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

Netzer, Markus; Palenga, Yannic; Goennheimer, Philipp; Fleischer, Jürgen

doi:10.36897/jme/132248

Cited by 5 publications

(4 citation statements)

References 8 publications

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“…In online pattern recognition, the analysed time series is not fully known. Because of this, in the instance of recognition, not all possible sub-sequences and data points are known for comparison [8].…”

Section: State Of the Art And Related Workmentioning

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%

“…The basic assumption of the approach presented here is that individual sequences recur even in the case of a production run with a batch size of 1 [8,9]. It is further assumed that the state of the respective tool decreases steadily between the cycles [46].…”

Section: Unsupervised Condition-cycle Classification and Detectionmentioning

confidence: 99%

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

View full text Add to dashboard Cite

show abstract

“…With the additional availability of sensor data, this can also be integrated to enable more precise detections. For the application of the approach, it is necessary to enable standardized access to the data in the controller, which can be achieved by implementing an OPC UA server in combination with intelligent parameter identification [23].…”

Section: Anomaly Detection For Indirect Tool Condition Monitor-mentioning

confidence: 99%

“…To enable re-detection of these positional signals in on-line data, a sliding buffer on the on-line data is used, matching positional signals in the offline database to signals appearing in the data stream. To reduce matching time, the patterns in the offline database are matched at different positions in the signal buffer using the mean absolute error [23]. Using iterative calculations of the distance between the offline patterns and the signal in the buffer in combination with stopping distance calculations for individual offline patterns early if the distance increases above a specified threshold leads to pattern matching that can be applied for streaming data.…”

Section: Pattern Recognitionmentioning

confidence: 99%

Machine tool process monitoring by segmented timeseries anomaly detection using subprocess-specific thresholds

Netzer

Palenga

Fleischer

2022

Prod. Eng. Res. Devel.

Self Cite

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Time series data generated by manufacturing machines during processing is widely used in mass part production to assess if processes run without errors. Systems that make use of this data use machine learning approaches for flagging a time series as a deviation from normal behaviour. In single part production, the amount of data generated is not sufficient for learning-based classification. Here, methods often focus on global signal variance but have trouble finding anomalies that present as local signal deviations. The referencing of the process states of the machine is usually performed by state indexing which, however, is not sufficient in highly flexible production plants. In this paper, a system that learns granular patterns in time series based on mean shift clustering is used for detecting processing segments in varying machine conditions. An anomaly detection then finds deviating patterns based on the previously identified processing segments. The anomalies can then be labeled by a human-in-the-loop approach for enabling future anomaly classification using a combination of machine learning algorithms. The method of anomaly detection is validated using an industrial machine tool and multiple test series.

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Model-Based Diagnosis of Feed Axes with Contactless Current Sensing

Hansjosten

Bott

Puchta

et al. 2023

Lecture Notes in Production Engineering

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

Cited by 5 publications

References 8 publications

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

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

Machine tool process monitoring by segmented timeseries anomaly detection using subprocess-specific thresholds

Model-Based Diagnosis of Feed Axes with Contactless Current Sensing

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