Fault Detection for Shipboard Monitoring – Volterra Kernel and Hammerstein Model Approaches

Lajic, Zoran; Blanke, Mogens; Nielsen, Ulrik Dam

doi:10.3182/20090630-4-es-2003.00004

Cited by 11 publications

(5 citation statements)

References 5 publications

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“…Wittkuhn et al [57]. Moreover, for DSS and the combined use of onboard monitoring systems there is also a (foreseen) great potential in looking at ideas developed within control engineering with regard to (automatic) fault monitoring and detection, so that the decision support system can be developed as a fault tolerant system; studies in this area have begun, see Lajic et al [26] and Lajic and Nielsen [27].…”

Section: 4mentioning

confidence: 99%

A novel approach for navigational guidance of ships using onboard monitoring systems

Nielsen

Jensen

2011

Ocean Engineering

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Section: 4mentioning

confidence: 99%

A novel approach for navigational guidance of ships using onboard monitoring systems

Nielsen

Jensen

2011

Ocean Engineering

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“…The residuals were evaluated by the GLR test, and all the faults were detected and isolated. Using the model presented in the paper, we are able not only to detect, as in case of previous models given by [7] and [8], but also to isolate all the faults. Having fault isolation is very important for the design of a fault tolerant monitoring and decision support system.…”

Section: Discussionmentioning

confidence: 99%

“…Several papers deal with fault-tolerant monitoring and/or control of ship responses to the waves. Fault detection and fault-tolerant approaches for the SeaSense system have been presented in [7] and [8] using a different model than the model described in this paper. With the "old" model, purely based on the ship kinematics, it was possible to detect, but not to isolate all the faults.…”

mentioning

confidence: 99%

Fault Isolation and Quality Assessment for Shipboard Monitoring

Lajic

Nielsen

Blanke

2010

29th International Conference on Ocean, Offshore and Arctic Engineering: Volume 4

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In this paper a new approach for increasing the overall reliability of a monitoring and decision support system will be explained. The focus is on systems used for ship operator guidance with respect to, say, speed and heading. The basic idea is to convert the given system into a fault tolerant system and to improve multi-sensor data fusion for the particular system. Fault isolation is an important part of the fault tolerant design for in-service monitoring and decision support systems for ships. In the paper, a virtual example of fault isolation will be presented. Several possible faults will be simulated and isolated using residuals and the generalized likelihood ratio (GLR) algorithm. It will be demonstrated that the approach can be used to increase accuracy of sea state estimations employing sensor fusion quality test.

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“…To support these approaches, different data collection practices employed in the shipping industry for acquiring data on-board ships (Armstrong & Banks, 2015;Aldous, et al, 2015), in specific: (a) Noon Reports (NR), where the crew is responsible of recording one data entry every 24 hours, and (b) Automatic Data Logging Monitoring (ADLM) systems, which can record a number of parameters at any specified sampling rate that usually results in a vast amount of high-quality data (Nielsen, et al, 2012;Lajic, et al, 2009;Perera & Mo, 2016). By employing the NR method, the data parameters recorded are limited and the data quality may be doubtable owing to the involvement of the human element.…”

Section: Introductionmentioning

confidence: 99%

A novel systematic methodology for ship propulsion engines energy management

Tsitsilonis

Theotokatos²

2018

Journal of Cleaner Production

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Establishing an energy monitoring and management methodology is a quintessential milestone for informed energy savings decision making as well as for effectively reducing the cost and the environmental impact of shipping operations. In this study, a novel systematic methodology is proposed for the energy management of the ship propulsion engine, which is the largest ship energy producer. The methodology employs a combination of tools including statistical analysis, predicting the engine air flow via compressor modelling and energy and exergy analyses, whereas its output includes the engine operating profile, the most frequently occurring propeller curves and the engine most frequent operating points, the key performance indicators for quantitatively assessing the recorded parameters quality as well as the energy and exergy flows and exergy destruction of the engine components. The methodology is implemented for three case studies for a very large crude carrier, a container ship and a bulk carrier, for which recorded data were available by using different monitoring techniques from either noon reports of automatic data acquisition systems. The derived results provide the engine operating profile demonstrating that the investigated vessels were operating in slow steaming conditions with a lower engine efficiency associated with a greater exhaust gas wasted energy. The measured data analysis demonstrates the better quality of the data recorded by automated monitoring systems and pinpoint maintenance issues of the engine components. Lastly, the exergy analysis results demonstrate that the exhaust gas and jacket cooling water provide the greater exergy flows rendering them attractive for energy saving initiatives, whereas the engine block, compressor and turbine are the engine components with the greater exergy destruction, thus requiring closing monitoring for timely identifying mitigating measures.

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Fault Detection for Shipboard Monitoring – Volterra Kernel and Hammerstein Model Approaches

Cited by 11 publications

References 5 publications

A novel approach for navigational guidance of ships using onboard monitoring systems

A novel approach for navigational guidance of ships using onboard monitoring systems

Fault Isolation and Quality Assessment for Shipboard Monitoring

A novel systematic methodology for ship propulsion engines energy management

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