Shearer drum load identification method based on audio recognition

In order to solve the problems of the existing shearer drum load identification methods, such as difficult implementation of related algorithms, complex engineering implementation mode and high application difficulty, through analyzing the characteristics of the audio signal during shearer operation, a shearer drum load identification method based on audio recognition is proposed. In order to ensure that the audio signal in each analysis period has the same load condition under the same operation standard, the cutting current and the traction speed are introduced into the dynamic energy calculation as variables, and the dynamic energy normalization algorithm (DENA) is adopted to normalize the original audio signal of the shearer. The normalized signal is compared and analyzed with the signal in the standard operation condition library, and the difference between the two is judged by the maximum dissimilarity coefficient, so as to determine the characteristics of the drum load and realize the identification and judgment of the drum load. The test results show that DENA can effectively suppress the noise energy in the audio signal and improve the resolution of the key characteristic values in the audio signal. The boundary of the characteristic parameters of the audio signal is obvious when the shearer cuts coal and rock, and there is no cross aliasing phenomenon. Under ideal conditions, that is, when the maximum dissimilarity coefficient is less than 0.189, the total coal-rock interface recognition rate can reach 78.6%

Vibration signal identification of coal-rock cutting of shearer based on cepstral distance

In order to solve problems that conventional time-frequency domain analysis methods were sensitive to noise and had poor adaptability of energy change in coal-rock interface identification based on vibration signal analysis, a vibration signal identification method of coal-rock cutting of shearer based on cepstral distance was proposed. By analyzing cutting vibration signals of shearer acquired by vibration sensor under different load conditions, following conclusions are gotten. Compared with condition of cutting rock, the cepstral distance between vibration signal under condition of cutting coal and standard signal under no load condition is larger. The cepstral distance of the vibration signal under condition of rock cutting is obviously periodic, and the cycle is one rotation cycle of shearer drum, while the vibration signal under condition of cutting coal has no periodic feature. The industrial test result shows that the method has identification accuracy of 75% when hardness difference between coal and rock is more than 10 MPa

Mining machine cutting load classification based on vibration signal

There are some errors and lags in the way of judging the cutting load type of the mining machine manually. In order to solve the above problem, a classification method of mining machine cutting load based on wavelet packet decomposition and sparrow search algorithm optimized BP neural network (SSA-BPNN) is proposed. The method comprises two parts of signal feature extraction and mode classification. In the part of signal feature extraction, the collected vibration signal of the mining machine rocker arm is decomposed by wavelet packet to obtain the energy of each subband and the total energy of the signal. After normalization, feature vectors representing different load types are obtained. The principal component analysis is used to reduce the dimensions of the feature vector. In the mode classification part, SSA is used to optimize the initial weight and threshold of BPNN. The feature vector is used as the input of SSA-BPNN to realize the load classification and recognition. Taking the MG500/1170-AWD1 mining machine as an object, the magnetic acceleration sensor is attached to the shell of the rocker arm of the mining machine near the bracket side. The vibration signals of the mining machine drum under three working conditions of no-load, cutting bauxite and rock are collected and tested. The experimental results show that the vibration signals under different cutting loads have some differences in the energy of each sub-band. This result indicates that the energy features obtained by wavelet packet decomposition can be used as feature vectors to distinguish different load types. Compared with BPNN, SSA-BPNN has faster convergence speed and higher recognition accuracy, and the recognition accuracy of load classification is 95.3%

The IBA-ISMO Method for Rolling Bearing Fault Diagnosis Based on VMD-Sample Entropy

Rolling bearings are important supporting components of large-scale electromechanical equipment. Once a fault occurs, it will cause economic losses, and serious accidents will affect personal safety. Therefore, research on rolling bearing fault diagnosis technology has important engineering practical significance. Feature extraction with high price density and fault identification are two keys to overcome in the field of fault diagnosis of rolling bearings. This study proposes a feature extraction method based on variational modal decomposition (VMD) and sample entropy and also designs an improved sequence minimization algorithm with optimal parameters to identify the fault. Firstly, a variational modal decomposition system based on vibration signals is designed, and the sample entropy of the components is extracted as the eigenvalue of the signal. Secondly, in order to improve the accuracy of fault diagnosis, the sequence minimum optimization algorithm optimized by the bat algorithm is used as the classifier. Certainly, the traditional bat algorithm (BA) and the sequence minimum optimization algorithm (SMO) are improved, respectively. Therefore, a fault diagnosis algorithm based on IBA-ISMO is obtained. Finally, the experimental verification is designed to prove that the algorithm model has a good state recognition rate for bearings

Efficacy and safety of fluzoparib combined with anlotinib in extensive stage small cell lung cancer after first-line platinum-based chemotherapy: a multi-center, single-arm prospective phase II clinical study (STAMP study)

Abstract Background Small-cell lung cancer (SCLC) is a highly aggressive and lethal malignancy that accounts for 10–15% of lung cancers, and it is generally divided into limited and extensive stage. The standard of care for patients with newly diagnosed extensive-stage SCLC (ES-SCLC) is still platinum-based chemotherapy and as maintenance therapy scheme. Although most parts of patients experience a significant tumor response to first-line therapy, the disease recurs invariably. Anlotinib hydrochloride, a novel oral multitarget tyrosine kinase inhibitor, has significant inhibitory activity against angiogenesis-related kinases, such as VEGFR, FGFR, PDGFR, and c-Kit kinase associated with tumor cell proliferation. Fluzoparib is a type of inhibitor of poly ADP ribose polymerase (PARP, including PARPl, PARP2 and PARP3). Previous studies have shown that Fluzoparib has a strong inhibitory effect on PARP1 activity at the molecular and cellular levels. Methods This is a multi-center, prospective, single-arm phase II clinical study. A total of 50 ES-SCLC patients who experienced disease progression after first-line standard platinum-based chemotherapy with/without immune checkpoint inhibitors scheme, or within 6 months after the completion of treatment will be recruited. Those who had prior treatment with any PARP inhibitor or antiangiogenic agent includes anlotinib, bevacizumab, sorafenib, and thalidomide are excluded. Eligible patients will receive oral anlotinib 8 mg once daily and oral fluzoparib 150 mg twice daily until disease progression or intolerable toxicity. The primary endpoint is objective response rate (ORR). Discussion The addition of fluzoparib to anlotinib is expected to increase the clinical benefit in ES-SCLC patients after platinum-based chemotherapy. Trial registration This study protocol was prospectively registered on June 17, 2021. ClinicalTrials.gov Identifier: NCT04933175

Olfactory genomics of the Coleoptera

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