Tool wear condition monitoring in drilling operations using hidden Markov models (HMMs)

Ertunç, H. Metin; Loparo, Kenneth A.; Ocak, Hasan

doi:10.1016/s0890-6955(00)00112-7

Cited by 159 publications

(60 citation statements)

References 20 publications

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“…HHM is a state of the art technique for time series modeling and classification and has also been successfully applied to many other fields such as tool wear condition monitoring [36] and bearing diagnosis [37]. Li et al Chinam and Baruah [20] have HMM to assess the degradations of bearings and to estimate the RUL.…”

Section: Related Workmentioning

confidence: 99%

Bearings Prognostic Using Mixture of Gaussians Hidden Markov Model and Support Vector Machine

Sloukia¹,

Bouarfa²,

Medromi³

et al. 2013

IJNSA

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Prognostic of future health state relies on the estimation of the Remaining Useful Life (RUL) of physical systems or components based on their current health state. RUL can be estimated by using three main approaches: model-based, experience-based and data-driven approaches. This paper deals with a datadriven prognostics method which is based on the transformation of the data provided by the sensors into models that are able to characterize the behavior of the degradation of bearings. For this purpose, we used Support

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Section: Related Workmentioning

confidence: 99%

Bearings Prognostic Using Mixture of Gaussians Hidden Markov Model and Support Vector Machine

Sloukia¹,

Bouarfa²,

Medromi³

et al. 2013

IJNSA

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“…Numerous domains such as the time domain [39], the spectrum [39], and the hybrid [40] have been utilized so far throughout the literature along with reviews. There are also statistical processing [41], pattern recognition methods [42], or even semi-empirical methods, such as autoregressive models [43]. Even more sophisticated methods of processing and decision making involve the hidden Markov models, ANFIS [44], fractal characterization [45], or support vector machines [46].…”

Section: Introductionmentioning

confidence: 99%

“…Even more sophisticated methods of processing and decision making involve the hidden Markov models, ANFIS [44], fractal characterization [45], or support vector machines [46]. Besides, there are numerous works [41,45,13] that correlate the tool wear with various parameters (such as RPMs and feed rate).…”

Section: Introductionmentioning

confidence: 99%

Tool wear predictability estimation in milling based on multi-sensorial data

Stavropoulos

Papacharalampopoulos

Vasiliadis

et al. 2015

Int J Adv Manuf Technol

137

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The safe and reliable operations in industrial manufacturing processes play a crucial role in the economic productivity. Machining process disturbances such as collision, overload, breakdown, and tool wear tend to cause production system failures. The current study aims at investigating the limitations of tool wear prediction on the milling of CGI 450 plates, through the simultaneous detection of acceleration and spindle drive current sensor signals. Tool wear prediction has been accomplished, by utilizing the experimental results that derived from third degree regression models and pattern recognition systems. These results indicate that predictability is affected by the mean signal energy, acquired from the vibration acceleration signals.

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“…Hidden Markov models (HMMs) have been shown to be effective pattern recognition tools in a large variety of recognition tasks. They have been successfully applied to condition monitoring of manufacturing machines [12,13] as well as in other application domains ranging from speech recognition [14] to intrusion detection in computer systems [15].…”

Section: Introductionmentioning

confidence: 99%

Online Adaptive Bearings Condition Assessment Using Continuous Hidden Markov Models

Cartella

Sahli

2014

Advances in Mechanical Engineering

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Condition assessment (CA) of bearings can be performed by using left-right hidden Markov models, because of the monotonically increasing pattern of the bearings degradation. Classical CA approaches assume that all possible system states are fixed and known a priori. The training of the system is performed offline at once with data from all of the system states. These assumptions significantly impede condition assessment applications in case that all the possible states of the system are not known in advance, or changes in environmental or operative conditions occur during the tool's usage. To overcome these limitations, we propose combining left-right continuous HMMs (CHMM) with a change point detection algorithm for (i) estimating, from historical observations, the initial number of the CHMM states and the initial guess of its model parameters and (ii) updating the state space as well as the model parameters during monitoring. Moreover, to deal with multidimensional sensor measurements, we propose using kernel principal component analysis for dimensionality reduction. Qualitative and quantitative evaluations of the proposed methodology have been performed using both simulated and real data from the NASA benchmark repository. Compared to state of the art techniques, the proposed methodology results in (i) an improvement of the HMM training phase in terms of iterations number; (ii) the detection of unknown states at an early stage; and (iii) an effective change of the CHMM's structure to represent the degradation processes more accurately in presence of unknown conditions. IntroductionMachines and equipments breakdown in industrial environments can have significant impact in the profitability of a business. Maintaining the engineering assets in operating conditions is an industrial and economical requirement. Nowadays optimization strategies for maintenance are an important part of research for companies [1,2], since, next to energy costs, maintenance spending due to equipment or machine failure can be the largest part of the operational budget [3].Corrective maintenance [1,4], which is the system maintenance strategy subject of this work, is based on condition monitoring and is performed after a degradation modality emerges in a system, with the goal of preventing the system from failing. A fundamental requirement of this strategy is the detection of the abnormal behavior, that will lead to the system failure, at an early stage in order to give enough time to perform the repair operations.Industrial equipments and their components are often characterized by a very complex structure. For this reason the usage of mathematical models for condition monitoring can represent an arduous task. On the other hand, industrial machines are usually equipped with monitoring devices (sensors placed in critical parts of the system under study) which are able to record and collect large amount of measurements. Given the availability of these huge amounts of data, the methodology proposed in this paper will focus on datadriven mo...

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Tool wear condition monitoring in drilling operations using hidden Markov models (HMMs)

Cited by 159 publications

References 20 publications

Bearings Prognostic Using Mixture of Gaussians Hidden Markov Model and Support Vector Machine

Bearings Prognostic Using Mixture of Gaussians Hidden Markov Model and Support Vector Machine

Tool wear predictability estimation in milling based on multi-sensorial data

Online Adaptive Bearings Condition Assessment Using Continuous Hidden Markov Models

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