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针对工程陶瓷磨削中金刚石砂轮磨损状态判别准确度不高的问题,在部分稳定氧化锆陶瓷金刚石砂轮精密磨削的声发射智能监测实验中,在深入研究部分稳定氧化锆陶瓷磨削机理的基础上,对磨削声发射信号进行了5层离散小波分解。研究结果表明:金刚石砂轮磨损后,磨削声发射信号小波分解系数的有效值和方差,以及声发射信号小波能谱系数在低频率段都有所增大;利用部分稳定氧化锆磨削声发射信号的小波能谱系数或小波分解系数的有效值和方差值的组合,作为判别金刚石砂轮磨损状态的特征值,采用基于遗传算法支持向量机对金刚石砂轮的磨损状态判别准确度达100%,判别准确度明显优于BP神经网络方法。 相似文献
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以小波分析理论为基础,提出了以对数熵理论确定最佳小波包分解树结构的方法,提出了基于声发射信号最佳小波基最佳小波分量频段能量的声发射信号小波特征,开发了基于最佳小波基小波特征的神经网络刀具磨损状态在线监测系统,实验结果表明,该系统具有较高的监测精度,能满足工业现场对刀具磨损状态实时在线监测的要求. 相似文献
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利用小波变换模的极大值和信号奇异点的关系,分析了用Lip指数来描述的切削力信号局部奇异性.通过观察奇异点的位置等信息得到切削刀具的磨损情况.通过对实际刀具磨损的在线监测数据分析,证明了采用小波变换检测刀具磨损这一方法的有效性. 相似文献
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针对轴向柱塞泵故障振动信号呈现出的非平稳和非线性特点,提出了一种基于小波包能量法与小波脊线法相结合的信号解调方法,将其用于液压泵故障诊断中的信号解调过程。该方法首先对原始振动信号进行功率谱分析,明确故障振动信号反映出的能量集中频带带宽;根据确定的带宽和原始信号分析频率设定小波包分解的层数,采用小波包能量法提取出分解系数对应频带能量最大的特征信息进行信号重构;利用小波脊线法对重构后的频带信号进行解调处理,通过信号的包络解调谱提取故障的特征频率,利用解调后的时频谱对液压泵单柱塞滑靴磨损、斜盘磨损以及中心弹簧故障进行分析。通过实验结果验证,该方法能有效地对液压泵的故障信号进行解调,并能找出反映故障的敏感特征频率。 相似文献
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基于人工神经网络的铣削磨损监测研究 总被引:1,自引:0,他引:1
利用铣削刀具磨损多参量信号进行预处理及特征量提取,采用特征融合方法建立信号级、模型级、特征级和融合级层次结构实验方案,通过样本训练模糊小波神经网络逼近系统,建立刀具补偿系统的最优控制策略,从而对被检测对象进行有效的识别与估计.由实验结果对比可见,人工神经网络模型的预测精度基本在范围之内.实验表明该模型适用于切削条件下的铣刀磨损监控,可以较准确地监控铣刀的剧烈磨损. 相似文献
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在阐述和构造了正交频分复用(OFDM)水声通信系统的基础上,利用小波包分解与重构对OFDM水声通信系统进行语音信号消噪处理.小波包分解方法依据信号与噪声小波变换系数分布特性不同来进行,首先将语音信号分层,确定最佳小波包分解树,再进行阈值量化,完成小波包分解,并对所得阈值进行消噪处理,最后利用小波包逆变换重构传输信号.计算机仿真结果表明在OFDM水声通信系统中利用小波包分解方法对语音信号进行处理,可有效消噪,并可较为完整地保存有效信号. 相似文献
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Tool condition monitoring based on numerous signal features 总被引:2,自引:2,他引:0
Krzysztof Jemielniak Tomasz Urbański Joanna Kossakowska Sebastian Bombiński 《The International Journal of Advanced Manufacturing Technology》2012,59(1-4):73-81
This paper presents a tool wear monitoring strategy based on a large number of signal features in the rough turning of Inconel 625. Signal features (SFs) were extracted from time domain signals as well as from frequency domain transforms and their wavelet coefficients (time–frequency domain). All of them were automatically evaluated regarding their relevancy for tool wear monitoring based on a determination coefficient between the feature and its low-pass-filtered course as well as the repeatability. The selected SFs were used for tool wear estimation. The accuracy of this estimation was then used to evaluate the sensor and signal usability. 相似文献
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基于云理论与LS-SVM的刀具磨损识别方法 总被引:1,自引:0,他引:1
针对刀具磨损过程中产生声发射信号的不确定性以及神经网络学习算法收敛速度慢、易陷入局部极小值、对特征要求较高等问题,提出了基于云理论和最小二乘支持向量机的刀具磨损状态识别方法。首先,对声发射信号进行小波包分解与重构,滤除干扰频段对求取特征参数的影响;其次,对重构后的信号利用逆向云算法提取云特征参数:期望、熵、超熵,分析刀具磨损声发射信号的云特性及磨损状态与云特征参数之间的关系;最后,将云特征参数组成特征向量送入最小二乘支持向量机进行识别。研究结果表明:所提取的特征可以很好地反映刀具的磨损状态,云-支持向量机方法可以有效地实现刀具磨损状态的识别,与传统神经网络识别方法相比具有更高的识别率,识别率达到96.67%。 相似文献
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Yi Jiang Wansheng Zhao Xuecheng Xi Lin Gu Xiaoming Kang 《The International Journal of Advanced Manufacturing Technology》2012,61(1-4):171-183
Discharge waveforms contain information representing the gap discharge status of an EDM process. The gap discharge status has a great influence on the machining performance including the machining efficiency, workpiece surface integrity, and tool wear rate in EDM processes. In order to identify the gap discharge status effectively, wavelet transform is used to analyze the discharge waveforms. A data acquisition and processing system based on DSP is developed for high-speed wavelet transforms and related calculations. The wavelet transform result shows that each EDM pulse can be classified by judging the approximation coefficients of the wavelet transform result. Experimental results demonstrate that the wavelet transform detection is capable of capturing the primary features of each single discharge pulse, which are usually unable to be discovered by conventional discharge detection methods such as the average gap voltage detection. By analyzing the local extreme values of approximation coefficients, the numbers of different pulses within a detection time period can be identified. The gap discharge status coefficient, which is a function of the numbers of different pulses, is then calculated and used as a feedback signal to an adaptive EDM process controller. A small-hole machining test demonstrates that, with the online adaptive controller based on the wavelet transform method, the machining efficiency and stability are improved significantly. 相似文献
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《Mechanical Systems and Signal Processing》2007,21(6):2665-2683
In this paper, combinations of signal processing techniques for real-time estimation of tool wear in face milling using cutting force signals are presented. Three different strategies based on linear filtering, time-domain averaging and wavelet transformation techniques are adopted for extracting relevant features from the measured signals. Sensor fusion at feature level is used in search of an improved and robust tool wear model. Isotonic regression and exponential smoothing techniques are introduced to enforce monotonicity and smoothness of the extracted features. At the first stage, multiple linear regression models are developed for specific cutting conditions using the extracted features. The best features are identified on the basis of a statistical model selection criterion. At the second stage, the first-stage models are combined, in accordance with proven theory, into a single tool wear model, including the effect of cutting parameters. The three chosen strategies show improvements over those reported in the literature, in the case of training data as well as test data used for validation—for both laboratory and industrial experiments. A method for calculating the probabilistic worst-case prediction of tool wear is also developed for the final tool wear model. 相似文献
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Y. Choi R. Narayanaswami A. Chandra 《The International Journal of Advanced Manufacturing Technology》2004,23(5-6):419-428
Tool wear identification and estimation present a fundamental problem in machining. With tool wear there is an increase in cutting forces, which leads to a deterioration in process stability, part accuracy and surface finish. In this paper, cutting force trends and tool wear effects in ramp cut machining are observed experimentally as machining progresses. In ramp cuts, the depth of cut is continuously changing. Cutting forces are compared with cutting forces obtained from a progressively worn tool as a result of machining. A wavelet transform is used for signal processing and is found to be useful for observing the resultant cutting force trends. The root mean square (RMS) value of the wavelet transformed signal and linear regression are used for tool wear estimation. Tool wear is also estimated by measuring the resulting slot thickness on a coordinate measuring machine. 相似文献
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Karali Patra Surjya K. Pal Kingshook Bhattacharyya 《Machining Science and Technology》2013,17(2):280-300
Thriving automation in industries leads to more research on the tool condition monitoring systems for better accuracy and fast recognition/evaluation of tool wear. Research on the applicability of the new advances in the soft-computing as well as in the signal processing fields is the inevitable consequence. In this work, a new soft-computing modeling technique, fuzzy radial basis function (FRBF) network has been applied to the prediction of drill wear using the vibration signal features. This work presents the wear prediction performance comparison of this new model with three other already tried and established soft-computing models, such as back propagation neural network (BPNN), radial basis function network (RBF) and normalized radial basis function network (NRBF), for both time-domain as well as wavelet packet approaches of feature extraction. Experimental results show that FRBF model with wavelet packet approach produces the best performance of predicting flank wear. 相似文献
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基于连续小波变换的钻削力信号灰度矩特征提取 总被引:1,自引:1,他引:1
利用小波分析良好的时频特性,分析了钻削过程中钻削力信号在时间-尺度域中的变化特征,提出用“灰度矩”的概念来描述连续小波变换的统计特性,并通过实验研究了整个钻头磨损历程中钻削力信号小波变换结果的“1 1”阶矩的变化规律。结果表明:随着钻头磨损的增加,其“1 1”阶矩统计特征呈上升趋势,根据其变化特征可有效实现钻头磨损状态的监测。 相似文献