On-machine tool prediction of flank wear from machined surface images using texture analyses and support vector regression

In this paper, a method for on-machine tool condition monitoring by processing the turned surface images has been proposed. Progressive monitoring of cutting tool condition is inevitable to maintain product quality. Thus, image texture analyses using gray level co-occurrence matrix, Voronoi tessellation and discrete wavelet transform based methods have been applied on turned surface images for extracting eight useful features to describe progressive tool flank wear. Prediction of cutting tool flank wear has also been performed using these eight features as predictors by utilizing linear support vector machine based regression technique with a maximum 4.9% prediction error.     

A comparison study of support vector machines and hidden Markov models in machinery condition monitoring

Condition classification is an important step in machinery fault detection, which is a problem of pattern recognition. Currently, there are a lot of techniques in this area and the purpose of this paper is to investigate two popular recognition techniques, namely hidden Markov model and support vector machine. At the beginning, we briefly introduced the procedure of feature extraction and the theoretical background of this paper. The comparison experiment was conducted for gearbox fault detection and the analysis results from this work showed that support vector machine has better classification performance in this area.     

Surface texture assessment of ultra-precision machined parts based on laser speckle pattern analysis

Surface texture plays an important role in overall product specification, because the surface quality of the product is dominated by the nano-scale surface texture. This paper presents a surface texture assessment method for evaluating roughness and periodicity of surface structure with a laser speckle pattern analysis. By investigating the relation between surface texture and laser speckle pattern, characteristic parameters for describing a laser speckle pattern are proposed. The proposed characteristic parameters can evaluate the surface texture in entire observed area and in limited area in any given direction. Furthermore, the surface texture can be qualitatively assessed with a radar chart of the proposed laser speckle characteristic parameters.     

基于狄利克雷混合模型的刀具磨损量在线估计

提出了一种基于狄利克雷混合模型的刀具磨损状态监测和磨损量估计的新方法。该方法将刀具磨损过程描述为磨损量的累积过程,通过对磨损增量的连续估计获得刀具当前的磨损量估计。首先对原始力信号进行特征提取,接着在不确定磨损增量状态数量的前提下采用狄利克雷混合模型对特征自动分类,然后利用吉布斯采样方法确定模型参数,最终得到描述力信号特征与磨损增量映射关系的刀具磨损状态混合模型。根据该混合模型以及当前的力信号信息即可完成刀具磨损量的在线估计。真实应用案例证明了该方法能自适应学习磨损状态并有效估计刀具的连续磨损值。     

基于隐马尔可夫的网络控制系统稳定性分析

讨论了一类具有随机通信时延的网络控制系统的建模及稳定性分析,其中网络诱导时延受控于一概率分布未知的马尔可夫链,其概率分布可通过Baum-Welch算法计算.基于隐马尔町夫模型理论,将采用状态反馈的闭环网络控制系统建模成跳变线性系统,给出了这类网络控制系统随机稳定的允分条件,并将状态反馈控制器的求解问题转化为线性矩阵不等式的解的问题.最后,通过一个仿真算例说明了上述判定系统稳定性条件的有效性.     

Generation of reference data of 3D surface texture using the non-causal 2D AR model

In this paper, the non-causal 2D AR model was proposed as a new method for generation of reference data of the 3D surface texture. Specification parameters to engender various topographical properties were the correlation distances in the x and y directions, the power index which determined the decaying pattern of the auto-correlation function, the root mean deviation, the skewness and the kurtosis. Isotropic surfaces were generated and compared with results by the causal 2D AR model which has been proposed in the past researches. It was found that the error of generation by the non-causal 2D AR model was smaller than that by the causal 2D AR model. Anisotropic surfaces and non-Gaussian distributed surfaces were also generated successfully.     

Automatic measurement of volume percentage stroma in endometrial images using texture segmentation

The popularity of digital microscopy and tissue microarrays allow the use of high-throughput imaging for pathology research. To coordinate with this new technique, it is essential to automate the process of extracting information from such high amount of images. In this paper, we present a new model called the Subspace Mumford-Shah model for texture segmentation of microscopic endometrial images. The model incorporates subspace clustering techniques into a Mumford-Shah model to solve texture segmentation problems. The method first uses a supervised procedure to determine several optimal subspaces. These subspaces are then embedded into a Mumford-Shah objective function so that each segment of the optimal partition is homogeneous in its own subspace. The method outperforms a widely used method in bioimaging community called k-means segmentation since it can separate textures which are less separated in the full feature space, which confirm the usefulness of subspace clustering in texture segmentation. Experimental results also show that the proposed method is well performed on diagnosing premalignant endometrial disease and is very practical for segmenting image set sharing similar properties.     

Automated classification of wear particles based on their surface texture and shape features

In this study, the automated classification system, developed previously by the authors, was used to classify wear particles. Three kinds of wear particles, fatigue, abrasive and adhesive, were classified. The fatigue wear particles were generated using an FZG back-to-back gear test rig. A pin-on-disk tribometer was used to generate the abrasive and adhesive wear particles. Scanning electron microscope (SEM) images of wear particles were acquired, forming a database for further analysis. The particle images were divided into three groups or classes, each class representing a different wear mechanism. Each particle class was first examined visually. Next, area, perimeter, convexity and elongation parameters were determined for each class using image analysis software and the parameters were statistically analysed. Each particle class was then assessed using the automated classification system, based on particle surface texture. The results of the automated particle classification were compared to both the visual assessment of particle morphology and the numerical parameter values. The results showed that the texture-based classification system was a more efficient and accurate way of distinguishing between various wear particles than classification based on size and shape of wear particles. It seems that the texture-based classification method developed has great potential to become a very useful tool in the machine condition monitoring industry.     

Influence of tool wear on surface roughness in hard turning using differently shaped ceramic tools

Hard turning has been applied in many cases in producing bearings, gears, cams, shafts, axels, and other mechanical components since the early 1980s. Mixed ceramics (aluminum oxide plus TiC or TiCN) is one of the two cutting tool materials (apart from PCBN) widely used for finish machining of hardened steel (HRC 50–65) parts, especially under dry machining conditions and moderate cutting speed ranging from 90 to 120 m/min. This paper reports an extensive characterization of the surface roughness generated during hard turning (HT) operations performed with conventional and wiper ceramic tools at variable feed rate and its changes originated from tool wear. Moreover, it compares some predominant tool wear patterns produced on the two types of ceramic inserts and their influence on the alteration of surface profiles. After the hard turning tests, the relevant changes of surface profiles and surface roughness parameters were successively registered and measured by a stylus profilometer. In this investigation, a set of 2D surface roughness parameters, as well as profile and surface characteristics, such as the amplitude distribution functions, bearing area curves and symmetrical curves of geometrical contact obtained for the machined surface, were determined and analyzed. A novel aspect of this research is that the notch wear progress at the secondary cutting (trailing) edges was found to produce the substantial modifications of the individual irregularities, and constitute the altered surface profiles. Moreover, this research contributes to practical aspects of HT technology due to exploring the relations between the tool state at different times within the tool life and the relevant surface roughness characterization.     

The fault feature extraction and classification of gear using principal component analysis and kernel principal component analysis based on the wavelet packet transform

The vibration signal of a gear system is selected as the original information of fault diagnosis and the gear system vibration equipment is established. The vibration acceleration signals of the normal gear, gear with tooth root crack fault, gear with pitch crack fault, gear with tooth wear fault and gear with multi-fault (tooth root crack & tooth wear fault) is collected in four kinds of speed conditions such as 300 rpm, 900 rpm, 1200 rpm and 1500 rpm. Using the method of wavelet threshold de-noising to denoise the original signal and decomposing the denoising signal utilizing the wavelet packet transform, then 16 frequency bands of decomposed signal are got. After restructuring the decomposing signal and obtaining the signal energy in each frequency band, the signal energy of the 16 bands is as the shortlisted fault characteristic data. Based on this, using the methods of principal component analysis (short for PCA) and kernel principal component analysis (short for KPCA) to extract the feature from the fault features of shortlisted 16-dimensional data feature, then the effect of reducing dimension analysis are compared. The fault classifications are displayed through the information that got from the first and the second principal component and kernel principal component, and these demonstrate they have a different and good effect of classification. Meanwhile, the article discusses the effect of feature extraction and classification that caused by the kernel function and the different options of its parameters. These provide a new method for a gear system fault feature extraction and classification.     

Flow state monitoring of gas-water two-phase flow using multi-Gaussian mixture model based on canonical variate analysis

As a highly complex and time-varying process, gas-water two-phase flow is commonly encountered in industries. It has a variety of typical flow states and transition flow states. Accurate identification and monitoring of flow states is not only beneficial to further study of two-phase flow but also helpful for stable operation and economic efficiency of process industry. Combining canonical variate analysis (CVA) and Gaussian mixture model (GMM), a strategy called multi-CVA-GMM is proposed for flow state monitoring in gas-water two-phase flow. CVA is used to extract flow state features from the perspective of correlation between historical data and future data, which solves the cross correlation and temporal correlation of multi-sensor measurement data. GMM calculates the possibility that the current flow state belongs to each typical flow pattern and judges the current flow state by probability indicators. It is conducive to follow-up use of Bayesian inference probability and Mahalanobis distance-based (BID) indicator for flow state monitoring, which avoids repeated traversal of multiple CVA-GMM models and improves the efficiency of the monitoring process. The probability indicators can also be used to analyze transition flow states. The method combining the probabilistic idea of GMM with the deterministic idea of multimodal modeling can accurately identify the current flow state and effectively monitor the evolution of flow state. The multi-CVA-GMM method is validated by using the measured data of the horizontal flow loop of gas-water two-phase flow experimental facility, and its effectiveness is proved.