Accurate SM Disturbance Observer-Based Demagnetization Fault Diagnosis With Parameter Mismatch Impacts Eliminated for IPM Motors

Han, Yaofei; Chen, Shaofeng; Gong, Chao; Zhao, Xing; Zhang, Fanggang; Li, Yunwei

doi:10.1109/tpel.2023.3245052

Cited by 52 publications

(20 citation statements)

References 16 publications

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“…The reduction in the number of characteristics was accomplished by correlation analysis between every characteristic using the Pearson correlation coefficient [42] (20).…”

Section: Data Preprocessingmentioning

confidence: 99%

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Ship Diesel Engine Fault Diagnosis Using Data Science and Machine Learning

Pająk,

Kluczyk,

Muślewski

et al. 2023

Electronics

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One of the most important elements of the reliability structure of a motor vessel is its power subsystem, with the most crucial component being the engine. An engine failure excludes the ship from operation or significantly limits its operation. Therefore, accurate fault diagnosis should be a crucial issue for modern maintenance strategies. In mechanical engineering, the vibration and acoustic signals recorded during the operation of the device are the most meaningful data used to identify the reliability state. In this paper, a novel system-oriented method of reliability state identification is proposed. The method consists of the analysis of the vibration and noise signals collected on each of the engine cylinders using supervised machine learning. The main novelty of this method is data augmentation application and SVM classifier implementation. Due to these aspects, the method becomes robust in the case of poor-quality data or a limited and incomplete learning dataset. The quality of the proposed identification method was evaluated by addressing a new industrial issue (Sulzer 6AL20/24 marine engine reliability state identification). During the tests, the efficiency of the method was analyzed in the case of a complete learning data set (all types of inability states were presented in the learning data set) and an incomplete learning data set (in the testing data set, there were new types of inability states). As a result, in both cases, a very high (100%) identification accuracy of the reliability state and the type of inability state was obtained. This is a significant increase in accuracy (4.6% for the complete and 22% for the incomplete learning data set) in comparison to the efficiency of the same method without the use of machine learning and data science.

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“…The reduction in the number of characteristics was accomplished by correlation analysis between every characteristic using the Pearson correlation coefficient [42] (20).…”

Section: Data Preprocessingmentioning

confidence: 99%

“…In the case of weak failure symptoms and signals overwhelmed by noise, Bayesian network-based solution applications can be found [18,19]. However, vibration analysis in the frequency domain does not always provide the best results [20].…”

Section: Introductionmentioning

confidence: 99%

Ship Diesel Engine Fault Diagnosis Using Data Science and Machine Learning

Pająk,

Kluczyk,

Muślewski

et al. 2023

Electronics

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“…Figure 11a shows the open-circuit radial air-gap flux density obtained by FEA. Then, the Φ i rpm is calculated by Equation (15). The comparison results between the FEA method and the proposed Φ i rpm calculation method are shown in Figure 11b.…”

Section: φ I Rpm Calculationmentioning

confidence: 99%

“…Until now, there are three kinds of DF diagnosis methods, namely signal processing-based [3][4][5][6][7][8][9], artificial intelligence (AI)-based [10][11][12] and model analysis-based [13][14][15]. For the signal processing-based method, fault diagnosis is achieved by extracting the fault characteristics from the fault signal using signal processing technique, which mainly includes fast Fourier transform, Hilbert-Huang transform, wavelet transform and empirical mode decomposition.…”

Section: Introductionmentioning

confidence: 99%

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Enhanced robust diagnosis of multiple‐type demagnetisation fault for permanent magnet synchronous motor based on D‐axis magnetic network model using magnetic field reconstruction

He,

Hang,

Ding

2023

IET Electric Power Appl

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Demagnetisation fault (DF) is a common rotor fault in the permanent magnet synchronous motor (PMSM). DF can cause obvious changes in the magnetic field of PMSMs. As a result, previous DF diagnosis methods mainly depends on magnetic field analysis. However, conventional analysis methods based on the magnetic potential‐permeability method, sub‐domain method and magnetic equivalent circuit method are not suitable for DF condition. In addition, DF diagnosis is further complicated by various operating conditions. To address these issues, a robust DF diagnosis method is proposed for PMSM based on d‐axis magnetic network model using magnetic field reconstruction. The proposed fault diagnosis method employs magnetic field reconstruction to obtain the radial air‐gap flux density under the open‐circuit condition. Subsequently, a d‐axis magnetic network is established to solve the demagnetisation coefficient matrix in the DF state. Finally, the effectiveness of the proposed method is validated through simulations and experiments. Both results demonstrate that the proposed method can accurately recognise uniform DF and partial DF with multiple demagnetised permanent magnets, exhibiting great robustness against different operating conditions.

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Robust multiple fault detection and estimation approaches based on adaptive finite‐time observer for nonlinear systems

Qiu,

Ban,

et al. 2024

Intl J Robust & Nonlinear

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This article investigates robust multiple fault detection (FD) and fault estimation (FE) schemes, based on adaptive finite‐time observer (AFTO), applied to nonlinear systems with disturbances, actuator faults, and sensor faults. The AFTO is designed, and a parameter adaptive law is constructed to continuously optimize the residual based performance function to improve the AFTO robustness. The schemes are able to simultaneously detect and estimate unknown states, disturbances, and faults. The AFTO's design, finite‐time convergence and stability conditions are guaranteed in terms of linear matrix inequalities. Finally, simulation results, based on two typical practical systems, are given to illustrate the effectiveness of the proposed approaches. The simulation results also show that the designed AFTO can offer faster convergence speed, higher estimation accuracy, and stronger robustness than conventional observer and sliding mode observer.

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Accurate SM Disturbance Observer-Based Demagnetization Fault Diagnosis With Parameter Mismatch Impacts Eliminated for IPM Motors

Cited by 52 publications

References 16 publications

Ship Diesel Engine Fault Diagnosis Using Data Science and Machine Learning

Ship Diesel Engine Fault Diagnosis Using Data Science and Machine Learning

Enhanced robust diagnosis of multiple‐type demagnetisation fault for permanent magnet synchronous motor based on D‐axis magnetic network model using magnetic field reconstruction

Robust multiple fault detection and estimation approaches based on adaptive finite‐time observer for nonlinear systems

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