Yongsheng Zhu

Intelligent Fault Diagnosis of Rolling Element Bearing Based on SVMs and Statistical Characteristics

In this paper, the statistical characteristics of time, frequency and time-frequency domain are applied to discriminate various fault types and evaluate various fault conditions of rolling element bearing, and the classification performance of them is evaluated by using SVMs. Experimental results showed that the statistical characteristics Mean, Variance, Root, RMS and Peak of the 25 sub frequency bands in frequency domain obtain higher classification accuracy rate on all the fault datasets than the statistical characteristics in the whole time and frequency domain. Wavelet packet decomposition is an efficient time-frequency analysis tool, and it can decompose the original signal into independent frequency bands. Experiment on the statistical characteristics of the 5th level wavelet packet decomposition showed that the statistical characteristics Variance, Root, RMS and Peak can discriminate various fault types and evaluate various fault conditions well on all the datasets. Compared with the statistical characteristics of sub frequency bands in frequency domain, the classification performance of the statistical characteristics of the wavelet packet transform is a little lower than that of the statistical characteristics of sub frequency bands in frequency domain.© 2007 ASME

Experimental study of the transient thermal effect and the oil film thickness of the equalizing thrust bearing in the process of start-stop with load

It was important to note the load could not be distributed equally over the thrust pads when the thrust bearing was in operation, especially at the stage of start-up and stop with load. An experimental study that investigated the transient thermal effect and the oil film thickness of the equalizing thrust bearing in the process of start-stop with load was presented in this article. Four static loads and three supplied oil pressures were used in this study. The transient thermal effect and the oil film thickness were simultaneously obtained from a horizontal thrust-bearing test rig. It was shown in experimental results that the transient thermal effect and the oil film thickness varied rapidly at the stage of start-up and stop, and had an unobvious variance when the rotational speed was constant. The structure of the equalizing thrust bearing allowed it to work well at the stage of start-up and stop with load.

Application of the multi-kernel non-negative matrix factorization on the mechanical fault diagnosis

Although the computation amount involved in the image processing is very large, image information which is very intuitive and easy to be understood has attracted great many attentions in the fault diagnosis of machines. In order to extract useful features from the images accurately and perfectly, a novel mechanical fault diagnosis method was proposed by the combination of the multi-kernel non-negative matrix factorization and multi-kernel support vector machine. The genetic algorithm was used to optimize the parameters of both multi-kernel non-negative matrix factorization and multi-kernel support vector machine. Experiments were used to validate the efficacy of the proposed method. It is shown that the multi-kernel function combined with the polynomial kernel function and radial-based kernel function can describe the fault feature more perfectly in the kernel space than a single kernel function. Sound accuracy can be obtained in the application of the bearing fault diagnosis. Compared with the fault diagnosis method based on the sparse non-negative matrix factorization, the proposed method is more accurate in the condition identification of rotor.

Research on condition assessment method based on projection one-class classifier

In order to solve the problem that the anomalous samples are scarce and the model is susceptible to abnormal data, this paper introduces the idea of kernel trick in the process of constructing the projection classifier and constructs three kinds of projection one-class classifiers: Projection Support Vector Data Description (PSVDD), Projection K-means (PK-means) and Projection K-centers (PK-centers) by means of the Feature Vector Selection and Projection (FVSP). In order to further improve the performance of the condition assessment model, this paper uses the method of ensemble learning and evidence theory with PSVDD, PK-means and PK-center and puts forward a new assessment index: Health Index (HI). Finally, the fatigue life experiment of rolling bearing is carried out to verify the effectiveness of the proposed method.

Experimental Investigation on the Film Pressure Measurement in Microgap Water-Lubricated Hybrid Journal Bearing

ABSTRACT The film pressure of a microgap water-lubricated hybrid journal bearing (HJB) was investigated in this article. The pressure transmission system (PTS) formed by the flow exchange from clearance to the membrane of the sensor was also studied. Three static loads, five rotational speeds, and five water supply pressures were used in this study. The pressure was obtained from a microgap water-lubricated HJB test rig. The experimental results showed that the variation trend of the film pressure agreed with the theoretical results except that the pressure values were lower than the theoretical data. The experimental results should be corrected by the PTS to reflect the real film pressure. The influence of PTS should be considered to improve the precision of experimental results of microgap HJBs.

Design of Quantum Dots for Rolling Bearing Inner Ring-Cage Thermal Monitoring

Temperature is one of the most important parameters affecting the service life and performance of rolling element bearings. Based on the temperature sensitive properties of quantum dots (QDs), a non-intrusive temperature measurement method is developed to monitor the temperature variation of the inner ring and cage during bearing operation. The CdTe QDs were synthesized in our laboratories and used in constructing of a sensor film by means of Layer-by-layer Electrostatic Self-assembly method. The fluorescence spectrum properties of the sensors were characterized. The experiment was performed to measure the inner ring and cage temperature of angular contact ball bearing in high speed running condition. Bearing inner ring and cage temperature rise curves were obtained in this paper by the CdTe QDs sensors and compared with the outer ring temperature gotten by platinum thermal resistance sensors.

The investigation of the temperature of high speed and heavy haul tilting pad journal bearing

Purpose – This paper aims to present the theoretical and experimental investigation of the temperature of high speed and heavy haul tilting pad journal bearing. Design/methodology/approach – The bearing is 152.15 mm in diameter with three slenderness ratios (L/D) and three clearance ratios. The equations that govern the flow and energy transport are solved by the finite difference method, and the experimental tests are conducted in a test rig of high speed and heavy haul tilting pad journal bearing. The shaft speed ranges from 3,000 to 16,500 r/min (the highest linear-velocity equals 131.4 m/s), and the three static loads are 10, 20 and 30 KN. Findings – The comparisons between numerical results and experimental results show better correlations. It is shown in the theoretical and experimental results that the temperature increases with static load and shaft speed and decreases with clearance ratio and L/D. Originality/value – The theoretical models presented in this paper can be used to predict the temper...

Nonnegative matrix factorization and artificial immune based classification for fault diagnosis of diesel valve train

To efficiently mine the classification model for machine fault diagnosis based on images, a hybrid classification algorithm, which inspired by combining nonnegative matrix factorization and artificial immune system, was put forward. In the algorithm, nonnegative matrix factorization was employed for dimensionality reduction of the time-frequency spectral images. An artificial immune based classification model was constructed by means of training of data samples mapped into low-dimensional space to recognize the machine conditions and diagnose faults. Experimental results on the fault classification of diesel valve train demonstrate the effectiveness of the algorithm. Compared with probabilistic neural network classifiers, the hybrid classifier achieves better fault diagnosis performance.