Numerical Control Machine Tool Fault Diagnosis Using Hybrid Stationary Subspace Analysis and Least Squares Support Vector Machine with a Single Sensor

Gao, Chen; Wang, Xue; Ren, Yan; Zhou, Yuqing

doi:10.3390/app7040346

Cited by 19 publications

(15 citation statements)

References 32 publications

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“…Several ML approaches for tool-wear monitoring and tool-condition identification have been released during the last two decades [4,[16][17][18][19][20][21][22][23][24][25][26][27][28][29]. A monitoring strategy establishing a combination of four static and two dynamic NNs was presented by Scheffer et al [4].…”

Section: Methods For Assessing Rulmentioning

confidence: 99%

“…Fuzzy logic systems appear to have great accuracy for tool-condition monitoring (TCM) in milling processes, but they are characterized by big computational times, affecting negatively their applicability for online monitoring. Support Vector Machines (SVM) are also popular ML alternatives [20,[24][25][26][27]. SVMs exhibit more moderate accuracy compared to FL methods, but they are much faster and more suitable for online tool life prediction.…”

Section: Methods For Assessing Rulmentioning

confidence: 99%

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Energy-Based Prognosis of the Remaining Useful Life of the Coating Segments in Hot Rolling Mill

2020

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The field of prognostic maintenance aims at predicting the remaining time for a system or component to continue being used under the desired performance. This time is usually named as Remaining Useful Life (RUL). The current study proposes a novel approach for the RUL estimation of coating segments placed on a hot rolling mill machine. A prediction method was developed, providing real-time updates of the RUL prediction during the rolling milling process. The proposed approach performs energy analysis on measurements of segment surface temperatures and hydraulic forces. It uses nonparametric statistical processes to update the predictions, within a prediction horizon/window, indicating the number of remaining products to be processed. To assess the probability of failure within the defined prediction window, Maximum Likelihood Estimation is used. The proposed methodology was implemented in a software prototype in the MATLAB environment and tested in an industrial use case coming from a steel parts manufacturer, facilitating testing and validation of the suggested approach. Real-world data were acquired from the operational machine, while the validation results support that the proposed methodology demonstrates reasonable performance and robustness against product type variations.

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Section: Methods For Assessing Rulmentioning

confidence: 99%

Section: Methods For Assessing Rulmentioning

confidence: 99%

Energy-Based Prognosis of the Remaining Useful Life of the Coating Segments in Hot Rolling Mill

2020

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“…To overcome the drawback of features in a single domain, which lose some useful information related to the tool condition, in this study, the multidomain features of multisensor signal were extracted in the time, frequency, and time–frequency domains. According to previous papers [ 34 , 43 , 44 ] and our experimental studies [ 45 ], a few dimensional and dimensionless statistical feature parameters in the time, frequency, and time–frequency (wavelet) domains were chosen.…”

Section: Methodsmentioning

confidence: 99%

A Multisensor Fusion Method for Tool Condition Monitoring in Milling

Zhou

Wang

2018

Sensors

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Tool fault diagnosis in numerical control (NC) machines plays a significant role in ensuring manufacturing quality. Tool condition monitoring (TCM) based on multisensors can provide more information related to tool condition, but it can also increase the risk that effective information is overwhelmed by redundant information. Thus, the method of obtaining the most effective feature information from multisensor signals is currently a hot topic. However, most of the current feature selection methods take into account the correlation between the feature parameters and the tool state and do not analyze the influence of feature parameters on prediction accuracy. In this paper, a multisensor global feature extraction method for TCM in the milling process is researched. Several statistical parameters in the time, frequency, and time–frequency (Wavelet packet transform) domains of multiple sensors are selected as an alternative parameter set. The monitoring model is executed by a Kernel-based extreme learning Machine (KELM), and a modified genetic algorithm (GA) is applied in order to search the optimal parameter combinations in a two-objective optimization model to achieve the highest prediction precision. The experimental results show that the proposed method outperforms the Pearson’s correlation coefficient (PCC) based, minimal redundancy and maximal relevance (mRMR) based, and Principal component analysis (PCA)-based feature selection methods.

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“…Dimensionality reduction methods such as principal component analysis (PCA) and least square analysis (LSA) are popular among the literature to perform this task. Chen et al compared the results of a least square support vector machine based algorithm with dimensionality reduction using PCA and LSA and without these methods [12].…”

Section: Introductionmentioning

confidence: 99%

Tool Condition Monitoring Using Spectral Subtraction Algorithm and Artificial Intelligence Methods in Milling Process

Aghazadeh¹,

Tahan²,

Thomas³

2017

IJMERR

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Process monitoring is necessary in machining operation to increase productivity, improve surface quality and reduce unscheduled downtime. Tool wear and breakage are important and common sources of machining problems due to high temperatures and forces of machining process. Therefore, it is highly beneficial to develop an online tool condition monitoring system. This paper investigates a robust tool wear monitoring system for milling operation. Spindle current is employed as the fault indicator due to its cost-effectiveness and ease of use in an industrial environment. Wavelet time-frequency transform is used as a superior tool to simultaneously investigate time-varying characteristics of the signal and its frequency components. After the time-frequency step, spectral subtraction algorithm is employed to intensify the effect of tool wear in the signal and reduce the effect of other cutting parameters. Based on this method, the average signal spectrum of a healthy case is subtracted from all the signals with the same cutting parameters. After further processing and noise reduction, fault features and indicators are extracted from the results of the processed signal. Finally, five advanced machine-learning algorithms are implemented for modeling the system. Gaussian process regression, support vector regression, Bayesian rigid regression, nearest neighbor regression and decision tree methods are compared. The methods are validated based on the experimental data. Results show a high accuracy for the tool wear estimation while decision tree method was superior to others with accuracy of 91.6%. 

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Numerical Control Machine Tool Fault Diagnosis Using Hybrid Stationary Subspace Analysis and Least Squares Support Vector Machine with a Single Sensor

Cited by 19 publications

References 32 publications

Energy-Based Prognosis of the Remaining Useful Life of the Coating Segments in Hot Rolling Mill

Energy-Based Prognosis of the Remaining Useful Life of the Coating Segments in Hot Rolling Mill

A Multisensor Fusion Method for Tool Condition Monitoring in Milling

Tool Condition Monitoring Using Spectral Subtraction Algorithm and Artificial Intelligence Methods in Milling Process

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