State-based and Self-adapting Algorithm for Condition Monitoring

Pütz, Matthias; Frieß, Uwe; Wabner, Markus; Friedrich, Axel; Zander, Andreas; Schlegel, Holger

doi:10.1016/j.procir.2016.06.073

Cited by 7 publications

(6 citation statements)

References 3 publications

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“…The last step is the assignment of a group with maximum comparability. The change in the system behaviour which likely characterises a different operation state can be either a trigger or an anomaly [9,10]. Anomaly detection in time series is a well-researched area, with various methods being employed, and they can be classified into different categories.…”

Section: State Of the Art And Related Workmentioning

confidence: 99%

“…The number of produced parts can influence the choice of algorithm for process segmentation, especially because most Machine Learning-based methods need a large training dataset for accurate estimations. Especially for small production runs, statistical process control methods may be more appropriate due to the limited amount of data available [10]. Here, approaches that work without fixed thresholds to adapt themselves for autonomous usage can enable a broader usage.…”

Section: State Of the Art And Related Workmentioning

confidence: 99%

“…By an adjustment of the production in the machine, such as by the initiation of a tool change, Digital Twins can be realised. One basic assumption of the presented approach is that the production of lot size one of the highly individualised small batches can be understood as a series of individual repetitive motion sequences [10]. This will be exploited here by detecting and quantifying anomalies at a segment level.…”

Section: Conceptmentioning

confidence: 99%

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Monitoring of Tool and Component Wear for Self-Adaptive Digital Twins: A Multi-Stage Approach through Anomaly Detection and Wear Cycle Analysis

Ströbel,

Bott,

Wortmann

et al. 2023

Machines

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In today’s manufacturing landscape, Digital Twins play a pivotal role in optimising processes and deriving actionable insights that extend beyond on-site calculations. These dynamic representations of systems demand real-time data on the actual state of machinery, rather than static images depicting idealized configurations. This paper presents a novel approach for monitoring tool and component wear in CNC milling machines by segmenting and classifying individual machining cycles. The method assumes recurring sequences, even with a batch size of 1, and considers a progressive increase in tool wear between cycles. The algorithms effectively segment and classify cycles based on path length, spindle speed and cycle duration. The tool condition index for each cycle is determined by considering all axis signals, with upper and lower thresholds established for quantifying tool conditions. The same approach is adapted to predict component wear progression in machine tools, ensuring robust condition determination. A percentage-based component state description is achieved by comparing it to the corresponding Tool Condition Codes (TCC) range. This method provides a four-class estimation of the component state. The approach has demonstrated robustness in various validation cases.

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Section: State Of the Art And Related Workmentioning

confidence: 99%

Section: State Of the Art And Related Workmentioning

confidence: 99%

Section: Conceptmentioning

confidence: 99%

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Monitoring of Tool and Component Wear for Self-Adaptive Digital Twins: A Multi-Stage Approach through Anomaly Detection and Wear Cycle Analysis

Ströbel,

Bott,

Wortmann

et al. 2023

Machines

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“…A typical pre-defined MS is characterized by a subset of TPs as presented in [7] ( Table 1). The MSs depict in Table 1 are represented by using different TPs for an axis stroke (see Fig.…”

Section: Auto-definition Of Mss By Segmentation Of Tps For Different mentioning

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%

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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