Crack detection is important for compressor blades. Due to the interference of strong noise, the existing methods based on single signal reach unsatisfactory performance. To improve the reliability and accuracy, a novel crack detection method is proposed by fusing the information from acoustic and vibration signals (AVS) in data and decision level. Firstly, the data-level fusion method is proposed by calculating Hoyer and improved cosine similarity, which is applied for vibration signals fusion based on its sparsity and similarity. Then, the raw AVS and data-level fusion samples are trained in the one-dimensional convolutional neural network (1D CNN), and the preliminary classification is obtained based on the probabilities of different cracks. Finally, the decision-level fusion method with changed reliability assignment is proposed through modifying and correcting the preliminary results of 1D CNN, which can reach reliable decisions and realize crack detection for compressor blade. The proposed method is tested by compressor experiments with three cracked blades under four single and one mixed working conditions. The results illustrate that the proposed method can make full use of AVS and detect cracks reliably under five working conditions. Furtherly, the superiority of the proposed method is validated by comparing with other crack detection approaches.

Nowadays, acoustic emission (AE) has been widely used to the nondestructive evaluation (NDE) and structural health monitoring (SHM) for compressor blades. However, traditional AE features and feature selection methods are generally difficult to identify the cracked blades of compressor due to its complex structure and background noise. To solve this problem, the crack damage monitoring method based on novel feature and hybridized feature selection is proposed to identify crack of compressor blades, which is aimed at improving the crack identification accuracy with optimal features. First, the novel feature of spectral centroid with energy shift (SCES) is established. Besides, the hybridized feature selection method is proposed based on Laplacian random forest scores (LRFS), which can evaluate and select features adaptively. By fusing information of selected features from AE sensors, the long short-term memory (LSTM) network is used to classify cracked blades. The proposed method is applied experimentally to identify cracks at different speeds and locations of AE sensors, which has the average accuracy of 98.93%. The comparative results demonstrate the effectiveness and superiority of the proposed method in AE-based SHM for compressor blades under different working conditions.

Objective: Recent studies have shown that irisin, a novel peptide hormone derived from muscles, could be used as a potential therapeutic drug against ischemic stroke. Moreover, electroacupuncture (EA) is widely used in the treatment of ischemic stroke. Yet, whether irisin is involved in the EA neuroprotection remains unclear. The following study investigated the association between serum and peri-lesional cortex irisin and EA-induced post-stroke motor recovery in rats. Methods: The middle cerebral artery occlusion (MCAO) method was used to induce ischemic stroke in rats. Rats were randomly divided into two groups: a middle cerebral artery occlusion (MCAO) group (MCAO rats without treatment) and an electroacupuncture (EA) group (MCAO rats treated with EA). On the 3rd day post-stroke, infarct volume, behavioral deficits, surviving neurons, irisin protein expression in peri-infarction cortex, muscle tissue, and serum were evaluated to identify the neuroprotective of EA in acute ischemic stroke. Results: Compared with the MCAO group, the EA group showed better behavioral performance, a smaller cerebral infarct volume, more surviving neurons, and a significant increase in irisin expression in the peri-infarction cortex and serum (p< 0.05). However, no difference in irisin expression in the muscle tissue was found between the MCAO group and the EA group (p> 0.05). Conclusion: EA promotes motor function recovery, reduces the volume of cerebral infarction, and alleviates neuronal death following ischemic stroke by enhancing the expression of irisin in both the blood and peri-lesional cortex.

Nowadays, acoustic and vibration signals have been widely used to detect crack and its degree on compressor blades for non-destructive evaluation and structural health monitoring. Due to complex working conditions and interference of strong noise, single signal-based methods reach unsatisfactory accuracy. To improve the reliability and accuracy of crack detection, the two-level fusion method based on Mahalanobis distance (MD) and self-attention mechanism is proposed based on multi-acoustic, vibration, and acoustic emission information. First, the MD is calculated to measure the similarity between raw samples and group, which can fuse the same type of samples in data level. Then, the processed raw and data-level fusion samples are inputted to one-dimensional convolutional neural network, and the preliminary decisions are obtained. Finally, the decision-level fusion based on self-attention mechanism and credit assignment is proposed to modify and correct the preliminary decisions. The compressor experiments are implemented to test the proposed method under single and mixed working conditions with strong noise intensity. Further, the results illustrate that the proposed method can accurately detect crack for compressor blades. By comparing with other data fusion approaches, the advantage of the proposed method is validated under complex conditions.