A fundamental approach for data acquisition on machine tools as enabler for analytical Industrie 4.0 applications

Gittler, Thomas; Gontarz, Adam; Weiss, Lukáš; Wegener, Konrad

doi:10.1016/j.procir.2019.02.088

Cited by 18 publications

(6 citation statements)

References 23 publications

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“…This ability to cope with unknown failure types distinguishes it from conventional supervised classification approaches. It is applicable to various component and also machine types and natures: by the distinction of Gittler et al [29], it can cope with test-cycle data of constant, controlled-constant and varying components. Moreover, the principle remains identical for translatory and rotary components.…”

Section: Advantages Over the Current State Of The Artmentioning

confidence: 99%

Machine Tool Component Health Identification with Unsupervised Learning

Gittler

Scholze²,

Rupenyan

et al. 2020

JMMP

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Unforeseen machine tool component failures cause considerable losses. This study presents a new approach to unsupervised machine component condition identification. It uses test cycle data of machine components in healthy and various faulty conditions for modelling. The novelty in the approach consists of the time series representation as features, the filtering of the features for statistical significance, and the use of this feature representation to train a clustering model. The benefit in the proposed approach is its small engineering effort, the potential for automation, the small amount of data necessary for training and updating the model, and the potential to distinguish between multiple known and unknown conditions. Online measurements on machines in unknown conditions are performed to predict the component condition with the aid of the trained model. The approach was exemplarily tested and verified on different healthy and faulty states of a grinding machine axis. For the accurate classification of the component condition, different clustering algorithms were evaluated and compared. The proposed solution demonstrated encouraging results as it accurately classified the component condition. It requires little data, is straightforward to implement and update, and is able to precisely differentiate minor differences of faults in test cycle time series.

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Section: Advantages Over the Current State Of The Artmentioning

confidence: 99%

Machine Tool Component Health Identification with Unsupervised Learning

Gittler

Scholze²,

Rupenyan

et al. 2020

JMMP

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“…Considerations for preliminary analysis and data acquisition for monitoring are exemplarily described e.g. by Gittler et al in [13] and [14]. For the diagnostics and health assessment tasks, Wang et al [15] give an overview of current PHM analytics with a focus of vibration signal-based health indicators.…”

Section: Prognostics Health and Monitoring In Machining And Manufacturingmentioning

confidence: 99%

International Conference on Advanced and Competitive Manufacturing Technologies milling tool wear prediction using unsupervised machine learning

Gittler

Glasder

Öztürk

et al. 2021

Int J Adv Manuf Technol

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Degraded or defect machine components and consumables negatively impact manufacturing quality and productivity. Diagnosing and predicting the wear or degradation status of critical machine components or parts are therefore of general interest. To tackle this challenge, data-driven approaches based on supervised machine learning principles have demonstrated promising results. However, supervised learning models capable of degradation identification require large quantities of data. In practice, run-to-failure data in large amounts is usually not available and expensive to obtain. To overcome this issue, this study proposes an unsupervised learning approach for degradation prognostics of machine tool components and consumables. It uses time series of multi-sensor signal data, which are transformed into a feature representation. The features consist of various characterizations of the time series, allowing to make different signal measurements comparable, and cluster them according to their feature values. The herewith obtained density-based clustering model is used to diagnose and predict the degradation states of components and parts in unknown conditions. The novelty in the proposed approach lies within the identification of continuous component and part degradation states based on unsupervised learning principles. The proposal is verified and demonstrated on an exemplary data set containing a small sample of run-to-failure multi-sensor signals of milling inserts and their corresponding wear state. By the application of the proposed procedure on the exemplary data set, we demonstrate that an unsupervised clustering approach is capable of separating wear data such that meaningful and accurate estimations of the part condition are possible. The advantages are its ability to cope with scarce data sets, its limited engineering and hyperparameter tuning effort, and its straightforward implementation to a multitude of degradation and wear diagnostics scenarios.

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“…Even in developed manufacturing landscapes, a lot of data is saved in unstructured form, as producers only slowly adapt to the needs of Industry 4.0. Data can be extracted from logs or time series in order to be used for manufacturing prognostics and health management [2]. However, unified approaches for this are largely missing as difficulties arise case-wise, since manufacturing equipment is used for a vast variety of different tasks.…”

Section: Related Workmentioning

confidence: 99%

Offline-Online pattern recognition for enabling time series anomaly detection on older NC machine tools

Netzer¹,

Palenga²,

Goennheimer³

et al. 2021

Journal of Machine Engineering

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Intelligent IoT functions for increased availability, productivity and component quality offer significant added value to the industry. Unfortunately, many old machines and systems are characterized by insufficient, inconsistent IoT connectivity and heterogeneous parameter naming. Furthermore, the data is only available in unstructured form. In the following, a new approach for standardizing information models from existing plants with machine learning methods is described and an offline-online pattern recognition system for enabling anomaly detection under varying machine conditions is introduced. The system can enable the local calculation of signal thresholds that allow more granular anomaly detection than using only single indexing and aims to improve the detection of anomalous machine behaviour especially in finish machining.

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A fundamental approach for data acquisition on machine tools as enabler for analytical Industrie 4.0 applications

Cited by 18 publications

References 23 publications

Machine Tool Component Health Identification with Unsupervised Learning

Machine Tool Component Health Identification with Unsupervised Learning

International Conference on Advanced and Competitive Manufacturing Technologies milling tool wear prediction using unsupervised machine learning

Offline-Online pattern recognition for enabling time series anomaly detection on older NC machine tools

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