Development of a New Expert System for Diagnosing Marine Diesel Engines Based on Real-Time Diagnostic Parameters

Gharib, Hla; Kovács, György

doi:10.5545/sv-jme.2022.298

Cited by 6 publications

(5 citation statements)

References 13 publications

(23 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Fuzzy expert systems deal with imprecise and uncertain information. Neural network systems simulate reasoning, and genetic algorithm systems use a genetic algorithm to find optimal solutions [72,73].…”

Section: Knowledge-based Methods (Expert Systems)mentioning

confidence: 99%

“…In addition, knowledge-based methods may not be able to detect new or unexpected problems, as these types of problems may not be represented in the database, which can affect the accuracy of the diagnosis. In addition, maintaining and updating the knowledge database can be time-consuming and resource-intensive, as new information about the engine and its components needs to be constantly added to the database [73].…”

Section: Knowledge-based Methods (Expert Systems)mentioning

confidence: 99%

See 1 more Smart Citation

A Review of Prognostic and Health Management (PHM) Methods and Limitations for Marine Diesel Engines: New Research Directions

Gharib

Kovács

2023

Machines

Self Cite

View full text Add to dashboard Cite

Prognostic and health management (PHM) methods focus on improving the performance and reliability of systems with a high degree of complexity and criticality. These systems include engines, turbines, and robotic systems. PHM methods involve managing technical processes, such as condition monitoring, fault diagnosis, health prognosis, and maintenance decision-making. Various software and applications deal with the processes mentioned above independently. We can also observe different development levels, making connecting all of the machine’s technical processes in one health management system with the best possible output a challenging task. This study’s objective was to outline the scope of PHM methods in real-time conditions and propose new directions to develop a decision support tool for marine diesel engines. In this paper, we illustrate PHM processes and the state of the art in the marine industry for each technical process. Then, we review PHM methods and limitations for marine diesel engines. Finally, we analyze future research opportunities for the marine industry and their role in developing systems’ performance and reliability. The main added value of the research is that a research gap was found in this research field, which is that new advanced PHM methods have to be implemented for marine diesel engines. Our suggestions to improve marine diesel engines’ operation and maintenance include implementing advanced PHM methods and utilizing predictive analytics and machine learning.

show abstract

Section: Knowledge-based Methods (Expert Systems)mentioning

confidence: 99%

Section: Knowledge-based Methods (Expert Systems)mentioning

confidence: 99%

A Review of Prognostic and Health Management (PHM) Methods and Limitations for Marine Diesel Engines: New Research Directions

Gharib

Kovács

2023

Machines

Self Cite

View full text Add to dashboard Cite

show abstract

“…Therefore, it is necessary to go back to the one cited earlier in this paper and re-examine the importance of the passage of time in any process, not only in the S.C. process. For example, in considering the impact of the passage of time on the condition of a technical object, there is also the concept of looking at the passage of time in terms of "frames of film" (Masalimov et al, 2022;Gharib, Kovács, 2022). The current state of an object is the state belonging to a point on the timeline that we can refer to as "now".…”

Section: Diagram Of the Matrix Linking The Process Stakeholders (Know...mentioning

confidence: 99%

Untitled

2023

SPSUTOM

View full text Add to dashboard Cite

The purpose of this paper is to explore the ways in which Corporate Social Responsibility (CSR) results can be significantly influenced by ethical management. Design/methodology/approach: There is combined in the paper the research analysis and narrative approach. The social side of ethical management was explored by looking at the history of these companies, focusing on the unethical practices they had to deal with. Findings: As a result of the research performed it became apparent that ethical management is more than just a theory. It affects how businesses interact with staff, comply with legal requirements, safeguard whistleblowers, and evaluate their ethical performance. Research limitations/implications:There are some limitations of the investigation. Deeper research need to be conducted to fully capture the diversity of organizational contexts and industries to understand in what way ethical management can impact their CSR strategy. Practical implications: Implementing these results to the real world means that ethical management is more than a slogan used by businesses. It's about establishing ethically conscious workplaces, encouraging values, and, in the end, influencing how companies interact with society. Originality/value: The personal perspective that was brought to the frequently theoretical field of ethical management sets these investigation apart. The goal was to provide insights that will benefit both academics and individuals navigating the challenges of ethical leadership in the real world by combining research with true stories.

show abstract

“…They developed an expert system for engine fault diagnosis that was applied to abnormal wear detection in a specific type of marine diesel engine; this system is capable of identifying concurrent fault modes. Moreover, Gharib et al [18] developed an improved expert system by selecting the most effective diagnostic parameters for diesel engine subsystems. This system determines the real-time technical conditions of marine diesel engines.…”

Section: Introductionmentioning

confidence: 99%

Fusion of Multi-Layer Attention Mechanisms and CNN-LSTM for Fault Prediction in Marine Diesel Engines

Sun,

Ren,

Duan

et al. 2024

JMSE

View full text Add to dashboard Cite

Timely and effective maintenance is imperative to minimize operational disruptions and ensure the reliability of marine vessels. However, given the low early warning rates and poor adaptability under complex conditions of previous data-driven fault prediction methods, this paper presents a hybrid deep learning model based on multi-layer attention mechanisms for predicting faults in a marine diesel engine. Specifically, this hybrid model first introduces a Convolutional Neural Network (CNN) and self-attention to extract local features from multi-feature input sequences. Then, we utilize Long Short-Term Memory (LSTM) and multi-head attention to capture global correlations across time steps. Finally, the hybrid deep learning model is integrated with the Exponential Weighted Moving Average (EWMA) to monitor the operational status and predict potential faults in the marine diesel engine. We conducted extensive evaluations using real datasets under three operating conditions. The experimental results indicate that the proposed method outperforms the current state-of-the-art methods. Moreover, ablation studies and visualizations highlight the importance of fusing multi-layer attention, and the results under various operating conditions and application scenarios demonstrate that this method possesses predictive accuracy and broad applicability. Hence, this approach can provide decision support for condition monitoring and predictive maintenance of marine mechanical systems.

show abstract

Development of a New Expert System for Diagnosing Marine Diesel Engines Based on Real-Time Diagnostic Parameters

Cited by 6 publications

References 13 publications

A Review of Prognostic and Health Management (PHM) Methods and Limitations for Marine Diesel Engines: New Research Directions

A Review of Prognostic and Health Management (PHM) Methods and Limitations for Marine Diesel Engines: New Research Directions

Untitled

Fusion of Multi-Layer Attention Mechanisms and CNN-LSTM for Fault Prediction in Marine Diesel Engines

Contact Info

Product

Resources

About