Fuzzy-clustering of machine states for condition monitoring

Frieß, Uwe; Kolouch, Martin; Friedrich, Axel; Zander, Andreas

doi:10.1016/j.cirpj.2018.09.001

Cited by 16 publications

(8 citation statements)

References 14 publications

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“…The expert therefore needs to define new relevant MSs and exclude old ones from the "pool" (see Fig. 9 in [8]). Further potentials can be obtained if the pre-definition of MSs is replaced by an auto-derivation of MSs and a subsequent fuzzy clustering of these MSs with the objective of a broad characterization of the machine tool configurations over time.…”

Section: Limits Of Cluster Analyses Based On Pre-defined Machine Statesmentioning

confidence: 99%

“…Auto-definition of MS by segmentation of MS parameters (section 2) 2. Deriving of Characteristic Values (CVs) for every state as described in [8] 3. MS-TP-reduction: Correlation analyses between MSs, CVs, parameter reduction and exclusion of non-significant MSs (section 3 and 4) 4.…”

Section: Limits Of Cluster Analyses Based On Pre-defined Machine Statesmentioning

confidence: 99%

“…The fuzzy clustering of MSs, as presented in [8] can be exercised without any consideration of possible correlations between TPs and CVs. This is possible for a limited number of pre-defined MSs based on practical considerations about components of interest and -heuristically anticipated -correlations between CVs and TPs.…”

Section: Regression Analysis For Correlation-based Machine State and mentioning

confidence: 99%

“…The clustering was deducted solely on base of the two relevant TPs for each of the two CVs as described in section 4. The algorithm is described in detail in [8] based on [9]. Every MS is gradually attributed to the cluster centres.…”

Section: Deduction Of Machine Characteristics Based On Clusteringmentioning

confidence: 99%

“…Integrally modules are predictive maintenance and cloud-based monitoring of production systems [4][5][6]. In [7] and [8] the authors introduced an approach to overcome limits in condition monitoring of large and special-purpose machine tools. The core challenge to address is the time-based change in nearly every internal and external constrainparameter ( Fig.…”

Section: Introductionmentioning

confidence: 99%

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Deduction of time-dependent machine tool characteristics by fuzzy-clustering

Frieß

Kolouch

Pütz

2018

Machine Learning for Cyber Physical Systems

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With the onset of ICT and big data capabilities, the physical asset and data computation is integrated in manufacturing through Cyber Physical Systems (CPS). This strategy also denoted as Industry 4.0 will improve any kind of monitoring for maintenance and production planning purposes. So-called bigdata approaches try to use the extensive amounts of diffuse and distributed data in production systems for monitoring based on artificial neural networks (ANN). These machine learning approaches are robust and accurate if the data base for a given process is sufficient and the scope of the target functions is curtailed. However, a considerable proportion of high-performance manufacturing is characterized by permanently changing process, workpiece and machine configuration conditions, e.g. machining of large workpieces is often performed in batch sizes of one or of a few parts. Therefore, it is not possible to implement a robust condition monitoring based on ANN without structured data-analyses considering different machine states-e.g. a certain machining operation for a certain machine configuration. Fuzzy-clustering of machine states over time creates a stable pool representing different typical machine configuration clusters. The time-depending adjustment and automatized creation of clusters enables monitoring and interpretation of machine tool characteristics independently of single machine states and pre-defined processes.

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Section: Limits Of Cluster Analyses Based On Pre-defined Machine Statesmentioning

confidence: 99%

Section: Limits Of Cluster Analyses Based On Pre-defined Machine Statesmentioning

confidence: 99%

Section: Regression Analysis For Correlation-based Machine State and mentioning

confidence: 99%

Section: Deduction Of Machine Characteristics Based On Clusteringmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Deduction of time-dependent machine tool characteristics by fuzzy-clustering

Frieß

Kolouch

Pütz

2018

Machine Learning for Cyber Physical Systems

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Fuzzy Condition Indicator of Spindle of Machining Center Based on Vibration Parameters

Augustyn,

Fidali

2023

Applied Condition Monitoring

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An IoT system for managing machine tool spindles in operation

Bachim

Martens

Gonçalves

et al. 2023

Int J Adv Manuf Technol

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With the development of production processes over the last decades, machine tools have evolved, and in everyday life today it is impossible not to visualize the new facilities arising from technological developments, such as IoT (Internet of Things) systems. Because they are so relevant, they have become a competitive differential in all markets and will not be different in the transformation industry or in its operational areas. Estimating when any machine will fail beforehand is fundamental to maximizing the company's results and consequently minimizing operating costs. In this sense, this research aimed to develop an IoT system for the online management of machine tool spindles in operation and to provide reliable data for maintenance management in the context of Industry 4.0 (I4.0). The development of the system took place through the Design Science Research (DSR) methodology. Thus, with the systematic review of the literature, the artifact/theoretical system of the method type for online monitoring of spindles in operation was developed, and its implementation was demonstrated and validated via a case study with participant observation in an automotive industry. The main contributions of the research are the artifact/system developed and validated and the associated predictive maintenance method. With the artifact, the normal behavior of the spindles in operation is demonstrated, contributing both to the academy eld and to the practice in the industry, in the sense of advancing knowledge and preventing catastrophic failures in machine tools.

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Fuzzy-clustering of machine states for condition monitoring

Cited by 16 publications

References 14 publications

Deduction of time-dependent machine tool characteristics by fuzzy-clustering

Deduction of time-dependent machine tool characteristics by fuzzy-clustering

Fuzzy Condition Indicator of Spindle of Machining Center Based on Vibration Parameters

An IoT system for managing machine tool spindles in operation

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