Identification of slam events experienced by a high-speed craft

Magoga, Teresa; Aksus, Seref; Cannon, Stuart; Ojeda, Roberto Suárez; Thomas, Giles

doi:10.1016/j.oceaneng.2016.07.017

Cited by 11 publications

(4 citation statements)

References 9 publications

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“…As mentioned earlier, various methods for identification of slamming events have been proposed previously including whipping-based criterion (Dessi, 2014), maximum rate of change of stress (Jacobi et al, 2014, Magoga et al, 2017, and wavelet methods (Amin et al, 2013). The approach proposed in Figure 8 considers bow vertical accelerations, strain gauge measurements and relative motions as the inputs for an unsupervised learning algorithm for the classification of the bow entry events.…”

Section: Time Series Segmentation and Feature Extractionmentioning

confidence: 99%

“…The first three features mentioned above represent some of the problems that were addressed by Ochi and Motter (Ochi and Motter, 1973) through a closed-form statistical model. More details of the Ochi slamming conditions and slamming probability can be found in (Dessi and Ciappi, 2013) and a review of slamming identification methods can be also found in (Magoga et al, 2017). Nevertheless, the alternative methods based on ML have yet to be developed to identify and classify slamming events in random waves.…”

Section: Introductionmentioning

confidence: 99%

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Using Remote Monitoring and Machine Learning to Classify Slam Events of Wave Piercing Catamarans

Shabani¹,

Ali-Lavroff²,

Holloway³

et al. 2021

IJME

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An onboard monitoring system can measure features such as stress cycles counts and provide warnings due to slamming. Considering current technology trends there is the opportunity of incorporating machine learning methods into monitoring systems. A hull monitoring system has been developed and installed on a 111 m wave piercing catamaran (Hull 091) to remotely monitor the ship kinematics and hull structural responses. Parallel to that, an existing dataset of a similar vessel (Hull 061) was analysed using unsupervised and supervised learning models; these were found to be beneficial for the classification of bow entry events according to key kinematic parameters. A comparison of different algorithms including linear support vector machines, naïve Bayes and decision tree for the bow entry classification were conducted. In addition, using empirical probability distributions, the likelihood of wet-deck slamming was estimated given a vertical bow acceleration threshold of 1 in head seas, clustering the feature space with the approximate probabilities of 0.001, 0.030 and 0.25.

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Section: Time Series Segmentation and Feature Extractionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Using Remote Monitoring and Machine Learning to Classify Slam Events of Wave Piercing Catamarans

Shabani¹,

Ali-Lavroff²,

Holloway³

et al. 2021

IJME

View full text Add to dashboard Cite

show abstract

“…As such, the data processing routines include the removal of spikes. High frequency noise is attenuated by applying a low-pass Chebyshev filter at 8 Hz, which is greater than the three-node bending vibration frequency of 5 Hz of the hull girder (Magoga, et al 2017). The stress signal was reduced to cycles using the rainflow counting method proposed by Rychlik (1987).…”

Section: Stress Spectramentioning

confidence: 99%

Through-life hybrid fatigue assessment of naval ships

Magoga

Aksu

Cannon

et al. 2019

Ships and Offshore Structures

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When a ship enters service it has an implied or calculated design fatigue life. Factors including construction quality, loads, material performance, and operational profile are assumed. However, these factors vary through life. Failure of structural items due to fatigue cracking can lead to increased cost of ownership and reduced capability. Therefore, to allow informed decision-making regarding fleet availability and economy, it is necessary to monitor the variables that influence a ship's fatigue life. This paper presents a new method whereby in-service data, fleet maintenance reports, and Finite Element Analysis are incorporated to predict ongoing fatigue life and hence support the management of the remaining service life of a ship. The method is applied to a naval High Speed Light Craft to demonstrate its merit. The fatigue lives of different welded details are predicted using a practical implementation of the S-N curve approach, employed with strain and operational data acquired from an on-board hull monitoring system. The proposed method is efficient because it is properly linked to stress analysis, inherently includes the influence of the operational profile, and is a balance between accuracy and effort.

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“…These assumptions are usually based on operational experience rather than measured data (Coraddu et al, 2016) and could mean that once a vessel enters service, the operational scenarios and usage profiles developed during requirements development may not reflect the actual usage of the vessel. As demonstrated in Magoga et al (2016) and Magoga et al (2014), and shown in Figure 1, the operational profile assumed during the design of a naval vessel (the baseline) did not accurately reflect the actual operational profile the crew adopted in response to the tactical situations they encountered. It can be further seen from the in-service operational profiles shown in Figure 1 that there can even be differences between the ways in which different vessels in the same class are operated.…”

Section: Introductionmentioning

confidence: 95%

A Framework for Identifying and Managing New Operational Requirements during Naval Vessel Batch‐Building Programs

Morris

2021

INCOSE International Symp

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One approach to building a class of naval vessels that has recently been adopted in Australia is to fix a design for a subset of the total number of vessels to be built. These subsets can be termed batches, or flights of vessels. The batch-building approach allows incremental changes to be made to the design for the follow-on batches and is analogous to evolutionary systems development. These design changes will be a response to updated operational requirements that typically result from the maturation of technology that needs to be integrated into the design or shifting geo-political circumstances that change the capability needs. A third key driver of new operational requirements that is not currently managed in a robust, traceable manner is the need to adapt the design of the vessel to account for how it is actually being used in-service. This need arises due to the potential mismatch between the operational scenarios and operational profiles developed during requirements definition activities and the operational profile the crew adopts when actually using the vessel in operations. Such a mismatch between the owner's original operational requirements and the in-service operational profile can result in sub-optimal outcomes for the vessel's performance. This paper investigates the research question: "how can new operational requirements be identified, managed and integrated to the design of follow-on batches of vessels in a naval vessel batch-building program?" The paper begins with an introduction into the effect a mismatched design and in-service operational profile can have on vessel performance. It is followed by a review of the open literature covering naval vessel batch-building and evolutionary system development. From this review, a high-level framework for incorporating updated operational requirements based on in-service operational data, new technology, or changes in strategic circumstances into the design of follow-on batches in naval vessel batch-building programs is synthesized. The paper concludes with some initial observations on how the framework's implementation could be supported by digital engineering and outlines key aspects that are required to support its implementation.

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Identification of slam events experienced by a high-speed craft

Cited by 11 publications

References 9 publications

Using Remote Monitoring and Machine Learning to Classify Slam Events of Wave Piercing Catamarans

Using Remote Monitoring and Machine Learning to Classify Slam Events of Wave Piercing Catamarans

Through-life hybrid fatigue assessment of naval ships

A Framework for Identifying and Managing New Operational Requirements during Naval Vessel Batch‐Building Programs

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