Weak crack identification of compressor impeller with hybrid methods of PNNs and SVD

Jiang, Yongying; Wang, Naige; Zhong, Yongteng; Shao, Liang

doi:10.1088/1361-6501/abe286

Cited by 8 publications

(3 citation statements)

References 55 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Among these weak signal detection methods, SVD has exhibited a very good performance and is widely used to extract the weak features [10][11][12]. The singular value of the SVD reflects the intrinsic characteristics of the data and has good stability and invariance.…”

Section: Introductionmentioning

confidence: 99%

A new selection method of singular values using correlation coefficients for detecting weak exponential damped sinusoidal signals under strong noise

Zhang

2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

Weak signal detection has attracted considerable attention in a wide range of research fields, especially the weak signal detection under strong noise is an urgent problem researches are concerned with. In this paper, a new criterion to select singular values using correlation coefficient is proposed for weak exponential damped sinusoidal signal detection, which has a wide variety of signal processing applications. The innovation of our method lies in selecting K most informative singular values rather than the most energetic singular values. The proposed method measures the similarity between component signals and useful signal through autocorrelation function and correlation coefficient, which can preserve more information of the original signal and be more suitable for weak signal detection scenarios under strong noise. The numerical experiments and analysis are performed to verify the efficiency and effectiveness of our method, which indicates the proposed method is superior to the singular value selection methods based on energy or simple difference principle for correlation coefficients. Compared with stochastic resonance methods suitable for weak signal detection under strong background noise, our proposed method also has significant advantages. Thus, the proposed method is beneficial to theoretical analysis and engineering applications.

show abstract

Section: Introductionmentioning

confidence: 99%

A new selection method of singular values using correlation coefficients for detecting weak exponential damped sinusoidal signals under strong noise

Zhang

2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…During engine maintenance, operators observe and record internal damage using a bore inspection instrument. [1][2][3][4] In this process, it is necessary to count the inspected blades to avoid duplicate inspections. Although some electric rotation devices can achieve counting, they are not adaptable to every engine model.…”

Section: Introductionmentioning

confidence: 99%

Repetition Count: Application to Aero-engine Blade Counting Tasks

Kaiyu,

Huitao,

Jun

et al. 2024

Preprint

View full text Add to dashboard Cite

Engine blades, being critical components of aircraft engines, pose a substantial threat to both the engine and the entire aircraft if they fracture during flight. Hence, inspecting and maintaining these blades are crucial to ensuring flight safety. In the process of blade damage detection, personnel typically utilize borescope inspection equipment to manually examine each blade and count them as they pass, thereby guaranteeing the examination of every individual blade within the engine to prevent any missed or duplicate inspections. This paper presents a new video interpretation method applied to the scenario of engine blade counting. The core of this algorithm involves employing the cosine correlation function to calculate the similarity between video frames captured during borescope inspections, followed by adaptively thresholding the processed signal for dynamic binarization, and ultimately counting the falling edges. By adopting frame-related approaches instead of relying on local image characteristics, this algorithm exhibits high robustness against smooth blade surfaces and metallic reflections. Additionally, it efficiently manages motion blur and directional variations that occur during the rapid movement of the blades. Compared to existing methods, this algorithm requires minimal training time, is compatible with various turbine engine blades, and guarantees real-time count updates.

show abstract

“…Its network structure is simple, convergence speed is fast, the number of neurons in each layer is relatively fixed, and it has a strong non-linear learning capability. PNN has achieved good results in fault diagnosis in power systems [9], aerospace [10], image processing [11] and mechanical engineering [12], etc. Ying et al [13] proposed an intelligent fault identification method based on PNN.…”

Section: Introductionmentioning

confidence: 99%

Fault monitoring and diagnosis of high-pressure heater system based on improved particle swarm optimization and probabilistic neural network

Wu¹,

Zhang²,

Dong³

et al. 2022

Meas. Sci. Technol.

View full text Add to dashboard Cite

The high-pressure heater system is an important part of the return heat system of thermal power units, which can significantly reduce the boiler fuel consumption and is of great significance to the safe and economic operation of the units. Aiming at the problem that the high-pressure heater system data has strong non-linear characteristics and the fault diagnosis accuracy is low, this paper proposes a hybrid model-based fault monitoring and diagnosis method for high-pressure heater system. Firstly, an improved particle swarm optimisation algorithm (IEDPSO) is proposed. A differential evolution operation is introduced to enhance particle diversity, and the inertia weight coefficients and learning factor parameters are improved to optimise the particle position and velocity update process. The problem that particle swarm optimisation (PSO) tends to fall into local optimum at the late stage of iterative optimisation search is solved. Numerical simulation experiments demonstrate that IDEPSO has high convergence speed and accuracy in the optimisation process of the test function. Secondly, a fault monitoring and diagnosis method based on a hybrid KPCA-IDEPSO-PNN model is proposed. Non-linear features are extracted using kernel principal component analysis (KPCA) for fault monitoring. The IDEPSO algorithm is used to iteratively find the best probabilistic neural network (PNN) to improve the fault diagnosis accuracy. Simulation experiments prove that compared with the traditional PNN model, the fault diagnosis accuracy of the KPCA-IDEPSO-PNN model is improved by 4.9% and the number of fault misclassifications is reduced by 34, effectively improving the fault diagnosis accuracy of the high-pressure heater system and ensuring the safe and stable operation of thermal power units.

show abstract

Weak crack identification of compressor impeller with hybrid methods of PNNs and SVD

Cited by 8 publications

References 55 publications

A new selection method of singular values using correlation coefficients for detecting weak exponential damped sinusoidal signals under strong noise

A new selection method of singular values using correlation coefficients for detecting weak exponential damped sinusoidal signals under strong noise

Repetition Count: Application to Aero-engine Blade Counting Tasks

Fault monitoring and diagnosis of high-pressure heater system based on improved particle swarm optimization and probabilistic neural network

Contact Info

Product

Resources

About