An HMM and polynomial regression based approach for remaining useful life and health state estimation of cutting tools

Kumar, Akhilesh; Chinnam, Ratna Babu; Tseng, Finn

doi:10.1016/j.cie.2018.05.017

Cited by 91 publications

(40 citation statements)

References 23 publications

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“…PdM turned out to be one of the most promising strategies amongst other strategies of maintenance that has the ability of achieving those characteristics [19], thus the strategy has been applied recently in many fields of studies. PdM captivates the attention of the industries, hence it has been applied in the era of I4.0 due to it is capability of optimizing the use and management of assets [1,20]. ML, within the contexts of artificial intelligence (AI) ( Figure 1, copyright permission of Figure 1 has taken on 20 September 2020), lately, has appeared to be one of the most powerful tools that can be applied in several applications to develop intelligent predictive algorithms.…”

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confidence: 99%

Machine Learning in Predictive Maintenance towards Sustainable Smart Manufacturing in Industry 4.0

et al. 2020

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Recently, with the emergence of Industry 4.0 (I4.0), smart systems, machine learning (ML) within artificial intelligence (AI), predictive maintenance (PdM) approaches have been extensively applied in industries for handling the health status of industrial equipment. Due to digital transformation towards I4.0, information techniques, computerized control, and communication networks, it is possible to collect massive amounts of operational and processes conditions data generated form several pieces of equipment and harvest data for making an automated fault detection and diagnosis with the aim to minimize downtime and increase utilization rate of the components and increase their remaining useful lives. PdM is inevitable for sustainable smart manufacturing in I4.0. Machine learning (ML) techniques have emerged as a promising tool in PdM applications for smart manufacturing in I4.0, thus it has increased attraction of authors during recent years. This paper aims to provide a comprehensive review of the recent advancements of ML techniques widely applied to PdM for smart manufacturing in I4.0 by classifying the research according to the ML algorithms, ML category, machinery, and equipment used, device used in data acquisition, classification of data, size and type, and highlight the key contributions of the researchers, and thus offers guidelines and foundation for further research.

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confidence: 99%

Machine Learning in Predictive Maintenance towards Sustainable Smart Manufacturing in Industry 4.0

et al. 2020

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“…Over the past decades, several advanced machine learning methods have been proposed for high-quality pattern recognition [45] In general, the research work of regression methods in machine learning can be roughly divided into five categories, including linear [18], kernel [39], tree and forest [60], nearest neighbors [47] and neural network [13]. Firstly, linear based method is the most basic model in machine learning.…”

Section: Introductionmentioning

confidence: 99%

“…For example, to tackle drug design problems, Lo et al [27] proposed a linear regression method to mine the chemical information and presented the basic principles in drug analysis Experiments validated that the proposed machine learning descriptor can be applied in drug discovery. Kumaret et al [18] introduced a more flexible approach to evaluate the health state of cutting tools. Specifically, this approach used a polynomial regression model based sequential clustering on time series sensor signals, which performed well for monitoring drill-bits.…”

Section: Introductionmentioning

confidence: 99%

MLLBC: A Machine Learning Toolbox for Modeling the Loss Rate of the Lining Bearing Capacity

Zhang

Wu²,

Zhao

et al. 2020

IEEE Access

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Testing the health of tunnels, as a branch of highway operation, has an extremely important application in public property and even life safety. Among them, there are many factors that cause the tunnel to deform or collapse. The conventional methods use the finite element method (FEM) which are to simulate the bearing capacity loss rate of the lining by using the mechanical method. However, it takes a long time to calculate the stress-strain-situation of the lining model under each condition. This paper explores the machine learning to calculate the loss rate of the lining bearing capacity under more conditions based on FEM simulation data. Here, we establish a machine learning toolbox for modeling the loss rate of the lining bearing capacity named ''MLLBC'', which contains three main components: 1) data loading; 2) machine learning model deployment; 3) performance evaluation. To ensure the fairness of model evaluation, ten machine learning models use a unified code library. We also conduct experiments on our new dataset which is the loss rate of the lining bearing capacity with different data amounts, as well as experiments on the goodness of model fitting under different ranges of various variables. INDEX TERMS Toolbox, the loss rate of the lining bearing capacity, machine learning, tunnel health.

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“…In previous research, a large number of machine learning approaches have been proposed, most of which construct prognostics models by analyzing correlative sensor sequential data and associating the discovered hierarchical patterns with a definite prognostics task [16]. These prediction models provide effective evidence to the manufacturers [17,18] and include, for instance, auto-regressive integrated moving average-based (ARIMA) models [19,20], hidden Markov models (HMM) [21][22][23], support vector regression (SVR) models [24][25][26], artificial neural networks (ANNs) [27,28], radial basis functions (RBFs) [28], random forest (RF) regression [29], among others [12,30].…”

Section: Introductionmentioning

confidence: 99%

Convolution and Long Short-Term Memory Hybrid Deep Neural Networks for Remaining Useful Life Prognostics

et al. 2019

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Reliable prediction of remaining useful life (RUL) plays an indispensable role in prognostics and health management (PHM) by reason of the increasing safety requirements of industrial equipment. Meanwhile, data-driven methods in RUL prognostics have attracted widespread interest. Deep learning as a promising data-driven method has been developed to predict RUL due to its ability to deal with abundant complex data. In this paper, a novel scheme based on a health indicator (HI) and a hybrid deep neural network (DNN) model is proposed to predict RUL by analyzing equipment degradation. Explicitly, HI obtained by polynomial regression is combined with a convolutional neural network (CNN) and long short-term memory (LSTM) neural network to extract spatial and temporal features for efficacious prognostics. More specifically, valid data selected from the raw sensor data are transformed into a one-dimensional HI at first. Next, both the preselected data and HI are sequentially fed into the CNN layer and LSTM layer in order to extract high-level spatial features and long-term temporal dependency features. Furthermore, a fully connected neural network is employed to achieve a regression model of RUL prognostics. Lastly, validated with the aid of numerical and graphic results by an equipment RUL dataset from the Commercial Modular Aero-Propulsion System Simulation(C-MAPSS), the proposed scheme turns out to be superior to four existing models regarding accuracy and effectiveness.

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An HMM and polynomial regression based approach for remaining useful life and health state estimation of cutting tools

Cited by 91 publications

References 23 publications

Machine Learning in Predictive Maintenance towards Sustainable Smart Manufacturing in Industry 4.0

Machine Learning in Predictive Maintenance towards Sustainable Smart Manufacturing in Industry 4.0

MLLBC: A Machine Learning Toolbox for Modeling the Loss Rate of the Lining Bearing Capacity

Convolution and Long Short-Term Memory Hybrid Deep Neural Networks for Remaining Useful Life Prognostics

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