Intelligent Diagnosis of V-Type Marine Diesel Engines Based on Multifeatures Extracted From Instantaneous Crankshaft Speed

Zhang, Mingquan; Ye, Zi; Niu, Linkai; Xi, Songtao; Li, Yiqing

doi:10.1109/tim.2018.2857018

Cited by 29 publications

(10 citation statements)

References 47 publications

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“…Acoustic emission data are potential to detect the incipient faults and the deformation of bearings [44,45] and gears [46][47][48], especially under the low-speed operation conditions and low-frequency-noise environment. Instantaneous speed data are commonly used in fault diagnosis of engines [49][50][51], which are strongly anti-interference.…”

Section: Step 1: Data Collectionmentioning

confidence: 99%

Applications of machine learning to machine fault diagnosis: A review and roadmap

Lei

Yang

Jiang

et al. 2020

Mechanical Systems and Signal Processing

1,690

603

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Section: Step 1: Data Collectionmentioning

confidence: 99%

Applications of machine learning to machine fault diagnosis: A review and roadmap

Lei

Yang

Jiang

et al. 2020

Mechanical Systems and Signal Processing

1,690

603

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“…Wang et al proposed using image correlation and periodicity algorithms [43]. Desbazeille and Zhang extended the analysis to V engines [44,45]. In his article, Dauglas attempted to combine the analysis of instantaneous rotational speed with vibroacoustic methods [46].…”

Section: State-of-the-artmentioning

confidence: 99%

Instantaneous Rotational Speed Algorithm for Locating Malfunctions in Marine Diesel Engines

Kazienko

Chybowski

2020

Energies

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This article suggested broadening the standard methods for diagnosing the technical condition of diesel engines to include an analysis of the instantaneous rotational speed of compression combustion engines with the use of a novel algorithm. The authors revised the subject concerning the use of the analysis of instantaneous changes in the rotational speed of an engine when assessing its technical condition and the location of the malfunction. An algorithm and its practical implementation in a prototype diagnostic system called SpeedMA were presented. This article reported the test results of the prototype in the context of indicating the engine cylinder in which ignition failed to occur. Tests were carried out for two marine engines: a low-speed trunk engine directly driving the fixed-pitch propeller and a medium-speed trunk engine driving the alternator. For each case, an analysis was carried out for different engine loads and at individual cylinders in which combustion failed to occur. The experimental results showed an unambiguous relation between the combustion process of the examined engines and changes in the instantaneous rotational speed. The results also confirmed the usefulness of the proposed method and showed the correct operation of the presented diagnostic algorithm. The proposed diagnostic system could be used during the operation of engines running in real ship engine rooms.

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“…10 Consequently, IAS signals present an excellent superiority under variable-speed conditions. Thanks to the above merits, IAS based diagnosis and prognosis are basically applicable to a variety of different rotating components/machines, such as the engine, 11 gearbox, 12–15 rolling bearing, 16,17 motor, 18 and so on. In this work, the IAS approach is introduced as an alternative or a complementary tool for more robust SAPP monitoring under non-stationary conditions.…”

Section: Introductionmentioning

confidence: 99%

Hydraulic pump fault diagnosis based on chaotic characteristics of speed signals under non-stationary conditions

Liu

Geng

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part C:

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Pumps are vital components in hydraulic equipment, and their malfunction is directly related to the normal operation of the whole system. The hydraulic system is accompanied by the conversion of multi-domain energy during operation. In particular, it exhibits non-stationarity and nonlinearity under variable operating conditions, which brings difficulties to fault diagnosis. In this paper, the instantaneous angular speed (IAS) signal obtained by the equal-angle measurement is studied to diagnose pump faults under non-stationary conditions. For this purpose, a synchroextracting normal S transform method is proposed to extract the time-frequency feature components properly. Then, the extracted signal is reconstructed into a high-dimensional phase space based on the improved C-C method. On this basis, the quantitative analysis indicators of pump state are obtained by the G-P algorithm and small-data method, including correlation dimension, Kolmogorov entropy, and largest Lyapunov exponent. Pump faults can be classified in a chaotic space by using these sensitive features. The results show that the proposed method is capable of diagnosing different states of pumps and robust to the variation of load values.

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Intelligent Diagnosis of V-Type Marine Diesel Engines Based on Multifeatures Extracted From Instantaneous Crankshaft Speed

Cited by 29 publications

References 47 publications

Applications of machine learning to machine fault diagnosis: A review and roadmap

Applications of machine learning to machine fault diagnosis: A review and roadmap

Instantaneous Rotational Speed Algorithm for Locating Malfunctions in Marine Diesel Engines

Hydraulic pump fault diagnosis based on chaotic characteristics of speed signals under non-stationary conditions

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