Study on Optimization of Data-Driven Anomaly Detection

Zhou, Yiqing; Li, Rui; Yongjia, Chen

doi:10.1109/icodsa55874.2022.9862914

Cited by 4 publications

(3 citation statements)

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“…However, by combining AI with human oversight, they can watch and manage the trends the model picks up. This usually improves the model’s predictions [ 23 , 24 , 25 , 26 , 27 , 28 , 29 ]. In [ 30 ], they proposed the autoencoder and LSTM for traffic flow prediction.…”

Section: Literature Review and Related Workmentioning

confidence: 99%

LSTM-Autoencoder for Vibration Anomaly Detection in Vertical Carousel Storage and Retrieval System (VCSRS)

Seok

Kareem

Hur

2023

Sensors

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Industry 5.0, also known as the “smart factory,” is an evolution of manufacturing technology that utilizes advanced data analytics and machine learning techniques to optimize production processes. One key aspect of Industry 5.0 is using vibration data to monitor and detect anomalies in machinery and equipment. In the case of a vertical carousel storage and retrieval system (VCSRS), vibration data can be collected and analyzed to identify potential issues with the system’s operation. A correlation coefficient model was used to detect anomalies accurately in the vertical carousel system to ascertain the optimal sensor placement position. This model utilized the Fisher information matrix (FIM) and effective independence (EFI) methods to optimize the sensor placement for maximum accuracy and reliability. An LSTM-autoencoder (long short-term memory) model was used for training and testing further to enhance the accuracy of the anomaly detection process. This machine-learning technique allowed for detecting patterns and trends in the vibration data that may not have been evident using traditional methods. The combination of the correlation coefficient model and the LSTM-autoencoder resulted in an accuracy rate of 97.70% for detecting anomalies in the vertical carousel system.

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Section: Literature Review and Related Workmentioning

confidence: 99%

LSTM-Autoencoder for Vibration Anomaly Detection in Vertical Carousel Storage and Retrieval System (VCSRS)

Seok

Kareem

Hur

2023

Sensors

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“…A comprehensive evaluation model is established using quantitative scoring, principal component analysis, and 0.1 planning. The proposed evaluation method is evaluated objectively [19]. This research deals with the issue of identifying faults in squirrel cage induction motors (SCIMs) when operating under conditions of low load.…”

Section: Literature and Related Workmentioning

confidence: 99%

ANN-Based Reliability Enhancement of SMPS Aluminum Electrolytic Capacitors in Cold Environments

Jeong,

Kareem,

Song

et al. 2023

Energies

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Due to their substantial energy density and economical pricing, switching-mode power supplies (SMPSs) often utilize electrolytic capacitors. However, their ability to function at low temperatures is essential for dependable operation in several sectors, including telecommunications, automotive, and aerospace. This study includes an experimental evaluation of how well standard SMPS electrolytic capacitors operate at low temperatures. This paper investigates the suitability of standard electrolytic capacitors used in switched-mode power supplies (SMPSs) for low-temperature applications. The experimental evaluation exposed the capacitors to temperatures ranging from −5 °C to −40 °C, assessing capacitance (Cp), impedance (Z), dissipation factor (DF), and equivalent series resistance (ESR) at each temperature. The capacitor’s time-domain electrical signals were analyzed using the Pearson correlation coefficient to extract discriminative features. These features were input into an artificial neural network (ANN) for training and testing. The results indicated a significant impact of low temperatures on capacitor performance. Capacitance decreased with lower temperatures, while the ESR and leakage current increased, affecting stability and efficiency. Impedance was a valuable diagnostic tool for identifying potential capacitor failure, showing a 98.44% accuracy drop at −5 °C and 88.75% at the peak temperature, indicating proximity to the manufacturer’s specified limit. The study suggests further research and development to improve the performance of electrolytic capacitors in SMPS systems under cold conditions, aiming to boost efficiency and reliability.

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“…Data preprocessing is an essential step for building a model; we perform missing value processing and outlier detection on sample data. Since no missing values in the data, we use a box diagram for outlier detection [20].…”

Section: Datamentioning

confidence: 99%

Multi-classification of national fitness test grades based on statistical analysis and machine learning

Yang,

Wang,

Cao

et al. 2023

PLoS ONE

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Physical fitness is a key element of a healthy life, and being overweight or lacking physical exercise will lead to health problems. Therefore, assessing an individual’s physical health status from a non-medical, cost-effective perspective is essential. This paper aimed to evaluate the national physical health status through national physical examination data, selecting 12 indicators to divide the physical health status into four levels: excellent, good, pass, and fail. The existing challenge lies in the fact that most literature on physical fitness assessment mainly focuses on the two major groups of sports athletes and school students. Unfortunately, there is no reasonable index system has been constructed. The evaluation method has limitations and cannot be applied to other groups. This paper builds a reasonable health indicator system based on national physical examination data, breaks group restrictions, studies national groups, and hopes to use machine learning models to provide helpful health suggestions for citizens to measure their physical status. We analyzed the significance of the selected indicators through nonparametric tests and exploratory statistical analysis. We used seven machine learning models to obtain the best multi-classification model for the physical fitness test level. Comprehensive research showed that MLP has the best classification effect, with macro-precision reaching 74.4% and micro-precision reaching 72.8%. Furthermore, the recall rates are also above 70%, and the Hamming loss is the smallest, i.e., 0.272. The practical implications of these findings are significant. Individuals can use the classification model to understand their physical fitness level and status, exercise appropriately according to the measurement indicators, and adjust their lifestyle, which is an important aspect of health management.

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Study on Optimization of Data-Driven Anomaly Detection

Cited by 4 publications

References 0 publications

LSTM-Autoencoder for Vibration Anomaly Detection in Vertical Carousel Storage and Retrieval System (VCSRS)

LSTM-Autoencoder for Vibration Anomaly Detection in Vertical Carousel Storage and Retrieval System (VCSRS)

ANN-Based Reliability Enhancement of SMPS Aluminum Electrolytic Capacitors in Cold Environments

Multi-classification of national fitness test grades based on statistical analysis and machine learning

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