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The objective of this work is to provide a novel aircraft allocation model for fractional business aviation. This model may provide decision-makers with alternative routing solutions that take into consideration preventive maintenance and failure prognostics information. The expected results are more efficient routing solutions when compared to conventional planning models, to help decision-makers improve operations and maintenance planning.

The model is a mixed integer linear problem formulation addressing and considering preventive maintenance and failure prognostics for optimal operations. Numerical experiments were performed using both field and synthetic data to validate the proposed method. All instances are solved using branch, price and cut algorithms from open-source software.

The results obtained in this study show that the use of failure prognostics information in aircraft routing can provide improvements in overall planning. By choosing slightly longer flight legs, the flight cost will increase, but putting an aircraft with a higher risk of failure on a leg inbound to a maintenance base can reduce maintenance and overall operating cost.

The model and method provide decision-makers with routing solutions that consider new aspects of planning, not used in previous works, such as failure. Most of the literature focuses on solving routing problems for large commercial airlines. Considering that, few solutions are found in literature for fractional business operators, which have their own operational particularities, such as a company managing a fleet of aircraft belonging to multiple shareowners. In such operation, clients may not always fly in the aircraft that they are shareowners, but an aircraft from the fractional fleet of the same category. Here, the company managing the aircraft guarantees that an aircraft will be ready to attend client demands in minimum time. One of the major differences from other models of operation is the dynamic nature of its flight demands, thus requiring flexible and agile planning limiting the available time to find a routing solution.

Social countermarketing (SCM) aims at influencing existing socio-cultural norms, public policies or political decision-making. Existing empirical accounts of SCM give limited insights into their success. The authors analyze SCM strategies and their public resonance by studying the diagnostic and prognostic frames and responsibility attributions that are used in the debates.

The authors focus on two online SCM campaigns in the Netherlands that are targeted against over-feeding of chickens for consumption and the selling of low-priced meat. The authors conducted a quantitative content analysis (N = 3,902) of these debates on Twitter for a two-year period (July 2015 to June 2017).

The results show that citizens play an important role for the amplification of SCM campaigns. Diagnostic and prognostic frames about meat selling practices are among the most popular ones while the importance of mobilization messages differs per case. This can be explained by the proximity of these frames to citizens' daily life experiences.

The apparent willingness of citizens to both tweet and retweet calls for mobilization might give messages by environmental NGOs third-party endorsement. This strengthens their position and visibility in the debates, which are both of strategic value. The analysis of actor responsibility can identify reputational risks for companies in contested industries such as mass meat production.

The findings enhance professional understanding of designing campaign messages and refine SCM success in terms of resonance, since resonance indicates amplification and third-party endorsement.

Condition-based maintenance (CBM) has become a central maintenance approach because it performs more efficient diagnoses and prognoses based on equipment health condition compared to time-based methods. CBM models greatly inform maintenance decisions. This research examines three CBM fault prognostics models: logical analysis of data (LAD), artificial neural networks (ANNs) and proportional hazard models (PHM). A methodology, which involves data pre-processing, formulating the models and analyzing model outputs, is developed to apply and compare these models. The methodology is applied on NASA’s Turbofan Engine Degradation data set and the structural health monitoring (SHM) data set from a Nova Scotia Bridge. Results are evaluated using three metrics: error, half-life error and a cost score. This paper concludes that the LAD and feedforward ANN models compares favorably to the PHM model. However, the feedback ANN does not compare favorably, and its predictions show much larger variance than the predictions from the other three methods. Based on these conclusions, the purpose of this paper is to provide recommendations on the appropriate situations in which to apply these three prognostics models.

LAD, ANNs and PHM methods are adopted to perform prognostics and to calculate the mean residual life (MRL) of eqipment using NASA’s Turbofan Engine Degradation data set and the SHM data set from a Nova Scotia Bridge. Statistical testing was used to evaluate the statistical differences between the approaches based on these metrics. By considering the differences in these metrics between the models, it was possible to draw conclusions about how the models perform in specific cases.

Results were evaluated using three metrics: error, half-life error and a cost score. It was concluded that the LAD and feedforward ANN models compares favorably to the PHM model. However, the feedback ANN does not compare favorably and its predictions show much larger variance than the predictions from the other three methods. Overall the models predict failure after it has already occurred (negative error) when the residual life is large and vice versa.

It was concluded that a good CBM prognostics model for practical implications can be determined based on three main considerations: accuracy, run time and data type. When accuracy is a main concern, as in the case where impacts of failure are large, LAD and feedforward neural network are preferred. The preference changes when run time is considered. If data can be easily collected and updating the model is performed often, the ANNs and LAD are preferred. On the other hand, if CM data are not easily obtainable and existing data are not representative of the population’s behavior, data type comes into play. In this case, PHM is preferred.

Previous research in the literature performed reviews of multiple independent studies on CBM techniques performed on different data sets. They concluded that it is typically harder to implement artificial intelligence models, because of difficulties in data procurement, but these approaches offer improved performance as compared to more traditional model-based and statistical approaches. In this research, the authors further investigate and compare the performance and results from two major artificial intelligence models, namely, ANNs and LAD, and one pioneer statistical model, PHM over the same two real life prognostics data sets. Such in-depth comparison and review of major CBM techniques was missing in current literature of CBM field.

The purpose of this paper is to perform an extensive literature review in the area of decision making for condition-based maintenance (CBM) and identify possibilities for proactive online recommendations by considering real-time sensor data. Based on these, the paper aims at proposing a framework for proactive decision making in the context of CBM.

Starting with the manufacturing challenges and the main principles of maintenance, the paper reviews the main frameworks and concepts regarding CBM that have been proposed in the literature. Moreover, the terms of e-maintenance, proactivity and decision making are analysed and their potential relevance to CBM is identified. Then, an extensive literature review of methods and techniques for the various steps of CBM is provided, especially for prognosis and decision support. Based on these, limitations and gaps are identified and a framework for proactive decision making in the context of CBM is proposed.

In the proposed framework for proactive decision making, the CBM concept is enriched in the sense that it is structured into two components: the information space and the decision space. Moreover, it is extended in a way that decision space is further analyzed according to the types of recommendations that can be provided. Moreover, possible inputs and outputs of each step are identified.

The paper provides a framework for CBM representing the steps that need to be followed for proactive recommendations as well as the types of recommendations that can be given. The framework can be used by maintenance management of a company in order to conduct CBM by utilizing real-time sensor data depending on the type of decision required.

The results of the work presented in this paper form the basis for the development and implementation of proactive Decision Support System (DSS) in the context of maintenance.

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.

Gliomas are common intracranial tumors with the characteristic of diffuse and invasive growth. The prognosis is poor, and the recurrence rate and mortality are higher. With the development of big data technology, many methods such as natural language processing, computer vision and image processing have been deeply applied in the medical field. This can help clinicians to provide personalized and precise diagnosis and therapeutic schedule for patients with different type of gliomas to achieve the best therapeutic effect. The purpose of this paper is to summarize and extract useful information from published research results by conducting a secondary analysis of the literature.

The PubMed and China National Knowledge Infrastructure (CNKI) literature database were used to retrieve published Chinese and English research papers about human gliomas. Comprehensive analysis was applied to conduct this research. The factors affecting survival and prognosis were screened and analyzed respectively in this paper, and different methods for multidimensional data of patients were discussed.

This paper identified biomarkers and therapeutic modalities associated with prognosis for different grade of gliomas. This paper investigated the relationship among these clinical prognostic factors and different histopathologic tying and grade of gliomas by comprehensive analysis. This paper summarizes the research progress of biomarker in medical imaging and genomics of gliomas to improve prognosis and the current status of treatment in China.

Combined with multimodal data such as genomics data, medical image data and clinical information data, this paper comprehensively analyzed the prognostic factors of glioma and provided guidance and evidence for rational treatment planning and improvement of clinical treatment prognosis.

Asset management has recently gained significance due to emerging business models such as Product Service Systems where the sale of asset use, rather than the sale of the asset itself, is applied. This leaves the responsibility of the maintenance tasks to fall on the shoulders of the manufacturer/supplier to provide high asset availability. The use of asset monitoring assists in providing high availability but the level of monitoring and maintenance needs to be assessed for cost effectiveness. There is a lack of available tools and understanding of their value in assessing monitoring levels. The paper aims to discuss these issues.

This research aims to develop a dynamic modelling approach using Discrete Event Simulation (DES) to assess such maintenance systems in order to provide a better understanding of the behaviour of complex maintenance operations. Interviews were conducted and literature was analysed to gather modelling requirements. Generic models were created, followed by simulation models, to examine how maintenance operation systems behave regarding different levels of asset monitoring.

This research indicates that DES discerns varying levels of complexity of maintenance operations but that more sophisticated asset monitoring levels will not necessarily result in a higher asset performance. The paper shows that it is possible to assess the impact of monitoring levels as well as make other changes to system operation that may be more or less effective.

The proposed tool supports the maintenance operations decision makers to select the appropriate asset monitoring level that suits their operational needs.

A novel DES approach was developed to assess asset monitoring levels for maintenance operations. In applying this quantitative approach, it was demonstrated that higher asset monitoring levels do not necessarily result in higher asset availability. The work provides a means of evaluating the constraints in the system that an asset is part of rather than focusing on the asset in isolation.

The purpose of this paper is to apply the situational theory of publics and framing theory in the context of environmental corporate social responsibility (CSR) communication research to determine how message frames impact information seeking and processing.

Using a 3 (message frame: diagnostic, prognostic, or motivational) × 2 (environmental issue: general vs specific) plus control between subjects experimental design, the study examines the attitudes, cognitions, and behavioral intentions different publics may form about two different environmental responsibility issues presented in this study. Furthermore, the study aims to examine how different types of message frames (diagnostic, prognostic, or motivational) and topics may impact how a company can move a public toward information seeking behaviors.

Based on theoretical considerations, structural equation modeling was used to examine significant paths between variables, thus creating a proposed new theoretical model that can be applied to CSR, public relations, and strategic communication literature.

The study offers a proposed new integrated theoretical model that can be applied to strategic communication literature and used to assist companies with enhancing their CSR communication and strategic communication planning efforts to determine how to move a public toward information seeking behaviors.

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.

This paper seeks to present a prognostics approach using the Mahalanobis distance (MD) method to predict the reliability of multilayer ceramic capacitors (MLCCs) in temperature‐humidity‐bias (THB) conditions.

Data collected during THB testing of 96 MLCCs were analyzed using the MD method. In the THB tests, three parameters (capacitance (C), dissipation factor (DF), and insulation resistance (IR)) were monitored in situ. A Mahalanobis space (MS) was formed from the MD values of a set of non‐failed MLCCs. MD values for the remaining MLCCs were compared with an MD threshold. Data for MLCCs which exceeded the threshold were examined using the failure criteria for the individual electrical parameters to identify failures and precursors to failure.

It was found that the MD method provided an ability to detect failures of the capacitors and identify precursors to failure, although the detection rate was not perfect.

It was observed that the quality and construction of the MS, together with the choice of the MD threshold, were the critical factors determining the sensitivity of the MD method. Recommendations are offered for improved sensitivity to enable assessment of intermittent failures.

MD analysis of the multivariate MLCC data set illustrates how detection of failures can be simplified in a system for which several parameters were monitored simultaneously. This makes the MD method of great potential value in a health‐monitoring system.