Rotordynamics of an Improved Face-grinding Spindle: Rotational Stiffness of Thrust Bearing Increases Radial Stiffness of Spindle

The support is a key factor affecting performance of face-grinding spindle. However, advantage of traditional rolling element bearing is not highlighted when it is for large-size face grinding. This technical brief aims to develop a combined support for the face-grinding spindle consisting of a water-lubricated hydrostatic thrust bearing and two types of radial rolling bearings, and the flexible rotor dynamics of the spindle with the combined support is analyzed using the modified transfer matrix method. The results show that the rotational stiffness of water-lubricated hydrostatic thrust bearing can increase the radial stiffness of the face-grinding spindle, so the small-size rolling bearings can be utilized as the radial support for the spindle by aid of such rotational stiffness. A comparative study of comprehensive performance between the spindle supported by rolling bearings and the replacement spindle designed with our proposed combined support shows that the proposed one has technical advantage of large axial load-carrying capacity, low frictional power loss, low temperature rise and etc.

Development of damage detection parameters over the lifetime of a rolling element bearing

This paper presents the trends of damage detection parameters over the lifetime of a rolling element bearing. In the experimental part, a series of bearing tests was performed using the twin-disc test device, until the monitored bearing was severely worn. This was followed by the analysis of measured acceleration and acoustic emission data in a constant-load condition, but also as loaded with impact-type loading. The results showed that traditionally used parameters, such as kurtosis and RMS, can indicate whether the bearing is damaged or not in a non-impact load condition. However, especially under impact-loading, the parameters based on acoustic emission data showed good performance and enabled monitoring of progress of the bearing damage.

Weak feature extraction of rolling element bearing based on self-adaptive blind de-convolution and enhanced envelope spectral

As the most commonly used support component in engineering, rolling element bearing is also the most prone-to-failure part. The vibration signal of faulty bearing will take on repetitive impact and modulation characteristics, and the two features are often difficult to be extracted by conventional fault feature extraction methods such as envelope spectral. The main reasons are due to the influence of strong background noise, the signal attenuation of the acquisition path, and the early weak failure characteristics. To solve the above problem, a weak fault feature extraction method of rolling element bearing by combing improved minimum entropy de-convolution with enhanced envelope spectral is proposed in the paper. The enhancement effect of improved minimum entropy de-convolution on impact features and the satisfactory extraction effect of EES on repetitive impact and modulation features are utilized comprehensively by the proposed method. Firstly, improved minimum entropy de-convolution is used to filter the vibration signal of faulty bearing to enhance the impact characteristics, and the influence of signal acquisition path on the attenuation of the signal characteristics is also weakened at the same time. Then, enhanced envelope spectral is performed on the filtered signal, and the repetitive impact and modulation characteristics of vibration signal are extracted synchronously. In order to solve the shortcomings of the current commonly used de-convolution methods, an improved minimum entropy de-convolution method based on D-norm is proposed, which can solve the interference caused by random impulsive signals effectively. In addition, compared with the conventional method such as envelope spectral, the enhanced envelope spectral method could extract the repetitive impact and modulation characteristics of the faulty signal simultaneously much more effectively. Effectiveness and superiority of the proposed method are verified through simulation, experiment, and engineering application.

Optimal solution of engineering design problems through differential gradient evolution plus algorithm: a hybrid approach

It is very necessary and applicable to optimize all disciplines. In practical engineering problems the optimization has been a significant component. This article presents the hybrid approach named as differential gradient evolution plus (DGE+) algorithm which is the combination of differential evolution (DE) algorithm and gradient evolution (GE) algorithm. DE was used to diversify and GE was used for intensification with a perfect equilibrium between exploration and exploitation with an improvised distribution of dynamic probability and offers a new shake-off approach to prevent premature convergence to local optimum. To describe the success, the proposed algorithm is compared to modern meta-heuristics. To see the accuracy, robustness, and reliability of DGE+ it has been implemented on eight complex practical engineering problems named as: pressure vessel, belleville spring, tension/compression spring, three-bar truss, welded beam, speed reducer, gear train and rolling element bearing design problem, the results revealed that DGE+ algorithm can deliver highly efficient, competitive and promising results.

A new fault diagnosis model of rolling element bearing based on a recurrent neural network

The rolling element bearing is an important part of mechanical equipment, it has various kinds of malfunctions, the location of the fault may occur in the inner ring, outer ring, or rolling element of the bearing. Therefore, traditional methods of classification are difficult to classify and identify effectively. To improve the accuracy of bearing fault diagnosis, the deep learning method is used to diagnose the fault of the rolling element bearing. In this paper, the long short-term memory and gated recurrent unit are combined to build a bearing fault diagnosis model. On the other hand, this paper adjusts the hidden layer structure and optimizes the network parameters to establish a better long short-term memory–gated recurrent unit–long short-term memory diagnostic model and classify the fault types of bearings with Softmax. The model proposed in this paper can effectively diagnose the bearing fault under the bearing data set of Case Western Reserve University and the University of Cincinnati. Compared with the traditional long short-term memory and the gated recurrent unit, the model proposed in this paper has high accuracy in fault diagnosis as well as certain reliability and generalization ability.

Experimental Study of the Effect of Misalignment on Rolling Element Bearing

Any single machine rotary component in the process could result in downtime costs. It is necessary to monitor the overall machine health while it is in use. Bearing failure is one of the primary causes of machine breakdown in industry at high and low speeds. A vibration signature evaluation has historically determined misalignments in shafting systems. These misalignments are also responsible for the bearing increase in temperature. The purpose of this work is to undertake a comparative study to obtain the reliability of the effect of the amount of misalignment on bearing by using thermography measurement. An experimental study was performed in this paper to indicate the existence of machine misalignment at an early stage by measuring the bearing temperature using a thermal imaging camera. The effects of load, velocity, and misalignment on the bearings and their temperature increase have been investigated. The test bench's rolling-element bearing is an NTN UCP213-208 pillow block bearing. It has been observed that by tracking the change of temperature in bearings could lead to misalignment detection and the effect of the amount of misalignment on it.