Semi-Supervised Learning Approach for Optimizing Condition-based-Maintenance (CBM) Decisions

Automatic classification of construction safety reports using semi-supervised YAKE-Guided LDA approach

In gas metal arc welding (GMAW) processes, including wire arc additive manufacturing (WAAM), machine learning (ML) is emerging as a powerful tool for monitoring both process and product anomalies. However, a significant challenge in real industrial environments is the reliance on large, balanced datasets for training supervised learning models. To address this issue, a shift toward unsupervised learning is gaining attention in this research field, offering the potential to work effectively with small and unbalanced datasets. However, different materials, sensors, and welding technologies have been used in the literature, making complex the comparison of the results. This work fills that gap by presenting a comprehensive comparison of both supervised and unsupervised learning methods. An experimental campaign was conducted on Invar 36 alloy—a material with limited WAAM research—where 15 wall structures were deposited with varying process parameters using the natural dip transfer process, aiming to identify the optimal parameters for this alloy. Data on welding current and voltage were captured, and during the qualification procedure, anomalies were detected, some of which led to product defects. Supervised, unsupervised, and semi-supervised ML approaches, along with a detailed frequency domain analysis of the collected signals, were applied to process the obtained unbalanced dataset. The results provide key insights: while supervised learning models can be applied to anomaly detection in small and unbalanced datasets, they are prone to overfitting, which limits their practical use due to the prevalence of normal cases over anomalies in the dataset, resulting in higher number of missed anomalies. In contrast, unsupervised models, with their lower generalization capability, tend to exhibit higher false alarm rates but better performance to identify anomalous data. This work not only compares in depth these data analytics methodologies but also offers guidance on selecting the appropriate ML algorithm based on specific industrial objectives and provides insights into the printability of Invar 36 for WAAM applications under natural dip transfer process.

Multireference (MR) diagnostics are common tools for identifying strongly correlated electronic structure that makes single-reference (SR) methods (e.g., density functional theory or DFT) insufficient for accurate property prediction. However, MR diagnostics typically require computationally demanding correlated wave function theory (WFT) calculations, and diagnostics often disagree or fail to predict MR effects on properties. To overcome these challenges, we introduce a semi-supervised machine learning (ML) approach with virtual adversarial training (VAT) of an MR classifier using 15 WFT and DFT MR diagnostics as inputs. In semi-supervised learning, only the most extreme SR or MR points are labeled, and the remaining point labels are learned. The resulting VAT model outperforms the alternatives, as quantified by the distinct property distributions of SR- and MR-classified molecules. To reduce the cost of generating inputs to the VAT model, we leverage the VAT model's robustness to noisy inputs by replacing WFT MR diagnostics with regression predictions in an MR decision engine workflow that preserves excellent performance. We demonstrate the transferability of our approach to larger molecules and those with distinct chemical composition from the training set. This MR decision engine demonstrates promise as a low-cost, high-accuracy approach to the automatic detection of strong correlation for predictive high-throughput screening.

Using a semi-supervised machine learning approach we present a real-time anomaly detection system based on an autoencoder used for online data quality monitoring of the CMS electromagnetic calorimeter operating at the CERN LHC. We introduce a novel method that maximizes the anomaly detection performance making use of the time-dependence of anomalies and the spatial variations in the detector response. The autoencoder-based system efficiently detects anomalies in real time and maintains a very low false discovery rate. We validate the performance of this novel system with anomalies from LHC collision data taken in 2018 and 2022. In addition, results are presented after deploying the autoencoder-based system in the CMS online Data Quality Monitoring workflow at the beginning of LHC Run 3 resulting in the system to detect issues that were missed by the existing system.

Blockchain and federated learning-based intrusion detection approaches for edge-enabled industrial IoT networks: a survey

This study uses a wind turbine case study as a subdomain of Industrial Internet of Things (IIoT) to showcase an architecture for implementing a distributed digital twin in which all important aspects of a predictive maintenance solution in a DT use a fog computing paradigm, and the typical predictive maintenance DT is improved to offer better asset utilization and management through real-time condition monitoring, predictive analytics, and health management of selected components of wind turbines in a wind farm. Digital twin (DT) is a technology that sits at the intersection of Internet of Things, Cloud Computing, and Software Engineering to provide a suitable tool for replicating physical objects in the digital space. This can facilitate the implementation of asset management in manufacturing systems through predictive maintenance solutions leveraged by machine learning (ML). With DTs, a solution architecture can easily use data and software to implement asset management solutions such as condition monitoring and predictive maintenance using acquired sensor data from physical objects and computing capabilities in the digital space. While DT offers a good solution, it is an emerging technology that could be improved with better standards, architectural framework, and implementation methodologies. Researchers in both academia and industry have showcased DT implementations with different levels of success. However, DTs remain limited in standards and architectures that offer efficient predictive maintenance solutions with real-time sensor data and intelligent DT capabilities. An appropriate feedback mechanism is also needed to improve asset management operations.

The present study presents an examination of fault detection and classification in manufacturing that is possible due to IoT and Artificial Intelligence in the Industry 4.0 structure. The study is based on IoT and compares three Artificial Intelligence algorithms such as Naive Bayes , Extreme Gradient Boosting , and k-Nearest Neighbors . The results displayed that in ten trials of the experiment achieving the accuracy from 0.82 to 0.89 . The interpretability tool allowed for the understanding of the models’ predictions. The consistent accuracy in both SHAP and LIME results, i.e. 0.80 and 0.89, suggest that both models performed effectively in model explanation. Thus, end users need to combine advanced machine learning algorithms with interpretable explanations to enhance fault detection and classification in manufacturing. This will consequently allow for better production processes and increased innovation in Industry 4.0 manufacturing.

A number of attempts to classify cancer samples using miRNA/gene expression profiles are known in literature. However, semi-supervised learning models have only been recently introduced to exploit the huge unlabeled expression profiles in enhancing sample classification. It is important to combine both miRNA and gene expression sets as that provides more information on the characteristics of cancer samples. The use of both of labeled and unlabeled miRNA and gene expression sets to enhance sample classification has not been explored yet. In this paper, two semi-supervised machine learning approaches, namely self-learning and co-training are adapted to enhance the quality of cancer sample classification. In self-learning, miRNA and gene based classifiers are enhanced independently. While in co-training, both miRNA and gene expression profiles are used simultaneously to provide different views of cancer samples. The approaches were evaluated using breast cancer, hepatocellular carcinoma (HCC) and lung cancer expression sets. Results show up to 20% improvement in F1-measure over Random Forests and SVM classifiers. Co-Training also outperforms Low Density Separation (LDS) approach by around 25% improvement in F1-measure in breast cancer.

The modern developments in the Internet of Things (IoT) have been producing an increasing number of interconnected devices, allowing a variety of elegant applications. These IoT devices generate huge information that require smart data study and processing methods, for instance, deep learning (DL). Remarkably, the DL algorithms, when amalgamated with the industrial IoT (IIoT), can facilitate a mixture of applications, for example, elegant assembling, effective manufacturing, efficient networking, accident identifying, and prevention of errors. Also, IIoT can change the way in which industries use to produce autonomous self-healing machines and improve stocks using machine learning (ML). Alternatively, ML carries hidden imminence in IoT data for quick, automated answers, and enhanced assessment making. ML for IoT can be used to venture future trends, become aware of anomalies, and augment intelligence by consuming images, videos, and audios. Various industries currently use ML in IoT to keep up acceptable standards as well as maintain predictable things in terms of benefits and profits at different times. Banks and other businesses in the financial industry make use of ML technology for two main reasons: to recognise significant imminence in data and to avoid scams. Data mining can also be used to make out clients with high-risk outlines or use cyber-surveillance to locate notifying indications of fraud. IIoT has been a means to digital transformation in manufacturing. It takes up a network of sensors to implement a serious building of data and brings cloud software into action to use this data in expensive approaches about the effectiveness of manufacturing procedures. Machines can gain knowledge of the data and algorithms that are accountable for origin faults in the system to identify the errors before those can do damage. Manufacturers can make use of ML to get better safeguarding processes and facilitate them to construct real-time, smart judgments derived from data. So the main aim of this chapter is to focus on data acquisition, integration, and predictive control in the industry using ML for IIoTs for real-time collection and analysis of thousands of measurement points, an early detection of equipment failures and process disturbances, detection of new types of anomalies as they occur for the first time, and Web-based UI for handling alerts.

Enabled by the increasing availability of sensor data monitored from production machinery, condition monitoring and predictive maintenance methods are key pillars for an efficient and robust manufacturing production cycle in the Industrial Internet of Things. The employment of machine learning models to detect and predict deteriorating behavior by analyzing a variety of data collected across several industrial environments shows promising results in recent works, yet also often requires transferring the sensor data to centralized servers located in the cloud. Moreover, although collaborating and sharing knowledge between industry sites yields large benefits, especially in the area of condition monitoring, it is often prohibited due to data privacy issues. To tackle this situation, we propose an Autoencoder-based Federated Learning method utilizing vibration sensor data from rotating machines, that allows for a distributed training on edge devices, located on-premise and close to the monitored machines. Preserving data privacy and at the same time exonerating possibly unreliable network connections of remote sites, our approach enables knowledge transfer across organizational boundaries, without sharing the monitored data. We conducted an evaluation utilizing two real-world datasets as well as multiple testbeds and the results indicate that our method enables a competitive performance compared to previous results, while significantly reducing the resource and network utilization.

Intelligent data-driven condition monitoring of power electronics systems using smart edge–cloud framework

Industrial Internet of Things (IIoT) networks are very important to modern manufacturing because they allow processes to be monitored, controlled, and improved in real time. IoT systems are linked to each other, which makes them vulnerable to cyberattacks, system breakdowns, and communication problems. This makes them less reliable and secure. To deal with these problems, we need advanced technologies that can find problems before they happen, reduce risks, and keep processes running smoothly. In this paper, we suggest a new way to make IIoT networks safer and more reliable by using machine learning methods together. Our system uses machine learning techniques to look at network traffic trends, spot strange behaviors, and find possible security holes in real time, using the huge amounts of data that IIoT devices produce. Our system can successfully find and stop a wide range of cyberattacks, such as hacking attempts, malware infections, and denial-of-service (DoS) attacks, because it is always learning from past data and changing to new threats. We also use machine learning models to predict when systems might fail or perform worse, so we can do preventative maintenance and keep downtime to a minimum.To prove that our method worked, we did a lot of tests using real-world IIoT datasets and checked how well our system worked by looking at how accurate it was at finding things, how often it gave false positives, and how fast it responded. These results show that our approach based on machine learning makes IIoT networks much safer and more reliable than standard rule-based approaches. In addition, our framework is strong against new and unknown threats, which shows that it could be used in a wide range of business settings.Overall, the paper research shows that machine learning has a lot of potential to make IIoT networks more reliable and to make sure that processes in industrial settings run smoothly and safely

Network Traffic Classification is the crucial phase of network monitoring. The network traffic once categorized per application it can be imposed with appropriate security policies to improve the performance of the network. The port and payload based traffic classification techniques used in the past decade relapsed owing to new techniques of encryption and tunneling emerging day-by-day. Recently, the statistical classification employing data mining techniques and analyzing the attributes for characterization of network traffic proved high efficiency. This proposed work characterized the network traffic based on semi-supervised machine learning approach. It initially clustered the auxiliary and content flow of application to precisely track the footprint of the application using improved k-means clustering which in turn is fed into C5.0 classifier to construct the classifier model. This system is designed to classify the network traffic accurately that resulted in high F-Score value of 0.993.

Ontology learning (OL) arises as an area to support semantic engineering because it enables to recover and to extract knowledge from the Web documents to improve the development of domain ontologies. One of the most fruitful fields of OL is artificial intelligence (AI), since it sustains new methods, techniques and tools, particularly related with Web- and text-mining. In this work, we are dealing with a meta-model incrementally developed, proposing an OL experiment with open source tools. To reach that, firstly, ontology about a university institution previously developed is increasable synthesized. Secondly, a particular methodology and strategy for OL is used to illustrate how could be included these text-mining and OL tools into a kind of (semi-) intelligent-agent, and how the overall ontological development process could be improved. Particularly, the OL "agent" test tool is applied to update/extend the university ontology by a semi-supervised machine learning approach.

Condition monitoring has come to the forefront of intelligent manufacturing and is particularly important in Computer Numerical Control (CNC) machining processes, where reliability, precision, and productivity are crucial. The traditional methods of monitoring, which are mostly premised on single sensors, the localized capture of data, and offline interpretation, are proving too small to handle current machining processes. Being limited in their scale, having limited computational power, and not being responsive in real-time, they do not fit well in a dynamic and data-intensive production environment. Recent progress in the Industrial Internet of Things (IIoT), cloud computing, and edge intelligence has led to a push into distributed monitoring architectures capable of obtaining, processing, and interpreting large amounts of heterogeneous machining data. Such innovations have facilitated more adaptive decision-making approaches, which have helped in supporting predictive maintenance, enhancing machining stability, tool lifespan, and data-driven optimization in manufacturing businesses. A structured literature search was conducted across major scientific databases, and eligible studies were synthesized qualitatively. This systematic review synthesizes over 180 peer-reviewed studies found in major scientific databases, using specific inclusion criteria and a PRISMA-guided screening process. It provides a comprehensive look at sensor technologies, data acquisition systems, cloud–edge–IoT frameworks, and digital twin implementations from an architectural perspective. At the same time, it identifies ongoing challenges related to industrial scalability, standardization, and the maturity of deployment. The combination of cloud platforms and edge intelligence is of particular interest, with emphasis placed on how the two ensure a balance in the computational load and latency, and improve system reliability. The review is a synthesis of the major advances associated with sensor technologies, data collection approaches, machine operations, machine learning, deep learning methods, and digital twins. The paper concludes with what can and cannot be performed to date by providing a comparative analysis of what is known about this topic and the reported industrial case applications. The main issues, such as the inconsistency of data, the lack of standardization, cyber threats, and old system integration, are critically analyzed. Lastly, new research directions are touched upon, including hybrid cloud–edge intelligence, advanced AI models, and adaptive multisensory fusion, which is oriented to autonomous and self-evolving CNC monitoring systems in line with the Industry 4.0 and Industry 5.0 paradigms. The review process was made transparent and repeatable by using a PRISMA-guided approach to qualitative synthesis and literature screening.