Industrial Monitoring Research Articles

An Accuracy-Aware Energy-Efficient Multipath Routing Algorithm for WSNs.

In the fields of industrial production or safety monitoring, wireless sensor networks are often content with unreliable and time-varying channels that are susceptible to interference. Consequently, ensuring both transmission reliability and data accuracy has garnered substantial attention in recent years. Although multipath routing-based schemes can provide transmission reliability for wireless sensor networks, achieving high data accuracy simultaneously remains challenging. To address this issue, an Energy-efficient Multipath Routing algorithm balancing data Accuracy and transmission Reliability (EMRAR) is proposed to balance the reliability and accuracy of data transmission. The multipath routing problem is formulated into a multi-objective programming problem aimed at optimizing both reliability and power consumption while adhering to data accuracy constraints. To obtain the solution of the multi-objective programming, an adaptive artificial immune algorithm is employed, in which the antibody initialization method, antibody incentive calculation method, and immune operation are improved, especially for the multipath routing scheme. Simulation results show that the EMRAR algorithm effectively balances data accuracy and transmission reliability while also saving energy when compared to existing algorithms.

PENERAPAN METODE NAÏVE BAYES DALAM KLASIFIKASI KESEGARAN IKAN BERDASARKAN WARNA PADA CITRA AREA MATA

As a maritime nation, fish is a staple in the Indonesian diet, rich in nutrition and a crucial protein source. It is imperative to maintain the freshness of fish to ensure the quality of fish production. However, the practice of mixing fresh and non-fresh fish poses a serious threat to consumer health and diminishes the overall quality of fish production. Therefore, the development of an automated and efficient method is necessary to distinguish between fresh and non-fresh fish. This research proposes the application of the Naïve Bayes method in classifying fish freshness based on color analysis in the eye area image. This approach involves the extraction of entropy features after segmenting fish images using the RGB and YCbCr color models. A total of 40 datasets of fish eye images were used for training and testing the model. The research results indicate that the proposed classification method achieved an accuracy rate of 97.5%. This success signifies the potential of the color analysis method and entropy features in distinguishing levels of fish freshness. These findings contribute to the development of automated techniques for monitoring and processing fish quality in the fisheries industry.

Design and Development of Industrial Practice Monitoring and Assessment Systems using Tsukamoto Fuzzy Logic

Vocational high schools are given flexibility for their students to carry out direct learning in the industry as part of the practical education activities of implementing student skills. The implementation of industrial practice requires a special way to find out and monitor each student's activities so that the achievements of the implementation of industrial practice can be carried out properly. The implementation of industrial work practice assessment has several assessment criteria. These criteria include attendance, neatness, attitude, skills, and knowledge. The problems found in the assessment system are still done manually so that the effectiveness is minimal. This study aims to create a system that can monitor and assess the implementation of industrial practices. The system developed will be tested as a medium for monitoring and assessing industrial practices. This research uses Fuzzy Tsukamoto’s logic approach as a scoring logic model and uses the waterfall method as a development model consisting of analysis, design, coding, and testing. The results of the research conducted resulted in a system that can monitor and assess the implementation of industry practices. The test was carried out by 24 people consisting of guidance teachers and students. Testing is done by testing aspects of functionality and aspects of usability. Based on the test results, the functionality aspect scored 100% (very feasible) and the usage aspect got a score of 84.8% (very feasible)

A review on flexible wearables – Recent developments in non-invasive continuous health monitoring

Wearable sensors hold profound significance in our increasingly interconnected world, revolutionizing the way we monitor and manage our health, daily activities, and environments. These compact, unobtrusive tools have transcended the realm of mere gadgets to become indispensable tools for individuals and healthcare professionals alike. By continuously collecting data on vital signs, physical activity, sleep patterns, and more, wearables empower users to gain valuable insights into their well-being, facilitating proactive health management. Beyond preventive medicine, wearables find applications in diverse fields, from sports performance optimization to industrial safety monitoring, making them catalysts for transformative change in the way we live, work, and thrive. As technology continues to advance, the significance of wearables in enhancing our lives and pushing the boundaries of knowledge will only continue to grow. In this review, we focused on the recent advances in the field of wearables for continuous non-invasive monitoring of cardiovascular, body temperature and blood glucose. These sensors are commonly incorporated into garments or stylish accessories such as watches, allowing individuals to monitor different facets of their health, including metabolic processes and vital signs. In premise, the worldwide wearables market is undergoing substantial growth propelled by technological advancements, expanding applications, and the need for inventive preventive healthcare solutions. Despite existing challenges, the industry is well-positioned for ongoing expansion as it tackles these issues and pushes the frontiers of wearable technology.

Efficient Control and Automation: Exploring Siemens LOGO PLC and PLC-Based Industrial Timer Controllers

Siemens LOGO PLC is a compact system for small automation projects, programmed via LOGO Soft Comfort. Its graphical interface enables easy circuit program creation, facilitating customized control systems swiftly. LOGO Soft Comfort allows offline and online testing, simulation, and program archiving. PLC timers, integral to the system, regulate input and output signals, functioning like relay timers without physical presence or wiring. This paper delves into PLC-based industrial timer controllers, designed to supersede traditional hard-wired relay and timer logic systems. Common PLC timers include Pulse, On-delay, Retentive On-delay, and Off-delay timers, pivotal for time delays and production monitoring in industries. They facilitate actions like turning machines off after set periods or initiating them after specific delays upon sensor activation or button press

Material management in automobile industry: Era 4.0

Nowadays, a lot of places worldwide are using Industry 4.0 technology. Smart manufacturing, supply chains, warehousing, logistics, and materials are all developed with the usage of these technologies, especially in automobile sector. It can potentially resolve complex production issues from a commercial perspective. Industry 4.0 technologies that are available for enhancing manufacturing procedures and material quality of automobile industry are thoroughly examined in this article. In the upcoming era of vehicles, we have enumerated the several technologies that are currently available, together with their attributes and advantages for efficient management. Automobile businesses may optimise their inventory and material waste with the use of digital technologies, which lowers costs. When necessary, employees who are located on different sites can work together with ease due to remote collaboration. By helping them stay current with knowledge, these tools eventually close communication barriers. Proper equipment monitoring in industries utilising Industry 4.0 technology will be aided by smart predictive analysis. Early detection and resolution of material defects is possible with the help of these advance technologies. Additionally, this meets the customer's expectations and offers transparency in the systems and procedures.

Metric Learning-Based Fault Diagnosis and Anomaly Detection for Industrial Data With Intraclass Variance.

Industrial system monitoring includes fault diagnosis and anomaly detection, which have received extensive attention, since they can recognize the fault types and detect unknown anomalies. However, a separate fault diagnosis method or anomaly detection method cannot identify unknown faults and distinguish between different fault types simultaneously; thus, it is difficult to meet the increasing demand for safety and reliability of industrial systems. Besides, the actual system often operates in varying working conditions and is disturbed by the noise, which results in the intraclass variance of the raw data and degrades the performance of industrial system monitoring. To solve these problems, a metric learning-based fault diagnosis and anomaly detection method is proposed. Fault diagnosis and anomaly detection are adaptively fused in the proposed end-to-end model, where anomaly detection can prevent the model from misjudging the unknown anomaly as the known type, while fault diagnosis can identify the specific type of system fault. In addition, a novel multicenter loss is introduced to restrain the intraclass variance. Compared with manual feature extraction that can only extract suboptimal features, it can learn discriminant features automatically for both fault diagnosis and anomaly detection tasks. Experiments on three-phase flow (TPF) facility and Case Western Reserve University (CWRU) bearing have demonstrated that the proposed method can avoid the interference of intraclass variances and learn features that are effective for identifying tasks. Moreover, it achieves the best performance in both fault diagnosis and anomaly detection.

Industry Monitoring System Using IoT through Interdisciplinary Approaches

Now-a-days in dynamic industrial landscape, the need for real-time monitoring, data-driven decision making, and operational optimization has become paramount. The Industry Monitoring System using IoT emerges as a transformative solution that leverages the capabilities of interconnected devices and sensors to provide comprehensive visibility into industrial processes and assets. This innovative system deploys a network of sensors and IoT devices strategically with in industrial facilities to capture critical data parameters, ranging from environmental conditions to equipment performance metrics. These sensors communicate seamlessly through wired or wireless connections, transmitting data to a centralized hub or cloud-based platform. This proactive approach not only enhances equipment reliability but also reduces downtime and maintenance costs. Furthermore, the integration of IoT in industry monitoring facilitates data-driven decision-making, fostering a more agile and responsive industrial ecosystem.

Preparation of carbon quantum dots and their application in the detection of vitamin B2.

A novel metal-doped carbon quantum dot, zinc-chlorine co-doped carbon quantum dots (Zn/Cl-CQDs), has been developed for the fluorescent probe detection of vitamin B2 and the analysis of the correlation properties of this carbon quantum dot and vitamin B2. Stability experiments demonstrate that Zn/Cl-CQDs possess good fluorescence properties under alkaline conditions. However, when vitamin B2 is added into Zn/Cl-CQDs, the fluorescence intensity decreases sharply, indicating that the fluorescence sensor shows rapid and sensitive detection of vitamin B2 under the static quenching. Lastly, the use of Zn/Cl-CQDs in the detection of vitamin B2 tablets and vitamin B2-rich fruits resulted in recovery rates of 98.2% and 100.6%, respectively. Therefore, this method can be well applied to the detection and analysis of vitamin B2, and has great development prospects in the pharmaceutical industry and food monitoring fields.

Performance Analysis of Deep Learning Algorithms Implemented Using PyTorch in Image Recognition

With the rapid development of artificial intelligence technology, image recognition has become a key technology in many fields. This paperaims to analyze the performance of image recognition tasks through the implementation of deep learning algorithms using PyTorch. This paperintroduces the selection and preprocessing techniques of datasets, including normalization, data augmentation, size adjustment, and batch processing, which provide high-quality input data for model training. In terms of deep learning model design, Convolutional Neural Network was adopted, CNN was used as the infrastructure and the cross entropy loss function and Adam optimizer were selected for model training. The implementation details of PyTorch demonstrate the model construction, training process, and debugging, as well as how to use PyTorch's DataLoader class to improve data loading efficiency. The Results and Discussion section presents the training results of the model, where the CNN model achieves the highest accuracy of 99% in image recognition tasks, and the loss function value ultimately stabilizes at 0.02, indicating that the model has efficient learning and generalization capabilities. Performance comparison experiments show that, the CNN implemented by PyTorch outperforms MXNet and Caffe in terms of frame rate and processing delay, with a frame rate between 51FPS-79FPS and a minimum processing delay of 308ms. The analysis of practical application scenarios further explores the potential application of the model in fields such as medical image analysis, autonomous driving, retail industry, and safety monitoring, and points out challenges such as data privacy, model generalization ability, and computational resource limitations.

Transforming Time Petri Nets into Heterogeneous Petri Nets for Hybrid System Monitoring

Increasing industrial system complexity challenges maintenance and health monitoring, that involves both diagnosis and prognosis tasks. While formalisms like Time Petri Nets (TPN) simplify the explicit expression of temporal uncertainties, Heterogeneous Petri Nets (HtPN) offer a formalism for hybrid system health monitoring that lacks explicit time representation. This article demonstrates HtPN’s ability to explicitly represent temporal information. Key contributions include a lossless transformation from a subclass of the TPN to a subclass of the HtPN and the provision of a Python code for conversion. This transformation enables simulation, diagnosis, and prognosis with explicit time representation.

Remote training platform for industrial control automation

Industrial automation environments are undergoing profound changes due to the high level of digitization in the industry. This means that teaching methods and software tools need to be updated. Teaching automation and control concepts to students involves the use of many different software tools that are not easily accessible or configurable. In addition, the new teaching methods lead to delocalized and remote teaching. Therefore, this paper proposes the development of a remote access platform that uses pre-configured virtual machines with all the necessary software for teaching industrial automation, control and monitoring concepts. This platform allows students not only to access a real physical system and work remotely with all the software already configured, but also to download the virtual machine to run it on their own computers and easily switch between a connection to the real system and a realistic simulation of it. The platform also allows the management of different hands-on tasks for subjects of Master's degree related to industrial process automation.

Notice of Violation of IEEE Publication Principles: Fault Detection Using Industrial Video Information

Notice of Violation of IEEE Publication Principles Fault Detection Using Industrial Video Information by Y. Zhang, Z. Wang, Y. Fu and X. Li, in the IEEE Transactions on Industrial Electronics, vol. PP, no. 99 After careful and considered review of the content of this paper and associated multimedia content by a duly constituted expert committee, this work has been found to be in violation of IEEE's Publication Principles. This paper and associated multimedia files contain fraudulent data and images. Due to the nature of this violation, reasonable effort should be made to remove all past references to this paper and refrain from future references. In this study, a new data-modelling algorithm, referred to as relaxed independent component analysis, is proposed for the purpose of industrial video process monitoring. The contributions are as follows. (1) Physical variables and image data are unified in a framework to propose fault detection method of industrial process for the first time. (2) We find that independent components are sensitive to extract features of image and physical variables. In original KICA method, KPCA+ICA are used to extract nonlinear independent components. Since ICA is linear method, the original method only extracts semi-nonlinear independent components. In our method, the complete nonlinear independent components are extracted using the independence criterion. (3) A process monitoring approach of relaxed independent component analysis is presented. When applied to the monitoring of an electrical fused magnesia furnace, it can effectively improve the accuracy of forecasting the non-Gaussian process and reduce the ratio of false alarms compared to kernel principle component analysis and traditional kernel independent component analysis.

Federated Episodic Learning to Extrapolate Unseen from Seen Conditions for Industrial IoT Monitoring

Federated Episodic Learning to Extrapolate Unseen from Seen Conditions for Industrial IoT Monitoring

A Novel Hybrid Acquisition System for Industrial Condition Monitoring and Predictive Maintenance

A Novel Hybrid Acquisition System for Industrial Condition Monitoring and Predictive Maintenance

IAR-Net: A Human–Object Context Guided Action Recognition Network for Industrial Environment Monitoring

IAR-Net: A Human–Object Context Guided Action Recognition Network for Industrial Environment Monitoring

A Comprehensive Investigation of Anomaly Detection Methods in Deep Learning and Machine Learning: 2019–2023

Almost 85% of companies polled said they were looking into anomaly detection (AD) technologies for their industrial image anomalies. The present problem concerns detecting anomalies often occupied by redundant data. It can be either in images or in videos. Finding a correct pattern is a challenging task. AD is crucial for various applications, including network security, fraud detection, predictive maintenance, fault diagnosis, and industrial and healthcare monitoring. Many researchers have proposed numerous methods and worked in the area of AD. Multiple anomalies and considerable intraclass variation make industrial datasets tough. Further, research is needed to create robust, efficient techniques that generalize datasets and detect anomalies in complex industrial images. The outcome of this study focuses on various AD methods from 2019 to 2023. These techniques are categorized further into machine learning (ML), deep learning (DL), and federated learning (FL). It explores AD approaches, datasets, technologies, complexities, and obstacles, emphasizing the requirement for effective detection across domains. It explores the results achieved in various ML, DL, and FL AD methods, which helps researchers explore these techniques further. Future research directions include improving model performance, leveraging multiple validation techniques, optimizing resource utilization, generating high‐quality datasets, and focusing on real‐world applications. The paper addresses the changing environment of AD methods and emphasizes the importance of continuing research and innovation. Each ML and DL AD model has strengths and shortcomings, concentrating on accuracy and performance while applying quality parameters for evaluation. FL provides a collaborative way to improve AD using distributed data sources and data privacy.

Variational Discriminative Stacked Auto-Encoder: Feature Representation Using a Prelearned Discriminator, and Its Application to Industrial Process Monitoring.

In deep-learning-based process monitoring, obtaining an effective feature representation is a critical step in constructing a reliable deep-learning monitoring model. Conventional deep-learning methods like stacked auto-encoders (SAEs) capture feature representation by minimizing the data reconstruction errors, which lack the expression of essential information and ultimately lead to degradation of the monitoring performance. To solve this problem, variational discriminative SAE (VDSAE) is proposed in this article. First, a variational generative discriminative structure is designed to obtain a reliable prelearned discriminator. Based on this new variational discriminator, the authenticity of the reconstructed data is evaluated as an important criterion for feature learning. Then, an SAE incorporating the prelearned discriminator is trained by both minimizing the reconstruction error and maximizing the data authenticity. In this way, the prelearned discriminator makes the network effectively capture the essential expression of the reconstructed data. The proposed approach enables SAE to learn a better feature representation owing to the excellent reconstruction performance. Finally, the feature representation and fault detection performance of VDSAE are verified in two cases. The results show that the average fault detection rates (FDRs) of the multiphase flow facility and the waste-water treatment process (WWTP) can be improved to 72% and 97%, respectively, compared with the other fault detection methods.

An Intelligent Industrial Visual Monitoring and Maintenance Framework Empowered by Large-Scale Visual and Language Models

An Intelligent Industrial Visual Monitoring and Maintenance Framework Empowered by Large-Scale Visual and Language Models

Photoelectric sensors constructed based on nanomaterials and their applications

Photoelectric sensors are electronic components that use the photoelectric effect to convert optical signals into electrical signals and are widely used in industrial automation, environmental monitoring, medical devices, and other fields. With continuous progress in fields such as information technology, material science, and manufacturing technology, optoelectronic sensor technology has developed rapidly. Nanomaterials have broad application prospects in optoelectronic sensors due to their unique properties. On the one hand, nanomaterials can enhance the electromagnetic field of light, leading to charge separation and enhanced electron transport, thereby achieving higher photoelectric conversion efficiency. On the other hand, nanomaterials have a larger specific surface area and higher light absorption and photosensitivity. In addition, due to the small size of nanomaterials, their surface energy has a greater impact and their response to the environment is more sensitive. This makes optoelectronic sensors based on nanomaterials a current research hotspot. Therefore, this article mainly introduces photoelectric sensors constructed using nanomaterials and their applications. This article first introduces various optoelectronic sensors made of nanomaterials, including inorganic semiconductors, organic photovoltaic materials, and two-dimensional materials. In addition, its applications in food safety, environmental monitoring, and medical fields were emphasized. It has reference significance for the development of future photoelectric sensors.