VD
V.S.V. Dhanisetty
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8 records found
1
Journal article
(2020)
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Philippe F.R. Massart, V. S.Viswanath Dhanisetty, Christos Kassapoglou, W. J.C.(Wim) Verhagen, Richard Curran
This paper proposes an analytical model that uses historical damage dimension data to deduce physical impactor characteristics (size and energy) that has caused a certain resulting damage. Maintenance tasks occur in operations due to impact, however the source of the damage caused in the event remains in most cases unknown. Consequently, by inferring what has caused a certain type of damage from the distribution of the damage type and severity relative to impactor types, maintainers can be better prepared in terms of what to expect from a given impactor source. The developed model introduces a novel transition deformation region between the local deformation and the global plate deflection, allowing for fast and accurate predictions of the impact event. Using the known aluminium structural properties and damage dimensions, the damage data is converted into impactor data. The model is applied in a case study using 120 fuselage dent damages dimensions (length, width, and depth) from a Boeing 777 fleet. The results show that the model deduces impactor characteristics for 94% of the considered damages, ranging up to 240 J and 110 mm for impactor energy and radius respectively.
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This paper proposes an analytical model that uses historical damage dimension data to deduce physical impactor characteristics (size and energy) that has caused a certain resulting damage. Maintenance tasks occur in operations due to impact, however the source of the damage caused in the event remains in most cases unknown. Consequently, by inferring what has caused a certain type of damage from the distribution of the damage type and severity relative to impactor types, maintainers can be better prepared in terms of what to expect from a given impactor source. The developed model introduces a novel transition deformation region between the local deformation and the global plate deflection, allowing for fast and accurate predictions of the impact event. Using the known aluminium structural properties and damage dimensions, the damage data is converted into impactor data. The model is applied in a case study using 120 fuselage dent damages dimensions (length, width, and depth) from a Boeing 777 fleet. The results show that the model deduces impactor characteristics for 94% of the considered damages, ranging up to 240 J and 110 mm for impactor energy and radius respectively.
Working paper
(2019)
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Viswanath Dhanisetty, Philippe Massart, Fardin Esrail, Wim Verhagen, Christos Kassapoglou, Richard Curran
This paper proposes an analytical model capable of relating damage found on a composite plate to a given impactor characteristic (size and energy). The model addresses a gap in knowledge regarding the types of damages to be expected over the lifetime of a new generation of composite aircraft. The damage type and dimensions are estimated using a superposition of local indentation and global plate deflection. The analytical approach, validated by drop-weight experiments, uniquely uses the impact characteristics predicted from metal aircraft damages as inputs to model the impact event response for composite plates under the same impact event conditions. The case study demonstrates that impact data from metal aircraft can be used to anticipate damage for a composite aircraft. The results from the model indicate that of the impactors that previously damaged metal aircraft, 75% will cause surface dent damage, fibre breakage, or penetration. As an extension of the analytical model application, a risk assessment is conducted on the predicted impactors, incorporating maintenance cost as the primary indicator for event consequences. This assessment shows the risks the similar events pose on metal vs. a comparable composite structure and allows aircraft operators to anticipate and plan maintenance actions.
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This paper proposes an analytical model capable of relating damage found on a composite plate to a given impactor characteristic (size and energy). The model addresses a gap in knowledge regarding the types of damages to be expected over the lifetime of a new generation of composite aircraft. The damage type and dimensions are estimated using a superposition of local indentation and global plate deflection. The analytical approach, validated by drop-weight experiments, uniquely uses the impact characteristics predicted from metal aircraft damages as inputs to model the impact event response for composite plates under the same impact event conditions. The case study demonstrates that impact data from metal aircraft can be used to anticipate damage for a composite aircraft. The results from the model indicate that of the impactors that previously damaged metal aircraft, 75% will cause surface dent damage, fibre breakage, or penetration. As an extension of the analytical model application, a risk assessment is conducted on the predicted impactors, incorporating maintenance cost as the primary indicator for event consequences. This assessment shows the risks the similar events pose on metal vs. a comparable composite structure and allows aircraft operators to anticipate and plan maintenance actions.
Airline operators face accidental damages on their fleet of aircraft as part of operational practice. Individual occurrences are hard to predict; consequently, the approach towards repairing accidental damage is reactive in aircraft maintenance practice. However, by aggregating occurrence data and predicting future occurrence rates, it is possible to predict future long-term (strategic) demand for maintenance capacity. In this paper, a novel approach for integration of reliability modelling and inventory control is presented. Here, the concept of a base stock policy has been translated to the maintenance slot capacity problem to determine long-term cost-optimal capacity. Demand has been modelled using a superposed Non-homogeneous Poisson Process (NHPP). A case study has been performed on damage data from a fleet of Boeing 777 aircraft. The results prove the feasibility of adopting an integrated approach towards strategic capacity identification, using real-life data to predict future damage occurrence and associated maintenance slot requirements.
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Airline operators face accidental damages on their fleet of aircraft as part of operational practice. Individual occurrences are hard to predict; consequently, the approach towards repairing accidental damage is reactive in aircraft maintenance practice. However, by aggregating occurrence data and predicting future occurrence rates, it is possible to predict future long-term (strategic) demand for maintenance capacity. In this paper, a novel approach for integration of reliability modelling and inventory control is presented. Here, the concept of a base stock policy has been translated to the maintenance slot capacity problem to determine long-term cost-optimal capacity. Demand has been modelled using a superposed Non-homogeneous Poisson Process (NHPP). A case study has been performed on damage data from a fleet of Boeing 777 aircraft. The results prove the feasibility of adopting an integrated approach towards strategic capacity identification, using real-life data to predict future damage occurrence and associated maintenance slot requirements.
Impact damage repair decision-making for composite structures
Predicting impact damage on composite aircraft using aluminium data
There is a growth in the use of composites for the new generation of wide-body aircraft such as the Boeing 787 and Airbus A350. This shift from using aluminium as the primary material is motivated by the benefits of using composites in design, manufacturing and operations. Composites offer the aircraft manufacturer the ability to create more complex shapes and optimise the design such that it is light-weight. This, in tandem with other design improvements, leads to lower fuel burn. Consequently, airlines see the advantage of these new aircraft to reduce their operational cost. Therefore, as airlines continue to renew their ageing fleets of aluminium aircraft, there is going to be an increased need for composite maintenance. However, fulfilling the increased demand for composite repairs is impeded by limited availability of historical damage data, due to the young operational age of these aircraft. Composites are particularly sensitive to impact damage, and understanding the likelihood and the consequence of this type of damage is valuable for maintenance processes such as repair decision-making. The purpose of this dissertation is to predict the risk of impact damage for future composite aircraft and use it to substantiate maintenance decision-making in an operational setting...
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There is a growth in the use of composites for the new generation of wide-body aircraft such as the Boeing 787 and Airbus A350. This shift from using aluminium as the primary material is motivated by the benefits of using composites in design, manufacturing and operations. Composites offer the aircraft manufacturer the ability to create more complex shapes and optimise the design such that it is light-weight. This, in tandem with other design improvements, leads to lower fuel burn. Consequently, airlines see the advantage of these new aircraft to reduce their operational cost. Therefore, as airlines continue to renew their ageing fleets of aluminium aircraft, there is going to be an increased need for composite maintenance. However, fulfilling the increased demand for composite repairs is impeded by limited availability of historical damage data, due to the young operational age of these aircraft. Composites are particularly sensitive to impact damage, and understanding the likelihood and the consequence of this type of damage is valuable for maintenance processes such as repair decision-making. The purpose of this dissertation is to predict the risk of impact damage for future composite aircraft and use it to substantiate maintenance decision-making in an operational setting...
This paper proposes an approach towards multi-criteria decision making (MCDM) for operational maintenance processes. It focuses on decision alternative identification and evaluation for short time horizons, thereby addressing problems that need to be resolved in hours or a few days at maximum. This addresses a gap in literature, where MCDM methods are predominantly proposed for strategic maintenance decision making. The proposed approach addresses two distinct steps of decision making: 1) identification of decision alternatives and 2) evaluation of decision alternatives. For identification of decision options, the Boolean Decision Tree (BDT) method is selected to accommodate for the qualitative and discrete operational factors that determine the available, feasible decision alternatives in operational maintenance processes. The feasible alternatives are subsequently evaluated using the weighted sum method (WSM). The approach is applied to a Boeing 777 outboard flap damage case, using real maintenance and operational data. A decision tool has been developed and verified, showing the capability of the approach to systematically identify and evaluate operational maintenance decision making problems in a few minutes. The results suggest that the proposed approach could save in excess of 50% on decision process time, with added benefits in full identification of the available set of decision alternatives at problem onset. In addition, sensitivity analysis on the basis of a global evaluation of the weight space is provided to investigate the impact of weight settings on the decision outcomes.
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This paper proposes an approach towards multi-criteria decision making (MCDM) for operational maintenance processes. It focuses on decision alternative identification and evaluation for short time horizons, thereby addressing problems that need to be resolved in hours or a few days at maximum. This addresses a gap in literature, where MCDM methods are predominantly proposed for strategic maintenance decision making. The proposed approach addresses two distinct steps of decision making: 1) identification of decision alternatives and 2) evaluation of decision alternatives. For identification of decision options, the Boolean Decision Tree (BDT) method is selected to accommodate for the qualitative and discrete operational factors that determine the available, feasible decision alternatives in operational maintenance processes. The feasible alternatives are subsequently evaluated using the weighted sum method (WSM). The approach is applied to a Boeing 777 outboard flap damage case, using real maintenance and operational data. A decision tool has been developed and verified, showing the capability of the approach to systematically identify and evaluate operational maintenance decision making problems in a few minutes. The results suggest that the proposed approach could save in excess of 50% on decision process time, with added benefits in full identification of the available set of decision alternatives at problem onset. In addition, sensitivity analysis on the basis of a global evaluation of the weight space is provided to investigate the impact of weight settings on the decision outcomes.
Airline Maintenance and Engineering (M&E) organizations face accidental damages on their fleet of aircraft as part of daily practice. As this type of damage is stochastic in nature, the approach towards repairing accidental damage is reactive in practice. However, it is possible to predict future long-term (strategic) demand for maintenance resources associated with accidental damages and use this to identify required capacity. To achieve the mutually related goals of prediction of future repairs and determination of capacity, a novel approach for integration of reliability modelling and inventory control is presented in this paper. Here, the concept of inventory control has been specifically applied to determine the maintenance capacity by taking into account the stochastic demand related to unscheduled repairs following from accidental damages. To predict demand, a Non-homogeneous Poisson Process (NHPP) reliability model has been adopted. The reliability model includes superpositioning, through which failure behaviour at aircraft fleet-level can be estimated and subsequently simulated. The resulting demand is fed into a single-system, single location base-stock inventory model. This allows for determination of strategic capacity based on optimum costs as well as service level requirements. A case study has been performed on a fleet of Boeing 777 aircraft of a major European airline. The results prove the feasibility of adopting an integrated approach towards strategic capacity identification, using real-life data to predict future demand occurrence.
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Airline Maintenance and Engineering (M&E) organizations face accidental damages on their fleet of aircraft as part of daily practice. As this type of damage is stochastic in nature, the approach towards repairing accidental damage is reactive in practice. However, it is possible to predict future long-term (strategic) demand for maintenance resources associated with accidental damages and use this to identify required capacity. To achieve the mutually related goals of prediction of future repairs and determination of capacity, a novel approach for integration of reliability modelling and inventory control is presented in this paper. Here, the concept of inventory control has been specifically applied to determine the maintenance capacity by taking into account the stochastic demand related to unscheduled repairs following from accidental damages. To predict demand, a Non-homogeneous Poisson Process (NHPP) reliability model has been adopted. The reliability model includes superpositioning, through which failure behaviour at aircraft fleet-level can be estimated and subsequently simulated. The resulting demand is fed into a single-system, single location base-stock inventory model. This allows for determination of strategic capacity based on optimum costs as well as service level requirements. A case study has been performed on a fleet of Boeing 777 aircraft of a major European airline. The results prove the feasibility of adopting an integrated approach towards strategic capacity identification, using real-life data to predict future demand occurrence.
Decision making in daily maintenance requires consideration of multiple factors. The importance of each of the factors fluctuates depending on the repair scenario and the needs of the maintainer. In order to include the prioritisation of multiple criteria, a weighted decision making model is developed. The model evaluates all repair options and rates them individually for three decision making factors: survivability, cost, and downtime. The factor ratings are aggregated using designated weights, resulting in a final score for each of the repair options. This type of decision making evaluation provides flexibility in considering repair options that may otherwise be deemed unfavourable because of one factor. Case study results show one of five considered options as the best, for three of the four weight sets. The resulting best option of the other weight set demonstrates that definition of best repair is dependent on the priority of decision factors.
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Decision making in daily maintenance requires consideration of multiple factors. The importance of each of the factors fluctuates depending on the repair scenario and the needs of the maintainer. In order to include the prioritisation of multiple criteria, a weighted decision making model is developed. The model evaluates all repair options and rates them individually for three decision making factors: survivability, cost, and downtime. The factor ratings are aggregated using designated weights, resulting in a final score for each of the repair options. This type of decision making evaluation provides flexibility in considering repair options that may otherwise be deemed unfavourable because of one factor. Case study results show one of five considered options as the best, for three of the four weight sets. The resulting best option of the other weight set demonstrates that definition of best repair is dependent on the priority of decision factors.
This paper details the development of a decision-making model that evaluates the multiple repair levels that a composite structure can undergo, each with its inherent achievable survivability and consequence to operations in terms of availability, costs, and scheduling. The goal of this model is to provide the maintainer an integrated approach to all feasible repair solutions within the operational and structural integrity constraints, applicable to any given damage levels found during monitoring. At its core, the model incorporates various stochastic processes to model different types of repairable behavior: the non-homogeneous Poisson process and the renewal process. A case study on the carbon-fiber reinforced polymer flaps of a Boeing 777 has been performed to verify and validate the proposed decision-making model. With the case study providing the means for application of the model in an operational context, a standardized decision making process was delivered that is adaptable to any given failure scenario and implementable in practice.
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This paper details the development of a decision-making model that evaluates the multiple repair levels that a composite structure can undergo, each with its inherent achievable survivability and consequence to operations in terms of availability, costs, and scheduling. The goal of this model is to provide the maintainer an integrated approach to all feasible repair solutions within the operational and structural integrity constraints, applicable to any given damage levels found during monitoring. At its core, the model incorporates various stochastic processes to model different types of repairable behavior: the non-homogeneous Poisson process and the renewal process. A case study on the carbon-fiber reinforced polymer flaps of a Boeing 777 has been performed to verify and validate the proposed decision-making model. With the case study providing the means for application of the model in an operational context, a standardized decision making process was delivered that is adaptable to any given failure scenario and implementable in practice.