Search Results (41)

The integration of machine learning (ML) into industrial automation is fundamentally reshaping how manufacturing systems are monitored, inspected, and optimized. By applying machine learning to real-time sensor data and operational histories, advanced models enable proactive fault prediction, intelligent inspection, and dynamic process control—directly enhancing system reliability, product quality, and efficiency. This review explores the transformative role of ML across three key domains: Predictive Maintenance (PdM), Quality Control (QC), and Process Optimization (PO). It also analyzes how Digital Twin (DT) and Edge AI technologies are expanding the practical impact of ML in these areas. Our analysis reveals a marked rise in deep learning, especially convolutional and recurrent architectures, with a growing shift toward real-time, edge-based deployment. The paper also catalogs the datasets used, the tools and sensors employed for data collection, and the industrial software platforms supporting ML deployment in practice. This review not only maps the current research terrain but also highlights emerging opportunities in self-learning systems, federated architectures, explainable AI, and themes such as self-adaptive control, collaborative intelligence, and autonomous defect diagnosis—indicating that ML is poised to become deeply embedded across the full spectrum of industrial operations in the coming years. Full article

Tracing the root cause of defective products in liquid storage tank (LST) production poses a formidable challenge due to the complex dependencies between production and inspection processes. With associated coupling existing among multiple production processes, and the correspondence between the faults in production processes and inspection links being non-unique, these faults are usually difficult to be directly located via a single inspection process. In this paper, the problem of tracing the root cause of defective LST products, which is caused by process parameter deviations or human operation errors during production, is studied. A root cause tracing method that is based on the dynamic multi-fault diagnosis (DMFD) framework is proposed. First, a factorial hidden Markov model (FHMM) is established to depict the state transition process of the LST product, where its status changes over time and across production processes. This is achieved by considering the product state at each production process as a hidden state and the outcomes of each inspection process as an observation state. Then, the Viterbi algorithm is employed to solve the hidden state transition matrix and diagnostic matrix within the framework of the FHMM. Finally, experimental verification is carried out on a real LST assembly production line, and the influence of imperfect testing on the model accuracy is also considered. The experiment is carried out on an LST assembly line that encompasses three discrete links, including the welding of the upper and lower bodies, the installation of check valves, and the installation of sensors. Experimental results demonstrate that the proposed method achieves significantly more superior performance when compared to existing algorithms. Full article

Modern steel manufacturing processes demand rigorous quality control to rapidly and accurately detect and classify defects in steel plates. In this work, we propose an enhanced three-stage cluster-then-classify method (ETSCCM) that merges clustering-based data partitioning with strategic feature subset selection and refined hyperparameter tuning. Initially, the appropriate number of clusters is determined by combining K-means with hierarchical clustering, ensuring a more precise segmentation of the Steel Plates Fault dataset. Concurrently, various correlated feature subsets are assessed to identify those that maximize classification performance. The best-performing scenario is then used in conjunction with the most effective classifier, identified through comparative analyses involving widely adopted algorithms. Experimental outcomes on real-world fault data, as well as additional publicly available datasets, indicate that our approach can achieve a significant increase in prediction accuracy compared to conventional methods. This study introduces a new method by jointly refining cluster assignments and classification parameters through scenario-based feature subsets, going beyond single-stage methods in enhancing detection accuracy. Through this multi-stage process, pivotal data relationships are uncovered, resulting in a robust, adaptable framework that advances industrial fault diagnosis. Full article

Steam traps are essential for industrial systems, ensuring steam quality and energy efficiency by removing condensate and preventing steam leakage. However, their failure results in energy loss, operational disruptions, and increased greenhouse gas emissions. This paper proposes a novel predictive maintenance system for steam traps that integrates statistical time series features and transformer encoder–decoder models for fault diagnosis and visualization. The proposed system combines IoT sensor data, operational parameters, open data (e.g., weather information and public holiday calendars), machine learning, and two-dimensional diagnostic projection to improve reliability and interpretability. Experiments were conducted in two industrial plants: an aluminum processing plant and a food manufacturing plant, and the system achieved superior defect detection accuracy and diagnostic reliability compared to existing methods. The transformer-based model outperformed traditional methods, including random forest, gradient boosting, and variational autoencoder, in classification and clustering. The system also demonstrated an average 6.92% reduction in thermal energy across both sites, highlighting its potential to improve energy efficiency and reduce carbon emissions. This research highlights the transformative impact of AI-based predictive maintenance technologies in industrial operations and provides a framework for sustainable manufacturing practices. Full article

Many industrial processes, from manufacturing to food processing, incorporate rotating elements as principal components in their production chain. Failure of these components often leads to costly downtime and potential safety risks, further emphasizing the importance of monitoring their health state. Vibration signal analysis is now a common approach for this purpose, as it provides useful information related to the dynamic behavior of machines. This research aimed to conduct a comprehensive examination of the current methodologies employed in the stages of vibration signal analysis, which encompass preprocessing, processing, and post-processing phases, ultimately leading to the application of Artificial Intelligence-based diagnostics and prognostics. An extensive search was conducted in various databases, including ScienceDirect, IEEE, MDPI, Springer, and Google Scholar, from 2020 to early 2024 following the PRISMA guidelines. Articles that aligned with at least one of the targeted topics cited above and provided unique methods and explicit results qualified for retention, while those that were redundant or did not meet the established inclusion criteria were excluded. Subsequently, 270 articles were selected from an initial pool of 338. The review results highlighted several deficiencies in the preprocessing step and the experimental validation, with implementation rates of 15.41% and 10.15%, respectively, in the selected prototype studies. Examination of the processing phase revealed that time scale decomposition methods have become essential for accurate analysis of vibration signals, as they facilitate the extraction of complex information that remains obscured in the original, undecomposed signals. Combining such methods with time–frequency analysis methods was shown to be an ideal combination for information extraction. In the context of fault detection, support vector machines (SVMs), convolutional neural networks (CNNs), Long Short-Term Memory (LSTM) networks, k-nearest neighbors (KNN), and random forests have been identified as the five most frequently employed algorithms. Meanwhile, transformer-based models are emerging as a promising venue for the prediction of RUL values, along with data transformation. Given the conclusions drawn, future researchers are urged to investigate the interpretability and integration of the diagnosis and prognosis models developed with the aim of applying them in real-time industrial contexts. Furthermore, there is a need for experimental studies to disclose the preprocessing details for datasets and the operational conditions of the machinery, thereby improving the data reproducibility. Another area that warrants further investigation is differentiation of the various types of fault information present in vibration signals obtained from bearings, as the defect information from the overall system is embedded within these signals. Full article

The manufacturing industry has been operating within a constantly evolving technological environment, underscoring the importance of maintaining the efficiency and reliability of manufacturing processes. Motor-related failures, especially bearing defects, are common and serious issues in manufacturing processes. Bearings provide accurate and smooth movements and play essential roles in mechanical equipment with shafts. Given their importance, bearing failure diagnosis has been extensively studied. However, the imbalance in failure data and the complexity of time series data make diagnosis challenging. Conventional AI models (convolutional neural networks (CNNs), long short-term memory (LSTM), support vector machine (SVM), and extreme gradient boosting (XGBoost)) face limitations in diagnosing such failures. To address this problem, this paper proposes a bearing failure diagnosis model using a graph convolution network (GCN)-based LSTM autoencoder with self-attention. The model was trained on data extracted from the Case Western Reserve University (CWRU) dataset and a fault simulator testbed. The proposed model achieved 97.3% accuracy on the CWRU dataset and 99.9% accuracy on the fault simulator dataset. Full article

The effective and comprehensive utilization of forest resources has become the theme of the global “dual-carbon strategy”. Forestry restructured wood is a kind of wood-based panel made of wood-based fiber composite material by high-temperature and high-pressure restructuring–molding, and has become an important material in the field of construction, furniture manufacturing, as well as derivative processing for its excellent physical and mechanical properties, decorative properties, and processing performance. Taking Medium Density Fiberboard (MDF) as the recombinant material as the research object, an event-triggered synergetic control mechanism based on interventional three-way decision making is proposed for the viscoelastic multi-field coupling-distributed agile control of the “fixed thickness section” in the MDF continuous flat-pressing process, where some typical quality control problems of complex plate shape deviations including thickness, slope, depression, and bump tend to occur. Firstly, the idea of constructing the industrial event information of continuous hot pressing based on information granulation is proposed, and the information granulation model of the viscoelastic plate shape process mechanism is established by combining the multi-field coupling effect. Secondly, an FMEA-based cyber granular method for diagnosing and controlling the plate thickness diagnosis and control failure information expression of continuous flat pressing is proposed for the problems of plate thickness control failure and plate thickness deviation defect elimination that are prone to occur in the continuous flat-pressing process. The precise control of the plate thickness in the production process is realized based on event-triggered control to achieve the intelligent identification and processing of the various types of faults. The application test is conducted in the international mainstream production line of a certain type of continuous hot-pressing equipment for the production of 18 mm plate thickness; the synergistic effect is basically synchronized after 3 s, the control accuracy reaches 30%, and the average value of the internal bond strength is 1.40, which ensures the integrity of the slab. Practical tests show that the method in the actual production is feasible and effective, with detection and control accuracy of up to ±0.05 mm, indicating that in the production of E0- and E1-level products, the rate of superior products can reach more than 95%. Full article

Data-based equipment fault detection and diagnosis is an important research area in the smart factory era, which began with the Fourth Industrial Revolution. Steel manufacturing is a typical processing industry, and efficient equipment operation can improve product quality and cost. Steel production systems require precise control of the equipment, which is a complex process. A gearbox transmits power between shafts and is an essential piece of mechanical equipment. A gearbox malfunction can cause serious problems not only in production, quality, and delivery but in safety. Many researchers are developing methods for monitoring gearbox condition and for diagnosing failures in order to resolve problems. In most data-driven methods, the analysis data set is derived from a distribution of identical data with failure mode labels. Industrial sites, however, often collect data without information on the failure type or failure status due to varying operating conditions and periodic repair. Therefore, the data sets not only include frequent false alarms, but they cannot explain the causes of the alarms. In this paper, a framework called the Reduced Lagrange Method (R-LM) periodically assigns pseudolabels to vibration signals collected without labels and creates an input data set. In order to monitor the status of equipment and to diagnose failures, the input data set is fed into a supervised learning classifier. To verify the proposed method, we build a test rig using motors and gearboxes that are used on production sites in order to artificially simulate defects in the gears and to operate them to collect vibration data. Data features are extracted from the frequency domain and time domain, and pseudolabeling is applied. There were fewer false alarms when applying R-LM, and it was possible to explain which features were responsible for equipment status changes, which improved field applicability. It was possible to detect changes in equipment conditions before a catastrophic failure, thus providing meaningful alarm and warning information, as well as further promising research topics. Full article

The study introduces an Intelligent Diagnosis Framework (IDF) optimized using the Grasshopper Optimization Algorithm (GOA), an advanced swarm intelligence method, to enhance the precision of bearing defect diagnosis in electrical machinery. This area is vital for the energy sector and IoT manufacturing, but the evolving designs of electric motors add complexity to fault identification. Machine learning offers potential solutions but faces challenges due to computational intensity and the need for fine-tuning hyperparameters. The optimized framework, named GOA-IDF, is rigorously tested using experimental bearing fault data from the CWRU database, focusing on the 12,000 drive end and fan end datasets. Compared to existing machine learning algorithms, GOA-IDF shows superior diagnostic capabilities, especially in processing high-frequency data that are susceptible to noise interference. This research confirms that GOA-IDF excels in accurately categorizing faults and operates with increased computational efficiency. This advancement is a significant contribution to fault diagnosis in electrical motors. It suggests that integrating intelligent frameworks with meta-heuristic optimization techniques can greatly improve the standards of health monitoring and maintenance in the electrical machinery domain. Full article

To address the challenges in real-time process diagnosis within the semiconductor manufacturing industry, this paper presents a novel machine learning approach for analyzing the time-varying 10th harmonics during the deposition of low-k oxide (SiOF) on a 600 Å undoped silicate glass thin liner using a high-density plasma chemical vapor deposition system. The 10th harmonics, which are high-frequency components 10 times the fundamental frequency, are generated in the plasma sheath because of their nonlinear nature. An artificial neural network with a three-hidden-layer architecture was applied and optimized using k-fold cross-validation to analyze the harmonics generated in the plasma sheath during the deposition process. The model exhibited a binary cross-entropy loss of 0.1277 and achieved an accuracy of 0.9461. This approach enables the accurate prediction of process performance, resulting in significant cost reduction and enhancement of semiconductor manufacturing processes. This model has the potential to improve defect control and yield, thereby benefiting the semiconductor industry. Despite the limitations imposed by the limited dataset, the model demonstrated promising results, and further performance improvements are anticipated with the inclusion of additional data in future studies. Full article

Our objective is to present an exceptional case of a patient diagnosed with Paget’s disease of the bone (PDB) while being confirmed with Lynch syndrome (LS). A 44-year-old woman was admitted for progressive pain in the left forearm 2 years ago, and was partially relieved since admission by non-steroidal anti-inflammatory drugs. Suggestive imaging findings and increased blood bone turnover markers helped the diagnosis of PDB. She was offered zoledronate 5 mg. She had two more episodes of relapse, and a decision of new medication was taken within the following years (a second dose of zoledronate, as well as denosumab 60 mg). Her family history showed PDB (mother) and colorectal cancer (father). Whole exome sequencing was performed according to the manufacturer’s standard procedure (Ion AmpliSeq™ Exome RDY S5 Kit). A heterozygous pathogenic variant in the SQSTM1 gene (c.1175C>T, p.Pro392Leu) was confirmed, consistent with the diagnosis of PDB. Additionally, a heterozygous pathogenic variant of MSH2 gene (c.2634+1G>T) was associated with LS. The patient’s first-degree relatives (her brother, one of her two sisters, and her only daughter) underwent specific genetic screening and found negative results, except for her daughter, who tested positive for both pathogenic variants while being clinically asymptomatic. The phenotype influence of either mutation is still an open issue. To our current knowledge, no similar case has been published before. Both genetic defects that led to the two conditions appeared highly transmissible in the patient’s family. The patient might have an increased risk of osteosarcoma and chondrosarcoma, both due to PDB and LS, and a review of the literature was introduced in this particular matter. The phenotypic expression of the daughter remains uncertain and is yet to be a lifelong follow-up as the second patient harbouring this unique combination of gene anomalies. Full article

Production lines in manufacturing environments benefit from quality diagnosis methods based on learning techniques since their ability to adapt to the runtime conditions improves performance, and at the same time, difficult computational problems can be solved in real time. Predicting the divergence of a product’s physical parameters from an acceptable range of values in a manufacturing line is a process that can assist in delivering consistent and high-quality output. Costs are saved by avoiding bursts of defective products in the pipeline’s output. An innovative framework for the early detection of a product’s physical parameter divergence from a specified quality range is designed and evaluated in this study. This framework is based on learning automata to find the sequences of variables that have the highest impact on the automated sensor measurements that describe the environmental conditions in the production line. It is shown by elaborate evaluation that complexity is reduced and results close to optimal are feasible, rendering the framework suitable for deployment in practice. Full article

Surface defect inspection is vital for the quality control of products and the fault diagnosis of equipment. Defect inspection remains challenging due to the low level of automation in some manufacturing plants and the difficulty in identifying defects. To improve the automation and intelligence levels of defect inspection, a CNN model is proposed for the high-precision defect inspection of USB components in the actual demands of factories. First, the defect inspection system was built, and a dataset named USB-SG, which contained five types of defects—dents, scratches, spots, stains, and normal—was established. The pixel-level defect ground-truth annotations were manually marked. This paper puts forward a CNN model for solving the problem of defect inspection tasks, and three strategies are proposed to improve the model’s performance. The proposed model is built based on the lightweight SqueezeNet network, and a rich feature extraction block is designed to capture semantic and detailed information. Residual-based progressive feature integration is proposed to fuse the extracted features, which can reduce the difficulty of model fine-tuning and improve the generalization ability. Finally, a multi-step deep supervision scheme is proposed to supervise the feature integration process. The experiments on the USB-SG dataset prove that the model proposed in this paper has better performance than that of other methods, and the running speed can meet the real-time demand, which has broad application prospects in the industrial inspection scene. Full article

Machine-health-surveillance systems are gaining popularity in industrial manufacturing systems due to the widespread availability of low-cost devices, sensors, and internet connectivity. In this regard, artificial intelligence provides valuable assistance in the form of deep learning methods to analyze and process big machine data. In diverse industrial applications, gears are considered a condemning element; many contributing failures occur due to an unexpected breakdown of the gears. In recent research, anomaly-detection and fault-diagnosis systems have been the gears’ most contributing content. Thus, in work, we presented a smart deep learning-based system to detect anomalies in an industrial machine. Our system used vibrational analysis methods as a deciding tool for different machinery-maintenance decisions. We will first perform a data analysis of the gearbox data set to analyze the data’s insights. By calculating and examining the machine’s vibration, we aim to determine the nature and severity of the defect in the machine and hence detect the anomaly. A gearbox’s vibration signal holds the fault’s signature in the gears, and earlier fault detection of the gearbox is achievable by examining the vibration signal using a deep learning technique. Therefore, we aim to propose a 6-layer autoencoder-based deep learning framework for anomaly detection and fault analysis using a publically available data set of wind-turbine components. The gearbox fault-diagnosis data set is utilized for experimentation, including collecting vibration attributes recorded using SpectraQuest’s gearbox fault-diagnostics simulator. Through comprehensive experiments, we have seen that the framework gains good results compared to others, with an overall accuracy of 91%. Full article

The most often used motor in commercial drives is the induction motor. While the induction motor is operating, electrical, thermal, mechanical, magnetic, and environmental stresses can result in defects. Therefore, many researchers who are involved in condition monitoring have been interested in the development of reliable and efficient fault diagnostic technologies. This paper’s goal is to provide an overview of available fault detection methods for the broken rotor bar problem, one of several defects associated to induction motors. Despite the fact that it is less common than bearing or insulator failure, this fault may cause electrical machines to fail catastrophically. It can be quite harmful, especially in large motors, and it can develop as a result of manufacturing faults, repeated starting of the machine, mechanical stress, and thermal stress. Hence, a review on rotor defect diagnosis was conducted. In order to confirm rotor bar fracture, this research provides probable defect signatures that can be extracted from the current signal. Each defect signature is reported according to (a) loading level, (b) the number of BRBs, (c) validation, and (d) methodologies. Full article