Condition Monitoring and Fault Diagnosis of Roller Element Bearing

Rolling element bearings play a crucial role in determining the overall health condition of a rotating machine. An effective condition-monitoring program on bearing operation can improve a machine’s operation efficiency, reduce the maintenance/replacement cost, and prolong the useful lifespan of a machine. This chapter presents a general overview of various condition-monitoring and fault diagnosis techniques for rolling element bearings in the current practice and discusses the pros and cons of each technique. The techniques introduced in the chapter include data acquisition techniques, major parameters used for bearing condition monitoring, signal analysis techniques, and bearing fault diagnosis techniques using either statistical features or artificial intelligent tools. Several case studies are also presented in the chapter to exemplify the application of these techniques in the data analysis as well as bearing fault diagnosis and pattern recognition

Simulation Study on the Effect of Multi-layer Biological Tissue on Focus Shift in High-Intensity Focused Ultrasound Therapy

Introduction: During the treatment of soft tissue tumors with high-intensity focused ultrasound (HIFU), the focus may shift away from the desired point due to tissue heterogeneity. By studying the effect of biological tissue on focus shift, it can provide a theoretical basis for the safety and reliability of HIFU therapy. Material and Methods: The finite difference time domain (FDTD) method was used to construct the simulation model of HIFU irradiated multi-layer biological tissue. Based on the Westervelt nonlinear acoustic propagation equation, the focus position change caused by the thickness of biological tissue and ultrasonic transducer during HIFU irradiation were simulated and calculated. The effects of ultrasonic transducer's electric power, irradiation frequency and tissue thickness on the focus position shift were analyzed and discussed. Results: With the increase of electric power of HIFU transducer, the sound pressure at the focal point rose and the focal point approached the transducer side. With the increase of irradiation frequency of transducer, the sound pressure at the focus increased and the focus shifted away from transducer. With the increase of the thickness of biological tissue, the amplitude of sound pressure at the focal point decreased gradually. If the sound velocity of biological tissue was greater than that of water, the focus was close to the transducer side. If the sound velocity of biological tissue was less than the sound velocity of water, the focus moved to the side away from the transducer. For biological tissue with sound velocity greater than (or less than) water, the greater the sound velocity, the greater the relative shift distance difference of focal position. Conclusion: As the electric power and frequency of ultrasonic transducer increased, the focus of HIFU moved toward and away from the transducer, respectively. For multi-layer biological tissue, the focus shift direction depended on the sound velocity relationship between biological tissue and wate

Structures, deformation history and dynamic background of the Qianyingzi Coal Mine in the Huaibei Coalfield, eastern China

The Qianyingzi Coal Mine is located in the west of the Suxian Mining District of the Huaibei Coalfield, eastern China. The study on structural development patterns and genetic mechanisms in this mine lays an important foundation for safe and efficiently underground mining, and is also the key to understanding the regional tectonic evolution. In this study, based on the analysis of three-dimensional seismic, drilling and underground measured data and regional tectonic correlation, the structures, evolution history and dynamic background of the Qianyingzi Coal Mine are discussed. The Carboniferous-Permian coal measure strata in the mine are generally a gentle syncline with a NNE-trending axis, and cut by a series of faults. The faults developed in this mine are mainly medium- and small-sized with a throw of less than 20 m, and the number of reverse faults is significantly greater than that of normal faults. The strikes of reverse and normal faults are both mainly NE, followed by NNE and nearly N‒S. According to the characteristics of structural geometry, tectonic association, fault property and cross-cutting relation, the structural deformation of coal measure strata in the Qianyingzi Coal Mine can be divided into five stages, and the corresponding tectonic stress fields are NWW‒SEE compressive stress, nearly E‒W compressive stress, NW‒SE compressive stress, nearly E‒W and NW‒SE extensional stresses, respectively. It developed the Fengjia Syncline with a NNE-trending axis in the first stage and nearly N‒S-striking reverse faults in the second stage, which were the results of foreland deformation and subsequent continent-continent collision during the convergence of the North China Craton and South China Plate in the Indosinian period. The NNE-striking reverse sinistral faults and NE-striking reverse faults developed in the third stage is related to the rapid oblique subduction of the Izanagi Plate toward the East Asian continental margin at the beginning of the Early Cretaceous in the western Pacific region. Later, the fourth and fifth stages of the nearly N‒S- and NE-SW-striking normal faults were developed under the backarc extensional background in eastern China during the Early Cretaceous. These new results can be used to guide the rational arrangement for underground mining and also provide a new understanding for regional tectonic evolution of the Huaibei Coalfield

Cost-sensitive subspace learning for face recognition

Conventional subspace learning-based face recognition aims to attain low recognition errors and assumes same loss from all misclassifications. In many real-world face recog-nition applications, however, this assumption may not hold as different misclassifications could lead to different losses. For example, it may cause inconvenience to a gallery per-son who is mis-recognized as an impostor and not allowed to enter the room by a face recognition-based door-locker, but it could result in a serious loss or damage if an impos-tor is mis-recognized as a gallery person and allowed to enter the room. Motivated by this concern, we propose in this paper a cost-sensitive subspace learning approach for face recognition. Our approach incorporates a cost matrix, which specifies the different costs associated with misclas-sifications of subjects, into three popular subspace learn-ing algorithms and devise the corresponding cost-sensitive methods, namely, cost-sensitive principal component anal-ysis (CSPCA), cost-sensitive linear discriminant analysis (CSLDA), and cost-sensitive locality preserving projections (CSLPP), to achieve a minimum overall recognition loss by performing recognition in the low-dimensional subspaces derived. Experimental results are presented to demonstrate the efficacy of the proposed approach. 1

Effects of composite oxide scale on oxidation resistance of superalloy K273

A Si-containing K273 superalloy was made using intermediate frequency induction furnace in the study. In the testing of oxidation resistance, the oxidation process of the alloy specimens during the testing at 900 ℃ for 500 h was examined by oxidation weight gain method. The morphology and composition of the oxide scales were determined using scanning electron microscope (SEM) and X-ray diffraction (XRD), respectively. The effects of the transferring of ions and electrons on the oxidation resistance were further analyzed microscopically by semiconductor oxide models. The results show that the composite oxide scales consist of Cr2O3, SiO2 and spinel-type oxide MCr2O4, with flat and compact structure, and fine grains in uniform distribution. All of these endow the superalloy K273 with strong oxidation resistance. The reason for the powerful oxidation resistance of the composite scale is that the formation process of P+N type semiconductor oxide enables to consume most of the surplus negative and positive ions in the oxide scales, which makes the number of the mobile ions and electrons dropped enormously, and the transfer rate of them falls heavily. So the oxidation rate of the metal phase in the alloy matrix is reduced significantly

Quantitative Expression of a Slight Deviation of the Impact Angle in a Collision Atomizer

In the processing of colliding atomizers, a small change in the inclination of each orifice will double the impact angle, seriously affecting its atomization performance. In this paper, the influence of slight impact angle deviation on atomization performance was studied in steps of 1°, quantitatively, for the first time. The cavitation effect of the flow field was combined with the shape and parameters of the atomization field. FLUENT was used to simulate the internal flow field, and an independently designed atomizer with transparent nozzles was used to detect the internal flow field in real time. The collision atomization experimental platform and the laser interference particle measurement platform were built independently, and the collision angle was adjusted through a high-precision rotating table to establish the relationship between collision-angle deviation (60° ± 5°) and the atomization field performance (Sauter mean diameter, atomization cone angle, and spatial distribution of droplets). The experimental results showed that under the same injection pressure, the increase in the collision angle led to an decrease in the Sauter mean diameter and an increase in the atomization cone angle. Taking 60° as the benchmark, the particle size distribution was concentrated at ~150 μm to 300 μm within the variation range of ±2°, and the peak positions were very similar

Nonlinearity Correction in OFDR System Using a Zero-Crossing Detection-Based Clock and Self-Reference

Tuning nonlinearity of the laser is the main source of deterioration of the spatial resolution in optical frequency-domain reflectometry (OFDR) system. In this paper, we develop methods for tuning nonlinearity correction in an OFDR system from the aspect of data acquisition and post-processing. An external clock based on a zero-crossing detection is researched and implemented using a customized circuit. Equal-spacing frequency sampling is, therefore, achieved in real-time. The zero-crossing detection for the beating frequency of 20 MHz is achieved. The maximum sensing distance can reach the same length of the auxiliary interferometer. Moreover, a nonlinearity correction method based on the self-reference method is proposed. The auxiliary interferometer is no longer necessary in this scheme. The tuning information of the laser is extracted by a strong reflectivity point at the end of the measured fiber. The tuning information is then used to resample the raw signal, and the nonlinearity correction can be achieved. The spatial resolution test and the distributed strain measurement test were both performed based on this nonlinearity correction method. The results validated the feasibility of the proposed method. This method reduces the hardware and data burden for the system and has potential value for system integration and miniaturization

Co-Learned Multi-View Spectral Clustering for Face Recognition Based on Image Sets

Different from the existing approaches that usually utilize single view information of image sets to recognize persons, multi-view information of image sets is exploited in this paper, where a novel method called Co-Learned Multi-View Spectral Clustering (CMSC) is proposed to recognize faces based on image sets. In order to make sure that a data point under different views is assigned to the same cluster, we propose an objective function that optimizes the approximations of the cluster indicator vectors for each view and meanwhile maximizes the correlations among different views. Instead of using an iterative method, we relax the constraints such that the objective function can be solved immediately. Experiments are conducted to demonstrate the efficiency and accuracy of the proposed CMSC method.ASTAR (Agency for Sci., Tech. and Research, S’pore)Accepted versio

Deep transfer metric learning

Conventional metric learning methods usually assume that the training and test samples are captured in similar scenarios so that their distributions are assumed to be the same. This assumption doesn't hold in many real visual recognition applications, especially when samples are captured across different datasets. In this paper, we propose a new deep transfer metric learning (DTML) method to learn a set of hierarchical nonlinear transformations for cross-domain visual recognition by transferring discriminative knowledge from the labeled source domain to the unlabeled target domain. Specifically, our DTML learns a deep metric network by maximizing the inter-class variations and minimizing the intra-class variations, and minimizing the distribution divergence between the source domain and the target domain at the top layer of the network. To better exploit the discriminative information from the source domain, we further develop a deeply supervised transfer metric learning (DSTML) method by including an additional objective on DTML where the output of both the hidden layers and the top layer are optimized jointly. Experimental results on cross-dataset face verification and person re-identification validate the effectiveness of the proposed methods.Accepted versio