Research on fault diagnosis method of rolling bearing based on AMD and LabVIEW

Aiming at the problem of rolling bearing fault diagnosis, a fault diagnosis method of rolling bearing is proposed based on analytical mode decomposition (AMD) and LabVIEW. For the fault feature frequency of rolling bearing is predictable, the AMD method can be used to extract the signal in the frequency band of fault characteristic frequency in rolling bearing signal, and seek frequency spectrum of vibration signal. If the spectrum contains fault characteristic frequency, then the rolling bearing fault can be diagnosed by vibration signal. A rolling bearing fault diagnosis system is developed based on LabVIEW and AMD, and the application of AMD algorithm is realized. The validity of the method is proved by the analysis of actual fault signal of rolling bearing

Rolling bearing health status assessment based on ITD-GMM method

This paper proposed a Rolling bearing health state assessment based on ITD-GMM method to fully dig the favorable information of the vibration signal from the rolling bearing with decline trend. By data analytic, the six components of vibration signal were calculated, and each component has three feature vectors. Finally, the performance of rolling bearing was quantified, and the curve of performance was acquired. The experimental results indicate that the method is feasible and effective for the assessment of rolling bearing

Residual useful life predictions for train’s rolling bearing based on proportional hazard model

This paper studies the rolling bearing of the train and it puts forward a method of predicting the residual useful life (RUL) of the train’s rolling bearing using the proportional hazard model (PHM). First, the problem of RUL predictions for the train’s rolling bearing is described. Secondly, PHM is introduced, including the basic form, sample data, parameter estimation and prediction. Then, the method of RUL predictions using PHM is put forward. Finally, PHM has been validated by the total life data of the train’s rolling bearing

Time-Frequency Fault Feature Extraction for Rolling Bearing Based on the Tensor Manifold Method

Rolling-bearing faults can be effectively reflected using time-frequency characteristics. However, there are inevitable interference and redundancy components in the conventional time-frequency characteristics. Therefore, it is critical to extract the sensitive parameters that reflect the rolling-bearing state from the time-frequency characteristics to accurately classify rolling-bearing faults. Thus, a new tensor manifold method is proposed. First, we apply the Hilbert-Huang transform (HHT) to rolling-bearing vibration signals to obtain the HHT time-frequency spectrum, which can be transformed into the HHT time-frequency energy histogram. Then, the tensor manifold time-frequency energy histogram is extracted from the traditional HHT time-frequency spectrum using the tensor manifold method. Five time-frequency characteristic parameters are defined to quantitatively depict the failure characteristics. Finally, the tensor manifold time-frequency characteristic parameters and probabilistic neural network (PNN) are combined to effectively classify the rolling-bearing failure samples. Engineering data are used to validate the proposed method. Compared with traditional HHT time-frequency characteristic parameters, the information redundancy of the time-frequency characteristics is greatly reduced using the tensor manifold time-frequency characteristic parameters and different rolling-bearing fault states are more effectively distinguished when combined with the PNN

Fault diagnosis method for rolling bearings based on the interval support vector domain description

Aiming at the fault classification problem of the rolling bearing under the uncertain structure parameters work condition, this paper proposes a fault diagnosis method based on the interval support vector domain description (ISVDD). Firstly, intrinsic time scale decomposition is performed for vibration signals of the rolling bearing to get the time-frequency spectrum samples. These samples are divided into a training set and a test set. Then, the training set is used to train the ISVDD. Meanwhile, the dynamic decreasing inertia weight particle swarm optimization is applied to improve the training accuracy of ISVDD model. Finally, the performance of the four interval classifiers is calculated in rolling bearing fault test set. The experimental results show the advantages of the ISVDD model: (1) ISVDD can extend the support vector domain description to solve the uncertain interval rolling bearing fault classification problem effectively; (2) The proposed ISVDD has the highest classification accuracy in four interval classification methods for the different rolling bearing fault types

Dynamic analysis of localized defects in rolling bearing systems

In this paper, the dynamics of rolling bearing with localized defects of the outer ring and rolling element are investigated. In order to study the nonlinear dynamical behaviors of the rolling bearing precisely, a novel dynamic model of the rolling bearing is established based on the Lagrangian approach. By setting 0.2 mm, 0.4 mm and 0.6 mm local defects on the outer ring and rolling element of bearing respectively, the results demonstrate that the amplitude of the rolling bearing is more intense as the local defect size increases, and the acceleration amplitude fluctuation is more significant than the velocity. In addition, in the case of the same defect size, the vibration of the rolling element defect is more intense than the vibration response caused by the outer ring defect

Incipient defect identification in rolling bearings using adaptive lifting scheme packet

Defects on the surface of rolling bearing elements are some of the most frequent causes of malfunctions and breakages of rotating machines. Defect detection in rolling bearings via techniques that examine changes in measured signal is a very important topic of research due to increasing demands for quality and reliability. In this paper, incipient defect identification method based on adaptive lifting scheme packet is proposed. Adaptive lifting scheme packet operators which adapt to the signal characteristic are constructed. The shock pulse value in defect sensitive frequency band is used as the defect indicator to identify the defect location and severity of rolling bearing. The proposed defect identification method is applied to analyze the experimental signal from rolling bearing with incipient inner raceway defect. The result confirms that the proposed method is accurate and robust in rolling bearing incipient defect identification

Curve similarity recognition based rolling bearing degradation state estimation and lifetime prediction

The health state of a rolling bearing keeps changing from a normal state to a slight degradation state followed eventually by a severely degraded state. To make reasonable inspection and maintenance plans, it is necessary to estimate the degradation state and predict the lifetime of a running rolling bearing accurately and in a timely fashion. This paper presents a new method for rolling bearing degradation state estimation and lifetime prediction based on curve similarity recognition. Different from existing methods, this method employs a dynamic time warping algorithm to recognize the curve similarity of those extracted features of rolling bearings in different states of health, which can reflect the intrinsic state of the rolling bearing; it discretizes the bearing degradation state reasonably through curve similarity. Next, the curve similarity is used to train the degradation state estimation model and a support vector machine based lifetime prediction model. Finally, this paper conducts a case study for a rolling bearing with impact degradation and one with wear degradation, respectively. The experimental results indicate that the new proposed method is highly efficient in recognizing the bearing’s degradation state and predicting its lifetime