Planetary gearbox fault diagnosis using morphological gradient filters

As a key component of rotating machineries, fault diagnosis for planetary gearbox is very difficult compared to the fixed shaft gearbox. It is becoming a hot research topic recent years. Different fault type has different vibration characteristics. Different from the traditional signal analysis methods, morphological gradient filters are used to extract the fault frequencies in this paper. Planetary gearbox experiment signals are used to validate the proposed method

A recognition method of plunger wear degree of plunger pump using probability neural network

In order to increase the diagnosis efficiency of plunger wear fault, a recognition method is developed using sensitivity analysis and probability neural network. Firstly, 17 time domain characteristics of vibration signal are extracted. Then analyzed the sensitivity of characteristics to failure to select sensitive characteristics parameters. Finally, PNN method to identify the degree of plunger wear was proposed. A hydraulic pump fault simulation experiment was designed, and validated the proposed method by experimental data. The results show that the method can quickly and effectively identify the degree of plunger wear

Trajectory tracking control of a quadrotor UAV based on sliding mode active disturbance rejection control

This paper proposes a sliding mode active disturbance rejection control scheme to deal with trajectory tracking control problems for the quadrotor unmanned aerial vehicle (UAV). Firstly, the differential signal of the reference trajectory can be obtained directly by using the tracking differentiator (TD), then the design processes of the controller can be simplified. Secondly, the estimated values of the UAV's velocities, angular velocities, total disturbance can be acquired by using extended state observer (ESO), and the total disturbance of the system can be compensated in the controller in real time, then the robustness and anti-interference capability of the system can be improved. Finally, the sliding mode controller based on TD and ESO is designed, the stability of the closed-loop system is proved by Lyapunov method. Simulation results show that the control scheme proposed in this paper can make the quadrotor track the desired trajectory quickly and accurately. &nbsp

An adaptive stochastic resonance method based on multi-agent cuckoo search algorithm for bearing fault detection

Bearing is widely used in the rotating machinery and prone to failure due to the harsh working environment. The bearing fault-induced impulses are weak because of poor background noise, long vibration transmission path, and slight fault degree. Therefore, the bearing fault detection is difficult. A novel adaptive stochastic resonance method based on multi-agent cuckoo search algorithm for bearing fault detection is proposed. Stochastic resonance (SR) is like a nonlinear filter, which can enhance the weak fault-induced impulses while suppressing the noise. However, the parameters of the nonlinear system exert an influence on the SR effect, and the optimal parameters are difficult to be found. Multi-agent cuckoo search (MACS) algorithm is an excellent heuristic optimization algorithm and can be used to search the parameters of nonlinear system adaptively. Two bearing fault signals are used to validate the effectiveness of our proposed method. Three other adaptive SR methods based on cuckoo search algorithm, particle swarm optimization or genetic algorithm are also used for comparison. The results show that MACS can find the optimal parameters more quickly and more accurately, and our proposed method can enhance the fault-induced impulses efficiently

Spare support model based on gamma degradation process

Spare parts ordering is very important in the domain of system support based on condition-based maintenance. For a single-unit system with condition monitoring, a joint degradation and spare parts ordering model is established in this paper to achieve the lowest total cost rate as the objective. The degradation process of system is assumed to be followed a gamma process. A decision on optimal spare ordering time by the improved cost rate model based on the proposed degradation model is made. Finally, a case analysis is implemented to demonstrate the effectiveness of the proposed model in this paper. Analysis results show that the proposed model can reduce the cost rate effectively

An availability model based on a three-stage failure process under age based replacement

This paper proposes a joint optimal policy of inspection and age based replacement based on a three-stage failure process to jointly optimize the inspection and replacement intervals. The three-stage failure process divides the failure process of system into three stages: namely normal, minor defective and severe defective. When the minor defective stage is identified, the subsequent inspection interval is halved. Once identifying the severe defective stage, the maintenance action is carried out immediately. The system is replaced once it reaches the certain age. Finally, a numerical example is presented to demonstrate the efficiency of the proposed model

Inspection period determination for two-stage degraded system

At present studies on degradation process are mainly single stage degradation mode, however, in practice the system degradation process is generally multi-stage. Based on general degradation process modeling, the paper assumed degenerate distribution of two-stage mode obey various normal distribution, shock times obey Poisson process. Reliability modeling and mean time to failure modeling of two-stage degraded mode are studied. Functional check period determination methods are used to calculate inspection periods for different degradation stage. In numerical example, inspection periods for system with two-stage degradation process are analyzed

Gear fault diagnosis and damage level identification based on Hilbert transform and Euclidean distance technique

This paper deals with the problem of gear fault diagnosis with multiple possible fault modes and damage levels. Gears are the most essential parts in rotating machinery. Their health status is a significant index to indicate whether machines can run continually or not. So, gear fault diagnosis and damage level identification is very important in engineering practice. An accuracy way to identify the state of gears is urgently needed for the maintenance decision making. In this paper, a novel gear fault diagnosis and damage level identification method based on Hilbert transform (HT) and Euclidean distance technique (EDT) is developed. The energies of six frequency bands are used as the fault feature through the contrast with other two parameters, kurtosis and skewness. Then HT is used to obtain analytic signal. Finally, EDT is utilized to recognize the different fault modes and damage levels. This method is implemented by two stages, i.e., classifying different fault modes and identifying damage levels for every fault mode. The effectiveness of this methodology is demonstrated by compare to fisher discriminant analysis (FDA) using experiment data acquired from a real gearbox. In addition, industrial data is also used to validate the effectiveness of the proposed method

Correlation Between University Music Teachers’ Self-efficacy and Autonomous Learning

Music instructors' self-efficacy and autonomous learning impact their professional growth at the university and play an essential part in developing high-level music education. This study aims to look into the current state of university music instructors' self-efficacy and autonomous learning and analyses the link between them. The survey findings include that university music instructors' self-efficacy is greater overall, and the status quo of university music teachers' autonomous learning at the medium level is powerful for autonomous learning motivation

Analysis of the linearly extrapolated BDF2 fully discrete Modular Grad-div stabilization method for the micropolar Navier-Stokes equations

We investigate a fully discrete modular grad-div (MGD) stabilization algorithm for solving the incompressible micropolar Navier-Stokes equations (IMNSE) model, which couples the incompressible Navier-Stokes equations and the angular momentum equation together. The mixed finite element (FE) method is applied for the spatial discretization. The time discretization is based on the BDF2 implicit scheme for the linear terms and the two-step linearly extrapolated scheme for the convective terms. The considered algorithm constitutes two steps, which involve a post-processing step for linear velocity. First, we decouple the fully coupled IMNSE model into two smaller sub-physics problems at each time step (one is for the linear velocity and pressure, the other is for the angular velocity), which reduces the size of the linear systems to be solved and allows for parallel computing of the two sub-physics problems. Then, in the post-processing step, we only need to solve a symmetrical positive determined grad-div system of linear velocity at each time step, which does not increase the computational complexity by much. However, the post-processing step can improve the solution quality of linear velocity. Moreover, we obtain unconditional stability, and error estimates of the linear velocity and angular velocity. Finally, several numerical experiments involving three-dimensional and two-dimensional settings are used to validate the theoretical findings and demonstrate the benefits of the modular grad-div (MGD) stabilization algorithm