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For many decades, it has been recognized that maintenance activities should be adapted to the specific usage of a system. For that reason, many advanced policies have been developed, such as condition-based and load-based maintenance policies. However, these policies require advanced monitoring techniques and rather detailed understanding of the failure behavior, which requires the support of an OEM or expert, prohibiting application by an operator in many cases. The present work proposes a maintenance policy that relieves the high (technical) demands set by these existing policies and provides a more accurate specification of the required (dynamic) maintenance interval than traditional usage-based maintenance.

The methodology followed starts with a review and critical assessment of existing maintenance policies, which are classified according to six different aspects. Based on the need for a technically less demanding policy that appears from this comparison, a new policy is developed. The consecutive steps required for this functional usage profiles based maintenance policy are then critically discussed: usage profile definition, monitoring, profile severity quantification and the possible extension to the fleet level. After the description of the proposed policy, it is demonstrated in three case studies on real systems.

A maintenance policy based on a simple usage registration procedure appears to be feasible, which enables a significantly more efficient maintenance process than the traditional usage-based policies. This is demonstrated by the policy proposed here.

The proposed maintenance policy based on functional usage profiles offers the operators of fleets of systems the opportunity to increase the efficiency and effectiveness of their maintenance process, without the need for a high investment in advanced monitoring systems and in experts interpreting the results.

The original contribution of this work is the explicit definition of a new maintenance policy, which combines the benefits of considering the effects of usage or environment severity with a limited investment in monitoring technology.

This paper proposes a heuristic, data-driven approach to the rapid performance evaluation of periodic maintenance on complex production plants. Through grouping, maintenance interval (MI)-based evaluation and performance assessment, potential nonvalue-adding maintenance elements can be identified in the current maintenance structure. The framework reduces management complexity and supports the decision-making process for further maintenance improvement.

The evaluation framework follows a prescriptive research approach. The framework is structured in three steps, which are further illustrated in the case study. The case study utilizes real-life data to verify the feasibility and effectiveness of the proposed framework.

Through a case study conducted on 9,538 pieces of equipment from eight offshore oil and gas production platforms, the results show considerable potential for maintenance performance improvement, including up to a 23% reduction in periodic maintenance hours.

The problem of performance evaluation under limited data availability has barely been addressed in the literature on the plant level. The proposed framework aims to provide a quantitative approach to reducing the structural complexity of the periodic maintenance evaluation process and can help maintenance professionals prioritize the focus on maintenance improvement among current strategies.

The proposed framework is especially suitable for initial performance assessment in systems with a complex structure, limited maintenance records and imperfect data, as it reduces management complexity and supports the decision-making process for further maintenance improvement. A similar application has not been identified in the literature.

Current models of transaction credit in the e-commerce network face many problems, such as the one-sided measurement, low accuracy and insufficient anti-aggression solutions. This paper aims to address these problems by studying the transaction credit problem in the crowd transaction network.

This study divides the transaction credit into two parts, direct transaction credit and recommended transaction credit, and it proposes a model based on the crowd transaction network. The direct transaction credit comprehensively includes various factors influencing the transaction credit, including transaction evaluation, transaction time, transaction status, transaction amount and transaction times. The recommendation transaction credit introduces two types of recommendation nodes and constructs the recommendation credibility for each type. This paper also proposes a “buyer + circle of friends” method to store and update the transaction credit data.

The simulation results show that this model is superior with high accuracy and anti-aggression.

The direct transaction credit improves the accuracy of the transaction credit data. The recommendation transaction credit strengthens the anti-aggression of the transaction credit data. In addition, the “buyer + circle of friends” method fully uses the computing of the storage ability of the internet, and it also solves the failure problem of using a single node.

3-ply cross-laminated timber (CLT) is used to investigate the thermo-mechanical performance of intermediate-size assemblies comprised of T-shaped welded slotted-in steel doweled connections and CLT beams at ambient temperature (AT), after and during non-standard fire exposure.

The first set of experiments was performed as a benchmark to find the load-carrying capacity of the assembly and investigate the failure modes at AT. The post-fire performance (PFP) test was performed to investigate the residual strength of the assembly after 30-min exposure to a non-standard fire. The fire-performance (FP) test was conducted to investigate the thermo-mechanical behavior of the loaded assembly during non-standard fire exposure. In this case, the assembly was loaded to 67% of AT load-carrying capacity and partially exposed to a non-standard fire for 75 min.

Embedment failure and plastic deformation of the dowels in the beam were the dominant failure modes at AT. The load-carrying capacity of the assembly was reduced to 45% of the ambient capacity after 30 min of fire exposure. Plastic bending of the dowels was the principal failure mode, with row shear in the mid-layer of the CLT beam and tear-out failure of the header sides also observed. During the FP test, ductile embedment failure of the timber in contact with the dowels was the major failure mode at elevated temperature.

This paper presents for the first time the thermo-mechanical performance of CLT beam-to-girder connections at three different thermal conditions. For this purpose, the outside layers of the CLT beams were aligned horizontally.

1. Load-carrying capacity and failure modes of CLT beam-to-girder assembly with T-shaped steel doweled connections at ambient temperature presented.

2. Residual strength and failure modes of the assembly after 30-min partially exposure to the non-standard fire provided throughout the post-fire performance test.

3. Fire resistance of the assembly partially exposed to the non-standard fire highlighted.

Load-carrying capacity and failure modes of CLT beam-to-girder assembly with T-shaped steel doweled connections at ambient temperature presented.

Residual strength and failure modes of the assembly after 30-min partially exposure to the non-standard fire provided throughout the post-fire performance test.

Fire resistance of the assembly partially exposed to the non-standard fire highlighted.

A generalized distribution with wide range of skewness and elongation will be suitable for the data mining and compatible for the misspecification of the distribution. Hence, the purpose of this paper is to present a distribution-based approach for estimating degradation reliability considering these conditions.

Tukey’s g-and-h distribution with the quantile expression is introduced to fit the degradation paths of the population over time. The Newton–Raphson algorithm is used to approximately evaluate the reliability. Simulation verification for parameter estimation with particle swarm optimization (PSO) is carried out. The effectiveness and validity of the proposed approach for degradation reliability is verified by the two-stage verification and the comparison with others’ work.

Simulation studies have proved the effectiveness of PSO in the parameter estimation. Two degradation datasets of GaAs laser devices and crack growth are performed by the proposed approach. The results show that it can well match the initial failure time and be more compatible than the normal distribution and the Weibull distribution.

Tukey’s g-and-h distribution is first proposed to investigate the influence of the tail and the skewness on the degradation reliability. In addition, the parameters of the Tukey’s g-and-h distribution is estimated by PSO with root-mean-square error as the object function.

In his 1943 paper “A Theory of Human Motivation”, Maslow suggested the “Hierarchy of Needs” as a classification system that described the stimuli for human behaviour. Presently, project behaviour research, which inspired this work, encourages undertaking research on behavioural aspects within and across organisational and project settings. Therefore, the purpose of this paper is to analyse project-based organisations’ (PBOs) seemingly reluctance in engaging in organisational learning from past project failures by drawing upon both institutional theory (since it focuses on how firms interact) and Maslow’s model within a project behaviour piece of research.

Interviews were held with purposively selected construction professionals from the UK construction industry, and data were analysed using thematic analysis.

Besides the need to learn from failures, PBOs’ main competing needs revolve around their “competitiveness”; “profitability and productivity”; “repeat business” and “reputation and partnering”. Mirroring these needs against Maslow’s hierarchy of needs, “competitiveness” and “profitability” are analogous to foundational “physiological” and “safety” needs. The need for “repeat business” and “reputation” is approximated with Maslow’s “affiliation” and “self-esteem” needs, and organisational learning is associated with “self-actualisation”. From an institutional theory perspective, such response to failure is influenced by the need to show legitimacy and conformity imposed by institutional factors.

Instead of taking a simplified approach to learning from failure such as the use of technological tools, PBOs and the sector at large should consider more robust approaches, by appreciating the influence of institutional factors and the external environment on their efforts to learn from failure.

Unlike past studies that present organisational learning within PBOs as a straightforward process, this study instead highlights the need of understanding various competing needs within a PBO and the external pressure.

This paper aims to study the reliability of the high-speed train operation control system in the Chinese Train Control System Level 3 (CTCS-3) operating mode.

Dynamic fault tree and Bayesian network method are adopted to analyze the reliability and weakness of the CTCS-3 system.

First, a physical architecture and data flow diagram of the CTCS-3 system are established according to the typical structure and functions of the CTCS-3 system. Second, the dynamic fault tree of the CTCS-3 system is constructed. Considering the prior probability of the bottom event and the existence of dynamic redundancy, the dynamic fault tree is transformed into a Bayesian net. The reliability of the CTCS-3 system is carried out based on the prior probability and the weakness that affects the reliability of the system based on the posterior probability is also analyzed by the Bayesian network. Finally, it is disclosed that the impact of the on-board subsystem on the reliability of the CTCS-3 system is generally greater than that of the ground subsystem. The two weakest modules in the onboard subsystem are the driver-machine interface (DMI) and balise transmission module (BTM) and the weakest one in the ground subsystem is Balise. The analysis results are generally consistent with the malfunctions in the field operation of China’s high-speed railway.

(1) By reasoning, the reliability of the train operation control system in the CTCS-3 operating mode meets the standard requirements.

(2) Through backward reasoning, it is found that the failure of the onboard subsystem leads to a greater probability of failure of the train control system.

(3) The DMI, BTM and automatic train protection computer unit modules are weak components in the onboard subsystem. Vital digit input&output, train interface unit and train security gateway are rarely involved in previous research, the result in this paper shows that these three modules are also weak components in the subsystem, which requires attention.

This paper aim at providing decision support for operational adjustment. In order to effectively reduce the external interference impact on train operation.

According to the reality that there are two kinds of high-speed trains running in China, considering the reality that there are two speed level train running in Chinese high-speed railway, this paper proposed the high-speed railway operation adjustment model based on priority, and the objective function is to minimize interference impact on train operation.

Adjustment strategy based on priority than based on sequence can effectively reduce the interference influence on train operation, which makes the train resume the planned operation back on schedule more quickly.

The solution of large-scale cases is too slow, and the practical application is limited.

The model was verified by a case, and the case results proved that adjustment strategy based on priority than based on sequence can effectively reduce the interference influence on train operation, which makes the train resume the planned operation back on schedule more quickly.

This paper examines how the macroeconomic environment affects the determinants of prepayment of mortgage loans from October 2004 to February 2020. For more accurate analysis, the authors define the timing of prepayment not only before the loan maturity but also at the time when 50% or more of the loan principal is repaid. The results show that, during the global financial crisis as well as the recent period of low interest rates, macroeconomic variables such as interest rate spreads and housing prices have a different effect compared to the normal situation. Also, significant explanatory variables, such as debt to income (DTI) ratio, loan amount ratio and poor credit score, have different effects depending on the macroenvironment. On the other hand, in all periods, the possibility of prepayment increases as comprehensive loan to value (CLTV) increases, and the younger the age, the shorter the loan maturity. The results suggest that, in the case of ultralong (40 years) mortgage loans recently introduced to support young people purchasing houses, the prepayment risk can be, at least partially, migrated by offsetting the increase in prepayment by young people and the decrease in prepayment due to long loan maturity. In addition, this study confirms that the accelerated time failure model compared to the logit model and COX proportional risk model has the potential to be more appropriate as a prepayment model for individual borrower analysis in terms of the explanatory power.

Poor maintenance management leads to non-negligible economic, environmental and social impacts and obstacles to the sustainable manufacturing paradigm. Studies evaluating maintenance impacts on sustainability underline growing interest in the topic, but reports on the industrial field are lacking. Therefore, this paper investigates the industrial environment and the indicators that manufacturing companies use for measuring their maintenance impacts.

In this pilot survey study, several stakeholders of production enterprises in the south of Italy were interviewed to unveil the spread of the measurement of maintenance impacts on sustainability and the indicators used by those companies.

The interview results showed a low level of awareness among stakeholders about maintenance impacts on sustainability. Maintenance stakeholders are mainly focused on technical and economic factors, whereas environmental, quality and safety stakeholders are becoming more aware of maintenance impacts on environmental and social factors. However, both groups need guidelines to define sustainability indicators to assess such impacts.

This exploratory study allowed us to investigate the current situation in industrial organisations and achieve the first variegated and diversified vision of the awareness of company stakeholders on maintenance impacts on the sustainability of several business functions. This paper provides a valuable contribution to “maintenance and sustainability” research area in production contexts and sheds light on non-negligible maintenance impacts on sustainability, providing preliminary insights on the topic and an effective basis for defining future research opportunities. Moreover, this study enables increased awareness among internal and external manufacturing company stakeholders on the role of maintenance in sustainable production.