Steel surfaces often display intricate texture patterns that can resemble defects, posing a challenge in accurately identifying actual defects. Therefore, it is crucial to develop a highly robust ...defect detection model. This study proposes a defect detection method for steel infrared images based on a Regularized YOLO framework. Firstly, the Coordinate Attention (CA) is embedded within the C2F framework, utilizing a lightweight attention module to enhance the feature extraction capability of the backbone network. Secondly, the neck part design incorporates the Bi-directional Feature Pyramid Network (BiFPN) for weighted fusion of multi-scale feature maps. This creates a model called BiFPN-Concat, which enhances feature fusion capability. Finally, the loss function of the model is regularized to improve the generalization performance of the model. The experimental results indicate that the model has only 3.03 M parameters, yet achieves a mAP@0.5 of 80.77% on the NEU-DET dataset and 99.38% on the ECTI dataset. This represents an improvement of 2.3% and 1.6% over the baseline model, respectively. This method is well-suited for industrial detection applications involving non-destructive testing of steel using infrared imagery.
As the core equipment of the high-pressure diaphragm pump, the working conditions of the check valve are complicated, and the vibration signal generated during operation displays non-stationary and ...nonlinear characteristics. In order to accurately describe the non-linear dynamics of the check valve, the smoothing prior analysis (SPA) method is used to decompose the vibration signal of the check valve, obtain the tendency term and fluctuation term components, and calculate the frequency-domain fuzzy entropy (FFE) of the component signals. Using FFE to characterize the operating state of the check valve, the paper proposes a kernel extreme-learning machine (KELM) function norm regularization method, which is used to construct a structurally constrained kernel extreme-learning machine (SC-KELM) fault-diagnosis model. Experiments demonstrate that the frequency-domain fuzzy entropy can accurately characterize the operation state of check valve, and the improvement of the generalization of the SC-KELM check valve fault model improves the recognition accuracy of the check-valve fault-diagnosis model, with an accuracy rate of 96.67%.
Due to the complicated engineering operation of the check valve in a high−pressure diaphragm pump, its vibration signal tends to show non−stationary and non−linear characteristics. These leads to ...difficulty extracting fault features and, hence, a low accuracy for fault diagnosis. It is difficult to extract fault features accurately and reliably using the traditional MPE method, and the ELM model has a low accuracy rate in fault classification. Multi−scale weighted permutation entropy (MWPE) is based on extracting multi−scale fault features and arrangement pattern features, and due to the combination of extracting a sequence of amplitude features, fault features are significantly enhanced, which overcomes the deficiency of the single−scale permutation entropy characterizing the complexity of vibration signals. It establishes the check valve fault diagnosis model from the twin extreme learning machine (TELM). The TELM fault diagnosis model established, based on MWPE, aims to find a pair of non−parallel classification hyperplanes in the equipment state space to improve the model’s applicability. Experiments show that the proposed method effectively extracts the characteristics of the vibration signal, and the fault diagnosis model effectively identifies the fault state of the check valve with an accuracy rate of 97.222%.
How to extract characteristic parameters from vibration signals with noise is a key problem of bearing fault diagnosis. A novel method based on Morlet wavelet-Singular Value Decomposition（ SVD） and ...Variable Predictive Model based Class Discriminate（ VPMCD） was proposed in this paper aiming to solve this problem. Firstly,Morlet wavelet transform was used to pre-process the signals in the time domain to obtain a time-frequency coefficient matrix,then SVD was applied to the matrix to remove noise and extract the weak fault information in the corresponding dimensions according to the singular value curvature spectrum; Secondly,the signal components near the optimal meature were selected,and the Shannon energy entropy were used as the characteristic parameters to construct the feature vectors,which were then used to establish the fault identification model based on VPMCD. Finally,5-fold cross validation method and Jackknife test method were adopted to verify the proposed method,and the results have demonstrated it
Due to buried pipes’ complex and changeable operating conditions and insufficient effective data samples, the multiple-blockage recognition model lacks great adaptability and generalization. A ...multiple-blockage identification scheme for buried pipeline via acoustic signature model and SqueezeNet is proposed, including three stages of acoustic signature model, feature learning with SqueezeNet, and blockage recognition and classification. First, indirect sound intensity and dynamic threshold are proposed to ensure segmentation that the typical individual acoustic signature images. Then, the lightweight SqueezeNet is used to extract the acoustic signature feature. Among them, only the data of healthy and single blockage to train the SqueezeNet recognition model. Finally, the unknown multiple blockages are divided into multiple independent individual parts. According to the results, the model can distinguish between the testing samples that contain all the blocking types and the unknown type of multiple blockages and achieve an average accuracy of 99.88% and 97%, respectively.
•Acoustical signature model is built to represent the specificity and stability relationship among different blockage patterns.•The proposed AS-SqueezeNet without implementing manual feature extraction and selection.•SqueezeNet training process only using the healthy and single blockage dataset.•Capability of identifying unknown multiple blockages.
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.
Support Vector Data Description (SVDD) concerns the characterization of a data set. A good description covers all target data but includes no superfluous space. The boundary of a data set can be used ...to detect outliers. SVDD is affected by noises during being trained. In this paper, Grid-based Fuzzy Support Vector Data Description (G-FSVDD) is presented to deal with the problem. G-FSVDD reduces the effects of noises by a new fuzzy membership model, which is based on grids. Each grid is a hypercube in data set. After obtaining enough grids, Apriori algorithm is used to find grids with high density. In G-FSVDD, different training data make different contributions to the domain description according to their density. The advantage of G-FSVDD is shown in the experiment.
In order to solve the problem that the fault feature information of rolling bearing is difficult to be separated,a new method of fault feature extraction is presented,which is based on the ...complementary ensemble empirical mode decomposition（ CEEMD） and fast independent component analysis（ Fast ICA）. First,analyze the CEEMD vibration signals,decompose them into intrinsic mode function（ IMF） components signal of different scales; then through the sensitivity evaluation algorithm,decompose and recombine the signals,and use Fast ICA to reduce their noise; in the end,conduct Hilbert envelope spectrum analysis to the signals separated by the Fast ICA,to obtain the fault feature information. This method is applied to the fault analysis of rolling bearing vibration signal,and was proved to be valid.
Since the working process of rolling bearings is a complex and nonstationary dynamic process, the common time and frequency characteristics of vibration signals are submerged in the noise. Thus, it ...is the key of fault diagnosis to extract the fault feature from vibration signal. Therefore, a fault feature extraction method for the rolling bearing based on the local mean decomposition (LMD) and envelope demodulation is proposed. Firstly, decompose the original vibration signal by LMD to get a series of production functions (PFs). Then dispose the envelope demodulation analysis on PF component. Finally, perform Fourier Transform on the demodulation signals and judge failure condition according to the dominant frequency of the spectrum. The results show that the proposed method can correctly extract the fault characteristics to diagnose faults.
Background Recent studies on bone have shown an endocrine role of the skeleton,which could be impaired in various human diseases,including osteoporosis,obesity,and diabetes-associated bone ...diseases.As a sensor and regulator of energy metabolism,AMP-activated protein kinase （AMPK） may also play an important role in the regulation of bone metabolism.The current study aimed to establish the expression profiles and phosphorylation patterns of AMPK subunits in several mesenchymal cell types.Methods Reverse transcription-polymerase chain reaction （PCR） for relative quantification,real-time PCR for absolute quantification,and Western blotting were used to investigate the gene expression profiles and phosphorylation patterns of AMPK subunits in several mesenchymal cell types,including primary human mesenchymal stem cells （hMSCs） and hFOB,Saos-2,C3H/10T1/2,MC3T3-E1,3T3-L1,and C2C12 cells.Results AMPKα1 and AMPKβ1 mRNAs were abundantly expressed in all cell types.AMPKY1 mRNA was abundantly expressed in C3H/10T1/2,MC3T3-E1,3T3-L1,and C2C12 but not detected in human-derived cell types.AMPKY2 mRNA was mildly expressed in all cell types.AMPKα1 protein was highly expressed in all cell types and AMPKα2 protein was highly expressed only in hFOB and Saos-2 cells.AMPKβ1 protein was abundantly expressed in all cell types except for Saos-2,in which AMPKβ2 protein overwhelmed AMPKβ1 expression.AMPKy1 and AMPKY2 proteins were expressed in C3H/10T1/2,MC3T3-E1,3T3-L1,and C2C12 cells and only AMPKY2 protein was expressed in hMSCs,hFOB and Saos2 cells.AMPKα was phosphorylated at Thr172 and Ser485 and AMPKβ1 was phosphorylated at Ser108 and Ser182 in all cell types with a specific pattern in each cell type.Conclusion The combination of AMPK α,β,and Y subunits and phosphorylation of AMPKα （Thr172 and Ser485） and AMPKβ1 （Ser108 and Ser182） showed a specific pattern in each cell type.
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