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Monitoring System for Industry 4.0: AI-Driven, Data Analysis and Health Maintenance

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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (20 October 2024) | Viewed by 21693

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Dr. Yusen He

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Guest Editor

Department of Industrial and System Engineering, University of Iowa, Iowa City, IA 52246, USA
Interests: deep learning; statistical modeling; image analysis; time-series analysis
Special Issues, Collections and Topics in MDPI journals

Dr. Huajin Li

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Guest Editor

School of Architecture and Civil Engineering, Chengdu University, Chengdu 610106, China
Interests: deep learning; geo-mathematics; image analysis; time-series analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the development of IoT and “Industry 4.0”, industrial systems become more intelligent and complex, and monitoring systems’ health is very important to guarantee stability, security, and economy. This shift also concerns diverse research areas, e.g., detection of abnormal data, unhealthy status, fault diagnosis, adversarial attacks, robustness analysis, and so on. On the other hand, with the development of sensor systems, large quantities of data have become easily available, bringing challenges to industrial systems’ condition monitoring.

A number of methodologies and algorithms related to data mining, big data analysis, and deep learning have been developed in this research area. However, there are still many challenging problems worth exploring and solving. Therefore, this Research Topic aims to select potential contributions related to advanced theoretical findings, technologies, algorithms, and industrial applications in the monitoring of industrial systems’ health (i.e., condition monitoring).

Subtopics of interest include:
• Theory development on monitoring systems’ health:
-Machine learning;
-Deep learning;
-Data Mining;
-Big data analytics;
-Graph theory.
• Engineering applications related to monitoring systems’ health:
-Data cleaning;
-Abnormal data detection;
-Anomaly detection;
-Condition monitoring;
-Fault diagnosis.
• Anomaly detection in energy-related industrial systems.

Dr. Yusen He
Dr. Huajin Li
Guest Editors

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Keywords

data-driven modeling
data mining
big data
machine learning
deep learning
condition monitoring
anomaly detection
fault diagnosis

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Published Papers (10 papers)

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Research

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18 pages, 5894 KiB

Open AccessArticle

Research on Detection of Icing Cover Transmission Lines Under Different Weather Conditions Based on Wide-Field Dynamic Convolutional Network LDKA-NET

by Xinsheng Dong, Yuanhao Wan, Yongcan Zhu and Chao Ji

Appl. Sci. 2024, 14(24), 11486; https://doi.org/10.3390/app142411486 - 10 Dec 2024

Viewed by 667

Abstract

The safety of transmission lines is a crucial guarantee for the operation of the power grid. To address the issue of low detection accuracy for icing transmission line defects with existing models, this paper proposes a defect detection algorithm for icing transmission line defects under different weather conditions based on a Large Dynamic Kernel Aggregation Net (LDKA-NET). First, a wide field of view convolutional network (WFVC Net) is introduced to enhance the network’s perception and generalization capabilities, enabling better adaptation to complex scene targets. Secondly, a full-dimensional dynamic convolutional feature fusion network is proposed, which strengthens the model’s feature extraction ability by learning linear combinations of multiple convolution kernels and their weighted input-related attention. Finally, an Expectation Maximization Dynamic Convolutional Attention (EM-DCA) mechanism is introduced, which focuses on and utilizes important information in the input data to help the model better allocate attention, thereby improving its generalization and robustness. Experimental results show that on the dataset proposed in this paper, the average accuracy of the improved algorithm (mAP@0.5) reaches 99.01%. The model parameters decreased by 47.7 M compared with the baseline model and 91.26 M compared with the Faster region with CNN feature (Faster R-CNN) model. The accuracy is 4.81% and 3.11% higher than the SSD model and YOLOv5-L model, respectively. Compared with the existing models, our model is smaller and has higher detection accuracy. It can accurately detect ice-covered wires and complete the task of the ice-covered detection of transmission lines. Full article

(This article belongs to the Special Issue Monitoring System for Industry 4.0: AI-Driven, Data Analysis and Health Maintenance)

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21 pages, 648 KiB

Open AccessArticle

Leveraging Swarm Intelligence for Invariant Rule Generation and Anomaly Detection in Industrial Control Systems

by Yunkai Song, Huihui Huang, Hongmin Wang and Qiang Wei

Appl. Sci. 2024, 14(22), 10705; https://doi.org/10.3390/app142210705 - 19 Nov 2024

Cited by 1 | Viewed by 1198

Abstract

Industrial control systems (ICSs), which are fundamental to the operation of critical infrastructure, face increasingly sophisticated security threats due to the integration of information and operational technologies. Conventional anomaly detection techniques often lack the ability to provide clear explanations for their detection, and their inherent complexity can impede practical implementation in the resource-constrained environments typical of ICSs. To address these challenges, this paper proposes a novel approach that leverages swarm intelligence algorithms for the extraction of numerical association rules, specifically designed for anomaly detection in ICS. The proposed approach is designed to effectively identify and precisely localize anomalies by analyzing the states of sensors and actuators. Experimental validation using the Secure Water Treatment (SWaT) dataset demonstrates that the proposed approach can detect over 84% of attack instances, with precise anomaly localization achievable by examining as few as two to six sensor or actuator states. This significantly improves the efficiency and accuracy of anomaly detection. Furthermore, since the method is based on the general control dynamics of ICSs, it demonstrates robust generalization, making it applicable across a wide range of industrial control systems. Full article

(This article belongs to the Special Issue Monitoring System for Industry 4.0: AI-Driven, Data Analysis and Health Maintenance)

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22 pages, 7138 KiB

Open AccessArticle

Milling Machine Fault Diagnosis Using Acoustic Emission and Hybrid Deep Learning with Feature Optimization

by Muhammad Umar, Muhammad Farooq Siddique, Niamat Ullah and Jong-Myon Kim

Appl. Sci. 2024, 14(22), 10404; https://doi.org/10.3390/app142210404 - 12 Nov 2024

Cited by 9 | Viewed by 1719

Abstract

This paper presents a fault diagnosis technique for milling machines based on acoustic emission (AE) signals and a hybrid deep learning model optimized with a genetic algorithm. Mechanical failures in milling machines, particularly in critical components like cutting tools, gears, and bearings, account for a significant portion of operational breakdowns, leading to unplanned downtime and financial losses. To address this issue, the proposed method first acquires AE signals from the milling machine. AE signals, capturing the dynamic responses of machine components, are transformed into continuous wavelet transform (CWT) scalograms for further analysis. Gaussian filtering is applied to enhance the clarity of these scalograms, effectively reducing noise while maintaining essential features. A convolutional neural network (CNN) based on the VGG16 architecture is utilized for spatial feature extraction, followed by a bidirectional long short-term memory (BiLSTM) network to capture the temporal dependencies of the scalograms. The genetic algorithm (GA) is used to optimize feature selection and ensure the selection of the most relevant features to further improve the model’s performance. The optimized features are finally fed into a fully connected (FC) layer of the proposed hybrid model for fault classification. The proposed method achieves an accuracy of 99.6%, significantly outperforming traditional approaches. This method offers a highly accurate and efficient solution for fault detection in milling machines, allowing for more reliable predictive maintenance and operational efficiency in industrial settings. Full article

(This article belongs to the Special Issue Monitoring System for Industry 4.0: AI-Driven, Data Analysis and Health Maintenance)

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20 pages, 2483 KiB

Open AccessArticle

Time Series Forecasting of Thermal Systems Dispatch in Legal Amazon Using Machine Learning

by William Gouvêa Buratto, Rafael Ninno Muniz, Rodolfo Cardoso, Ademir Nied, Carlos Tavares da Costa, Jr. and Gabriel Villarrubia Gonzalez

Appl. Sci. 2024, 14(21), 9806; https://doi.org/10.3390/app14219806 - 27 Oct 2024

Cited by 1 | Viewed by 1025

Abstract

This paper analyzes time series forecasting methods applied to thermal systems in Brazil, specifically focusing on diesel consumption as a key determinant. Recognizing the critical role of thermal systems in ensuring energy stability, especially during low rain seasons, this study employs bagged, boosted, and stacked ensemble learning methods for time series forecasting focusing on exploring consumption patterns and trends. By leveraging historical data, the research aims to predict future diesel consumption within Brazil’s thermal energy sector. Based on the bagged ensemble learning approach a mean absolute percentage error of 0.089% and a coefficient of determination of 0.9752 were achieved (average considering 50 experiments), showing it to be a promising model for the short-time forecasting of thermal dispatch for the electric power generation system. The bagged model results were better than for boosted and stacked ensemble learning methods, long short-term memory networks, and adaptive neuro-fuzzy inference systems. Since the thermal dispatch in Brazil is closely related to energy prices, the predictions presented here are an interesting way of planning and decision-making for energy power systems. Full article

(This article belongs to the Special Issue Monitoring System for Industry 4.0: AI-Driven, Data Analysis and Health Maintenance)

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20 pages, 5266 KiB

Open AccessArticle

Unsupervised Deep Anomaly Detection for Industrial Multivariate Time Series Data

by Wenqiang Liu, Li Yan, Ningning Ma, Gaozhou Wang, Xiaolong Ma, Peishun Liu and Ruichun Tang

Appl. Sci. 2024, 14(2), 774; https://doi.org/10.3390/app14020774 - 16 Jan 2024

Cited by 13 | Viewed by 4476

Abstract

With the rapid development of deep learning, researchers are actively exploring its applications in the field of industrial anomaly detection. Deep learning methods differ significantly from traditional mathematical modeling approaches, eliminating the need for intricate mathematical derivations and offering greater flexibility. Deep learning technologies have demonstrated outstanding performance in anomaly detection problems and gained widespread recognition. However, when dealing with multivariate data anomaly detection problems, deep learning faces challenges such as large-scale data annotation and handling relationships between complex data variables. To address these challenges, this study proposes an innovative and lightweight deep learning model—the Attention-Based Deep Convolutional Autoencoding Prediction Network (AT-DCAEP). The model consists of a characterization network based on convolutional autoencoders and a prediction network based on attention mechanisms. The AT-DCAEP exhibits excellent performance in multivariate time series data anomaly detection without the need for pre-labeling large-scale datasets, making it an efficient unsupervised anomaly detection method. We extensively tested the performance of AT-DCAEP on six publicly available datasets, and the results show that compared to current state-of-the-art methods, AT-DCAEP demonstrates superior performance, achieving the optimal balance between anomaly detection performance and computational cost. Full article

(This article belongs to the Special Issue Monitoring System for Industry 4.0: AI-Driven, Data Analysis and Health Maintenance)

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17 pages, 4763 KiB

Open AccessArticle

Transfer Learning-Based Remaining Useful Life Prediction Method for Lithium-Ion Batteries Considering Individual Differences

by Borui Gu and Zhen Liu

Appl. Sci. 2024, 14(2), 698; https://doi.org/10.3390/app14020698 - 14 Jan 2024

Cited by 5 | Viewed by 2128

Abstract

With the wide utilization of lithium-ion batteries in the fields of electronic devices, electric vehicles, aviation, and aerospace, the prediction of remaining useful life (RUL) for lithium batteries is important. Considering the influence of the environment and manufacturing process, the degradation features differ between the historical batteries and the target ones, and such differences are called individual differences. Currently, lithium battery RUL prediction methods generally use the characteristics of a large group of historical samples to represent the target battery. However, these methods may be vulnerable to individual differences between historical batteries and target ones, which leads to poor accuracy. In order to solve the issue, this paper proposes a prediction method based on transfer learning that fully takes individual differences into consideration. It utilizes an extreme learning machine (ELM) twice. In the first stage, the relationship between the capacity degradation rate and the remaining capacity is constructed by an ELM to obtain the adjusting factor. Then, an ELM-based transfer learning method is used to establish the connection between the remaining capacity and the RUL. Finally, the prediction result is adjusted by the adjusting factor obtained in the first stage. Compared with existing typical data-driven models, the proposed method has better accuracy and efficiency. Full article

(This article belongs to the Special Issue Monitoring System for Industry 4.0: AI-Driven, Data Analysis and Health Maintenance)

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17 pages, 8947 KiB

Open AccessArticle

The Influence of Vertical Seismic Acceleration on the Triggering of Landslides Constrained by Bedding Faults under an Inertial Frame Reference: The Case of the Daguangbao (DGB) Landslide

by Guoping Xiang, Tao Jiang, Qingwen Yang, Shenghua Cui, Ling Zhu, Yuhang He and Huajin Li

Appl. Sci. 2023, 13(23), 12911; https://doi.org/10.3390/app132312911 - 2 Dec 2023

Cited by 3 | Viewed by 1331

Abstract

The Daguangbao (DGB) landslide was the largest landslide that was triggered by the 2008 Wenchuan earthquake with a magnitude of Ms8.0. The sliding surface of this landslide was constrained on a bedding fault 400 m below the ground surface. Seismic records show that the landslide suffered not only from strong horizontal but also vertical ground shaking that was almost equal to the horizontal component. In this study, to reveal the landslide triggering mechanism of the DGB landslide, this study ignores the steep dipping tension fracture section and the leading edge-locking section of the trailing edge of the DGB landslide, and the geological model of the large optical package landslide is generalized into a block model with the bottom controlled slip soft zone as the interface. Based on the improved Newmark method that considers vertical ground motion, the three-way seismic acceleration data and the shear strength parameter of the sliding surface being taken as a variable are used to calculate the cumulative permanent displacement of the slider. Then, by considering the cumulative permanent displacement ratio of vertical seismic acceleration or not and the cumulative permanent displacement ratio value considering the inertial force as the index, the response characteristics of the cumulative permanent displacement of the block-to-vertical ground motion and inertial forces were analyzed. The results show that both the horizontal inertial force and the vertical acceleration significantly increased the permanent displacement. The permanent displacement is 4.9 cm when considering the vertical acceleration, whereas it is only 2.0 cm without taking this into account. The contribution of vertical acceleration is significantly enlarged (87.8–90.7%) by the decreasing of the internal friction angle of the slide surface, while it is less influenced (5–27.4%) by the cohesion. Compared with the lower shear strength parameter of the sliding surface, the contributions of vertical acceleration and inertial force to the permanent displacement are more obvious when the shear strength parameter of the sliding surface is higher. When ϕ > 18°, the D/D* is greater than 1, and the maximum D/D* reaches 7. The fast accumulation event of permanent displacement is triggered in the concentration stage of the seismic energy release. In the DGB landslide area, 50% of the energy is released within 30–50 s, as indicated by the acceleration peaks recorded at the nearest seismic station, Qingping station. It is assumed that the DGB landslide may be triggered at 30–50 s due to half of the seismic energy being released during that time span. Full article

(This article belongs to the Special Issue Monitoring System for Industry 4.0: AI-Driven, Data Analysis and Health Maintenance)

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11 pages, 13525 KiB

Open AccessArticle

Noise-to-Norm Reconstruction for Industrial Anomaly Detection and Localization

by Shiqi Deng, Zhiyu Sun, Ruiyan Zhuang and Jun Gong

Appl. Sci. 2023, 13(22), 12436; https://doi.org/10.3390/app132212436 - 17 Nov 2023

Cited by 4 | Viewed by 1771

Abstract

Anomaly detection has a wide range of applications and is especially important in industrial quality inspection. Currently, many top-performing anomaly detection models rely on feature embedding-based methods. However, these methods do not perform well on datasets with large variations in object locations. Reconstruction-based methods use reconstruction errors to detect anomalies without considering positional differences between samples. In this study, a reconstruction-based method using the noise-to-norm paradigm is proposed, which avoids the invariant reconstruction of anomalous regions. Our reconstruction network is based on M-net and incorporates multiscale fusion and residual attention modules to enable end-to-end anomaly detection and localization. Experiments demonstrate that the method is effective in reconstructing anomalous regions into normal patterns and achieving accurate anomaly detection and localization. On the MPDD and VisA datasets, our proposed method achieved more competitive results than the latest methods, and it set a new state-of-the-art standard on the MPDD dataset. Full article

(This article belongs to the Special Issue Monitoring System for Industry 4.0: AI-Driven, Data Analysis and Health Maintenance)

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14 pages, 6667 KiB

Open AccessArticle

Research on a Ship Mooring Motion Suppression Method Based on an Intelligent Active Anti-Roll Platform

by Feng Gao, Yougang Tang, Chuanqi Hu and Xiaolei Xie

Appl. Sci. 2023, 13(13), 7979; https://doi.org/10.3390/app13137979 - 7 Jul 2023

Cited by 3 | Viewed by 2144

Abstract

Conventional ship mooring in ports has many shortcomings such as a high safety risk, low efficiency and high labor intensity. In order to explore and develop the theory and key technologies of intelligent automatic mooring systems, this paper takes an intelligent mooring system based on a parallel anti-rolling mechanism as the research and development object. A new mooring method integrating ship hydrodynamics, mechanism kinematics and intelligent algorithms is proposed. Through numerical simulation and comparative analysis of the model, the motion inhibition effect of mooring ships under different working conditions is obtained. The results show that the control strategy and intelligent algorithm of the system can realize the active control of the wharf mooring ships and achieve the goal of improving wharf stability conditions through an intelligent mooring system. Full article

(This article belongs to the Special Issue Monitoring System for Industry 4.0: AI-Driven, Data Analysis and Health Maintenance)

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Other

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39 pages, 5394 KiB

Open AccessSystematic Review

Condition Monitoring of Electrical Transformers Using the Internet of Things: A Systematic Literature Review

by Mzamo R. Msane, Bonginkosi A. Thango and Kingsley A. Ogudo

Appl. Sci. 2024, 14(21), 9690; https://doi.org/10.3390/app14219690 - 23 Oct 2024

Viewed by 4042

Abstract

The adoption of Internet of Things (IoT) technology for transformer condition monitoring is increasingly replacing traditional methods. This systematic review aims to evaluate the existing research on IoT frameworks used in transformer condition monitoring, providing insights into their effectiveness and research trends. This review seeks to identify the leading IoT frameworks employed in transformer condition monitoring; analyze the key research objectives, methods, and outcomes; and assess the global research distribution and technological tools used in this field. A systematic literature review was conducted by searching published databases using keywords related to “Internet of Things”, “transformers”, “condition monitoring”, and “fault diagnosis”. The search spanned publications released between 2014 and 2024, yielding 262 articles. Of these, 120 met the predefined review criteria and were included for further analysis. This review found that Arduino boards are the most used microcontrollers for monitoring and analyzing transformer operational parameters, with Arduino IDE 1.8 being the predominant software for programming. The primary research focus in the reviewed literature is the identification of transformer faults. The geographical distribution of research contributions shows that India leads with 65% of the studies, followed by China (11%) and Pakistan (5%). The findings indicate a strong global interest in developing IoT-based transformer condition monitoring systems, particularly in India. This review highlights the potential of IoT technologies to enhance transformer monitoring and diagnostics. The insights gained from this review can guide future research and the development of more advanced IoT frameworks for transformer condition monitoring. Full article

(This article belongs to the Special Issue Monitoring System for Industry 4.0: AI-Driven, Data Analysis and Health Maintenance)

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