The Experts below are selected from a list of 86250 Experts worldwide ranked by ideXlab platform
Peter J Blau - One of the best experts on this subject based on the ideXlab platform.
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a wavelet based methodology for grinding wheel condition monitoring
International Journal of Machine Tools & Manufacture, 2007Co-Authors: Warren T Liao, Jun Qu, Chifen Ting, Peter J BlauAbstract:Grinding wheel surface condition changes as more material is removed. This paper presents a wavelet-based methodology for grinding wheel condition monitoring based on acoustic emission (AE) signals. Grinding experiments in creep feed mode were conducted to grind alumina specimens with a resinoid-bonded diamond wheel using two different conditions. During the experiments, AE signals were collected when the wheel was 'sharp' and when the wheel was 'dull'. Discriminant features were then extracted from each raw AE signal segment using the discrete wavelet Decomposition procedure. An adaptive genetic clustering algorithm was finally applied to the extracted features in order to distinguish different states of grinding wheel condition. The test results indicate that the proposed methodology can achieve 97% clustering accuracy for the high material removal rate condition, 86.7% for the low material removal rate condition, and 76.7% for the combined grinding conditions if the base wavelet, the Decomposition Level, and the GA parameters are properly selected.
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grinding wheel condition monitoring with hidden markov model based clustering methods
Machining Science and Technology, 2006Co-Authors: Warren T Liao, Jun Qu, Peter J BlauAbstract:Hidden Markov model (HMM) is well known for sequence modeling and has been used for condition monitoring. However, HMM-based clustering methods are developed only recently. This article proposes a HMM-based clustering method for monitoring the condition of grinding wheel used in grinding operations. The proposed method first extract features from signals based on discrete wavelet Decomposition using a moving window approach. It then generates a distance (dissimilarity) matrix using HMM. Based on this distance matrix several hierarchical and partitioning-based clustering algorithms are applied to obtain clustering results. The proposed methodology was tested with feature sequences extracted from acoustic emission signals. The results show that clustering accuracy is dependent upon cutting condition. Higher material removal rate seems to produce more discriminatory signals/features than lower material removal rate. The effect of window size, wavelet Decomposition Level, wavelet basis, clustering algorithm, ...
Jianmin Liu - One of the best experts on this subject based on the ideXlab platform.
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a partial least squares and wavelet transform hybrid model to analyze carbon content in coal using laser induced breakdown spectroscopy
Analytica Chimica Acta, 2014Co-Authors: Tingbi Yuan, Zhe Wang, Jianmin LiuAbstract:A partial least squares (PLS) and wavelet transform hybrid model are proposed to analyze the carbon content of coal by using laser-induced breakdown spectroscopy (LIBS). The hybrid model is composed of two steps of wavelet analysis procedures, which include environmental denoising and background noise reduction, to pretreat the LIBS spectrum. The processed wavelet coefficients, which contain the discrete line information of the spectra, were taken as inputs for the PLS model for calibration and prediction of carbon element. A higher signal-to-noise ratio of carbon line was obtained after environmental denoising, and the best Decomposition Level was determined after background noise reduction. The hybrid model resulted in a significant improvement over the conventional PLS method under different ambient environments, which include air, argon, and helium. The average relative error of carbon decreased from 2.74 to 1.67% under an ambient helium environment, which indicated a significantly improved accuracy in the measurement of carbon in coal. The best results obtained under an ambient helium environment could be partly attributed to the smallest interference by noise after wavelet denoising. A similar improvement was observed in ambient air and argon environments, thereby proving the applicability of the hybrid model under different experimental conditions.
Seyed Hossein Hosseinian - One of the best experts on this subject based on the ideXlab platform.
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power quality disturbance classification using a statistical and wavelet based hidden markov model with dempster shafer algorithm
International Journal of Electrical Power & Energy Systems, 2013Co-Authors: Hamed Dehghani, Behrooz Vahidi, Ramezan Ali Naghizadeh, Seyed Hossein HosseinianAbstract:A novel approach for power quality disturbance classification using Hidden Markov Model (HMM) and Wavelet Transform (WT) is proposed in this paper. The energy distributions of the signals are obtained by wavelet transform at each Decomposition Level which are then used for training HMM. The statistical parameters of the extracted disturbance features are used to initialize the HMM training matrices which maximize the classification accuracy. Fifteen different types of power quality disturbances are considered for training and evaluating the proposed method. The Dempster–Shafer algorithm is also used for improving the accuracy of classification. In addition, the effect of the noise is studied and the performance of a denoising method is also investigated. Simulation results in a 34-bus distribution system verify the performance and reliability of the proposed approach. Also the results obtained for practical data prove the capability of the proposed method for implementing in experimental systems.
Warren T Liao - One of the best experts on this subject based on the ideXlab platform.
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a wavelet based methodology for grinding wheel condition monitoring
International Journal of Machine Tools & Manufacture, 2007Co-Authors: Warren T Liao, Jun Qu, Chifen Ting, Peter J BlauAbstract:Grinding wheel surface condition changes as more material is removed. This paper presents a wavelet-based methodology for grinding wheel condition monitoring based on acoustic emission (AE) signals. Grinding experiments in creep feed mode were conducted to grind alumina specimens with a resinoid-bonded diamond wheel using two different conditions. During the experiments, AE signals were collected when the wheel was 'sharp' and when the wheel was 'dull'. Discriminant features were then extracted from each raw AE signal segment using the discrete wavelet Decomposition procedure. An adaptive genetic clustering algorithm was finally applied to the extracted features in order to distinguish different states of grinding wheel condition. The test results indicate that the proposed methodology can achieve 97% clustering accuracy for the high material removal rate condition, 86.7% for the low material removal rate condition, and 76.7% for the combined grinding conditions if the base wavelet, the Decomposition Level, and the GA parameters are properly selected.
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grinding wheel condition monitoring with hidden markov model based clustering methods
Machining Science and Technology, 2006Co-Authors: Warren T Liao, Jun Qu, Peter J BlauAbstract:Hidden Markov model (HMM) is well known for sequence modeling and has been used for condition monitoring. However, HMM-based clustering methods are developed only recently. This article proposes a HMM-based clustering method for monitoring the condition of grinding wheel used in grinding operations. The proposed method first extract features from signals based on discrete wavelet Decomposition using a moving window approach. It then generates a distance (dissimilarity) matrix using HMM. Based on this distance matrix several hierarchical and partitioning-based clustering algorithms are applied to obtain clustering results. The proposed methodology was tested with feature sequences extracted from acoustic emission signals. The results show that clustering accuracy is dependent upon cutting condition. Higher material removal rate seems to produce more discriminatory signals/features than lower material removal rate. The effect of window size, wavelet Decomposition Level, wavelet basis, clustering algorithm, ...
Xiaoyan Zeng - One of the best experts on this subject based on the ideXlab platform.
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accuracy improvement of quantitative analysis in laser induced breakdown spectroscopy using modified wavelet transform
Optics Express, 2014Co-Authors: Xiaoheng Zou, Lianbo Guo, Meng Shen, Zhongqi Hao, Qingdong Zeng, Zemin Wang, Xiaoyan ZengAbstract:A modified algorithm of background removal based on wavelet transform was developed for spectrum correction in laser-induced breakdown spectroscopy (LIBS). The optimal type of wavelet function, Decomposition Level and scaling factor γ were determined by the root-mean-square error of calibration (RMSEC) of the univariate regression model of the analysis element, which is considered as the optimization criteria. After background removal by this modified algorithm with RMSEC, the root-mean-square error of cross-validation (RMSECV) and the average relative error (ARE) criteria, the accuracy of quantitative analysis on chromium (Cr), vanadium (V), cuprum (Cu), and manganese (Mn) in the low alloy steel was all improved significantly. The results demonstrated that the algorithm developed is an effective pretreatment method in LIBS to significantly improve the accuracy in the quantitative analysis.
