Automatic Time Series Segmentation as the Basis for Unsupervised, Non-Intrusive Load Monitoring of Machine Tools

Seevers, J-P.; Johst, J.; Weiss, T.J.; Meschede, Henning; Hesselbach, Jens

doi:10.1016/j.procir.2019.03.178

Cited by 21 publications

(6 citation statements)

References 18 publications

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“…Some segments in the cycle are constant while others are not. This behaviour of many household appliances is typical and has been described by Seevers et al [34]. Some detailed differences can easily be observed in the case of electricity receivers.…”

Section: Selection and Preparation Of Input And Output Variables Of Msupporting

confidence: 60%

“…Figure 8 shows an example of the arithmetic average of plug meter readings (with a measurement frequency of 1 Hz) recorded in ten minutes. The course of real power consumption is highly cyclical (appliances typically have their own specific work cycles [34]); however, certain differences between cycles might be observed. Some segments in the cycle are constant while others are not.…”

Section: Selection and Preparation Of Input And Output Variables Of Mmentioning

confidence: 99%

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Modelling the Disaggregated Demand for Electricity in Residential Buildings Using Artificial Neural Networks (Deep Learning Approach)

Jasiński

2020

Energies

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The paper addresses the issue of modelling the demand for electricity in residential buildings with the use of artificial neural networks (ANNs). Real data for six houses in Switzerland fitted with measurement meters was used in the research. Their original frequency of 1 Hz (one-second readings) was re-sampled to a frequency of 1/600 Hz, which corresponds to a period of ten minutes. Out-of-sample forecasts verified the ability of ANNs to disaggregate electricity usage for specific applications (electricity receivers). Four categories of electricity consumption were distinguished: (i) fridge, (ii) washing machine, (iii) personal computer, and (iv) freezer. Both standard ANNs with multilayer perceptron architecture and newer types of networks based on deep learning were used. The simulations included over 10,000 ANNs with different architecture (number of neurons and structure of their connections), type and number of input variables, formulas of activation functions, training algorithms, and other parameters. The research confirmed the possibility of using ANNs to model the disaggregation of electricity consumption based on low frequency data, and suggested ways to build highly optimised models.

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Section: Selection and Preparation Of Input And Output Variables Of Msupporting

confidence: 60%

Section: Selection and Preparation Of Input And Output Variables Of Mmentioning

confidence: 99%

Modelling the Disaggregated Demand for Electricity in Residential Buildings Using Artificial Neural Networks (Deep Learning Approach)

Jasiński

2020

Energies

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“…Segmentation techniques proposed in the literature are a well-established signal processing method that are mainly focused on improving the signal's quality or detecting operational change points in the signal for a more efficient and focused analysis. This can be observed, for instance, in the presented research work by [18]. An automatic time series segmentation technique was proposed to identify and characterize machine tool operational states.…”

Section: Related Workmentioning

confidence: 65%

Linear Axis Guide Rail Misalignment Detection and Localization Using a Novel Signal Segmentation Analysis Technique

Hurtado Carreon,

DePaiva,

Veldhuis

2024

Applied Sciences

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Maintenance of the linear axis and its components such as the linear guide can be significantly costly due to the difficult nature of the repair procedure and the downtime the machine exhibits while being repaired. This is a decision that must be made carefully and with proper justification. Therefore, it is crucial that the condition-based monitoring (CBM) system in the machine can detect and localize faults in the linear axis. The presented paper proposes a novel vibration signal segmentation analysis technique that detects and localizes misalignment in the linear guide rail, which is considered a leading root-cause failure fault. The results demonstrated that the usability of time domain features such as RMS was doubled by applying segmentation analysis. Also, evaluating both stroke directions aided in the localization of the misalignment. Overall, the practical value of the proposed technique is to function as both a localization and repair verification tool when performing linear axis maintenance.

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“…This can influence the data processing or possible predictions. Because of this, the segmentation process divides the time series into sub-sequences to maximise comparability within a group [7]. Here, two cases have to be differentiated: online and offline pattern recognition.…”

Section: State Of the Art And Related Workmentioning

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

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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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Automatic Time Series Segmentation as the Basis for Unsupervised, Non-Intrusive Load Monitoring of Machine Tools

Cited by 21 publications

References 18 publications

Modelling the Disaggregated Demand for Electricity in Residential Buildings Using Artificial Neural Networks (Deep Learning Approach)

Modelling the Disaggregated Demand for Electricity in Residential Buildings Using Artificial Neural Networks (Deep Learning Approach)

Linear Axis Guide Rail Misalignment Detection and Localization Using a Novel Signal Segmentation Analysis Technique

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

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