Bayesian and machine learning-based fault detection and diagnostics for marine applications

Cheliotis, Michail; Lazakis, Iraklis; Cheliotis, Angelos

doi:10.1080/17445302.2021.2012015

Cited by 14 publications

(7 citation statements)

References 51 publications

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“…ANNs have also recently been used to diagnose defects in marine ICEs [19][20][21][22][23]. However, they rarely dealt with the classification of damage to the injection apparatus [2,15,17,[24][25][26][27], which the co-author has been dealing with for many years.…”

Section: Existing Methods Of Damage Recognitionmentioning

confidence: 99%

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Application of Machine Learning to Classify the Technical Condition of Marine Engine Injectors Based on Experimental Vibration Displacement Parameters

Monieta,

Kasyk

2023

Energies

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The article presents the possibility of using machine learning (ML) in artificial intelligence to classify the technical state of marine engine injectors. The technical condition of the internal combustion engine and injection apparatus significantly determines the composition of the outlet gases. For this purpose, an analytical package using modern technology assigns experimental test scores to appropriate classes. The graded changes in the value of diagnostic parameters were measured on the injection subsystem bench outside the engine. The influence of the operating conditions of the fuel injection subsystem and injector condition features on the injector needle vibration displacement waveforms was subjected to a neural network (NN) ML process and then tested. Diagnostic parameters analyzed in the amplitude, frequency, and time–frequency domains were subjected after a learning process to recognize simulated various regulatory and technical states of suitability and unfitness with single and complex damage of new and worn injector nozzles. Classification results were satisfactory in testing single damage and multiple changes in technical state characteristics for unfitness states with random wear injectors. Testing quality reached above 90% using selected NNs of Statistica 13.3 and MATLAB R2022a environments.

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Section: Existing Methods Of Damage Recognitionmentioning

confidence: 99%

“…The article by Cheliotis et al [19] presents developed diagnostics for marine vessels based on operational data and damage probabilities. It was shown that complex physicsbased models could detect the primary cause of damage by using "black-box neural networks" [19].…”

Section: Existing Methods Of Damage Recognitionmentioning

confidence: 99%

Application of Machine Learning to Classify the Technical Condition of Marine Engine Injectors Based on Experimental Vibration Displacement Parameters

Monieta,

Kasyk

2023

Energies

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“…In Zhao et al (2022), a dynamic state feedback controller is constructed after the average dwell time of the switching system is constrained, which improves the degree of freedom of the switching system and realizes the robust control and fault detection work at the same time. At the same time, Bayesian and machine learning methods can also be applied to solve SFDC problems (Cheliotis et al, 2022). Gong et al (2021) proposed a new distributed system for the distributed SFDC problem, and establishes the corresponding residual system.…”

Section: Introductionmentioning

confidence: 99%

Simultaneous attitude fault detection and control of the six-rotor UAV based on event trigger mechanism

Huang,

Zhang,

Dai

et al. 2024

Transactions of the Institute of Measurement and Control

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This paper analyzes the problem of simultaneous fault detection and control of the six-rotor unmanned aerial vehicle control system, considering external disturbance, measurement disturbance, and actuator fault. The integral event trigger mechanism is introduced in the control side and sensor side of the system. Based on Lyapunov stability and H∞ control theory, the sufficient conditions to make the fault system asymptotically stable and have certain performance indexes are given by means of linear matrix inequalities; at the same time, the design criteria of event trigger parameters are also given. The linear matrix inequality is decoupled, and the calculation method of gain matrix of simultaneous fault detection and control module is given. The effectiveness of the proposed method is verified by simulation experiments.

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“…(3) Among clustering techniques, it has the advantages of short computation times and excellent performance in outlier detection [22]. Because of these advantages, DBSCAN is widely used for fault detection and FD [23][24][25][26]. Li et al applied DBSCAN to diagnose the potential thermal runaway in batteries installed in electric vehicles [27].…”

Section: Introductionmentioning

confidence: 99%

Research on Clustering-Based Fault Diagnosis during ROV Hovering Control

Park,

Cho,

Gil

et al. 2024

Applied Sciences

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The objective of this study was to perform fault diagnosis (FD) specific to various faults that can occur in the thrusters of remotely operated vehicles (ROVs) during hovering control. Underwater thrusters are predominantly utilized as propulsion systems in the majority of ROVs and are essential components for implementing motions such as trajectory tracking and hovering. Faults in the underwater thrusters can limit the operational capabilities of ROVs, leading to permanent damage. Therefore, this study focused on the FD for faults frequently caused by external factors such as entanglement with floating debris and propeller breakage. For diagnosing faults, a data-based technique that identifies patterns according to data characteristics was utilized. In imitation of the fault situations, data for normal, breakage and entangled conditions were acquired, and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) was employed to differentiate between these fault conditions. The proposed methodology was validated by configuring an ROV and conducting experiments in an engineering water tank to verify the performance of the FD.

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Bayesian and machine learning-based fault detection and diagnostics for marine applications

Cited by 14 publications

References 51 publications

Application of Machine Learning to Classify the Technical Condition of Marine Engine Injectors Based on Experimental Vibration Displacement Parameters

Application of Machine Learning to Classify the Technical Condition of Marine Engine Injectors Based on Experimental Vibration Displacement Parameters

Simultaneous attitude fault detection and control of the six-rotor UAV based on event trigger mechanism

Research on Clustering-Based Fault Diagnosis during ROV Hovering Control

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