the process of predicting the remaining useful life (RUL) of components, subsystems, or systems. However, until now, the prognostics has often been approached from a component view without considering interactions between components and effects of the environment, leading to a misprediction of the complex systems failure time. In this work, a prognostics approach to system-level is proposed. This approach is based on a new modeling framework: the inoperability input-output model (IIM), which allows tackling the issue related to the interactions between components and the mission profile effects and can be applied for heterogeneous systems. Then, a new methodology for online joint system RUL (SRUL) prediction and model parameter estimation is developed based on particle filtering (PF) and gradient descent (GD). In detail, the state of health of system components is estimated and predicted in a probabilistic manner using PF. In the case of consecutive discrepancy between the prior and posterior estimates of the system health state, the proposed estimation method is used to correct and to adapt the IIM parameters. Finally, the developed methodology is verified on a realistic industrial system: The Tennessee Eastman Process. The obtained results highlighted its effectiveness in predicting the SRUL in reasonable computing time