Adaptive Stochastic Resonance Method Research Articles

Optimizing the Adaptive Stochastic Resonance and Its Application in Fault Diagnosis

This paper presents an adaptive stochastic resonance method based on the improved artificial fish swarm algorithm. By this method, we can enhance the weak characteristic signal which is submerged in a heavy noise. We can also adaptively lead the stochastic resonance to be optimized to the greatest extent. The effectiveness of the proposed method is verified by both numerical simulation and lab experimental vibration signals including normal, a chipped tooth and a missing tooth of planetary gearboxes under the loaded condition. Both theoretical and experimental results show that this method can effectively extract weak characteristics in a heavy noise. In the experiment, each weak fault feature is extracted successfully from the fault planetary gear. When compared with the ensemble empirical mode decomposition (EEMD) method, the method proposed in this paper has been found to give remarkable performance.

Blade Crack Detection of Centrifugal Fan Using Adaptive Stochastic Resonance

Centrifugal fans are widely used in various industries as a kind of turbo machinery. Among the components of the centrifugal fan, the impeller is a key part because it is used to transform kinetic energy into pressure energy. Crack in impeller’s blades is one of the serious hidden dangers. It is important to detect the cracks in the blades as early as possible. Based on blade vibration signals, this research applies an adaptive stochastic resonance (ASR) method to diagnose crack fault in centrifugal fan. The ASR method, which can utilize the optimization ability of the grid search method and adaptively realize the optimal stochastic resonance system matching input signals, may weaken the noise and highlight weak characteristic and thus can diagnose the fault accurately. A centrifugal fan test rig is established and experiments with three cases of blades are conducted. In comparison with the ensemble empirical mode decomposition (EEMD) analysis and the traditional Fourier transform method, the experiment verified the effectiveness of the current method in blade crack detection.

Adaptive bistable stochastic resonance and its application in mechanical fault feature extraction

Stochastic resonance (SR) is an important approach to detect weak vibration signals from heavy background noise. In order to increase the calculation speed and improve the weak feature detection performance, a new bistable model has been built. With this model, an adaptive and fast SR method based on dyadic wavelet transform and least square system parameters solving is proposed in this paper. By adding the second-order differential item into the traditional bistable model, noise utilization can be increased and the quality of SR output signal can be improved. The iteration algorithm for implementing the adaptive SR is given. Compared with the traditional adaptive SR method, this algorithm does not need to set up the searching range and searching step size of the system parameters, but only requires a few iterations. The proposed method, discrete wavelet transform and the traditional adaptive SR method are applied to analyzing simulated vibration signals and extracting the fault feature of a rotor system. The contrastive results verify the superiority of the proposed method, and it can be effectively applied to weak mechanical fault feature extraction.

Adaptive Stochastic Resonance Method for Early Fault Detection of Rotating Machinery Based on Alpha Stable Distribution

This paper proposes an adaptive stochastic resonance (SR) method based on alpha stable distribution for early fault detection of rotating machinery. By analyzing the SR characteristic of the impact signal based on sliding windows, SR can improve the signal to noise ratio and is suitable for early fault detection of rotating machinery. Alpha stable distribution is an effective tool for characterizing impact signals, therefore parameter alpha can be used as the evaluating parameter of SR. Through simulation study, the effectiveness of the proposed method has been verified.

Adaptive stochastic resonance method based on quantum particle swarm optimization

In order to enhance the usefulness of the theory of stochastic resonance in the areas of weak signal detection, a new method based on quantum particle swarm optimization is proposed to conquer with the problem of adaptive stochastic resonance. First, the problem of adaptive stochastic resonance is converted into the problem of multi-parameter optimization. Then simulation experiments are conducted respectively under a Langevin system and Duffing oscillator system. At the same time, Point detection method is chosen as the comparative test in the Langevin system. While in the Duffing system, the optimization results are compared with those from the Langevin system directly. Results show that the method based on quantum particle swarm optimization is obviously superior to the point detection method and optimization result in the Duffing oscillator is better than that from Langevin system under the same condition. Besides, it is also found that the lower the SNR of input signal, the more effective the quantum particle swarm optimization is. Finally, the regularity of optimization results of the stochastic resonance system parameters is summarized.

Wayside acoustic diagnosis of defective train bearings based on signal resampling and information enhancement

The diagnosis of train bearing defects plays a significant role to maintain the safety of railway transport. Among various defect detection techniques, acoustic diagnosis is capable of detecting incipient defects of a train bearing as well as being suitable for wayside monitoring. However, the wayside acoustic signal will be corrupted by the Doppler effect and surrounding heavy noise. This paper proposes a solution to overcome these two difficulties in wayside acoustic diagnosis. In the solution, a dynamically resampling method is firstly presented to reduce the Doppler effect, and then an adaptive stochastic resonance (ASR) method is proposed to enhance the defective characteristic frequency automatically by the aid of noise. The resampling method is based on a frequency variation curve extracted from the time–frequency distribution (TFD) of an acoustic signal by dynamically minimizing the local cost functions. For the ASR method, the genetic algorithm is introduced to adaptively select the optimal parameter of the multiscale noise tuning (MST)-based stochastic resonance (SR) method. The proposed wayside acoustic diagnostic scheme combines signal resampling and information enhancement, and thus is expected to be effective in wayside defective bearing detection. The experimental study verifies the effectiveness of the proposed solution.

Adaptive stochastic resonance method for impact signal detection based on sliding window

Aiming at solving the existing sharp problems in impact signal detection by using stochastic resonance (SR) in the fault diagnosis of rotating machinery, such as the measurement index selection of SR and the detection of impact signal with different impact amplitudes, the present study proposes an adaptive SR method for impact signal detection based on sliding window by analyzing the SR characteristics of impact signal. This method can not only achieve the optimal selection of system parameters by means of weighted kurtosis index constructed through using kurtosis index and correlation coefficient, but also achieve the detection of weak impact signal through the algorithm of data segmentation based on sliding window, even though the differences between different impact amplitudes are great. The algorithm flow of adaptive SR method is given and effectiveness of the method has been verified by the contrastive results between the proposed method and the traditional SR method of simulation experiments. Finally, the proposed method has been applied to a gearbox fault diagnosis in a hot strip finishing mill in which two local faults located on the pinion are obtained successfully. Therefore, it can be concluded that the proposed method is of great practical value in engineering.

Planetary gearbox fault diagnosis using an adaptive stochastic resonance method

Planetary gearboxes are widely used in aerospace, automotive and heavy industry applications due to their large transmission ratio, strong load-bearing capacity and high transmission efficiency. The tough operation conditions of heavy duty and intensive impact load may cause gear tooth damage such as fatigue crack and teeth missed etc. The challenging issues in fault diagnosis of planetary gearboxes include selection of sensitive measurement locations, investigation of vibration transmission paths and weak feature extraction. One of them is how to effectively discover the weak characteristics from noisy signals of faulty components in planetary gearboxes. To address the issue in fault diagnosis of planetary gearboxes, an adaptive stochastic resonance (ASR) method is proposed in this paper. The ASR method utilizes the optimization ability of ant colony algorithms and adaptively realizes the optimal stochastic resonance system matching input signals. Using the ASR method, the noise may be weakened and weak characteristics highlighted, and therefore the faults can be diagnosed accurately. A planetary gearbox test rig is established and experiments with sun gear faults including a chipped tooth and a missing tooth are conducted. And the vibration signals are collected under the loaded condition and various motor speeds. The proposed method is used to process the collected signals and the results of feature extraction and fault diagnosis demonstrate its effectiveness.

New Adaptive Stochastic Resonance Method and Its Application to Fault Diagnosis

The performance of stochastic resonance methods is mostly decided by its system parameters.The existing stochastic resonance methods have the fatal problems;for example,subjectively selecting parameters or optimizing only one parameter therefore ignoring the interactive effect between parameters.To solve the problems mentioned above,a new adaptive stochastic resonance method is proposed.Compared with the existing methods,the proposed method utilizes the optimization ability of ant colony algorithms,synchronously selecting and optimizing multiple system parameters and considering the interactive effect between parameters,and adaptively realizes the optimal stochastic resonance system matching input signals.Thus,the problems in selecting parameters are solved by using the proposed method.Therefore noise is weakened and weak characteristics are enhanced effectively,and the early faults are diagnosed accurately as well.Both simulations and a real case of locomotive rolling element bearings with an early fault demonstrate that the proposed adaptive stochastic resonance method obtains the improved results compared with the existing methods.