Maintenance, Reliability and Condition Monitoring
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Controlled cascading waterfall technology of crushing raw materials on the value of its current strongholds
Get PDFThe results of laboratory studies of innovative technology for grinding raw materials are presented. Grinding technology is one of the most energy-intensive industries in the industry. The obtained data confirm the possibility of obtaining breakthrough technical and economic indicators in the implementation of the proposed innovative technology of grinding raw materials in ball mills. The latter is achieved by maintaining in them the optimal trajectories of intra mill loading on the basis of the use of a controlled cascade-waterfall grinding mode, depending on the constantly changing physical and mechanical properties (grind ability, strength) of the ground product. At the same time, the mill effectively implements the most efficient grinding regimes (blow, abrasion, crushing), depending on the current strength of the crushed raw materials. Tests have shown the possibility of reducing the specific energy consumption depending on the strength of the crushed material by 31-44 % with a simultaneous increase in the performance of the mill for raw materials of various strength classes by 22-57 %. For the industrial tests of the proposed technology, two systems of automatic control of the strength of the comminuted material in the mill have been developed
Experimental setup for unbalance fault detection and vibration analysis in a cardan shaft (Part B)
Get PDFThis paper experiments a vibration measurement on a twin-rotor connected by a Hooke’s joints. An experimental set-up for vibration analysis and unbalance fault detection in rotating machinery has been conducted to reveal the unbalance effect in signal transmission of the interconnected shaft through Hooke’s joint. Vibrations signals were measured and analysed using the Fast Fourier Transform (FFT), orbit patterns and time displacement of each rotor centre, to extract the signature of the unbalance fault. The results indicate the accuracy of the discussed approach for the twin-rotor unbalances diagnosis in an interconnected shafts system through a Hooke’s joint
Simulation and experiment research of vibration characteristic on star compressor
Get PDFAiming at the vibration defects of star compressor, simulation and experiment are carried out to investigate the compressor. By drilling holes in the valve stem, the pressure changes of each cylinder are measured without damaging the cylinder block. The dynamic model of crank-connecting rod mechanism is established. After the measured cylinder pressure loaded on the dynamic model, the force acting on the main motion pair is obtained. The finite element model of the compressor is established for the first time, the force of the motion pair is loaded on the finite element model by MPC coupling method, and the vibration acceleration of the machine base is tested. The result shows that the main modal shapes between 50-150 Hz are body deformation, modal shapes between 150-200 Hz are base deformation, the simulation acceleration of the base is higher than that of the experiment, and the main working frequency and frequency doubling can correspond. The conclusions provide reference for vibration isolation design of star compressor
Fault identification technology for gear tooth surface wear based on MPE method by MI and improved FNN algorithm
Get PDFMultiscale Permutation Entropy (MPE) is a presented nonlinear dynamic technology for measuring the randomness and detecting the nonlinear dynamic change of time sequences and can be used effectively to extract the nonlinear dynamic wear fault feature of gear tooth surface from vibration signals of gear set. To solve the subjectivity drawback of threshold parameter selection process in MPE method, a joint calculation method based on the Mutual Information (MI) and improved False Nearest Neighbor (FNN) principle for calculating threshold parameters for MPE method was presented in this article. Then, the influence of threshold parameters on the identification accuracy of fault features with the MPE was studied by analyzing simulation data. Through the simulation analysis, the effectiveness of the proposed MPE method is validated. Finally, the wear failure test of spur gear was carried out, and the proposed method was applied to analyze the experimental data of fault signal. Meanwhile, the vibration characteristics of the fault signal are acquired. The analysis results show that the proposed method can effectively realize the fault diagnosis of gear box and has higher fault identification accuracy than the existing methods
Construction of 12 DOFs spur gear coupling dynamic model
Get PDFA 12-degree-of-freedom (DOF) spur gear dynamic model is constructed, which is coupled by the mesh gear pair and the gearbox. The construction method of spur gear coupling dynamic model, based on lumped mass method, is better than finite element method, due to higher modeling efficiency. The work would be benefit to spur gear coupling dynamic modeling and analyses
Safety evaluation method of bridge plate rubber bearing based on SVM
Get PDFIn order to comprehensively evaluate the working state and safety performance of the bridge plate rubber bearing (PRB), the basic principle and method of SVM in machine learning theory (Support Vector Machine) was introduced in this paper. Firstly, the performance indices (such as the average compression stress, tangent value of the shear angle and the rotation angle of the PRB) were analyzed and calculated through its finite element model considering the different working conditions (axial compression, shear deformation and the rotation deformation of the PRB). Subsequently, considering the relevant indices affecting the safety performance of the PRB, the Pearson Correlation Coefficients (PCC) were calculated by SVM method for assessing the internal correlation of the performance indices of the PRB for different cases, respectively. The threshold values of the influential factors for different cases considering the PCC and the weight coefficient were obtained for grading the working state of the PRB as five levels. Moreover, in order to verify the validity and suitability of the evaluation of the working state of the plate rubber bearing by the SVM method, three apparent indices for grading the performance level of the PRB such as the crack, the undistributed spalling deformation and the dislocation of the PRB were analyzed and compared with the qualitative descriptions and the quantitative values. Finally, the corresponding threshold values for grading the performance levels of the PRB working states were proposed. It was shown that the SVM based evaluation method for the PRB is feasible and convenient, especially for the bridges with real-time monitoring devices to get the response data of the PRB
Vibration-based damage growth monitoring in beam-like structures
Get PDFDamage growth monitoring plays an important role in providing early warning of structural failure. The existing methods for damage growth monitoring are mainly local inspection methods, such as acoustic emission. These methods need a priori knowledge of accessible damage vicinity, which may not be realized in practice. Hence, vibration-based global approach is adopted to overcome these difficulties. Natural frequency, as a global modal parameter, can be measured easily and is used for vibration-based damage growth monitoring in this study. A concept of damage-induced relative natural frequency change (RNFC) curve is defined first and its relation with mode shape is then derived analytically, giving a good way to approximate RNFC curves. For monitoring damage growth, a damage growth indicator is proposed based on RNFCs between two damaged stages of a beam. The effectiveness of the indicator for damage growth monitoring is proved by both numerical and experimental cases in beam-like structures
Damage detection in sluice hoist beams subject to excitation at resonance frequency band based on local primary frequency
Get PDFCracks of sluice hoist beams due to the load and aging of the material threaten the safety of sluice structural system. As the one of the main methods of damage detection, the non-destructive detection method based on natural frequency is still insensitive to local damage. Therefore, this paper proposes a method for hoist beams damage detection driven by resonance frequency band based on local primary frequency in the local mode. Firstly, the possibility of damage detection based on local primary frequency is discussed and the procedure of determining resonance frequency band is explained. Then the damage identification index based on the change ratio of local primary frequency is provided. Finally, numerical results demonstrate the correctness and effectiveness of the proposed method. The proposed method can provide reference for damage detection of hoist beams and health monitoring of sluice structural system
Chaotic time series prediction using wavelet transform and multi-model hybrid method
Get PDFIn order to further improve the prediction accuracy of the chaotic time series and overcome the defects of the single model, a multi-model hybrid model of chaotic time series is proposed. First, the Discrete Wavelet Transform (DWT) is used to decompose the data and obtain the approximate coefficients (low-frequency sequence) and detailed coefficients (high-frequency sequence) of the sequence. Secondly, phase space reconstruction is performed on the decomposed data. Thirdly, the chaotic characteristics of each sequence are judged by correlation integral and Kolmogorov entropy. Fourthly, in order to explore the deeper features of the time series and improve the prediction accuracy, a sequence of Volterra adaptive prediction models is established for the components with chaotic characteristics according to the different characteristics of each component. For the components without chaotic characteristics, a JGPC prediction model without chaotic feature sequences is established. Finally, the multi-model fusion prediction of the above multiple sequences is carried out by the LSTM algorithm, and the final prediction result is obtained through calculation, which further improves the prediction accuracy. Experiments show that the multi-model hybrid method of Volterra-JGPC-LSTM is more accurate than other comparable models in predicting chaotic time series