A Method for Monitoring Lubrication Conditions of Journal Bearings in a Diesel Engine Based on Contact Potential

Wan, Biao; Yang, Jianguo; Sun, Sicong

doi:10.3390/app10155199

Cited by 10 publications

(2 citation statements)

References 21 publications

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“…Then the selected seven characteristic parameters are collected and compared with the normal values (NCs). In order to facilitate the comparison of abnormal values with NC when faults occur, the deviation rate (DR), which can be calculated by equation (1), is used to reflect the degree of abnormal values deviation from NC. The DR of the selected characteristic parameters is shown in Figure 7.…”

Section: Abnormal Fuel Injection Timingmentioning

confidence: 99%

“…For diesel engine fault diagnosis, there are three traditional diagnostic technologies including oil analysis technology, 1 vibration analysis technology, 2 and thermal parametric analysis technology, 3 as shown in Figure 1. Oil analysis technology is used to detect the quality of lubricating oil in the crankcase of a diesel engine, which can diagnose the wear fault of moving parts.…”

Section: Introductionmentioning

confidence: 99%

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Multiple faults diagnosis for ocean-going marine diesel engines based on different neural network algorithms

Zhu,

Huang,

Yin

et al. 2023

Science Progress

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Fault diagnosis technologies for ocean-going marine diesel engines play an important role in the safety and reliability of ship navigation. Although many fault diagnosis technologies have achieved acceptable results for single fault of diesel engines, the diagnosis of multiple faults is rarely involved. Due to the strong correlation, non-linearity and randomness of multiple faults, it is extremely difficult to make an accurate diagnosis. In this study, diagnosis methods based on thermal parametric analysis combined with different neural network algorithms were established and used for the diagnosis of multiple faults in the ocean-going marine diesel engine. The results show that the Levenberg Marquardt back propagation neural network has the highest diagnostic accuracy rate of 88.89% and 100% for multiple faults and single faults, respectively, and its diagnostic time is also relatively short, 0.78 s. The Bayesian regularization back propagation neural network can give a diagnostic accuracy rate of 100% for single faults, but for multiple faults, the diagnostic accuracy rate is only 55.56%, and the diagnosis time for the entire sample is the longest. As for the probabilistic neural network, although it has the fastest diagnosis speed, it has the lowest diagnostic accuracy rate for both single faults and multiple faults. The results may provide references for the online diagnosis of single faults and multiple faults in ocean-going marine diesel engines.

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Section: Abnormal Fuel Injection Timingmentioning

confidence: 99%