Real-time diagnostic system using acoustic emission for a cylinder liner in a large two-stroke diesel engine

Kim, Jang Kyu; Lee, Min Cheol

doi:10.1007/s12541-009-0047-7

Cited by 11 publications

(2 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Ultrasonic detection is fast and cost efficient compared to temperature, vibration and oil analysis as it does not require sensor installation on the specified components that are monitored (Kim and Lee, 2009). The importance of ultrasonic CM is also suggested by IACS as presented Unified Requirements and Procedural Requirements for Ultrasonic Thickness Measurements (UTM) (IACS, 2004).…”

Section: Acoustic and Ultrasonic Monitoringmentioning

confidence: 99%

Numerical analysis of tank heating coil heat transfer process

Dikis

Lazakis

Theotokatos

2015

Towards Green Marine Technology and Transport

View full text Add to dashboard Cite

This paper presents the development of the Machinery Risk Analysis (MRA) methodology for different ship machinery. This study is part of INCASS (Inspection Capabilities for Enhanced Ship Safety) FP7 EU funded research project, which tackles the issue of ship inspection and maintenance by accessing information related to ship surveys and incorporate harmonized cooperation of maritime stakeholders in order to avoid ship accidents, promote maritime safety and protect the environment. In this study, different systems are considered together with associated failure modes. The innovation of MRA is the consideration of multiple systems, sub-systems and components as well as failure interdependencies providing a holistic view of the reliability performance. Furthermore, MRA takes into account the system’s dynamic change of state, involving failure rate variation within time. The presented methodology involves the generation of a Markov Chain model integrated with the advantages of Bayesian Belief Networks (BBNs) developed in Java programming language

show abstract

Section: Acoustic and Ultrasonic Monitoringmentioning

confidence: 99%

Numerical analysis of tank heating coil heat transfer process

Dikis

Lazakis

Theotokatos

2015

Towards Green Marine Technology and Transport

View full text Add to dashboard Cite

show abstract

“…Consequently, the TDA method has been widely applied to rotary machinery component diagnosis, for example the gear fault detection is a typical application where the fault signal is closely related to the rotating speed of the component. [4][5][6][7][8][9][10][11] In the TDA process, the measured signal is first divided into many segments according to the period of the rotating component. Then, it averages all the segments.…”

Section: Introductionmentioning

confidence: 99%

Realization of the real-time time domain averaging method using the Kalman filter

Shin

2011

Int. J. Precis. Eng. Manuf.

View full text Add to dashboard Cite

NOMENCLATURE A = matrix of the state model H = matrix of the measurement function K k = Kalman gain matrix P k = error covariance matrix Q = process noise covariance matrix R = measurement noise covariance matrix v k = process noise vector w k = measurement noise vector x k = state vector at the k-th state f p = Fundamental frequency M = Number of segments N = number of elements in each segment Time Domain Averaging (TDA) is a traditional (though powerful) method of extracting periodic signals from a composite signal, based on averaging signal sections of the period chosen. The TDA method has been widely used for the condition monitoring of rotating machinery as a pre-process. However, the averaging process requires the measured data to be recorded, and thus may not be easily implemented as a real-time (or on-line) processor. This paper presents an alternative method of performing the TDA that can easily be realized as a real-time averaging processor by using the Kalman filter. The suggested method has another advantage over the traditional TDA method, which is to monitor the variance reduction continuously as the averaging process evolves. This may help to determine whether the averaging is further needed or not. The method is verified by using both simulated data and a measured signal.

show abstract