Fault Handling in Industry 4.0: Definition, Process and Applications

Webert, Heiko; Döß, Tamara; Kaupp, Lukas; Simons, Stephan

doi:10.3390/s22062205

Cited by 31 publications

(16 citation statements)

References 117 publications

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“…This decision model is responsible for recognizing the system's state as either normal or abnormal. Deep learning with artificial neural networks has enabled many effective solutions that address this decision-making challenge [8,9].…”

Section: Control Systemmentioning

confidence: 99%

Accuracy Is Not Enough: Optimizing for a Fault Detection Delay

Šprogar

Verber

2023

Mathematics

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This paper assesses the fault-detection capabilities of modern deep-learning models. It highlights that a naive deep-learning approach optimized for accuracy is unsuitable for learning fault-detection models from time-series data. Consequently, out-of-the-box deep-learning strategies may yield impressive accuracy results but are ill-equipped for real-world applications. The paper introduces a methodology for estimating fault-detection delays when no oracle information on fault occurrence time is available. Moreover, the paper presents a straightforward approach to implicitly achieve the objective of minimizing fault-detection delays. This approach involves using pseudo-multi-objective deep optimization with data windowing, which enables the utilization of standard deep-learning methods for fault detection and expanding their applicability. However, it does introduce an additional hyperparameter that needs careful tuning. The paper employs the Tennessee Eastman Process dataset as a case study to demonstrate its findings. The results effectively highlight the limitations of standard loss functions and emphasize the importance of incorporating fault-detection delays in evaluating and reporting performance. In our study, the pseudo-multi-objective optimization could reach a fault-detection accuracy of 95% in just a fifth of the time it takes the best naive approach to do so.

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Section: Control Systemmentioning

confidence: 99%

Accuracy Is Not Enough: Optimizing for a Fault Detection Delay

Šprogar

Verber

2023

Mathematics

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“…In general, fault detection methods for robot driving control systems can be divided into three main groups, including model-based, signal-based and data-driven methods [5]. Model-based fault detection methods are accomplished based on the comparison between the estimated and measured signals, which are highly dependent on accurate design of the system model.…”

Section: Introductionmentioning

confidence: 99%

Fault Detection for Motor Drive Control System of Industrial Robots Using CNN-LSTM-based Observers

Wang

Zhang

Wang

2023

Trans. Electr. Mach. Syst.

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The complex working conditions and nonlinear characteristics of the motor drive control system of industrial robots make it difficult to detect faults. In this paper, a deep learning-based observer, which combines the convolutional neural network (CNN) and the long short-term memory network (LSTM), is employed to approximate the nonlinear driving control system. CNN layers are introduced to extract dynamic features of the data, whereas LSTM layers perform time-sequential prediction of the target system. In terms of application, normal samples are fed into the observer to build an offline prediction model for the target system. The trained CNN-LSTM-based observer is then deployed along with the target system to estimate the system outputs. Online fault detection can be realized by analyzing the residuals. Finally, an application of the proposed fault detection method to a brushless DC motor drive system is given to verify the effectiveness of the proposed scheme. Simulation results indicate the impressive fault detection capability of the presented method for driving control systems of industrial robots.

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“…To be able to locate faults in a system, it is necessary to provide some processes such as: Fault detection: which is considered as a process of finding an occurrence of fault in an electrical unit based on measurement data provided by the system [43]; Fault identification: which consists in defining and representing a fault in a model [44]; Fault classification: which is considered as a process including fault detection of various fault types by clustering analysis and classification of detected faults in predefined fault classes [43]. …”

Section: Introductionmentioning

confidence: 99%

Fault detection and classification using deep learning method and neuro‐fuzzy algorithm in a smart distribution grid

Mbey,

Foba Kakeu,

Boum

et al. 2023

The Journal of Engineering

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This article proposes a deep learning (DL) model made of Long Short Term Memory (LSTM) and Adaptive Neuro Fuzzy Inference System (ANFIS) to detect fault in smart distribution grid assisted by communication systems using smart meter data. In smart grid, data analysis for fault identification and detection is crucial for grid monitoring. Nowadays, there are several DL techniques developed for smart grid data analysis applications. To solve this problem, a novel data analysis model based on deep learning and Neuro‐fuzzy algorithm is developed for fault location in a smart power grid. First, the LSTM is applied for training the data samples extracted from the smart meters. Then, an ANFIS algorithm is implemented for fault detection and identification from the trained data. Finally, faults are located with the higher accuracy. With this intelligent method proposed, single‐phase, two‐phase and three‐phase faults can be identified using a restricted amount of data. The novelty of the proposed method compared with other methods is the capability of fast training and testing even with large amount of data. To verify the effectiveness of our methodology, an intelligent model of the IEEE 13‐node network is used. The effectiveness and robustness of the proposed model are evaluated using several parameters such as accuracy, precision‐recall, F1‐score, Receiver Operating Characteristic (ROC) curve and complexity time. The obtained results indicate that the proposed deep learning model outperforms existing deep learning methods in the literature for fault detection and classification with 99.99% accuracy.

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Fault Handling in Industry 4.0: Definition, Process and Applications

Cited by 31 publications

References 117 publications

Accuracy Is Not Enough: Optimizing for a Fault Detection Delay

Accuracy Is Not Enough: Optimizing for a Fault Detection Delay

Fault Detection for Motor Drive Control System of Industrial Robots Using CNN-LSTM-based Observers

Fault detection and classification using deep learning method and neuro‐fuzzy algorithm in a smart distribution grid

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