Study of scintillation detector fault diagnosis based on ELM method

Ding, Tiansong; Yan, Yongjun; Li, Xiang; Liu, Linfei

doi:10.1016/j.nima.2022.166637

Cited by 3 publications

(1 citation statement)

References 12 publications

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“…Constructing a feature set that characterizes the operating state of equipment and using it to establish a fault diagnosis model is the key to the fault diagnosis of mechanical equipment. Ding et al [4] proposed a method for scintillation detector fault diagnosis based on the extreme learning machine (ELM), and this method could not only classify the faults of the failed detector but also intelligently determine the severity of various faults. Lee et al [5] proposed a novel remaining useful life (RUL) estimation method based on systematic feature engineering and the extreme learning machine (ELM) for seven out of eleven bearings; the proposed method reduced the mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE) in the RUL estimation by over 50%.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis of Check Valve Based on KPLS Optimal Feature Selection and Kernel Extreme Learning Machine

et al. 2022

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The check valve is the core part of high−pressure diaphragm pumps. It has complex operation conditions and has difficulty characterizing fault states completely with its single feature. Therefore, a fault signal diagnosis model based on the kernel extreme learning machine (KELM) was constructed to diagnose the check valve. The model adopts a multi−feature extraction method and reduces dimensionality through kernel partial least squares (KPLS). Firstly, we divided the check valve vibration signal into several non−overlapping samples. Then, we extracted 16 time−domain features, 13 frequency−domain features, 16 wavelet packet energy features, and energy entropy features from each sample to construct a multi−feature set characterizing the operation state of the check valve. Next, we used the KPLS method to optimize the 45 dimension multi−feature data and employed the processed feature set to establish a KELM fault diagnosis model. Experiments showed that the method based on KPLS optimal feature selection could fully characterize the operating state of the equipment with an accuracy rate of 96.88%. This result indicates the high accuracy and effectiveness of the multi−feature set constructed with the KELM fault diagnosis model.

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Section: Introductionmentioning

confidence: 99%

Fault Diagnosis of Check Valve Based on KPLS Optimal Feature Selection and Kernel Extreme Learning Machine

et al. 2022

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FD-ELM Diagnosis Method for Electric Actuator of Six-Degree-of-Freedom Motion Platform

Liu,

Zuo,

Wang

2024

Lecture Notes in Electrical Engineering

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Fault Diagnosis of Aircraft Hydraulic Pipeline Clamps Based on Improved KPCA and WOA–KELM

Liu,

Zhang,

Bai

2024

Processes

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Due to the complexity and diversity of aviation hydraulic pipeline systems, there has been a lack of qualitative formulas or characteristic indicators to describe clamp failures within these systems. In this paper, based on the data-driven idea, an improved KPCA-based feature extraction method is proposed and combined with the optimized KELM for fault diagnosis and condition monitoring of aviation hydraulic line clamps. Firstly, the kernel parameters of KPCA are combined using polynomial and Gaussian kernels based on their proportional weights. Secondly, a GA–PSO (Genetic Algorithm–Particle Swarm Optimization) hybrid algorithm is employed to optimize the kernel parameters, selecting 13 time-domain and 4 frequency-domain feature indicators to form the initial feature dataset, which is then subjected to dimensionality reduction using the improved KPCA. Finally, diagnosis is conducted using a KELM optimized by the whale optimization algorithm. The results indicate that, across multiple diagnostic trials, the average diagnostic accuracy can reach 99.99%, providing a feasible approach for the precise diagnosis of clamp faults in aviation hydraulic pipeline systems.

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Study of scintillation detector fault diagnosis based on ELM method

Cited by 3 publications

References 12 publications

Fault Diagnosis of Check Valve Based on KPLS Optimal Feature Selection and Kernel Extreme Learning Machine

Fault Diagnosis of Check Valve Based on KPLS Optimal Feature Selection and Kernel Extreme Learning Machine

FD-ELM Diagnosis Method for Electric Actuator of Six-Degree-of-Freedom Motion Platform

Fault Diagnosis of Aircraft Hydraulic Pipeline Clamps Based on Improved KPCA and WOA–KELM

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