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Integrated vehicle health management has been developed for several years in different industries, to be able to provide the required inputs to determine the optimal maintenance operations depending on the actual health status of the system. The purpose of this paper is to demonstrate the potential of a physics-based model (PbM) for prognostics with a real case study, based on the detection of incipient faults and estimate the remaining useful life of a planetary transmission of an aircraft system.

Most of the research in the area of health assessment algorithms has been focused on data-driven approaches that are not based on the knowledge of the physics of the system, while PbM approaches rely on the understanding of the system and the degradation mechanisms. A physics-based modelling approach to represent metal-metal contact and fatigue in the gears of the planetary transmission of an aircraft system is applied.

Both the failure mode caused by metal-metal contact as caused by fatigue in the gears is described. Furthermore, the real-time application that retrieves the results from the simulations to assess the health of the system is described. Finally the decision making that can be executed during flight in the aircraft is incorporated.

The paper proposes an innovative prognostics health management system that assesses two important failure modes of the planetary transmission that regulates the speed of the generators of an aircraft. The results from the models have been integrated in an application that emulates a real system in the aircraft and computes the remaining useful life in real time.

Prognostics and health management (PHM) can support product‐service systems (PSS) contracts, especially in the case of high technology products where their condition and performance can be monitored. The purpose of this paper is to investigate how PHM can support effective execution of some PSS contracts and to set out the future research agenda for the development of an online simulation modelling framework that will further harness the interaction between PHM and PSS.

The research methodology commenced by collating facts and figures from the existing body of knowledge, from which a set of key findings is presented from both technical and business perspectives. Analysis of the key findings highlights the current state of PHM‐PSS interaction, the capability of existing tools and techniques and a comprehensive analysis of PSS performances, with and without PHM.

Increased demand for total asset performance from the customers has been the main driver for PSS providers to adopt PHM technology. In the case of high value assets, PHM is used to capture the condition of the assets and to feed this information back to the PSS operations management which, in turn, will be used to plan a maintenance regime, spare parts provision, as well as to mitigate the dynamic behaviour which commonly occurs in PSS. Simulation modelling, driven by asset health condition, shows a considerable potential as an effective tool to control the execution of the PSS contract. In addition to the benefits from the maintenance services, the PHM‐PSS interaction can increase the controllability of the PSS contract execution and allow future modifications to PSS contracts.

The value of this paper lies in the comprehensive analysis of the interaction between PHM and PSS, especially focusing on the interaction during the PSS contract execution. This paper demonstrates the strengths and weaknesses of existing research in the research domain, and highlights the opportunities for future research.

The demand for contracts on assets availability has increased. Recently published papers show that the use of asset health monitoring technologies is being encouraged to improve the asset performance. This is based on reason rather than analysis. This paper aims to understand and assess the effect of different types of business processes for maintenance resource levels on the behaviour of the maintenance operations and asset availability located at different customer locations using different asset monitoring levels.

A discrete event simulation (DES) model was developed to mimic complex maintenance operations with different monitoring levels (reactive, diagnostics, and prognostics). The model was created to understand and assess the influence of resources (labour and spare parts) on a particular maintenance operation. The model was created to represent different levels of asset monitoring to be applied in a case study. Subsequently, different levels of spare parts (ranging from deficient inventory to a plentiful spares inventory) and labour were applied to show the effects of those resources on the asset availability.

This research has found that the DES was able to discern different processes for asset monitoring levels in complex maintenance operations. It also provided numerical evidence about applying such asset monitoring levels and proved that the higher asset monitoring level does not always guarantee higher asset availability.

The developed model is a unique model that can provide the decision makers of maintenance operations with numerical evidence to select an appropriate asset monitoring level based on their particular maintenance operations.

A novel DES model was developed to support maintenance operations decision makers in selecting the appropriate asset monitoring level for their particular operations. This unique approach provides numerical evidence rather than reasoning, and also proves that the higher asset monitoring level does not always guarantee higher asset availability.

A reliable and safe operation of fuel cells (FCs) is imperative for their application in aviation, especially within the main powertrain. Moreover, performance and lifetime requirements for technical and economic viability are demanding compared to their stationary or road transportation counterparts, while the operating conditions are considered challenging. Prognostics and health management (PHM) could represent a powerful tool for enhancing reliability, durability and performance by detecting, predicting and/or mitigating relevant degradation and failure mechanisms. Against this backdrop, the authors consider it of high relevance to obtain an understanding of the effectiveness of PHM approaches for polymer electrolyte fuel cells (PEFCs) for future aircraft applications, which represents the aim of this paper.

In this study, the authors first discuss application relevant failure modes, review state-of-the-art PHM approaches and, consecutively, assess the potential of FC control strategies for aviation. Aiming for a tangible, comparable metric for this initial assessment, the authors apply a published remaining useful life prediction method to load profiles for a range of aviation-specific applications.

The authors’ analysis shows significant potentials for lifetime improvement by (partial) avoidance of high power operation and rapid load change through control strategies. Tapping into these theoretical potentials, however, requires significant developments in the field of PEFC PHM and a focus on aviation specific degradation and performance testing.

The novelty of this study lies in creating an understanding of the potential of avoiding or preventing certain degradation modes by means of PHM in the PEFC specifically in aviation applications.

Resilience as a novel concept has attracted the most attention in the management of engineering systems. The main goal of engineering systems is production assurance and increasing customer satisfaction which depends on the suitable performance of mechanical equipment. “A resilient system is defined as a system that is resistant to disruption and failures and can recover itself and returns to the state before failure as soon as possible in the case of failure.” Estimate the value of the system’s resilience to increase its resilience by covering the weakness in the resilience indexes of the system.

In this article, a suitable approach to estimating resilience in complex engineering systems management in the field of mining has been presented. Accordingly, indexes of reliability, maintainability, supportability, efficiency index of prognostics and health management of the system, and ultimately the organization resilience index, have been used to evaluate the system resilience.

The results of applying this approach indicate the value of 80% resilience if the risk factor is considered and 98% if the mentioned factors are ignored. Also, the value of 58% resilience of this organization’s management group indicates the weakness of situational awareness and weakness in the vulnerable points of the organization.

To evaluate the resilience in this article, five indicators of reliability, maintainability, and supportability are used as performance indicators. Also, organization resilience and the prognostic and health management of the system (PHM) are used as management indicators. To achieve more favorable results, the environmental and operational variables governing the system have been used in performance indicators, and expert experts' opinions have been used in management indicators.

From on-board automotive diagnostics to real-time aircraft state of health, the implementation of health monitoring and management systems are an increasing trend. Further, reductions in operating budgets are forcing many companies and militaries to consider new operating and support environments. Combined with longer service lives for aircraft and other systems, maintenance and operations processes must be reconsidered. The majority of research efforts focus on health monitoring techniques and technologies, leaving others to determine the maintenance and logistics impact on the systems. The paper aims to discuss these issues.

This research analyzes the impact of a health monitoring system on a squadron of aircraft. Flight, maintenance and logistics operations are stochastically modeled to determine the impact of program decisions on supply metrics. An arena discrete event simulation is utilized to conduct this research on 20 components on each of the 12 aircraft modeled. Costs and availability are recorded for comparison across three sparing scenarios to include economic order quantity (EOQ) for baseline and health monitoring cases and a just-in-time (JIT) health monitoring set of simulations.

Data are presented for EOQ and JIT supply methods. A comparison of health monitoring enabled supply to current methods shows cost savings and availability gains. The different methodologies are compared and discussed as a trade-space for programmatic decisions.

This work demonstrates the ability of health monitoring systems and condition based maintenance to affect supply ordering decisions. The development of trade-spaces within operating environments is demonstrated along with the ability to conduct cost benefit analyses.

The purpose of this paper is to present a weakly supervised learning method to perform health evaluation and predict the remaining useful life (RUL) of rolling bearings.

Based on the principle that bearing health degrades with the increase of service time, a weak label qualitative pairing comparison dataset for bearing health is extracted from the original time series monitoring data of bearing. A bearing health indicator (HI) quantitative evaluation model is obtained by training the delicately designed neural network structure with bearing qualitative comparison data between different health statuses. The remaining useful life is then predicted using the bearing health evaluation model and the degradation tolerance threshold. To validate the feasibility, efficiency and superiority of the proposed method, comparison experiments are designed and carried out on a widely used bearing dataset.

The method achieves the transformation of bearing health from qualitative comparison to quantitative evaluation via a learning algorithm, which is promising in industrial equipment health evaluation and prediction.

The method achieves the transformation of bearing health from qualitative comparison to quantitative evaluation via a learning algorithm, which is promising in industrial equipment health evaluation and prediction.

The purpose of the paper is to propose a novel approach, based on grey clustering decision, to fill in an omission of quantitative monitoring parameter selection methods.

The basic monitoring parameter selection criteria and the corresponding calculation methods are presented. Then, the grey clustering decision model for monitoring parameter optimization selection is constructed, and an integrated weight determination method based on analytic hierarchy process (AHP) and information entropy is provided.

Basic principle for monitoring parameter selection is proposed and quantitative description is carried out for selection principle in engineering application. Grey clustering decision‐making model for monitoring parameter optimization selection is established. Comprehensive weight ascertainment method based on AHP and information entropy is provided to make the index weight more scientific.

At system design stage, it is of significance to carry out selection and optimization of monitoring parameters. After the optimization of monitoring parameters is confirmed, measurability analysis and design in parallel are carried out for convenience of timely information feedback and system design revision. Therefore, the system integration efficiency is improved and the cost of research and manufacturing is reduced.

Monitoring parameter optimization selection process based on grey clustering decision‐making model is described and the analysis result shows that the proposed method has certain degree of effectiveness, rationality and universality.

Online health monitoring of large complex equipment has become a trend in the field of equipment diagnostics and prognostics due to the rapid development of sensing and computing technologies. The purpose of this paper is to construct a more accurate and stable grey model based on similar information fusion to predict the real-time remaining useful life (RUL) of aircraft engines.

First, a referential database is created by applying multiple linear regressions on historical samples. Then similarity matching is conducted between the monitored engine and historical samples. After that, an information fusion grey model is applied to predict the future degradation trajectory of the monitored engine considering the latest trend of monitored sensory data and long-term trends of several similar referential samples, and the real-time RUL is obtained correspondingly.

The results of comparative analysis reveal that the proposed model, which is called similarity-based information fusion grey model (SIFGM), could provide better RUL prediction from the early degradation stage. Furthermore, SIFGM is still able to predict system failures relatively accurately when only partial information of the referential samples is available, making the method a viable choice when the historical whole life cycle data are scarce.

The prediction of SIFGM method is based on a single monotonically changing health indicator (HI) synthesized from monitoring sensory signals, which is assumed to be highly relevant to the degradation processes of the engine.

The SIFGM can be used to predict the degradation trajectories and RULs of those online condition monitoring systems with similar irreversible degradation behaviors before failure occurs, such as aircraft engines and centrifugal pumps.

This paper introduces the similarity information into traditional GM(1,1) model to make it more suitable for long-term RUL prediction and also provide a solution of similarity-based RUL prediction with limited historical whole life cycle data.

The developments of complex systems have increased the demand for condition monitoring techniques so as to maximize operational availability and safety while decreasing the costs. Signal analysis is one of the methods used to develop condition monitoring in order to extract important information contained in the sensory signals, which can be used for health assessment. However, extraction of such information from collected data in a practical working environment is always a great challenge as sensory signals are usually multi-dimensional and obscured by noise. The paper aims to discuss this issue.

This paper presents a method for trends extraction from multi-dimensional sensory data, which are then used for machinery health monitoring and maintenance needs. The proposed method is based on extracting successive features from machinery sensory signals. Then, unsupervised feature selection on the features domain is applied without making any assumptions concerning the source of the signals and the number of the extracted features. Finally, empirical mode decomposition (EMD) algorithm is applied on the projected features with the purpose of following the evolution of data in a compact representation over time.

The method is demonstrated on accelerated degradation data set of bearings acquired from PRONOSTIA experimental platform and a second data set acquired form NASA repository.

The method showed that it is able to extract interesting signal trends which can be used for health monitoring and remaining useful life prediction.