P1 - Intelligentes Condition Monitoring von hydraulischen Anlagen

Helwig, Nikolai; Schütze, Andreas

doi:10.5162/ahmt2014/p1

Cited by 2 publications

(2 citation statements)

References 5 publications

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“…without making use of the system status, while feature selection-here based primarily on support vector machines-and LDA projection are supervised methods, i.e. require the knowledge of the system status [14]. The evaluation is based on a comprehensive training phase in which all combinations of all fault states are tested.…”

Section: Condition Monitoring Using Data-based Modelingmentioning

confidence: 99%

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Industrial condition monitoring with smart sensors using automated feature extraction and selection

Schneider¹,

Helwig²,

Schütze

2018

Meas. Sci. Technol.

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Smart sensors with internal signal processing and machine learning capabilities are a current trend in sensor development. This paper suggests a set of complementary and automated algorithms for feature extraction and selection to be used with smart sensors. The suggested methods for feature extraction can be applied on smart sensors and are capable of extracting signal characteristics from signal shape, time domain, time-frequency domain, frequency domain and signal distribution. Feature selection subsequently is capable of selecting the most important features for linear and nonlinear fault classification. The paper also highlights the potential of smart sensors in combination with the suggested algorithms that provide both data and further functionality from self-monitoring to condition monitoring in industrial applications. The first example applications are condition monitoring of a complex hydraulic machine where smart signal processing allows classification and quantification of four different fault scenarios. Additionally redundancies in the systems were used for self-monitoring and allowed to detect simulated sensor faults before they become critical for fault classification. The second example application is remaining lifetime prediction of electromechanical cylinders that shows applicability to big data and transparency of the solution by providing detailed information about sensor significance.

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Section: Condition Monitoring Using Data-based Modelingmentioning

confidence: 99%

“…A systematic validation, e.g. based on k-fold cross-validation and projected faults, completes the development of the statistical model and ensures that no overfitting occurs in spite of the high-dimensional input data set and the supervised training methods [14].…”

Section: Condition Monitoring Using Data-based Modelingmentioning

confidence: 99%

Industrial condition monitoring with smart sensors using automated feature extraction and selection

Schneider¹,

Helwig²,

Schütze

2018

Meas. Sci. Technol.

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Sensors 4.0 – smart sensors and measurement technology enable Industry 4.0

Schütze

Helwig²,

Schneider³

2018

J. Sens. Sens. Syst.

205

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Abstract. "Industrie 4.0" or the Industrial Internet of Things (IIoT) are two terms for the current (r)evolution seen in industrial automation and control. Everything is getting smarter and data generated at all levels of the production process are used to improve product quality, flexibility, and productivity. This would not be possible without smart sensors, which generate the data and allow further functionality from self-monitoring and selfconfiguration to condition monitoring of complex processes. In analogy to Industry 4.0, the development of sensors has undergone distinctive stages culminating in today's smart sensors or "Sensor 4.0". This paper briefly reviews the development of sensor technology over the last 2 centuries, highlights some of the potential that can be achieved with smart sensors and data evaluation, and discusses success requirements for future developments. In addition to magnetic sensor technologies which allow self-test and self-calibration and can contribute to many applications due to their wide spectrum of measured quantities, the paper discusses condition monitoring as a primary paradigm for introducing smart sensors and data analysis in manufacturing processes based on two projects performed in our group.

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P1 - Intelligentes Condition Monitoring von hydraulischen Anlagen

Cited by 2 publications

References 5 publications

Industrial condition monitoring with smart sensors using automated feature extraction and selection

Industrial condition monitoring with smart sensors using automated feature extraction and selection

Sensors 4.0 – smart sensors and measurement technology enable Industry 4.0

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