Metal-Matrix Embedded Phononic Crystals

Metal-matrix embedded phononic crystals (MMEPCs) can be applied for noise and vibration reduction. Metal-matrix embedded phononic crystals (MMEPCs) consisting of double-sided stubs (single “hard” stubs/composite stubs) were introduced. The introduced MMEPCs are deposited on a two-dimensional locally resonant phononic crystal plate that consists of an array of rubber fillers embedded in a steel plate. The lower frequency complete bandgap will be produced in the MMEPCs with composite stubs by decoupling the spring-mass system of the resonator by means of the rubber filler. Then, the out-of-plane bandgap and the in-plane bandgap can be adjusted into the same lowest frequency range by the composite stubs. The broad complete bandgap will be produced in the metal-matrix embedded phononic crystals with single “hard” stubs by producing new kinds of resonance modes (in-plane and out-of-plane analogous-rigid modes) by introducing the single “hard” stubs, and then the out-of-plane bandgap and the in-plane bandgap can be broadened into the same frequency range by the single “hard” stubs. The proposed MMEPCs can be used for noise and vibration reduction

Method for Vibration Response Simulation and Sensor Placement Optimization of a Machine Tool Spindle System with a Bearing Defect

sensor

Vibration Responses of the Bearing-Rotor-Gear System with the Misaligned Rotor

The bearing-rotor-gear system is an important mechanical component for transmitting motion and power. Due to the complex responses, the condition assessment of the transmission system becomes more difficult. Thus, a model of the bearing-rotor-gear system with a misaligned rotor is built for implementing the complex response analysis. The misalignment rotor is realized by offset connection of couplings, and the creative excitation force is transferred to the bearing inner ring through the rotor. The constructed model is checked by the corresponding experiment. From the simulation results, it is found that vibration responses are modulated by rotor frequencies, and there are rotor frequencies, harmonic frequencies of bearings, and gear pairs in the spectrum. When the misalignment defect is deepening, the high-order harmonic responses are excited. If the revolving speed increases, the modulation of the rotor frequencies is accentuated, the vibration intensity generated by gear pairs is attenuated, while the harmonic response and super-harmonic response of bearings can be suppressed, and the system vibrates mainly at the low-frequency band. When the load becomes higher, the amplitudes of the rotor frequencies, meshing frequencies, and defect frequencies are all increased

Defect Diagnosis of Gear-Shaft Bearing System Based on the OWF-TSCNN Composed of Wavelet Time-Frequency Map and FFT Spectrum 1

In the defect diagnosis of the gear-shaft-bearing system with compound defects, the generated vibration signals are complicated. In addition, the information acquired by a single sensor is easily affected by uncertain factors, and low diagnostic accuracy is caused when traditional defect diagnosis methods are used, which cannot meet the high-precision diagnosis requirements. Therefore, a method is developed to identify the defect types and defect degrees of the gear-shaft-bearing system efficiently. In this method, the vibration signals are collected using multiple sensors, the dual-tree complex wavelet and the optimal weighting factor (OWF) methods are used for the data layer fusion, and the preprocessing is realized through wavelet transform and FFT. A learning model based on two-stream CNN composed of 1D-CNN and 2D-CNN is established, and the obtained wavelet time-frequency map and FFT spectrum are used as the input. Then, the trained features from the output of the connected layer are classified by the SVM. Compared with the OWF-1DCNN and OWF-2DCNN models, the time consumption of the OWF-TSCNN model is increased by 14.5%–26.6%, and the convergence speed of the network is decreased. However, its accuracy reaches 100% and 99.83% in the training set and test set, and the loss entropy and over-fitting rate are also greatly reduced. The feature extraction ability and generalization ability of the OWF-TSCNN model are increased, reaching 100% diagnosis accuracy on different defect types and defect degrees, which is more suitable for defect diagnosis of the gear-shaft-bearing system