Model Selection for Predictive Quality in Hydraulic Testing

Neunzig, Christian; Fahle, Simon; Schulz, Jürgen; Möller, Matthias; Kuhlenkötter, Bernd

doi:10.1016/j.procir.2022.04.052

Cited by 3 publications

(1 citation statement)

References 7 publications

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“…Huo applied an RBF neural network for intelligent prediction for the digging load of hydraulic excavators [9], according to the flowchart shown in Figure 6. Guo [10] investigated the use of four different neural network types, including convolutional, BP, T-S, and Elman, to predict the internal leakage of a hydraulic cylinder, and Neunzig utilized machine learning methods to predict internal leakage of hydraulic valves [11]. Nie [12] developed a remaining useful life (RUL) prediction method for a high-speed on/off valve by combining the autoregressive integrated moving average (ARIMA) model and the long short-term memory (LSTM) neural network and Li carried out the research on the RUL prediction of a hydraulic pump [16] based on kernel principal component analysis (KPCA) and just in time learning (JITL) (Figure 7).…”

Section: Neural Predictionmentioning

confidence: 99%

Artificial Intelligence Methods in Hydraulic System Design

Filo

2023

Energies

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Reducing energy consumption and increasing operational efficiency are currently among the leading research topics in the design of hydraulic systems. In recent years, hydraulic system modeling and design techniques have rapidly expanded, especially using artificial intelligence methods. Due to the variety of algorithms, methods, and tools of artificial intelligence, it is possible to consider the prospects and directions of their further development. The analysis of the most recent publications allowed three leading technologies to be indicated, including artificial neural networks, evolutionary algorithms, and fuzzy logic. This article summarizes their current applications in the research, main advantages, and limitations, as well as expected directions for further development.

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Section: Neural Predictionmentioning

confidence: 99%

Artificial Intelligence Methods in Hydraulic System Design

Filo

2023

Energies

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ML Pro: digital assistance system for interactive machine learning in production

Neunzig,

Möllensiep,

Kuhlenkötter

et al. 2023

J Intell Manuf

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The application of machine learning promises great growth potential for industrial production. The development process of a machine learning solution for industrial use cases requires multi-layered, sophisticated decision-making processes along the pipeline that can only be accomplished by subject matter experts with knowledge of statistical mathematics, coding, and engineering process knowledge. By having humans and computers work together in a digital assistance system, the special characteristics of human and artificial intelligence can be used synergistically. This paper presents the development of a digital human-centered assistance system for employees in the production and development departments of industrial manufacturing companies. This assistance system enables users to apply production-specific data mining and machine learning techniques without programming to typical tabular production data, which is often inherently high-dimensional, nonstationary, and highly imbalanced data streams. Through tight interactive process guidance that considers the dependencies between machine learning process modules, users are empowered to build and optimize predictive models. Compared to existing commercial and academic tools with similar objectives, the digital assistance system offers the added value that both classical shallow and deep learning as well as generative and oversampling methods can be interactively applied to all feature table use cases for different user modes without programming.

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Enhanced classification of hydraulic testing of directional control valves with synthetic data generation

Neunzig

Möllensiep

Hartmann

et al. 2023

Prod. Eng. Res. Devel.

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Production environments bring inherent system challenges that are reflected in the high-dimensional production data. The data is often nonstationary, is not available in sufficient size and quality, and is class imbalanced due to the predominance of good parts. Data-driven manufacturing analytics requires data of sufficient quantity and quality. In order to predict quality characteristics, production data is collected across processes in the industrial use case at Bosch Rexroth AG for the purpose of inferring results in hydraulic final inspection using machine learning methods. Since high quality data generation is costly, synthetic data generation methodologies offer a promising alternative to improve prediction models and thus generate safer, more accurate predictions for manufacturing companies. Among the synthetic data generation methodologies used, variational autoencoders compared to generative adversarial networks and synthetic minority oversampling technique methods are best suited to synthesize the feature with highest feature importance from a small sample data set compared to the production data and improve the prediction for the target variable.

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Model Selection for Predictive Quality in Hydraulic Testing

Cited by 3 publications

References 7 publications

Artificial Intelligence Methods in Hydraulic System Design

Artificial Intelligence Methods in Hydraulic System Design

ML Pro: digital assistance system for interactive machine learning in production

Enhanced classification of hydraulic testing of directional control valves with synthetic data generation

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