Advantages of Condition-Based Maintenance over Scheduled Maintenance Using Structural Health Monitoring System

Dong, Ting; Haftka, Raphael T.; Kim, Nam Hoon

doi:10.5772/intechopen.83614

Cited by 10 publications

(8 citation statements)

References 30 publications

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“…An effective planning can save on maintenance costs and resources and it affects the safety and operational efficiency of the maintenance activities [124]. According to literature, condition-based maintenance typically generates savings ranging between 90% -95% with respect to the costs of the time based maintenance [125][126][127]. However, as a limitation of this approach, the separate analysis of the survey outcomes may fail in detecting decay trends, especially in case an accurate comparison between successive inspections is lacking.…”

Section: Benefits Of Network-level Satellite Remote Sensing and Ndt M...mentioning

confidence: 99%

Non-destructive technologies for sustainable assessment and monitoring of railway infrastructure: a focus on GPR and InSAR methods

et al. 2021

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Health monitoring of ballast in railway infrastructures is crucial to assure structural stability and efficiency of the operations. To this extent, an efficient and sustainable management of maintenance is fundamental for asset managers in setting up strategic and effective action plans.Amongst the available methods to assess the conditions of railway infrastructures, non-destructive technologies (NDT) are gaining popularity due to their capability to overcome main drawbacks of traditional routine methods, such as digging trenches and visually inspect locations assumed as critical along the track.The present paper presents an overview of the use of the Ground Penetrating Radar (GPR) and the Interferometric Synthetic Aperture (InSAR) technologies for a sustainable monitoring of railway infrastructures. Specifically, main traditional and non-destructive methods used for maintenance of railway ballast materials are presented with a special focus on their sustainability. A review about the research methods on the use of GPR and InSAR technologies for railway infrastructures also reported, including main investigations carried out in the laboratory and the real-life environments. Furthermore, a conceptual framework based on an integrated approach including satellite-based and ground-based investigations is proposed, where network and local level information can be merged for the detection of critical sections and the implementation of a more advanced predictive maintenance system.

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Section: Benefits Of Network-level Satellite Remote Sensing and Ndt M...mentioning

confidence: 99%

Non-destructive technologies for sustainable assessment and monitoring of railway infrastructure: a focus on GPR and InSAR methods

et al. 2021

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“…The authors included in the maintenance cost estimation just the costs affected by replacing the SHM equipment, starting from the assumption that the cost analysis of an aircraft over its lifecycle is too complicated. Later, Dong et al quantified the benefits of a condition-based maintenance based on SHM on the lifetime cost of an airplane fuselage [17]. Manufacturing costs, maintenance costs and fuel costs were considered in the analysis.…”

Section: Literature Reviewmentioning

confidence: 99%

Economic and Environmental Sustainability for Aircrafts Service Life

et al. 2020

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Aircrafts are responsible for a significant environmental impact mainly due to the air pollution caused by their motors. The use of composite materials for their production is a way to significantly reduce the weight of the structures and to maximise the ratio between the payload weight and the gasoline consumption. Moreover, the design phase has to consider the cost of different operations performed during the aircraft service life. During the entire life cycle, one of the main costs is the maintenance one. In the current literature, there is a lack of knowledge of methods for maintenance cost estimation in the aircraft industry; moreover, very few environmental assessment methods have been developed. Thus, the aim of this paper is to define a new method to support the aircraft design process; both the environmental and the economic dimensions have been included with the purpose of assessing the aircraft sustainability during its service life. A green index has been identified mixing the maintenance cost and an environmental parameter with the aim of identifying the greenest solution. A final practical application shows the feasibility and the simple application of the proposed approach.

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“…Companies in several fields are increasingly integrating Structural Health Monitoring (SHM) systems into maintenance programs, to move toward more efficient condition-based strategies. [1][2][3] In this context, diagnosis methods based on the analysis of Ultrasonic Guided Waves (UGWs) have been consistently used to monitor the health state of thin-walled structures. Although traditional methods have been successfully applied to detect and localize damage, their performance is usually conditioned by the manual features extraction process required for the definition of the damagesensitive features, or damage indices (DIs).…”

Section: Introductionmentioning

confidence: 99%

Enhancing Lamb wave-based damage diagnosis in composite materials using a pseudo-damage boosted convolutional neural network approach

Gonzalez-Jimenez,

Lomazzi,

Junges

et al. 2023

Structural Health Monitoring

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Damage diagnosis of thin-walled structures has been successfully performed through methods based on tomography and machine learning-driven methods. According to traditional approaches, diagnostic signals are excited and sensed on the structure through a permanently installed network of sensors and are processed to obtain information about the damage. Good performance characterizes methods that process Lamb waves, which are described by long propagation distances and high sensitivity to anomalies. Most of the methods require extracting damage-sensitive features from the diagnostic signals to drive the damage diagnosis task. However, this process can lead to loss of information, and the choice of the specific feature to extract may introduce biases that hamper damage diagnosis. Furthermore, traditional approaches do not perform well when composites are considered, due to the anisotropy, inhomogeneity, and complex damage mechanisms shown by this type of material. To boost the performance of methods for damage diagnosis of composite plates, this work proposes a convolutional neural network (CNN)-based algorithm that localizes damage by processing Lamb waves. Different from other methods, the proposed method does not require extracting features from the acquired signals and allows localizing damage through the regression approach. The method was tested against experimental observations of Lamb waves propagating in two composite panels and in a hybrid panel, and the performance of two different sensor arrays was investigated. The pseudo-damage approach was used to generate large enough datasets for training the CNNs, and the performance of the framework was evaluated by localizing pseudo-damage and real damage determined by low-velocity impacts. The CNN-driven method accurately localized damage in all the considered scenarios, and it also outperformed traditional damage indices-based approaches, such as the reconstruction algorithm for probabilistic inspection of defects.

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Advantages of Condition-Based Maintenance over Scheduled Maintenance Using Structural Health Monitoring System

Cited by 10 publications

References 30 publications

Non-destructive technologies for sustainable assessment and monitoring of railway infrastructure: a focus on GPR and InSAR methods

Non-destructive technologies for sustainable assessment and monitoring of railway infrastructure: a focus on GPR and InSAR methods

Economic and Environmental Sustainability for Aircrafts Service Life

Enhancing Lamb wave-based damage diagnosis in composite materials using a pseudo-damage boosted convolutional neural network approach

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