Identification and reconstruction of anomalous sensing data for combustion analysis of marine diesel engines

Ou, Shunhua; Yu, Yang; Yang, Jianguo

doi:10.1016/j.measurement.2022.110960

Cited by 5 publications

(2 citation statements)

References 36 publications

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“…Lazakis et al [34] classified critical marine main engine system parameters (e.g., exhaust gas temperatures) using fault tree analysis (FTA) and failure mode and effects analysis (FMEA), and predicted the diesel engine's exhaust gas temperature abnormalities using an artificial neural network (ANN). Ou et al [35] proposed the identification and reconstruction of anomalous sensing data for combustion in marine diesel engines by using cylinder pressure data and the corresponding crankshaft angle data.…”

Section: Other Data-oriented Approaches For Fault Detection and Diagn...mentioning

confidence: 99%

A Novel Time–Frequency Feature Fusion Approach for Robust Fault Detection in a Marine Main Engine

Je-Gal,

Lee,

Yoon

et al. 2023

JMSE

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Ensuring operational reliability in machinery requires accurate fault detection. While time-domain vibration pulsation signals are intuitive for pattern recognition and feature extraction, downsampling can reduce analytical complexity, but may result in low-precision data, affecting fault detection performance. To address this, we propose time–frequency feature fusion, combining information from both the time and frequency domains for fault detection. Our approach transforms vibrational pulse data into instantaneous revolutions per minute (RPM) and employs statistical analysis for the time-domain features. For the frequency-domain features, we use the combined method of empirical mode decomposition and independent component analysis (EMD-ICA), along with the Wigner bispectrum method to capture the nonlinear characteristics and phase conjugation. Using a deep neural network (DNN), we classify the anomaly states, demonstrating the effectiveness and versatility of our approach in detecting anomalies and improving diagnostic precision. Compared to using time or frequency features alone, our time–frequency feature fusion model achieves higher accuracy, with 100% accuracy at lower downsampling rates and 96.3% accuracy at a downsampling rate of 100×.

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Section: Other Data-oriented Approaches For Fault Detection and Diagn...mentioning

confidence: 99%

A Novel Time–Frequency Feature Fusion Approach for Robust Fault Detection in a Marine Main Engine

Je-Gal,

Lee,

Yoon

et al. 2023

JMSE

View full text Add to dashboard Cite

show abstract

“…Although different types of process data can be monitored and used for status assessment, online novelty detection and fault diagnosis are awkward in practice and it is often difficult to accurately locate fault positions. As a result, massive losses can occur due to the late detection of typical malfunctions and delayed maintenance [1][2][3].…”

Section: Introductionmentioning

confidence: 99%

Comparative Study on Health Monitoring of a Marine Engine Using Multivariate Physics-Based Models and Unsupervised Data-Driven Models

Liang

et al. 2023

Machines

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The marine engine is a complex-structured multidisciplinary system that operates in a harsh environment involving high temperatures and pressures and gas/fluid/solid interactions. Many malfunctions and faults can occur to the marine engine and efficient condition monitoring is critical to ensure the expected performance. In this paper, a marine engine test rig is established and its process data are recorded, including various temperatures and pressures. Two data-driven models, i.e., principal component analysis and the sparse autoencoder, and a physics-based model are applied to the marine engine for two classic faults, i.e., lubrication oil filter blocking and cylinder leakage. Comparative studies and discussions are conducted regarding their performance in terms of anomaly detection and fault isolation. The data points collected for the filter blocking fault are generally two times higher than the fault thresholds set by the data-driven models. In the physics-based model, it is observed that the lubrication oil pressure falls from the predicted 3.2–3.8 bar to around 2.3 bar. For the cylinder leakage fault, the fault test data are nearly four times higher than the thresholds in the data-driven models. The exhaust gas temperature of the leaked cylinder falls from an estimated 150–200 °C to about 100 °C. The transferability and interpretability of these models are finally discussed. The findings of the present study offer insights into the two types of models and can provide guidance for the effective condition monitoring of marine engines.

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Study on transient flow characteristics and measurement method for multi-point injection system of natural gas engine

Yang

Dong

Zhao³

2023

Measurement

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Identification and reconstruction of anomalous sensing data for combustion analysis of marine diesel engines

Cited by 5 publications

References 36 publications

A Novel Time–Frequency Feature Fusion Approach for Robust Fault Detection in a Marine Main Engine

A Novel Time–Frequency Feature Fusion Approach for Robust Fault Detection in a Marine Main Engine

Comparative Study on Health Monitoring of a Marine Engine Using Multivariate Physics-Based Models and Unsupervised Data-Driven Models

Study on transient flow characteristics and measurement method for multi-point injection system of natural gas engine

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