Gang Cheng

A new method of gear fault diagnosis in strong noise based on multi-sensor information fusion

In the work process of mining machinery gears, vibration signals are not only influenced by friction, nonlinear stiffness and non-stationary load, but also influenced by strong noise. How to extract the fault feature information effectively, identify the fault status accurately and eliminate the uncertainty in the identification process is the key to evaluate the fault status in strong noise. A new gear fault diagnosis method in strong noise is proposed based on multi-sensor information fusion, which combines wavelet correlation feature scale entropy (WCFSE), self-organizing feature map (SOM) neural network and Dempster-Shafer (D-S) evidence theory algorithm. The noise is reduced by the way of a wavelet transform correlation filter, to calculate the Shannon entropy of denoised wavelet coefficients, which can reflect the vibration signal complexity. The WCFSE of standard training samples defined as the input vectors of an SOM neural network is used to train the neural network, and the gear status clusters in a competitive layer. In order to improve the accuracy and completeness in the identification process, multi-sensor fusion technology is introduced to establish the recognition framework of D-S evidence theory and the basic belief function allocation method based on the recognition rate of statistical SOM neural network. Each sensor can provide sub-evidence, and the gear fault diagnosis is analyzed according to the combination rules and basic belief function. The experimental results show that the proposed gear diagnosis method in strong noise can identify the gear fault accurately, eliminate the identified uncertainty and imperfection with a single sensor. The recognition rates of two-sensor fusion beyond 80%, and the recognition rates of three-sensor fusion beyond 88%, especially the status with tooth loss beyond 95%. The fault recognition rates have been greatly improved compared with a single sensor, so this is an effective method of gear fault diagnosis in strong noise.

Planetary Gear Fault Diagnosis via Feature Image Extraction Based on Multi Central Frequencies and Vibration Signal Frequency Spectrum

Poor working environment leads to frequent failures of planetary gear trains. However, complex structure and variable transmission make the vibration signal strongly non-linear and non-stationary, which brings big problems to fault diagnosis. A method of planetary gear fault diagnosis via feature image extraction based on multi central frequencies and vibration signal frequency spectrum is proposed. The original vibration signal is decomposed by variational mode decomposition (VMD), and four components with narrow bands and independent central frequencies are decomposed. In order to retain the feature spectrum of the original vibration signal as far as possible, the corresponding feature bands are intercepted from the frequency spectrum of original vibration signal based on the central frequency of each component. Then, the feature images of fault signals are constructed as the inputs of the convolution neural network (CNN), and the parameters of the neural network are optimized by sample training. Finally, the optimized CNN is used to identify fault signals. The overall fault recognition rate is up to 98.75%. Compared with the feature bands extracted directly from the component spectrums, the extraction method of the feature bands proposed in this paper needs fewer iterations under the same network structure. The method of planetary gear fault diagnosis proposed in this paper is effective.

Planetary Gears Feature Extraction and Fault Diagnosis Method Based on VMD and CNN

Given local weak feature information, a novel feature extraction and fault diagnosis method for planetary gears based on variational mode decomposition (VMD), singular value decomposition (SVD), and convolutional neural network (CNN) is proposed. VMD was used to decompose the original vibration signal to mode components. The mode matrix was partitioned into a number of submatrices and local feature information contained in each submatrix was extracted as a singular value vector using SVD. The singular value vector matrix corresponding to the current fault state was constructed according to the location of each submatrix. Finally, by training a CNN using singular value vector matrices as inputs, planetary gear fault state identification and classification was achieved. The experimental results confirm that the proposed method can successfully extract local weak feature information and accurately identify different faults. The singular value vector matrices of different fault states have a distinct difference in element size and waveform. The VMD-based partition extraction method is better than ensemble empirical mode decomposition (EEMD), resulting in a higher CNN total recognition rate of 100% with fewer training times (14 times). Further analysis demonstrated that the method can also be applied to the degradation recognition of planetary gears. Thus, the proposed method is an effective feature extraction and fault diagnosis technique for planetary gears.

Fault diagnosis of gearbox based on local mean decomposition and discrete hidden Markov models

This paper proposes an intelligent diagnosis method for gearbox using local mean decomposition and discrete hidden Markov models, including local mean decomposition, the energy difference spectrum of singular value, multiscale sample entropy, and the discrete hidden Markov model. How to extract feature information effectively and identify the fault type is key to making a diagnosis in the presence of strong noise. Combined with the Kurtosis criterion and correlation coefficient, the product function that contains the main characteristic frequency is filtered out by local mean decomposition. Next, the filtered local mean decompositions are used to construct the Hankel matrix and complete singular value decomposition. The denoised and reconstructed signals are achieved by an energy difference spectrum of singular value. Furthermore, the feature information after denoising is extracted by multiscale sample entropy. After combining the discrete hidden Markov models, the mechanical condition is identified. Practical examples of diagnoses for four gear types used in the gearbox can accurately identify the gear types, and the recognition rates of the various types are above 92%. The experiments shown here verify the effectiveness of the method proposed in this paper.

Bifurcation and stability analysis for 3SPS+1PS parallel hip joint manipulator based on unified theory

For a parallel hip joint manipulator, the unified kinematics and stiffness model are established based on a novel unified theory, and then the bifurcation and stability are analyzed under the same unified theory framework. In bifurcation analysis, a chaos method is first applied to solve the non-linear bifurcation equations in order to get the full configuration of the parallel hip joint manipulator, which improves the convergence rate and accuracy. Based on the full-configuration solution, the single-parameter and double-parameter simulation for the bifurcation and stability of the parallel hip joint manipulator is performed. The bifurcation simulation results show that the configuration only changes along the corresponding path but cannot change to other paths when the configuration of the parallel hip joint manipulator is at a certain path. The stability simulation results show that when the parallel hip joint manipulator enters into an uncontrolled domain of a bifurcation posture along different paths, the posture component which changes dramatically will lose control first, and the other posture components will move along the changed configuration. In this paper, the kinematics, stiffness, bifurcation and stability of the parallel hip joint manipulator are solved under the same theory framework, which improves the solving efficiency and enriches the mechanical theory for the parallel manipulators.

Fault diagnosis of planetary gear based on entropy feature fusion of DTCWT and OKFDA

Planetary gears are often used in the key parts of the transmission systems of mechanical equipment, and faults are the main factors that determine the reliability of equipment operation. A fault diagnosis method for planetary gears based on the entropy feature fusion of dual-tree complex wavelet transform (DTCWT) and optimized kernel Fisher discriminant analysis (OKFDA) is proposed. The original vibration signal is decomposed by DTCWT, the frequency band signals are obtained, and the extraction models for the entropy features are built from multiple perspectives according to the definition of entropy theory. But the original entropy features, which are extracted from multiple perspectives, lead to excessive feature dimensions, and there are also many insensitive features that have small effects on the identification of the faults of planetary gears. Feature dimension reduction and sensitive feature extraction were achieved by OKFDA. The effectiveness of OKFDA and the extracted sensitive features were analyzed for the original features with different dimensions. Fault diagnosis for planetary gears can be achieved by analyzing sensitive features accurately.

Friction Compensation in Trajectory Tracking Control for a Parallel Hip Joint Simulator

To evaluate the friction and wear characteristics of hip joint prosthesis biomaterials, a hip joint simulator with a 3SPS + 1PS (P: prismatic joint, S: spherical joint) spatial parallel manipulator as the core module is proposed. To improve the control performance of the parallel hip joint simulator, a friction compensation control method is proposed. First, with the help of Lagrange’s Equations, the dynamic model of the parallel hip joint simulator is established, and a Coulomb + viscous friction model is adopted to describe the friction of the hip joint and the thrust ball bearing. Second, identification experiments are conduced, and parameters of the friction model are estimated. Third, a friction compensation controller is obtained based on computed torque control method, and the friction is served as the feed-forward compensation. As can be observed from the experiment results, the tracking accuracy of the parallel hip joint simulator is improved with the friction compensation.