An exTS based neuro-fuzzy algorithm for prognostics and tool condition monitoring

Massol, O.; Li, X.; Gouriveau, Rafael; Zhou, Junhong; Gan, Oon Peen

doi:10.1109/icarcv.2010.5707842

Cited by 9 publications

(5 citation statements)

References 16 publications

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“…In order to find the probability of being at each state in future, the model must be unrolled over the time horizon while there are no observations available from onwards. Hence, prognosis for can also be formulated similar to diagnosis case in a probabilistic manner as follows: (16) where is again included to indicate that calculations are based on the parameter set of the trained HMM and is a normalizing factor replaced by similar to (13). In the end, …”

Section: Diagnostics and Prognosticsmentioning

confidence: 99%

A Physically Segmented Hidden Markov Model Approach for Continuous Tool Condition Monitoring: Diagnostics and Prognostics

Geramifard

Zhou

et al. 2012

IEEE Trans. Ind. Inf.

108

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In this paper, a temporal probabilistic approach based on the hidden Markov model (HMM), named physically segmented HMM with continuous output, is introduced for continuous tool condition monitoring in machinery systems. The proposed approach has the advantage of providing an explicit relationship between the actual health states and the hidden state values. The provided relationship is further exploited for formulation and parameter estimation in the proposed approach. The introduced approach is tested for continuous tool wear prediction in a computer numerical control milling machine and compared with two well-established neural network (NN) approaches, namely, multilayer perceptron and Elman network. In the experimental study, the prediction results are provided and compared after adopting appropriate hyper-parameter values for all the approaches by cross-validation. Based on the experimental results, physically segmented HMM approach outperforms the NN approaches. Moreover, the prognosis ability of the proposed approach is studied.

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Section: Diagnostics and Prognosticsmentioning

confidence: 99%

A Physically Segmented Hidden Markov Model Approach for Continuous Tool Condition Monitoring: Diagnostics and Prognostics

Geramifard

Zhou

et al. 2012

IEEE Trans. Ind. Inf.

108

View full text Add to dashboard Cite

show abstract

“…There are 8 articles about FIS: [33,76,[83][84][85][86][87][88] and 18 articles for the neuro-fuzzy approach: [33,36,37,41,58,[89][90][91][92][93][94][95][96][97][98][99][100][101]. For the inference system, it is important to note that, out of the 8 articles, five articles have the author Balazinski Marek (and Baron Luc for four of these articles) in the author list which explains the similarity in the approaches presented in these articles ( [33,84,[86][87][88]).…”

Section: Presentation Of the Articlesmentioning

confidence: 99%

A Systematic Literature Review of Cutting Tool Wear Monitoring in Turning by Using Artificial Intelligence Techniques

et al. 2021

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In turning operations, the wear of cutting tools is inevitable. As workpieces produced with worn tools may fail to meet specifications, the machining industries focus on replacement policies that mitigate the risk of losses due to scrap. Several strategies, from empiric laws to more advanced statistical models, have been proposed in the literature. More recently, many monitoring systems based on Artificial Intelligence (AI) techniques have been developed. Due to the scope of different artificial intelligence approaches, having a holistic view of the state of the art on this subject is complex, in part due to a lack of recent comprehensive reviews. This literature review therefore presents 20 years of literature on this subject obtained following a Systematic Literature Review (SLR) methodology. This SLR aims to answer the following research question: “How is the AI used in the framework of monitoring/predicting the condition of tools in stable turning condition?” To answer this research question, the “Scopus” database was consulted in order to gather relevant publications published between 1 January 2000 and 1 January 2021. The systematic approach yielded 8426 articles among which 102 correspond to the inclusion and exclusion criteria which limit the application of AI to stable turning operation and online prediction. A bibliometric analysis performed on these articles highlighted the growing interest of this subject in the recent years. A more in-depth analysis of the articles is also presented, mainly focusing on six AI techniques that are highly represented in the literature: Artificial Neural Network (ANN), fuzzy logic, Support Vector Machine (SVM), Self-Organizing Map (SOM), Hidden Markov Model (HMM), and Convolutional Neural Network (CNN). For each technique, the trends in the inputs, pre-processing techniques, and outputs of the AI are presented. The trends highlight the early and continuous importance of ANN, and the emerging interest of CNN for tool condition monitoring. The lack of common benchmark database for evaluating models performance does not allow clear comparisons of technique performance.

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“…Statistical features in the TD and FD extracted from vibration and power signals via wavelet packet decomposition were also discussed by Niaki et al [9] and a RNN was used for tool wear estimation; the authors studied the application of sensor information fusion in order to increase the estimation performance of the NN and the results showed that only a maximum of 13% relative error in estimating tool wear. Massol et al [10] studied the relationship between tool condition and several features extracted from force and AE signal, and trained an ANFIS to monitor wear state. The authors developed an eXtended Takagi Sugeno (eXTS) to correlate sensory signals with several cutter health conditions, however, the accuracy of the model on unknown tool parameters is still low.…”

Section: DLmentioning

confidence: 99%

Using Multiple-Feature-Spaces-Based Deep Learning for Tool Condition Monitoring in Ultraprecision Manufacturing

Shi

Panoutsos

Luo

et al. 2019

IEEE Trans. Ind. Electron.

112

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ÑTool condition monitoring is critical in ultraprecision manufacturing in order to optimize the performance of the overall process, while maintaining the desired part quality. Recently, Deep Learning has been successfully applied in numerous classification tasks in manufacturing, often to forecast part quality. In this paper, a novel Deep Learning data-driven modeling framework is presented, which includes fusion of multiple stacked sparse autoencoders for tool condition monitoring in ultra-precision machining. The proposed computational framework consists of two main structures. A training model that is designed with the ability to process multiple parallel feature spaces to learn the lower-level features; and a feature fusion structure that is used to learn the higher-level features and associations to tool wear. To achieve this learning structure, a modified loss function is utilized that enhances the feature extraction and classification tasks. A dataset from a real manufacturing process is used to demonstrate the performance of the proposed framework. Experimental results and simulations show that the proposed method successfully classifies the ultra-precision machining case study with over 96% accuracy, while also outperforms comparable methodologies. Index TermsÑUltra-precision manufacturing process, tool condition monitoring, deep learning, feature spaces, feature fusion. NOMENCLATURE AE Acoustic emission ANFIS Adaptive neuro fuzzy inference system ANN Artificial neural network BPNN Back propagation neural network CNC Computer numerical control CNN Convolutional neural network DBN Deep belief network

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An exTS based neuro-fuzzy algorithm for prognostics and tool condition monitoring

Cited by 9 publications

References 16 publications

A Physically Segmented Hidden Markov Model Approach for Continuous Tool Condition Monitoring: Diagnostics and Prognostics

A Physically Segmented Hidden Markov Model Approach for Continuous Tool Condition Monitoring: Diagnostics and Prognostics

A Systematic Literature Review of Cutting Tool Wear Monitoring in Turning by Using Artificial Intelligence Techniques

Using Multiple-Feature-Spaces-Based Deep Learning for Tool Condition Monitoring in Ultraprecision Manufacturing

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