Tool Condition Monitoring and Remaining Useful Life Prognostic Based on a Wireless Sensor in Dry Milling Operations

Zhang, Cunji; Yao, Xifan; Zhang, Jianming; Jiang, Hong

doi:10.3390/s16060795

Cited by 126 publications

(84 citation statements)

References 53 publications

(60 reference statements)

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“…In the time–frequency domain, Wavelet transform (WT) can be used to extract candidate feature parameters. The Wavelet packet transform (WPT) conducts a multilevel band division over the entire signal band, which not only inherits the advantages of the good time–frequency localization from the WT, but it also further decomposes the high-frequency band to increase the frequency resolution [ 4 , 22 , 34 ]. Thus, the WPT was applied in order to extract the time–frequency domain features in this paper, and the wavelet energy feature is the energy of a 3-level wavelet packet decomposition using db1, which corresponds to the wavelet coefficient with a higher energy that is related to the characteristic frequency of the machine [ 46 ].…”

Section: Methodsmentioning

confidence: 99%

“…Too many feature parameters will greatly increase the model computation and affect the timeliness of online monitoring. In addition, irrelevant and redundant feature parameters have a negative impact on the performance of the monitoring model, and a few appropriate feature parameters can generate a more accurate and robust model [ 22 , 23 ].…”

Section: Literature Reviewmentioning

confidence: 99%

“…The feature selection methods select a few effective feature parameters from the preset candidate feature parameters. For example, Zhang et al [ 22 ] selected 13 parameters significantly related to tool wear values using Pearson’s correlation coefficient (PCC) from 144 feature parameters extracted in the time, frequency, and time–frequency domains of multiple sensory signals. In Reference [ 35 ], the minimal redundancy and maximal relevance (mRMR) algorithm is utilized to select the most prominent features.…”

Section: Literature Reviewmentioning

confidence: 99%

See 2 more Smart Citations

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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Section: Methodsmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

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A Multisensor Fusion Method for Tool Condition Monitoring in Milling

Zhou

Wang

2018

Sensors

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“…Using the current or power of the machine tool as the monitoring parameter of the processing state has the advantages of simple measurement, convenient extraction and low cost [23][24][25]. Wireless sensor methods have also been widely used in health, environment, home and agriculture sectors [26][27][28][29].…”

Section: Rag Facementioning

confidence: 99%

Review of Machining Equipment Reliability Analysis Methods based on Condition Monitoring Technology

Dai

Sun

Chi³

et al. 2019

Applied Sciences

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The condition of mechanical equipment during machining is closely related to the accuracy and roughness of the workpiece. In an intelligent sensing environment, a large amount of multi-source data reflecting status information are generated during processing, and a number of studies have been conducted for machining equipment reliability analysis. In this paper, the reliability analysis method of machining equipment based on condition monitoring technology is taken as the main line. And an up-to-date comprehensive survey of multi-source information during the cutting process, failure physical analysis for signal selection and reliability assessment based on condition information will be provided. Finally, the future challenges and trends will also be presented. It is a feasible and valuable research direction to evaluate the reliability of machining equipment for product quality characteristics.

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“…NFSs have been widely used in machinery performance prediction in recent years. Zhang, et al [107], used neuro-fuzzy network to predict tool wear and RUL, considering that tool condition monitoring is critical to the manufacturing industry. Gokulachandran, et al [108], used neuro-fuzzy and support vector regression to evaluate RUL of cutting tools.…”

Section: Nfssmentioning

confidence: 99%

Comprehensive Overview on Computational Intelligence Techniques for Machinery Condition Monitoring and Fault Diagnosis

Zhang

Jia

Zhu

et al. 2017

Chin. J. Mech. Eng.

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Computational intelligence is one of the most powerful data processing tools to solve complex nonlinear problems, and thus plays a significant role in intelligent fault diagnosis and prediction. However, only few comprehensive reviews have summarized the ongoing efforts of computational intelligence in machinery condition monitoring and fault diagnosis. The recent research and development of computational intelligence techniques in fault diagnosis, prediction and optimal sensor placement are reviewed. The advantages and limitations of computational intelligence techniques in practical applications are discussed. The characteristics of different algorithms are compared, and application situations of these methods are summarized. Computational intelligence methods need to be further studied in deep understanding algorithm mechanism, improving algorithm efficiency and enhancing engineering application. This review may be considered as a useful guidance for researchers in selecting a suitable method for a specific situation and pointing out potential research directions.

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Tool Condition Monitoring and Remaining Useful Life Prognostic Based on a Wireless Sensor in Dry Milling Operations

Cited by 126 publications

References 53 publications

A Multisensor Fusion Method for Tool Condition Monitoring in Milling

A Multisensor Fusion Method for Tool Condition Monitoring in Milling

Review of Machining Equipment Reliability Analysis Methods based on Condition Monitoring Technology

Comprehensive Overview on Computational Intelligence Techniques for Machinery Condition Monitoring and Fault Diagnosis

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