International Journal of Prognostics and Health Management

Life Cycle Assessment of Aircraft Maintenance

2025-05-08T18:07:15+00:00

The electrification of aircraft propulsion systems is currently being intensively investigated to reduce climate-damaging emissions during flight operations. Battery Electric Propulsion Systems (EPSs) are highly complex and require regular maintenance to ensure airworthiness. In particular, the limited lifespan of batteries necessitates frequent replacements, which can lead to significant environmental impact. This paper discusses possible maintenance activities for battery EPSs and their environmental implications. The environmental assessment is based on a Life Cycle Assessment (LCA), which considers the impact of individual maintenance tasks over the entire aircraft life cycle. The LCA results show that the environmental impact of maintenance increases significantly with the use of the electrical system compared to conventional propulsion systems. Two different battery scenarios with state-of-the-art and projected energy densities towards the year 2035 show significant potential for improvement. In our analysis, the batteries, which have to be replaced a total of eight times during the evaluated life cycle, account for the largest contribution to the environmental impact. At the same time, battery EPSs offer the potential to significantly reduce the environmental impact of flight operations, as there are no direct emissions into the atmosphere. The results highlight the need to consider maintenance-related environmental impact alongside operational improvements, providing a foundation for future strategies that minimise the impact while ensuring operational safety and efficiency. Nevertheless, implementing these systems presents significant challenges from a maintenance perspective, particularly in avoiding environmental burden shifting.

Time to Replace? A Guide for Sustainable Engine Maintenance Strategies

2025-06-30T12:15:01+00:00

Aircraft engine maintenance planning is traditionally focusing on questions of economic optimization. This includes repair-versus-replacement decisions and the cost-effective scheduling of maintenance events and relies on operational data, shop visit forecasts, and contractual obligations. Although this approach has been subject of extensive study, the integration of environmental considerations into maintenance planning remains largely overlooked. For example, replacing Life Limited Parts (LLPs) in shorter intervals can improve engine efficiency and reduce fuel consumption, but increases material demand and lead to a higher number of shop visits, causing additional environmental impacts. Postponing replacement conserves resources but may lead to higher operational inefficiencies and greater fuel consumption. Therefore, the objective of this study is to examine the environmental consequences associated with the timing and frequency of LLP replacements. Furthermore, it analyses the impact of different replacement intervals on climate change, material resource consumption and engine performance. A comparative Life Cycle Assessment (LCA) study is conducted to quantify the environmental impacts of replacing LLPs at shorter intervals versus an optimized replacement interval. By varying the LLP usage, this study demonstrates how longer utilization influences the overall environmental impacts. These repercussions are compared to increased fuel consumption due to engine degradation by using a simplified engine performance degradation approach. The findings of the study indicate the significance of varying impacts of different replacement intervals on the overall environmental performance. Furthermore, the study underscores the importance of recycling strategies for improving resource efficiency and sustainability in engine maintenance.

Prescriptive Decision-Making for Sustainable Production Management

2025-08-16T14:58:20+00:00

In response to the growing challenges posed by climate change and demographic shifts, industrial operations must move beyond traditional productivity metrics such as Overall Equipment Effectiveness (OEE). While OEE is a valuable key performance indicator, it fails to account for the ecological, social, and economic dimensions essential for long-term sustainability. This paper introduces an Overall Sustainable Equipment Effectiveness (OSEE) framework, designed to integrate sustainability factors into operational performance measurement, enabling a holistic assessment and optimization approach. Key sustainability factors and their interrelationships are identified through an extensive literature review and subsequently validated by industry experts to ensure practical relevance and applicability to real-world operational settings. To address the complexity of these interconnected factors, causal AI methods, in particular Dynamic Bayesian Networks (DBN) are employed. DBN allow a qualitative understanding of sustainability interrelationships (cause-effects) and enable a quantitative optimization of sustainability impacts on operational efficiency. The proposed OSEE framework offers a structured approach for balancing productivity with environmental and social factors, equipping decision-makers with insights for informed sustainable operational strategies. This research contributes to the broader agenda of twin transformation, aligning digitalization and sustainability, and provides a foundation for building resilient, future-ready industrial operations.

Assessing the Sustainability Impacts of Industry 4.0 on Maintenance Policies

2025-09-18T23:02:04+00:00

Maintenance strategies have traditionally been designed with a primary focus on cost reduction and operational efficiency, often overlooking their broader environmental and social impacts. However, in the current context where industries must align with European carbon neutrality 2050 objectives and the United Nations Sustainable Development Goals (SDGs), maintenance is recognized as a key lever for enhancing the three pillars of sustainability in industries: economic, social, and environmental. In addition, recent studies have shown that the ongoing digital transformation of industry through Industry 4.0 technologies such as artificial intelligence, Internet of Things, digital twins, and big data analytics, offers new opportunities to improve maintenance strategies. These developments have given rise to the concept of Maintenance 4.0, which opens new perspectives for aligning maintenance practices with broader sustainability objectives.
To better understand the impact of these technologies on maintenance sustainability, as well as the existing assessment initiatives in the current state of research, this paper conducts a systematic literature review (SLR). A total of 31 relevant studies were analyzed and classified into literature reviews, conceptual frameworks, and evaluation models. The review reveals that while economic and environmental benefits are increasingly supported by measurable indicators, the social dimension remains underexplored and lacks standardized metrics. In addition, most studies focus on short-term operational gains and do not address life cycle-wide perspective, including manufacturing and end-of-life stages.
Based on these findings, this paper (i) clarifies the current maturity of research and its exploratory nature; (ii) identifies major gaps which is the lack of lifecycle-based assessments and operational social indicators; (iii) highlights the weak operationalization of circular economy principles in maintenance 4.0 strategies; and (iv) proposes future research directions to develop holistic, life cycle-oriented, human-centric, and practically validated frameworks. These contributions aim to support the transition toward more sustainable maintenance practices, in alignment with sustainability goals.

Cost and Environmental Assessment of Tool Replacement Strategies Under Imperfect Wear Monitoring in Ti6Al4V Milling

2025-05-30T15:04:42+00:00

The policy for the replacement of cutting tools has a direct influence on the risk of producing out-of-tolerances workpieces, but can also avoid excessive consumption of new tools and production interruption. Current industrial practice tries to achieve an economical balance that does not directly incorporate the environmental impact of the policy. This often results in systematic preventive replacement of tools after a safely fixed number of produced workpieces, wasting a portion of the tool’s useful life (at an environmental cost) and increasing the machine’s downtime. In this study, we simulate the production of Ti6Al4V parts under three different tool replacement scenarios: (1) at fixed intervals, (2) using an imperfect cutting tool monitoring system where the tool can only be replaced between the production of two workpieces, and (3) with a cutting tool monitoring system that allows tool replacement every minute during machining. The simulation demonstrates that even with an imperfect monitoring system, condition-based replacement leads to improved economic performance (expressed in EUR) and environmental performance (expressed in kg CO2-eq). In comparison to systematic replacement, the condition monitoring allows reducing the environmental impact up to 8.7% and the cost up to 8.1%.

Impact of Sensor Degradation on Measurement Uncertainty in Prognostics and Maintenance Decision-Making:

2025-07-25T19:53:40+00:00

Prognostics and maintenance decision-making rely heavily on accurate and reliable measurements derived from sensors. However, sensor degradation introduces measurement uncertainties that compromise the precision of fault detection, remaining useful life estimation, and overall maintenance strategies. This paper provides a comprehensive review of the multifaceted impacts of sensor degradation on measurement uncertainty and its subsequent influence on prognostics and maintenance. The paper synthesizes various sensor degradation mechanisms and existing modelling techniques, emphasizing the growing research focus on developing accurate degradation models. The review also provides an in-depth analysis of how sensor degradation affects measurement uncertainty, exploring both qualitative and quantitative impacts through various modelling approaches and tools. Furthermore, this review examines the implications of this uncertainty on prognostics and maintenance decision-making methodologies, showcasing current mitigation methods and models. Finally, the review identifies key challenges and research gaps, outlining promising directions for future research in sensor degradation and its impact on prognostics and maintenance. By addressing these critical issues, this paper contributes to the advancement of more reliable, adaptive, and efficient Prognostics and Health Management (PHM) systems across various industrial and technological domains.

Editorial for the Special Issue on Sustainable Maintenance 4.0

2025-11-14T14:31:28+00:00

It is our great pleasure to introduce this Special Issue on “Sustainable Maintenance 4.0” in the International Journal of Prognostics and Health Management (IJPHM). This Special Issue addresses a timely and strategic shift in maintenance engineering and Prognostics and Health Management (PHM): the transition toward maintenance strategies that are not only efficient and reliable, but also environmentally responsible, resource-aware, and socially sustainable.
Global challenges such as climate change, resource depletion, and growing expectations for industrial responsibility call for a transformation of maintenance practices. Although maintenance strongly influences environmental and social performance, these dimensions remain underexplored in current research. Meanwhile, the rise of Industry 4.0/5.0—through digitalization, automation, AI, big-data analytics, and predictive maintenance—offers powerful opportunities to enhance sustainability, optimize resource use, reduce waste, and extend asset lifetime. These technologies also support the development of new sustainability-oriented KPIs that go beyond traditional economic indicators.
The contributions collected in this issue illustrate this evolution and provide new insights for both researchers and practitioners.