Performance degradation assessment has been proposed to realize equipment's near-zero downtime and maximum productivity. Exploring effective indices is crucial for it. In this study, rolling element bearing has been taken as a research object, spectral entropy is proposed to be as a complementary index for its performance degradation assessment, and its accelerated life test has been performed to collect vibration data over a whole lifetime (normal-fault-failure). Results of both simulation and experiment show that spectral entropy is an effective complementary index.
Bearing performance degradation assessment is more effective than fault diagnosis to realize condition-based maintenance. In this article, a hybrid model is proposed for it based on a support vector data description (SVDD) and fuzzy c-means (FCM). SVDD, which holds excellent robustness to outliers, is used to obtain the clustering centre of normal state. The subjection of tested data to normal state is defined as a degradation indicator, which is computed by a FCM algorithm with final failure data. The results of applying this hybrid model to an accelerated bearing life test show that it can effectively assess bearing performance degradation. Furthermore, it is robust to the outliers in the training set and is not influenced by the Gaussian kernel parameter.
The purpose of this article is to optimize the design of a pickup head that removes particles from road surface. A validated computational fluid dynamics model was proposed to evaluate the particle removal performance of the designed pickup head with different inclination angles. The gas-particle flow through the pickup head was modelled using the EulerianLagrangian approach. The realizable k model and the discrete particle model were adopted to simulate gas flow field and solid particle trajectories, respectively. The results indicate that the inclination angle of the rear edge wall and the pressure drop across the pickup head have great impact on the particle removal performance. Both the particle overall removal efficiency and the grade efficiency increase with the increment of inclination angle, and higher pressure drop can pick up more particles from the road surface, but it would induce unnecessary energy consumption. Therefore, it is necessary to design a pickup head with high removal efficiency and low pressure drop. Through simulation, the optimal angle should be 135 for the range of the inclination angle in this study, and pressure drop is about 2400Pa. Furthermore, more information can be acquired for pickup head design.
The non-linear characteristics of a cracked rotor are investigated in this paper. It is known that the cracked rotor system becomes non-linear when the rotor is not in a weight-dominant condition. In the case of the crack opening, the rotor is weakened not only in the crack direction but also in the perpendicular direction of the crack, and the breathing of the crack is determined by the vibration in the crack direction. Based on these assumptions, a model of a cracked rotor can be established in rotating coordinates and the governing equations can also be obtained. The discontinuities of the governing equations make it very difficult to obtain the vibration response of this kind of model analytically, so the numerical simulation method is applied. The chaos and bifurcation phenomena is observed through the numerical results. According to the results obtained in this paper, the Lyapunov exponent calculated from the simulated time series is proven to be effective in judging the chaotic property of a system. The non-linear effect of a crack can be reconstructed by using the state space theory when considering the crack as an external force.
As the result of vibration emission in air, machine sound signal carries affluent information about the working condition of machine and it can be used to make mechanical fault diagnosis. The fundamental problems with fault diagnosis are the estimation of the number of sound sources and the localization of sound sources. The wave superposition can be employed to identify and locate sound sources, which is based on the idea that an acoustic radiator can be approximated and represented by the sum of the fields due to a finite number of interior point sources. But, in practice, a large number of measurements must be used in order to achieve a desired resolution, which makes the reconstruction process very time-consuming and expensive. In this paper, a combined wave superposition method has been developed reconstruct to acoustic radiation from machine acoustical signals. This method combines the advantages of both the wave superposition and Helmholtz equationleast squares methods, and it allows for reconstruction of the acoustic field from an arbitrary object with relatively few measurements, thus significantly enhancing the reconstruction efficiency. After sound source localization, the blind source separation (BSS) is proposed to extract acoustical feature from the mixed measuring sound signals. In a semi-anechoic chamber, a cross-planar microphone array, which consists of 29 microphones, was successfully applied to obtain the two-dimensional mapping of the sound sources. The location, the sound pressure, and the properties in frequency domain of the sound sources can be found through this method precisely. The experimental results demonstrate that the methods presented can potentially become an acoustical diagnosis tool.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.