Proceedings of the Institution of Mechanical Engineers,Part O: Journal of Risk and Reliability

In this article, we present a novel approach of modelling risk management process for complex systems. To overcome difficulties of modelling dynamic large-scale systems, the main idea is to split it into various structural homogeneous units. The object-oriented paradigm is used to this end but, unlike previous works, the proposed methodology allows variation in terms of internal parameters throughout the objects. This novel approach based on Bayesian network techniques is referred to as extended object-oriented Bayesian network. The main contribution of this article consists in establishing algorithms and methods on how to build and run such models. This article is an extension of a communication presented at AMEST by mainly developing a more realistic case study along with other improvements.

After-sales maintenance services can be a very profitable source of income for original equipment manufacturers (OEMs) due to the increasing interest of assets’ users on performance-based contracts. However, when it concerns the product value-adding process, OEMs have traditionally been more focused on improving their production processes, rather than on complementing their products by offering after-sales services, consequently leading to difficulties in offering them efficiently. Furthermore, due to both the high uncertainty of the assets’ behaviour and the inherent challenges of managing the maintenance process (e.g. maintenance strategy to be followed or resources to be deployed), it is complex to make business out of the supply of after-sales services. Aiming at helping the business and maintenance decision-makers at this point, this paper proposes a framework for optimising the income of after-sales maintenance services through: (a) implementing advanced multi-objective opportunistic maintenance strategies that systematically consider the assets’ operational context in order to perform preventive maintenance during the most favourable conditions, (b) considering the specific OEMs’ and users’ needs and (c) assessing both endogenous and exogenous uncertainties that might condition the after-sales services’ profitability. The developed case study for the wind energy sector demonstrates the suitability of the presented framework for optimising the after-sales services.

The aim of this article is to investigate how to embed asset management in production companies. A framework is defined based on literature analysis and focus groups findings, in which the fundamentals to guide the integration of asset management are systematized. Two dimensions are identified—the asset life cycle and the hierarchical level of the asset-control activities—and four founding principles—life cycle, system, risk and asset-centric orientation—as levers to integrate asset management within an industrial organization. An empirical investigation is then developed through multiple case study involving eight production companies in Italy, with the purpose to map the elements of the framework against the real mechanisms in the industrial practices. This allows testing the relevance of the framework itself and demonstrating its potential as a support for companies to implement gap analysis on asset management practices. Empirical evidence on current practices of asset management in production companies is contextually unveiled.

Condition-based maintenance has been developed and successfully applied in various industrial systems to preventively maintain the correct equipment at the right time with regard to its current health “condition” such as oil temperature, harmonics data, and vibration. The monitoring of these conventional indicators may however be costly. Moreover, energy efficiency addressed by sustainability requirements has been recently considered as an emerging key performance indicator to be controlled. Nevertheless, this emerging key performance indicator is not yet integrated in condition-based maintenance decision-making. To face these issues, the main objective of this article is to investigate the interests to use energy efficiency for condition-based maintenance decision-making. The first original contribution of this article is to propose a new energy efficiency–based condition-based maintenance model using energy efficiency indicator which is defined as the amount of energy consumption to produce one useful output unit. The proposed model leads to consider not only the maintenance cost but also energy and useful output performance in the condition-based maintenance optimization process. The second contribution concerns an investigation of the proposed energy efficiency–based condition-based maintenance model for the case study of the TELMA platform. The performance of the proposed model is verified by comparing to an extended traditional one. The obtained results allow to highlight the impacts of energy efficiency on existing condition-based maintenance strategies and to conclude on the interest of a new energy efficiency indicator–based condition-based maintenance practice in terms of both cost and efficiency.

The IoT (Internet of Things) concept is being widely regarded as the fundamental tool of the next industrial revolution – Industry 4.0. As the value of data generated in social networks has been increasingly recognised, social media and the IoT have been integrated in areas such as product-design, traffic routing, etc. However, the potential of this integration in improving system-level performance in industrial environments has rarely been explored. This paper discusses the feasibility of improving system-level performance in industrial systems by integrating social networks into the IoT concept. We propose the concept of a social internet of industrial assets (SIoIA) which enables the collaboration between assets by sharing status data. We also identify the building blocks of SIoIA and characteristics of one of its important components – social assets. A sketch of the general architecture needed to enable a social network of collaborating industrial assets is proposed and two illustrative application examples are given.

Maintenance services of geographically dispersed industrial applications, such as oil transfer systems via pipelines and wastewater treatment plants, are affected by high logistics costs and risks of permanent downtimes. The increasing availability of smart technologies and devices has led to the introduction of advanced prognostic and diagnostic systems to support maintenance activities. In this context, artificial immune systems support the development of industrial applications, where machines and equipment are capable of self-repairing, healing and learning due to their ability to learn from experience. However, the applicability of artificial immune systems has a limited set of contexts along with a low incidence of real-word implementations in the literature, and thus, additional explorative studies are necessary. This article describes a proposed hybrid system conceived by integrating a multi-agent system–based architecture with the main features of artificial immune systems and evaluates its potential applications in two different industrial settings. The flexibility of the behaviour of artificial immune systems methodologies allows for the implementation of a reliable diagnostic and prognostic system, while the choice of multi-agent system architecture enables a mix of autonomy and distributed processing that overcomes the strong limitations of a reduced training dataset.

This work explores the challenges of handling the recovery phenomena in the degradation behavior of the proton exchange membrane fuel cells, from the perspective of the prognostics. An adaptive prognostics and health management approach with additional knowledge, such as the electrochemical impedance spectroscopy, from the state of health characterization, is applied on two fuel cell stacks under both stationary and quasi-dynamic operating regimes. Some improvements in the prognostic performance are obtained in the view of the remaining useful life predictions by comparing with a classical particle filtering–based prognostic approach.

The degradation process of complex multi-component systems is highly stochastic in nature. A major side effect of this complexity is that components of such systems may have unexpected reduced life and faults and failures that decrease the reliability of multi-component systems in industrial environments. In this work, we provide maintenance practitioners with an explanation of the nature of some of these unpredictable events, namely, the degradation interactions that take place between components. We begin by presenting a general wear model where the degradation process of a component may be dependent on the operating conditions, the component’s own state and the state of the other components. We then present our methodology for extracting accurate health indicators from multi-component systems by means of a time–frequency domain analysis. Finally, we present a multi-component system degradation analysis of experimental data generated by a gearbox-accelerated life testing platform. In doing so, we demonstrate the importance of modelling the interactions between the system components by showing their effect on component lifetime reduction.

There is a large number of industries that make extensive use of composite materials in their respective sectors. This rise in composites’ use has necessitated the development of new non-destructive inspection techniques that focus on manufacturing quality assurance, as well as in-service damage testing. Active infrared thermography is now a popular nondestructive testing method for detecting defects in composite structures. Non-uniform emissivity, uneven heating of the test surface, and variation in thermal properties of the test material are some of the crucial factors in experimental thermography. These unwanted thermal effects are typically coped with the application of a number of well-established thermographic techniques including pulse phase thermography and thermographic signal reconstruction. This article addresses this problem of the induced uneven heating at the pre-processing phase prior to the application of the thermographic processing techniques. To accomplish this, a number of excitation invariant pre-processing techniques were developed and tested in this article addressing the unwanted effect of non-uniform excitation in the collected thermographic data. Various fitting approaches were validated in light of modeling the non-uniform heating effect, and new normalization approaches were proposed following a time-dependent framework. The proposed pre-processing techniques were validated on a testing composite sample with pre-determined defects. The results demonstrated the effectiveness of the proposed processing algorithms in terms of removing the unwanted heat distribution effect along with the signal-to-noise ratio of the produced infrared images.

This article proposes a novel condition-based selective maintenance model for a multi-component system running multiple missions interspersed with scheduled intermission breaks. Each component in the system degrades according to a time-dependent stochastic process and fails whenever its degradation level reaches a prespecified threshold. Failures of system components are revealed only through periodic inspections performed during a mission. The decision to repair components found in a failed state is made at the beginning of the following break. However, a penalty cost proportional to the expected component downtime is incurred. To improve the probability of the system successfully completing its next mission, maintenance activities are carried out on its components during the breaks. Each component can be imperfectly maintained or replaced. The level at which maintenance is performed determines the improvement degree in the component health. Cost and time structures are developed to take into account the trade-offs between the cost of an imperfect maintenance action and its resulting health improvement. Given the limited duration of the break and the required reliability target for the next mission, the condition-based selective maintenance problem aims at finding an optimal subset of maintenance actions to be performed on the selected components to minimize the total expected cost which is the sum of the total expected maintenance, inspection and penalty costs. All parameters and components of this nonlinear selective maintenance optimization problem are developed and thoroughly discussed. Numerical experiments are provided to illustrate the modelling steps and show the validity of the proposed approach.

Identification and quantification of cost and value of industrial assets is a field in which much terminology has been developed. When we try to analyze the importance of an asset for our business, the discussion about its costs should not be separated from the value provided by the asset. Most of the time, managers use the term “cost” because it seems to be more objective. The concept of value is more subjective and more difficult to define. However, we shall henceforth use definitions as amortization, inflation, or replacement value in order to simplify the concept of “value” to improve our decisions. The economic retribution of the facilities is based on a legal normative for regulated companies, so the concept of “cost” may turn out to be quite useless. Therefore, it is important to use a methodology that allows us to estimate the value of our assets. We have developed a criticality analysis of our infrastructure in order to assess the relative value of these items for the company. The target is to optimize the operation and maintenance strategies at a corporate level. This must have a relevant impact on the OPEX of our company, and there may also be an impact on future CAPEX. This article is a case study of the methodology and presents clear examples of how operation and maintenance strategy is transformed according to criticality assessments.