Support vector machine in machine condition monitoring and fault diagnosis

Recently, the issue of machine condition monitoring and fault diagnosis as a part of maintenance system became global due to the potential advantages to be gained from reduced maintenance costs, improved productivity and increased machine availability. This paper presents a survey of machine condition monitoring and fault diagnosis using support vector machine (SVM). It attempts to summarize and review the recent research and developments of SVM in machine condition monitoring and diagnosis. Numerous methods have been developed based on intelligent systems such as artificial neural network, fuzzy expert system, condition-based reasoning, random forest, etc. However, the use of SVM for machine condition monitoring and fault diagnosis is still rare. SVM has excellent performance in generalization so it can produce high accuracy in classification for machine condition monitoring and diagnosis. Until 2006, the use of SVM in machine condition monitoring and fault diagnosis is tending to develop towards expertise orientation and problem-oriented domain. Finally, the ability to continually change and obtain a novel idea for machine condition monitoring and fault diagnosis using SVM will be future works.     

基于RVM的多功能自确认水质检测传感器

提出了一种多功能自确认水质检测传感器功能模型,可以同时测量水的温度、盐度和pH值,并可对自身工作状态进行自确认.提出了一种基于相关向量机的多功能自确认传感器故障诊断和数据恢复方法,在二分类机基础上利用层次扩展方法得到基于相关向量机的多分类机,对传感器进行故障诊断;利用水质检测过程中多个参数之间的相关信息,解决了非线性、小样本条件下的传感器故障数据恢复问题.构建了多功能自确认水质检测传感器实验平台,实验结果表明故障诊断识别率达到98％,数据恢复相对误差在士4％以内,提高了传感器的可靠性.     

Support vector machine based decision for mechanical fault condition monitoring in induction motor using an advanced Hilbert-Park transform

In this work we suggest an original fault signature based on an improved combination of Hilbert and Park transforms. Starting from this combination we can create two fault signatures: Hilbert modulus current space vector (HMCSV) and Hilbert phase current space vector (HPCSV). These two fault signatures are subsequently analysed using the classical fast Fourier transform (FFT). The effects of mechanical faults on the HMCSV and HPCSV spectrums are described, and the related frequencies are determined. The magnitudes of spectral components, relative to the studied faults (air-gap eccentricity and outer raceway ball bearing defect), are extracted in order to develop the input vector necessary for learning and testing the support vector machine with an aim of classifying automatically the various states of the induction motor.     

Optimization of dimensionality of symptom space in machine condition monitoring

With the modern tools of metrology we can measure almost all variables in the phenomenon field of a working machine, and some of measuring quantities can be symptoms of machine condition. On this basis we can form the symptom observation matrix for condition monitoring. From the other side we know that contemporary complex machines can have many modes of failure/damage, so called faults. The paper presents the method of extraction of fault information from the symptom observation matrix by means of singular value decomposition, in the form of generalized fault symptoms. However, at the beginning of monitoring we do not know the sensitivity of potential symptoms to the given machine faults and to its overall condition. Hence, some method of symptom observation matrix optimization leading to redundancy minimization is presented first time in this paper. This gives the possibility to assess the diagnostic contribution of every primary measured symptom. Also in the paper some possibility to assess symptom limit value, based on symptom reliability is considered. These concepts are illustrated by symptom observation matrix processing with the special program and the data are taken directly from the machine vibration condition monitoring area.     

基于支持向量机的供应链伙伴企业选择方法的研究

为了克服传统的机器学习方法在供应链管理领域应用存在的局限性,介绍了一种新的支持向量机的机器学习算法。以企业为背景,运用支持向量机算法来解决多类分类问题和函数回归问题。通过在某企业供应链伙伴选择中的实际应用,并与用神经元网络训练得出的结果进行对比,证明这种支持向量机的机器学习算法,不仅具有较高的训练效率,而且有更高的精确度。     

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.     

Predictive maintenance of abnormal wind turbine events by using machine learning based on condition monitoring for anomaly detection

Journal of Mechanical Science and Technology - The predictive maintenance of wind turbines has become a critical issue with the rapid development of wind power generation. The early detection of...     

Modeling cutter tilt and cutter-spindle stiffness for machine condition monitoring in face milling using high-definition surface metrology

In face milling, the spindle is intentionally tilted to avoid backcutting. The cutter tilt during machining is a combined effect of the intentional initial tilt and cutter-spindle deflection, which varies with cutting load during machining. An accurate estimation of the cutter tilt is critical to surface quality control and machine health monitoring. However, due to the small magnitude, the spindle tilt and deflection are difficult to measure in real time. Conventionally, the cutter tilt can be obtained through in-line sensors mounted on the machine tool but such in-line measurement is greatly influenced by the dynamic machining conditions such as vibration. This paper proposes a method to monitor the spindle setup tilt and deflection using surface data measured by high-definition metrology (HDM). Two parameters are proposed to characterize the cutter tilt, i.e., cutter tilt at idle state (initial cutter tilt) for spindle setup and cutter-spindle stiffness for the cutter-spindle deflection. Cutting force modeling is conducted to estimate these two parameters in conjunction with statistical procedures that fit the model to HDM surface data. The estimated cutter-spindle stiffness variation is also correlated to machine conditions such as a loose or worn bearing for process diagnosis. The method is demonstrated via experimental data from a machining process for automotive engine heads.     

A hybrid simulated annealing-tabu search algorithm for the part selection and machine loading problems in flexible manufacturing systems

Part selection and machine loading are two major problems in the production planning phase of the flexible manufacturing systems. The problems have a combinatorial structure and usually, in practice, it is difficult to deal with this kind of problems using a mathematical programming model. In this paper, the above problems are formulated as a mixed-integer programming model which is handled sequentially and solved by a diversification-strategy-added version of the hybrid tabu search/simulated annealing algorithm of Zhang et al. (Comput Oper Res 35:282–294, 2008) presented in 2008. The performance of the algorithm is tested on eight random-generated problems with different sizes. The results are compared with those of the mathematical programming model, the original version of Zhang et al.’s (Comput Oper Res 35:282–294, 2008) algorithm and also a tabu search algorithm developed earlier by the authors.     

Discriminant diffusion maps analysis: A robust manifold learner for dimensionality reduction and its applications in machine condition monitoring and fault diagnosis

Various features extracted from raw signals usually contain a large amount of redundant information which may impede the practical applications of machine condition monitoring and fault diagnosis. Hence, as a solution, dimensionality reduction is vital for machine condition monitoring. This paper presents a new technique for dimensionality reduction called the discriminant diffusion maps analysis (DDMA), which is implemented by integrating a discriminant kernel scheme into the framework of the diffusion maps. The effectiveness and robustness of DDMA are verified in three different experiments, including a pneumatic pressure regulator experiment, a rolling element bearing test, and an artificial noisy nonlinear test system, with empirical comparisons with both the linear and nonlinear methods of dimensionality reduction, such as principle components analysis (PCA), independent components analysis (ICA), linear discriminant analysis (LDA), kernel PCA, self-organizing maps (SOM), ISOMAP, diffusion maps (DM), Laplacian eigenmaps (LE), locally linear embedding (LLE) analysis, Hessian-based LLE analysis, and local tangent space alignment analysis (LTSA). Results show that DDMA is capable of effectively representing the high-dimensional data in a lower dimensional space while retaining most useful information. In addition, the low-dimensional features generated by DDMA are much better than those generated by most of other state-of-the-art techniques in different situations.     

Cutting tool condition monitoring by analyzing surface roughness,work piece vibration and volume of metal removed for AISI 1040 steel in boring

The vibration is one of the intensive problems in boring process. Machining and tool wear are affected more by vibration of tool due to length of boring bar. The present work is to estimate the effect of cutting parameters on work piece vibration, roughness on machined surface and volume of metal removed in boring of steel (AISI1040). A laser Doppler vibrometer (LDV) was used for online data acquisition and a high-speed FFT analyzer used to process the AOE signals for work piece vibration. A design of experiments was prepared with eight experiments with two levels of cutting parameters such as spindle rotational speed, feed rate and tool nose radius. Taguchi method has been used to optimize the cutting parameters and a multiple regression analysis is done to obtain the empirical relation of Tool life with roughness of machined surface, volume of metal removed and amplitude of work piece vibrations.