A statistical data-based approach to instability detection and wear prediction in radial turning processes

Cortadi, Alberto Jimenez; Irigoien, Itziar; Boto, Fernando; Sierra, Basilio; Suárez, Alejandro Rosete; Galar, Diego

doi:10.17531/ein.2018.3.8

Cited by 7 publications

(4 citation statements)

References 19 publications

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“…Jin et al [16] applied a one class SVM method for detecting change points in time series data, which would imply a degradation in the system. Maronna et al [17] proposed another method called the three-sigma edit rule, which was used in Jimenez Cortadi et al [18] for outlier detection on a radial turning process.…”

Section: Data Cleaningmentioning

confidence: 99%

Predictive Maintenance on the Machining Process and Machine Tool

Jimenez-Cortadi¹,

Irigoien²,

Boto

et al. 2019

Applied Sciences

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This paper presents the process required to implement a data driven Predictive Maintenance (PdM) not only in the machine decision making, but also in data acquisition and processing. A short review of the different approaches and techniques in maintenance is given. The main contribution of this paper is a solution for the predictive maintenance problem in a real machining process. Several steps are needed to reach the solution, which are carefully explained. The obtained results show that the Preventive Maintenance (PM), which was carried out in a real machining process, could be changed into a PdM approach. A decision making application was developed to provide a visual analysis of the Remaining Useful Life (RUL) of the machining tool. This work is a proof of concept of the methodology presented in one process, but replicable for most of the process for serial productions of pieces.

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Section: Data Cleaningmentioning

confidence: 99%

Predictive Maintenance on the Machining Process and Machine Tool

Jimenez-Cortadi¹,

Irigoien²,

Boto

et al. 2019

Applied Sciences

Self Cite

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“…They verified predictions with practical examples. Jimenez Cortadi et al [99] applied the Linear Mixed Model (LMM) for chatter prediction as well as for wear prediction with a good accuracy. Tian [100] proposed a neural network analysis for detecting chatter vibration in turning.…”

Section: On-line Chatter Classification Detection and Monitoringmentioning

confidence: 99%

Prediction Methods and Experimental Techniques for Chatter Avoidance in Turning Systems: A Review

et al. 2019

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The general trend towards lightweight components and stronger but difficult to machine materials leads to a higher probability of vibrations in machining systems. Amongst them, chatter vibrations are an old enemy for machinists with the most dramatic cases resulting in machine-tool failure, accelerated tool wear and tool breakage or part rejection due to unacceptable surface finish. To avoid vibrations, process designers tend to command conservative parameters limiting productivity. Among the different machining processes, turning is responsible of a great amount of the chip volume removed worldwide. This paper reports some of the main efforts from the scientific literature to predict stability and to avoid chatter with special emphasis on turning systems. There are different techniques and approaches to reduce and to avoid chatter effects. The objective of the paper is to summarize the current state of research in this hot topic, particularly (1) the mechanistic, analytical, and numerical methods for stability prediction in turning; (2) the available techniques for chatter detection and control; (3) the main active and passive techniques.

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“…This outcome is interesting but again, vibration assessment causes some noise in the final system state determination. Inspirational examples on specific diagnostic data forms analysis can be found in [9,13,14,25,28,32].…”

Section: State Of the Artmentioning

confidence: 99%

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2019

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Forecasting study oF mains reliability based on sparse Field data and perspective state space models prognozowanie niezawodności elementów sieci wodociągowej na podstawie rzadkich danych terenowych i modeli przestrzeni stanówThe elements of critical infrastructure have to meet demanding dependability, safety and security requirements. The article deals with the prognosis of water mains reliability while using sparse irregular filed data. The data are sparse because the only thing we know is the number of mains failures during a given month. Since it is possible to transform the data into a typical reliability measure (rate of failure occurrence -ROCOF), we can examine the course of this measure development in time. In order to model and predict the ROCOF development, we suggest novel single and multiple error state space models. The results can be used for i) optimizing mains operation and maintenance, ii) estimating life cycle cost, and iii) planning crisis management.

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A statistical data-based approach to instability detection and wear prediction in radial turning processes

Cited by 7 publications

References 19 publications

Predictive Maintenance on the Machining Process and Machine Tool

Predictive Maintenance on the Machining Process and Machine Tool

Prediction Methods and Experimental Techniques for Chatter Avoidance in Turning Systems: A Review

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