Tool wear predicting based on multi-domain feature fusion by deep convolutional neural network in milling operations

Huang, Zhiwen; Ji, Zhu; Lei, Jingtao; Li, Xiaoru; Tian, Fengqing

doi:10.1007/s10845-019-01488-7

Cited by 171 publications

(64 citation statements)

References 34 publications

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“…Finally, a sigmoid function will be applied to compute the final classification probabilities as the output result in the last layer. In this study, the rectified linear units (ReLU) function [24] is applied as the activation functions of the convolutional, pooling and fully connected layers, because the ReLU can effectively overcome deficiencies of gradient disappearance and slow convergence in the training process [12]. To further improve the performance of classification, the training process uses the back-propagation algorithm to minimize the loss function, which can be expressed as…”

Section: Figurementioning

confidence: 99%

“…In indirect methods, some researchers start to collect the maximum amount of information from multiple sensors for monitoring states of tools or machines due to different kinds of sensors can reveal specific features of interest [5], [8]- [12]. Although the use of multiple sensing systems could compensate for the limitations of a single sensor when collecting signals [5], the results of the mixed feature analysis cannot effectively reflect the current states of the detected objects due to interference between the signals.…”

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confidence: 99%

“…This technique also has attracted the attention of researchers in the field of tool wear monitoring [23] because it made great achievements in the classification and recognition of large dataset images [24]. Because deep convolutional neural networks (CNNs) are widely used in solving high dimensional and intricate nonlinear problems [12], and specifically designed for variable and complex signals and have shown remarkable success in various applications in the past few years [25], we developed an intelligent system based on CNNs to recognize different conditions of grinding wheel during grinding process.…”

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confidence: 99%

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An Intelligent System for Grinding Wheel Condition Monitoring Based on Machining Sound and Deep Learning

et al. 2020

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Immediate monitoring of the conditions of the grinding wheel during the grinding process is important because it directly affects the surface accuracy of the workpiece. Because the variation in machining sound during the grinding process is very important for the field operator to judge whether the grinding wheel is worn or not, this study applies artificial intelligence technology to attempt to learn the experiences of auditory recognition of experienced operators. Therefore, we propose an intelligent system based on machining sound and deep learning to recognize the grinding wheel condition. This study uses a microphone embedded in the grinding machine to collect audio signals during the grinding process, and extracts the most discriminated feature from spectrum analysis. The features will be input the designed CNNs architecture to create a training model based on deep learning for distinguishing different conditions of the grinding wheel. Experimental results show that the proposed system can achieve an accuracy of 97.44%, a precision of 98.26% and a recall of 96.59% from 820 testing samples. INDEX TERMS Grinding wheel wear, intelligent system, machining sound, audio signals, deep learning. I. INTRODUCTION CHENG-HSIUNG LEE received the B.I.M. and M.I.M. degrees in information management from the Chaoyang University of Technology, in 2002 and 2004, respectively, and the Ph.D. degree in computer science and engineering from the

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confidence: 99%

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confidence: 99%

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An Intelligent System for Grinding Wheel Condition Monitoring Based on Machining Sound and Deep Learning

et al. 2020

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“…In the onlooker bee stage, the onlooker bee will select a food source according to Equation (25), and this is a way of sharing information between the employed bees and the onlookers. The new solution is updated and selected as in the employed bee stage by Equation (24) and greedy rule.…”

Section: Improved Artificial Bee Colony (Iabc) Algorithmmentioning

confidence: 99%

Multi-Sensor Data Fusion for Remaining Useful Life Prediction of Machining Tools by IABC-BPNN in Dry Milling Operations

Yao

Zhang

Chen

et al. 2020

Sensors

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Inefficient remaining useful life (RUL) estimation may cause unpredictable failures and unscheduled maintenance of machining tools. Multi-sensor data fusion will improve the RUL prediction reliability by fusing more sensor information related to the machining process of tools. In this paper, a multi-sensor data fusion system for online RUL prediction of machining tools is proposed. The system integrates multi-sensor signal collection, signal preprocess by a complementary ensemble empirical mode decomposition, feature extraction in time domain, frequency domain and time-frequency domain by such methods as statistical analysis, power spectrum density analysis and Hilbert-Huang transform, feature selection by a Light Gradient Boosting Machine method, feature fusion by a tool wear prediction model based on back propagation neural network optimized by improved artificial bee colony (IABC-BPNN) algorithm, and the online RUL prediction model by a polynomial curve fitting method. An example is used to verify whether if the prediction performance of the proposed system is stable and reliable, and the results show that it is superior to its rivals.

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“…In this paper, Variational Mode Decomposition (VMD) is used to decompose the acceleration of rolling bearings because it has the advantages of high decomposition accuracy and strong noise robustness [11,12,13,14]. Then, time-domain features are adopted to construct feature sets from signal components [15][16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

A Fault Diagnosis Method Based on Improved Adaptive Filtering and Joint Distribution Adaptation

2020

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In the real environment of industrial equipment, the vibration signals of essential components show deviations due to the fault and noise. Notably, the noise in the signal will interfere with the diagnosis process of the signal and reduce the accuracy of fault diagnosis. Based on the above problem, adaptive filtering (AF) is used as an excellent method to attenuate noise without specifying the noise type. However, how to define the most appropriate length and type of morphological filter element is the most inherent problem which needs to be solved first. This paper proposed a cooperative diagnosis method of rolling bearings vibration signal based on improved adaptive filtering and joint distribution adaptation (JDA). First, the kurtosis under different element types and lengths is calculated as an index. The structural element corresponding to maximum kurtosis is selected as the most suitable morphological filter element because the different morphological filter elements reflect the effect of feature extraction. Then, JDA aims to improve both the marginal distribution and the conditional distribution to solve the chaotic distribution of time-domain features under variable working conditions. Finally, the improved least squares support vector machine (LSSVM) verified the effectiveness and improvement of the proposed method under bearing acceleration signal. At the same time, the comparative experiment proved that the proposed method not only directly corrects the most appropriate elements greatly optimizes the feature structure, but also enhances the accuracy of fault diagnosis. INDEX TERMS adaptive filters, morphological operation, time domain features, transfer learning, joint distribution adaptation, LSSVM, fault diagnosis.

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Tool wear predicting based on multi-domain feature fusion by deep convolutional neural network in milling operations

Cited by 171 publications

References 34 publications

An Intelligent System for Grinding Wheel Condition Monitoring Based on Machining Sound and Deep Learning

An Intelligent System for Grinding Wheel Condition Monitoring Based on Machining Sound and Deep Learning

Multi-Sensor Data Fusion for Remaining Useful Life Prediction of Machining Tools by IABC-BPNN in Dry Milling Operations

A Fault Diagnosis Method Based on Improved Adaptive Filtering and Joint Distribution Adaptation

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