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.

The engine system is critical for a marine vehicle, and its performance significantly affects the efficiency and safety of the whole ship. Due to the harsh working environment and the complex system structure, a marine system is prone to have many kinds of novelties and faults. Timely detection of faults via effective condition monitoring is vital for such systems, avoiding serious damage and economic loss. However, it is difficult to realize online monitoring because of the limitations of measurement and health monitoring methods. In this paper, a marine engine system simulator is set up with enhanced sensory placement for static and dynamic data collection. The test rig and processing for static and dynamic data are described. Then, a physics-based multivariate modeling method is proposed for the health monitoring of the system. Case studies are carried out considering the misfire fault and the exhaust valve leakage fault. In the misfire fault test, the exhaust gas temperature of the misfired cylinder dropped from the confidence interval 100–150 °C to 70–80 °C and the head vibration features decreased from the confidence interval 900–1300 m/s² to around 200–300 m/s². For the exhaust valve leakage fault, the engine body vibration main bearing impact RMS increased nearly 10 times. Comparisons between the model-predicted confidence interval and measured data reveal that the proposed model based on the fault-related static and dynamic features successfully identified the two faults and their positions, proving the effectiveness of the proposed framework.

Failure mode effects and criticality analysis (FMECA) is a commonly adopted approach to defining, assessing, and reducing possible failures in designs, systems, processes, products, and services. Traditional FMECA ranks the failure modes of products based on a risk priority number (RPN), which is obtained by multiplying the risk elements. Conventional FMECA has the shortcomings of badly handling unknown information and unreasonably assessing RPNs. To deal with these issues, an advanced FMECA method based on intuitionistic 2‐tuple linguistic variables (I2LVs) and the triangular fuzzy analytic hierarchy process (TFAHP) is proposed. In this method, the fuzzy evaluation of risk elements given by different FMECA members is represented by I2LVs, which can efficiently handle unknown information. The TFAHP method is adopted to assess the weights of risky elements and rank the risk priorities of different failure modes. Finally, an application case of an insulated‐gate bipolar transistor is used to verify the effectiveness and robustness of the proposed method.

With the vigorous development of integrated circuit (IC) manufacturing, the harmfulness of defects and hardware Trojans is also rising. Therefore, chip verification becomes more and more important. At present, the accuracy of most existing chip verification methods depends on high-precision sample data of ICs. Paradoxically, it is more challenging to invent an efficient algorithm for high-precision noiseless data. Thus, we recently proposed a fusion clustering framework based on low-quality chip images named High-Frequency Low-Rank Subspace Clustering (HFLRSC), which can provide the data foundation for the verification task by effectively clustering those noisy and low-resolution partial images of multiple target ICs into the correct categories. The first step of the framework is to extract high-frequency texture components. Subsequently, the extracted texture components will be integrated into subspace learning so that the algorithm can not only learn the low-rank space but also retain high-frequency information with texture characteristics. In comparison with the benchmark and state-of-the-art method, the presented approach can more effectively process simulation low-quality IC images and achieve better performance.