A Study on the Development of a Failure Simulation Database for Condition Based Maintenance of Marine Engine System Auxiliary Equipment

Kim, Jeong-Yeong; Lee, Tae-Hyun; Lee, Song-Ho; Lee, Jong-Jik; Shin, Dongmin; Lee, Won-kyun; Kim, Youg Jin

doi:10.3744/snak.2022.59.4.200

SNAK

2022

DOI: 10.3744/snak.2022.59.4.200

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A Study on the Development of a Failure Simulation Database for Condition Based Maintenance of Marine Engine System Auxiliary Equipment

Jeong-Yeong Kim¹,

Tae-Hyun Lee²,

Song-Ho Lee³

et al.

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Cited by 3 publications

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A Study on the Application of Discrete Wavelet Decomposition for Fault Diagnosis on a Ship Oil Purifier

Lee

Kim

et al. 2022

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With the development of the Internet of things, big data, and AI leading the 4th industrial revolution, it has become possible to acquire, manage, and analyze vast and diverse condition signals from various industrial machinery facilities. In addition, it has been revealed that various and large amounts of signals acquired from the facilities can be utilized for fault diagnosis. Currently, while data-driven fault diagnosis techniques applicable to the facilities are being developed, it has been tried to apply the techniques for the development of fully autonomous ships in the shipbuilding and shipping industry. Since the autonomous ships must be able to detect and diagnose the failures on their own in real time, the overall research is required on how to acquire signals from the ship facilities and use them to diagnose their failures. In this study, a fault diagnosis framework was proposed for condition-based maintenance (CBM) of ship oil purifiers, which are an auxiliary facility in the engine system of a ship. First, an oil purifier test-bed for simulating faults was built to obtain data on the state of the equipment. After extracting features using discrete wavelet decomposition from the data, the features were visualized by using t-distributed stochastic neighbor embedding, and were used to train support vector machine-based diagnostic models. Finally, the trained models were evaluated with Accuracy and F1 score, and some models scored 0.99 or higher, confirming high diagnostic performance. This study can be used as a reference for establishing CBM system and fault diagnosis system. Furthermore, this study is expected to improve the safety and reliability of oil purifiers in Degree 4 MASS.

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A Study on the Application of Discrete Wavelet Decomposition for Fault Diagnosis on a Ship Oil Purifier

Lee

Kim

et al. 2022

Processes

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The Study of Failure Mode Data Development and Feature Parameter’s Reliability Verification Using LSTM Algorithm for 2-Stroke Low Speed Engine for Ship’s Propulsion

Park¹,

Kwon²,

Kim³

et al. 2023

SNAK

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Development of Equipment for Generating and Measuring Vibration Data for Electric Motor

Jang,

Lee,

Lee

et al. 2024

SNAK

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A Study on the Development of a Failure Simulation Database for Condition Based Maintenance of Marine Engine System Auxiliary Equipment

Cited by 3 publications

References 4 publications

A Study on the Application of Discrete Wavelet Decomposition for Fault Diagnosis on a Ship Oil Purifier

A Study on the Application of Discrete Wavelet Decomposition for Fault Diagnosis on a Ship Oil Purifier

The Study of Failure Mode Data Development and Feature Parameter’s Reliability Verification Using LSTM Algorithm for 2-Stroke Low Speed Engine for Ship’s Propulsion

Development of Equipment for Generating and Measuring Vibration Data for Electric Motor

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