Rolling bearing fault feature extraction via improved SSD and a singular-value energy autocorrelation coefficient spectrum

Aerospace-sealed electronic components exhibit favourable anti-interference capability and high reliability and are widely utilised in satellites, rockets, and missiles. Loose particle detection is crucial to ensure high reliability. However, the classification problem of loose particle detection signals based on the Particle Impact Noise Detection (PIND) method has been a challenge for the high reliability of aerospace-sealed electronic components. To address this issue, this paper systematically proposes a synchronised classification method based on PIND and validates its feasibility. The proposed approach combines a self-developed empirical model (referred to as Algorithm 1) with a classical machine learning model (referred to as Algorithm 2). The key findings are as follows: this method has a recognition rate for loose particle signals of 91.86%. Compared to a single algorithmic mode, the overall speed improves by 300%. The maximal recognition accuracy rates for component signals, mixed signals, and excessive signals are 90.03%, 81.04%, and 95.17%, respectively. The test results demonstrate that the method effectively balances the accuracy and speed of loose particle detection and leverages the complementary advantages of the two algorithms, thereby addressing the multi-classification issue of loose particle detection signals.

Section: Introductionmentioning

confidence: 99%

Research on a synchronised classification method for loose particle detection signals of aerospace-sealed electronic components

Li,

Zhai,

Wang

et al. 2024

“…According to statistics, rolling bearings cause about 45%-55% of mechanical failures [1], and the rolling bearing's running condition will affect the equipment's operation. Xu et al [2] proposed an improved singular spectrum decomposition for obtaining weak fault characteristics in rolling bearings. A single failure in a local area can quickly develop into multiple * Author to whom any correspondence should be addressed.…”

Section: Introductionmentioning

confidence: 99%

Blind extraction of acoustic signals from rolling bearing composite faults based on ISCA-IMCKD

Jialu¹,

Zhou²,

Liu³

et al. 2023

In the production practice, the signal gathered by sensor often includes strong ambient noise, and its composition is complex. Focusing on the trouble that traditional methods are difficult to separate and extract fault frequency from strong background noise, a novel compound fault blind extraction means based on improved sparse component analysis (ISCA) and improved maximum correlation kurtosis deconvolution (IMCKD)-named ISCA‐IMCKD- is suggested. Initially, the signal that the sensor has collected is shifted into time-frequency area signal by short-time Fourier transform (STFT). In addition, the single source domain characteristic data is screened by improved single source point detection (ISSPD) to decide the number of sources. Secondly, ISCA method is optimized by using cosine distance improved fuzzy C-means clustering, which is utilized to further process the characteristic data to calculate the mixing matrix. Moreover, estimated source signal is initially extracted according to the membership degree of clustering results. Finally, estimated source signal is shift into the time area by inverse STFT transform, and the IMCKD is employed to enhance the characteristics of the projected source signals. Meanwhile, initially estimated source signal is completely separated, and the defect frequency of the composite faults are finally extracted by envelope analysis. Simulation experiments and measured data are employed to certify the viability of the proposed means. The defect detection of rolling bearings is finished while the time cost is significantly saved.

“…The traditional approach to analyzing one-dimensional (1D) spectra involves selecting characteristic wavelength spots and creating a concentration prediction model next [15][16][17]. Wang et al combined correlation coefficient (CC) with stability competitive adaptive reweighted sampling to screen the characteristics of near-infrared spectroscopy (NIRS) [18].…”

Section: Introductionmentioning

confidence: 99%

Prediction of brown tide algae using improved Gramian angular field and deep learning based on laser-induced fluorescence spectrum

Sui

Zhu

Chen

et al. 2023

The frequent occurrence of algal blooms has seriously affected the marine environment and human production activities. Therefore, it is crucial to monitor the phytoplankton concentration in water bodies. In this study, a prediction method for brown tide algae using improved Gramian angular field (IGAF) and deep learning based on the laser-induced fluorescence spectrum was proposed. The method combined one-dimensional(1D) fluorescence spectrum with improved Gramian angular field (IGAF) for image coding. The internal normalizing approach of the original Gramian angle field algorithm was upgraded from local to global, which can increase the difference between samples with various concentrations. Then, we established a novel technique that fully takes into account the Gramian angular difference field (GADF) and Gramian angular summation field (GASF) features, allowing it to control the main and sub-diagonal features and successfully convert 1D sequences into images by adding various weight factors. Using depthwise separable convolutional neural network (DSC) to extract image features helps reduce model training parameters, paired with long short-term memory network (LSTM) to rapidly predict the concentration of brown tide. To confirm the actual performance of the given approach, ablation and contrast experiments were carried out, and the results showed that the method's regression accuracy, R2 was 97.8%, with the lowest MSE and MAE. This study investigated the transformation of 1D spectra into images using IGAF, which not only explored the application of the fluorescence spectrum image coding method for algal regression but also enabled the introduction of the potent benefits of deep learning image processing into the field of spectral analysis.