共查询到18条相似文献,搜索用时 171 毫秒
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多传感器数据融合技术在刀具状态监测中的应用 总被引:1,自引:0,他引:1
提出了一种基于混合智能融合技术进行铣刀磨损量监测和预测方法。利用多传感器对切削力和振动信号进行监测,通过频率变换提取切削力特征量,采用小波包分解技术提取振动信号特征量。通过信号特征值的组合,分别探讨了几种混合智能数据融合技术——小波神经网络、遗传神经网络、遗传小波神经网络对刀具磨损量的预测效果。试验分析表明:提出的几种基于多传感器的混合智能数据融合技术均能够有效地完成刀具磨损量监测和预测,同时,对这几种数据融合技术各自的特点进行了比较分析。 相似文献
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本文提出了基于智能融合技术进行铣刀磨损量监测和预测方法。利用多传感器对切削力和振动信号进行监测,通过频率变换提取切削力特征量,采用小波包分解技术提取振动信号特征量。通过信号特征值的组合,分别探讨了几种计算智能数据融合技术-小波神经网络、遗传神经网络、遗传小波神经网络对刀具磨损量的预测效果。实验分析表明,本文提出的几种计算智能数据融合技术均能够有效地完成刀具磨损量预测。 相似文献
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选取能够充分反映刀具磨损状态的振动信号和功率信号作为研究对象,采用正交小波变换技术,提取刀具磨损特征信号,利用该特征信号建立了振动幅值变化与刀具磨损量间的关系,计算出基于振动信号的刀具状态特征值,定性地识别出刀具磨损状态;对功率信号,采用统计分析方法,通过均方根处理提取出刀具磨损特征信号,并以信号强度的变化来表征刀具的磨损情况;为了避免单一特征信号提供刀具状态信息的局限性,采用模糊数据融合方法对振动、功率特征信号进行融合,获得更加全面、准确的刀具磨损状态;实验结果表明,基于模糊数据融合的刀具磨损状态识别比单一传感器系统对刀具磨损状态识别更为可靠. 相似文献
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刀具的磨损状态直接影响产品加工质量、成本和效率,对刀具磨损量的实时监测识别具有重要意义。针对刀具磨损状态先验样本少和常规神经网络识别模型收敛速度慢、易陷入局部极小值等问题,提出了基于最小二乘支持向量机(LS-SVM)的刀具磨损识别方法,并针对支持向量机的惩罚因子和核参数对模型识别精度影响较大的问题,提出一种根据个体适应度来调整惯性权重的自适应粒子群算法进行自动参数寻优。以车削加工为研究对象,采集加工过程中的切削力信号,应用小波包分析技术提取反映刀具磨损状态的特征信息作为识别模型的输入,然后利用训练好的自适应粒子群算法优化后的LS-SVM识别模型进行刀具磨损量识别。实验结果表明,该自适应粒子群优化算法比标准粒子群优化算法参数寻优能力更强;粒子群优化LS-SVM模型能高效地实现刀具磨损量识别,与BP神经网络相比具有更高的精度,且所需样本数较少,训练速度更快。 相似文献
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为解决采用单一特征量预测轴承剩余寿命误差较大、有限数据样本条件下轴承剩余寿命难以估算的问题,提出了一种基于主元特征融合和支持向量机(SVM)的轴承剩余寿命预测方法。该方法采集振动加速度信号构建数据样本,提取有效值、峰值、小波熵等表征轴承退化趋势的特征指标;采用主元分析融合多个特征指标,消除特征间的冗余和相关性,构造出相对多特征的退化特征量;将退化特征量输入SVM模型中进行轴承剩余寿命预测。现场工程应用结果表明,基于主元特征融合和SVM的轴承剩余寿命预测方法可在小样本条件下筛选出包含信号绝大部分信息的主元,从而在保证预测精度的同时,减少了计算量。 相似文献
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基于小波包的频带能量特征提取及智能诊断 总被引:1,自引:0,他引:1
提出一种基于小波包和BRF神经网络的智能故障诊断方法。对滚动轴承故障信号进行小波包分解,选择合适的小波基函数和尺度,将故障信号分解到八个不同的频段上,提取这八个频段上的能量信息,组成特征问量,作为RBF神经网络的输入;建立RBF神经网络模型并进行训练,对三种滚动轴承故障信号进行智能分类与识别。实验结果表明这种智能诊断方法有效可行。 相似文献
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Drill wear detection and prognosis is one of the most important considerations in reducing the cost of rework and scrap and to optimize tool utilization in hole making industry. This study presents the development and implementation of two supervised vector quantization neural networks for estimating the flank-land wear size of a twist drill. The two algorithms are; the learning vector quantization (LVQ) and the fuzzy learning vector quantization (FLVQ). The input features to the neural networks were extracted from the vibration signals using power spectral analysis and continuous wavelet transform techniques. Training and testing were performed under a variety of speeds and feeds in the dry drilling of steel plates. It was found that the FLVQ is more efficient in assessing the flank wear size than the LVQ. The experimental procedure for acquiring vibration data and extracting features in the time-frequency domain using the wavelet transform is detailed. Experimental results demonstrated that the proposed neural network algorithms were effective in estimating the size of the drill flank wear. 相似文献
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KURAPATI VENKATESH MENGCHU ZHOU REGGIE J. CAUDILL 《Journal of Intelligent Manufacturing》1997,8(3):215-226
An on-line scheme for tool wear monitoring using artificial neural networks (ANNs) has been proposed. Cutting velocity, feed, cutting force and machining time are given as inputs to the ANN, and the flank wear is estimated using the ANN. Different ANN structures are designed and investigated to estimate the tool wear accurately. An existing analytical model is used to obtain the data for various cutting conditions in order to eliminate the huge cost and time associated with generation of training and evaluation data. Motivated by the fact that the tool wear at a given instance of time depends on the tool wear value at a previous instance of time, memory is included in the ANN. ANNs without memory, with one-phase memory, and with two-phase memory are investigated in this study. The effect of various training parameters, such as learning coefficient, momentum, temperature, and number of hidden neurons, on these architectures is studied. The findings and experience obtained should facilitate the design and implementation of reliable and economical real-time systems for tool wear monitoring and identification in intelligent manufacturing. 相似文献
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A wide variety of tool condition monitoring techniques has been introduced in recent years. Among them, tool force monitoring, tool vibration monitoring and tool acoustics emission monitoring are the three most common indirect tool condition monitoring techniques. Using multiple intelligent sensors, these techniques are able to monitor tool condition with varying degrees of success. This paper presents a novel approach for the estimation of tool wear using the reflectance of cutting chip surface and a back propagation neural network. It postulates that the condition of a tool can be determined using the surface finish and color of a cutting chip. A series of experiments has been carried out. The experimental data obtained was used to train the back propagation neural network. Subsequently, the trained neural network was used to perform tool wear prediction. Results show that the prediction is in good agreement with the flank wear measured experimentally. 相似文献
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A key aspect impacting the quality and efficiency of machining is the degree of tool wear. If the tool failure is not discovered in time, the quality of workpiece processing decreases, and even the machine tool itself may be harmed. To increase machining quality, efficiency and facilitate the intelligent advancement of the manufacturing industry, tool wear prediction is crucial. This research offers a multi-signal tool wear prediction method based on the Gramian angular field (GAF) and depth aggregation residual transform neural network (ResNext), enabling fast and accurate tool wear prediction. Specifically, the required one-dimensional signal is obtained through preprocessing including intercepting, splicing and wavelet threshold denoising of the force and vibration signals, and GAF is used to encode the obtained one-dimensional signal to generate a (224 × 224) data matrix. ResNext automatically extracts the features of the data matrix, establish the relationship between features and tool wear, and creates a tool wear prediction model based on GAF-ResNext. The ability of this method to predict tool wear has been trained and tested by milling experimental data. The experimental findings demonstrate the real-time, accuracy, dependability and universality of this method. This method has a better effect when compared to other research methods. The study's findings can boost machining productivity and offer technical support for intelligent tool wear early warning and intelligent manufacturing. 相似文献
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Rui G. Silva 《Journal of Intelligent Manufacturing》2010,21(6):823-829
This paper presents a new approach to sensor based condition monitoring using a self-organizing spiking neuron network map.
Experimental evidence suggests that biological neural networks, which communicate through spikes, use the timing of these
spikes to encode and compute information in a more efficient way. The paper introduces the basis of a simplified version of
the Self-Organizing neural architecture based on Spiking Neurons. The fundamental steps for the development of this computational
model are presented as well as some experimental evidence of its performance. It is shown that this computational architecture
has a greater potential to unveil embedded information in tool wear monitoring data sets and that faster learning occurs if
compared to traditional sigmoidal neural networks. 相似文献
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Sukhomay Pal P. Stephan Heyns Burkhard H. Freyer Nico J. Theron Surjya K. Pal 《Journal of Intelligent Manufacturing》2011,22(4):491-504
One of the big challenges in machining is replacing the cutting tool at the right time. Carrying on the process with a dull
tool may degrade the product quality. However, it may be unnecessary to change the cutting tool if it is still capable of
continuing the cutting operation. Both of these cases could increase the production cost. Therefore, an effective tool condition
monitoring system may reduce production cost and increase productivity. This paper presents a neural network based sensor
fusion model for a tool wear monitoring system in turning operations. A wavelet packet tree approach was used for the analysis
of the acquired signals, namely cutting strains in tool holder and motor current, and the extraction of wear-sensitive features.
Once a list of possible features had been extracted, the dimension of the input feature space was reduced using principal
component analysis. Novel strategies, such as the robustness of the developed ANN models against uncertainty in the input
data, and the integration of the monitoring information to an optimization system in order to utilize the progressive tool
wear information for selecting the optimum cutting conditions, are proposed and validated in manual turning operations. The
approach is simple and flexible enough for online implementation. 相似文献
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D. Avci 《Digital Signal Processing》2010,20(4):1196-1206
In this paper, an intelligent analog modulation identification system is presented for interpretation of the analog modulated signals. This paper especially deals with combination of the feature extraction and classification for analog modulated signals. The analog modulated signals used in this study are six types (AM, DSB, USB, LSB, FM, and PM). Here, a discrete wavelet neural network-adaptive wavelet entropy (DWNN-ANE) model is used, which consists of two layers: discrete wavelet-adaptive wavelet entropy and multi-layer perceptron neural networks for intelligent analog modulation identification. The discrete wavelet layer is used for adaptive feature extraction in the time-frequency domain and is composed of DWT and adaptive wavelet entropy. The performance of the used system is evaluated by using total 1080 analog modulated signals. These test results show the effectiveness of the used intelligent system presented in this paper. The rate of correct classification is about 98.34% for the sample analog modulated signals. 相似文献