Computers in Industry最新文献

{"title":"A comprehensive survey of image synthesis approaches for Deep Learning-based surface defect detection in manufacturing","authors":"Aru Ranjan Singh,&nbsp;Sumit Hazra,&nbsp;Abhishek Goswami,&nbsp;Kurt Debattista,&nbsp;Thomas Bashford-Rogers","doi":"10.1016/j.compind.2025.104360","DOIUrl":"10.1016/j.compind.2025.104360","url":null,"abstract":"<div><div>Detection of manufacturing defects is a crucial step in ensuring product quality and safety. The automation of defect detection processes and the enhancement of detection accuracy are pivotal objectives in industrial quality control. However, the complexities of manufacturing processes present significant hurdles in the development of effective defect detection models. Deep Learning (DL) models have emerged as a potential solution for defect detection by learning patterns from extensive datasets without necessitating an in-depth understanding of the manufacturing processes. However, training such DL models requires vast amounts of data, which are often difficult and costly to collect from real manufacturing environments. As a response to these challenges, researchers have proposed synthetic image generation to facilitate DL model training. The existing literature primarily focuses on two main approaches for synthetic defect image generation: computer graphics-based methods and DL-based methods. However, there are a limited number of literature reviews focused on DL-based methods and no reviews on recent developments particularly diffusion models in defect image synthesis. Moreover, no comprehensive review currently addresses the application of computer graphics-based techniques for defect image generation. Therefore, this article presents a comprehensive review covering both computer graphics-based methods and recent developments in DL-based methods employed in the synthesis of artificial images. The review addresses various techniques, their strengths and limitations, and their implications for advancing defect detection in manufacturing.</div></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"173 ","pages":"Article 104360"},"PeriodicalIF":9.1,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145120988","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A lightweight transformer winding condition assessment method with multi-scale image fusion and an improved attention mechanism","authors":"Yongteng Sun,&nbsp;Hongzhong Ma","doi":"10.1016/j.compind.2025.104377","DOIUrl":"10.1016/j.compind.2025.104377","url":null,"abstract":"<div><div>In recent years, vibration image analysis has emerged as a promising technique for assessing transformer winding conditions. This study proposes a lightweight assessment model for transformer windings, integrating an image fusion module and a recognition module to address the accuracy limitations of single-image analysis and the high computational demands of multi-scale analysis. First, a Parallel Efficient Mixed Attention Mechanism (PEMAM) is proposed, designed to enhance adaptability to transformer vibration signals while maintaining a low parameter count. This mechanism improves the feature extraction capability of the Image Fusion Framework based on a Convolutional Neural Network, significantly boosting the signal-to-noise ratio and enhancing resistance to distortion in fused images. Subsequently, multi-scale Markov field images, derived from the time and frequency domain features of vibration signals, are fused and fed into the PEMAM-enhanced recognition module for condition assessment. Experimental results indicate that the proposed method achieves 99.63 % accuracy in identifying transformer winding conditions while maintaining low model complexity and computational cost.</div></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"173 ","pages":"Article 104377"},"PeriodicalIF":9.1,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145109721","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Association Rule-Assisted Multi-Time-Series Forecasting method for non-production material consumption in the automotive sector","authors":"Baiqing Sun ,&nbsp;Changsheng Zhang ,&nbsp;Yuyang Bai ,&nbsp;Yang An ,&nbsp;Guosong Zhu ,&nbsp;Hongyan Yu","doi":"10.1016/j.compind.2025.104366","DOIUrl":"10.1016/j.compind.2025.104366","url":null,"abstract":"<div><div>Non-Production Materials (NPMs) are a vital category of materials in automotive manufacturing, including various items whose consumption is often interconnected, especially with maintenance activities. Predicting NPM consumption is complex due to these interdependencies. Most existing research tends to focus on forecasting the consumption of individual materials, overlooking the advantages of utilizing data from multiple materials. This narrow focus limits both the accuracy and the breadth of forecasting efforts. In this paper, we formulate a multi-NPM consumption forecasting problem, which aims to predict the consumption of several NPMs at once. To address this issue, we introduce an Association Rule-Assisted Multi-Time-Series Forecasting Method (AR-MTSF). Our approach combines data from multiple materials with similar attributes and employs association rules to enhance forecasting accuracy. We assessed the effectiveness of AR-MTSF using a real-world NPM consumption dataset from a collaborating multinational automotive manufacturer. The experimental findings reveal that when forecasting automotive NPM consumption, the AR-MTSF method, when paired with the same forecasting algorithm, improves accuracy by 5%–30%.</div></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"173 ","pages":"Article 104366"},"PeriodicalIF":9.1,"publicationDate":"2025-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145093794","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reliability analysis and remaining useful life estimation of a two-variable phased degradation system","authors":"Bincheng Wen,&nbsp;Xin Zhao,&nbsp;Haizhen Zhu,&nbsp;Jinjun Cheng,&nbsp;Changjun Li,&nbsp;Mingqing Xiao","doi":"10.1016/j.compind.2025.104368","DOIUrl":"10.1016/j.compind.2025.104368","url":null,"abstract":"<div><div>On the one hand, due to changes in the operating conditions or working environment of the equipment, the degradation process often exhibits characteristics of two-phase or even multi-phase. In contrast to single-phase degradation models, two-phase degradation modeling necessitates considering the variability of the change points and analyzing the characteristics of the degraded state at the change points. On the other hand, as sensor technology advances, multi-sensor data collection systems have become increasingly widespread, and combining data from several sources can considerably improve the accuracy of remaining useful life (RUL) estimation. However, the current research fails to simultaneously incorporate both of the aforementioned conditions. Consequently, constructing a multivariate phased deterioration model and estimating the RUL still present a significant challenge. With this particular consideration, this paper constructs a two-variable phased degradation model based on the Wiener process. The RUL analytic expression is derived by taking into account the diversity of individuals and the random nature of change points. A novel approach is provided to achieve precise detection of change points. The proposed model’s validity is ultimately confirmed through the use of a simulation dataset as well as two real working datasets.</div></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"173 ","pages":"Article 104368"},"PeriodicalIF":9.1,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145093795","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Road surface damage detection based on enhanced YOLOv8","authors":"Wenjin Chen ,&nbsp;Jia Sheng Yang ,&nbsp;Chenbo Xia ,&nbsp;Yaosong Li ,&nbsp;Xu Xiao","doi":"10.1016/j.compind.2025.104363","DOIUrl":"10.1016/j.compind.2025.104363","url":null,"abstract":"<div><div>The Road Damage Detection System (RDDS) is crucial in intelligent transportation networks, enhancing driving safety, comfort, and overall traffic efficiency. A key factor in the system's performance is the effectiveness of the underlying detection algorithm. Currently, the YOLOv8 algorithm is widely applied in defect detection, but it faces challenges due to the varying scales of road damage. Specifically, the convolutional downsampling module in the backbone network often has a limited receptive field, reducing its ability to capture global information, while the multi-scale feature fusion network may lose critical local defect details and deep location information. These limitations hinder YOLOv8’s performance in detecting pavement defects. To address these issues, we propose an enhanced algorithm, YOLOv8 with Context Capture and Slimneck Structure (YOLOv8-CCS), which targets multi-scale defect characteristics and the prevalence of small-sized targets in road damage detection. To overcome the limited receptive field and improve global context awareness, we have integrated an enhanced context-guided module downsampling component (E-ContextGuidedBlock_Down), which expands the receptive field and improves context capture. Additionally, we replace the existing multi-scale fusion network with Ghost Shuffle Convolution (GSConv)-Slimneck and introduce the Enhanced VoVNet-based Ghost Shuffle Cross Stage Partial (VoVGSCSP-E) module in specific layers. To further enhance feature extraction and minimize information loss during fusion, we incorporate the Content-Aware ReAssembly of Features (CARAFE) upsampling module and a weighted feature fusion method. Finally, the Multi-Level Context Attention Bottleneck (MLCABOT) module is added between the backbone network and the multi-scale feature fusion network, improving the connectivity and overall feature extraction capability. In validation, our proposed method outperformed YOLOv8 by 3 %, 4.7 % and 3.8 % on the RDD-2022, ROAD-MAS and Unmanned Aerial Vehicle Asphalt Pavement Distress Dataset (UAPD) datasets, respectively. It also achieved the highest F1 score among comparable detection models and ranked among the top three in inference speed. These results highlight the potential of YOLOv8-CCS for real-time road damage detection, providing a more accurate and comprehensive solution for urban pavement management. Such a system, equipped with an advanced detection algorithm, can significantly improve road maintenance efficiency and enhance driving safety.</div></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"173 ","pages":"Article 104363"},"PeriodicalIF":9.1,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145050103","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reliability evaluation of wind power systems by integrating granularity-related latin hypercube sampling with LSTM-based prediction","authors":"Yonggang Li ,&nbsp;Yaotong Su ,&nbsp;Lei Xia ,&nbsp;Yuanjin Zhang ,&nbsp;Weinong Wu ,&nbsp;Longjiang Li","doi":"10.1016/j.compind.2025.104365","DOIUrl":"10.1016/j.compind.2025.104365","url":null,"abstract":"<div><div>When evaluating the reliability of a wind power system, it is imperative to undertake differentiated sampling and meticulously predict extensive datasets. Existing studies frequently constrain raw data within narrowly defined parameter spaces to enhance their statistical significance. However, such an approach may inadvertently engender overly optimistic reliability evaluations, neglecting rare yet crucial failure scenarios. Consequently, this oversight potentially underestimates systemic risks and undermines robustness. To date, the dichotomy between high data acquisition rates and the intrinsic characteristics of collected data remains inadequately addressed. Concurrently, an urgent requirement persists for developing precise data distribution models capable of comprehensively assessing wind power system reliability. In response, Long Short-Term Memory (LSTM) models are employed to bridge this research gap, enabling predictions of wind power generation through analyses of data at varying granularities. Subsequently, an Improved Latin Hypercube Sampling (ILHS) methodology is implemented to partition sampling intervals, integrating seamlessly with the Monte Carlo (MC) method for wind power data sampling. This reliability assessment model fully exploits the flexibility of the proposed sampling technique, enhancing the precision of sample probability distributions, interval segmentation, and data stratification. Empirical evidence demonstrates that the proposed algorithm exhibits superior predictive accuracy and enhanced statistical efficacy relative to conventional methodologies. Thus, it offers a robust and efficacious solution for assessing the reliability of wind power integration. This study evaluates the practical reliability of a local wind power integration system in Southwest China. Additionally, methods for discerning vulnerabilities are systematically applied to fortify critical power buses and augment overall system reliability.</div></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"173 ","pages":"Article 104365"},"PeriodicalIF":9.1,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145050104","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel dynamic variational compensation network with tracking for quality prediction of multirate industrial processes","authors":"Shihao Duan,&nbsp;Hengqian Wang,&nbsp;Chuang Peng,&nbsp;Lei Chen,&nbsp;Kuangrong Hao","doi":"10.1016/j.compind.2025.104364","DOIUrl":"10.1016/j.compind.2025.104364","url":null,"abstract":"<div><div>Quality prediction holds significant importance in monitoring industrial processes, with soft sensors proving to be highly effective in this domain. However, industrial processes frequently exhibit multirate characteristics due to measurement and cost limitations. The characteristics lead to periodic missing and varying dynamics of variables at different sampling rates, further presenting substantial challenges to current soft sensor techniques. To tackle the obstacles, we propose a Multirate Dynamic Variational Compensation Network with Tracking (MR-TDVCN). Utilizing a generic preprocessor and dynamic variational inference, MR-TDVCN effectively captures and characterizes crucial and diverse temporal dynamics related to multiple sampling rates, enabling comprehensive dynamic modeling of inhomogeneous multirate data. Based on this, a feature prism dynamic compensation network is developed to process multirate sequences for local feature compensation and global temporal relationship correction hierarchically and progressively. This mitigates the information loss due to multirate sampling, providing richer and more holistic feature representations for quality prediction. Finally, a feature tracking strategy is customized for multirate processes to alleviate the label sparsity problem. MR-TDVCN demonstrates superior performance on the common debutanizer column dataset, outperforming existing models. It is further applied to the polyester esterification process dataset to address real-world multirate challenges.</div></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"173 ","pages":"Article 104364"},"PeriodicalIF":9.1,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145027168","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A controllable generative design framework for residential communities with multi-scale architectural representations","authors":"Shidong Wang,&nbsp;Renato Pajarola","doi":"10.1016/j.compind.2025.104367","DOIUrl":"10.1016/j.compind.2025.104367","url":null,"abstract":"<div><div>We present a novel human-in-the-loop framework, CLOD-ReCo, for controllable residential community (ReCo) layout design in the form of multiple levels-of-detail (LODs) for a given construction plot boundary. Unlike other existing end-to-end methods that can only predict a basic 2D raster ReCo plan (LOD0), our approach simulates the design process of architects, which can not only be automated to generate diverse, vector-based, and high-quality 3D ReCo plans (LOD1<span><math><mo>∼</mo></math></span>4), but can also interact with the users during the entire generation process, from sketching, including the building numbers and locations to LOD4 including a realistic representation of a group of buildings and their surroundings, making humans and AI co-design the final layout plan. Intensive experiments are conducted to demonstrate the strengths of our approach. The quantitative evaluation, the qualitative comparison, and the subjective evaluation by architects show the ability of our method to generate high-quality and plausible results, which are better than those produced by prior existing methods and comparable to the real-world ReCo plans designed by professional architects. Furthermore, the experiments on the variability of our automated method and user interaction show the ability of our approach to generate diverse results and to interact with users toward co-designing human-centric ReCo plans that meet the requirements of architects.</div></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"173 ","pages":"Article 104367"},"PeriodicalIF":9.1,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145027231","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A learning model for perceived visual complexity assessment of automotive 3D shapes based on visual perception elements","authors":"Chengyi Shen,&nbsp;Changao Liu,&nbsp;Shijian Luo,&nbsp;Deyin Zhang,&nbsp;Yao Wang","doi":"10.1016/j.compind.2025.104358","DOIUrl":"10.1016/j.compind.2025.104358","url":null,"abstract":"<div><div>As the automotive industry matures, automotive exterior design has become a key factor affecting market performance and user purchasing decisions. But the current assessment methods mainly rely on expert experience and lack systematic use of user perception knowledge. To remedy this issue, this study introduces a learning model for perceived visual complexity (PVC) assessment of automotive 3D shapes, grounded in user cognition. It aims to connect user perception with shape attributes. PVC offers key advantages, including quantifiability and relevance to both aesthetics and functionality, among others. To develop and validate this model, we first conducted paired comparison experiments to measure PVC of automotive 3D shapes, thereby establishing a dataset correlating user assessments with shape attributes influencing such evaluations. These attributes were then translated into computable features informed by human visual perception, followed by correlation analysis for feature selection. Finally, a variety of regression models and feature combinations were employed to construct learning models for assessment, from which the best-performing representative model was identified. The evaluation results demonstrated that the representative learning model underscored its efficacy in predicting the PVC of automotive 3D shapes. Its average Spearman correlation with human subjective evaluations was 0.7991 based on K-fold cross-validation. Notably, comparative analysis revealed that the representative model outperformed previous models of 3D complexity within the test set.</div></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"173 ","pages":"Article 104358"},"PeriodicalIF":9.1,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145027232","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A semi-automated compliance checking framework for shield tunnel design integrating ontology and natural language processing","authors":"Yuxian Zhang,&nbsp;Xuhua Ren,&nbsp;Jixun Zhang","doi":"10.1016/j.compind.2025.104362","DOIUrl":"10.1016/j.compind.2025.104362","url":null,"abstract":"<div><div>Conventional compliance checking for shield tunnel design models relies on two-dimensional drawings and the designer's subjective interpretation of specifications, which limits the efficiency and introduces potential errors. This study developed a semi-automated framework for shield tunnel design compliance checking using ontology and natural language processing. The adopted methodology establishes a shield tunnel design ontology (STDO) model, which includes six classes of information and relationships that need to be considered in the design phase. A novel method for converting text into a computer-readable format was proposed for design specification content. The design specification text is converted into a word sequence format, including STDO semantics, through word segmentation and semantic alignment. The pattern-matching method converts semantically enriched specification text into a Prolog rule format by extracting grammatical structure elements and transforming logical checking elements. The established design compliance checking framework generates facts through interaction with the building information model and performs compliance reasoning tasks using Prolog rules derived from the specification text. To demonstrate the effectiveness of the conversion method proposed in this study and the designed compliance checking framework, a shield tunnel project was selected for experimental verification. The results showed the following: (1) The proposed method of converting specification text into predicate logic achieved an <span><math><mrow><mi>F</mi><mn>1</mn></mrow></math></span> of 86.25 %, providing a convenient approach for transforming it into a computer-readable format. (2) The established semi-automated framework could provide a convenient solution to assist in conducting model compliance checking tasks according to both quantitative and non-quantitative clauses. The results of this study provide significant guidance for the intelligent design of shield tunnels.</div></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"173 ","pages":"Article 104362"},"PeriodicalIF":9.1,"publicationDate":"2025-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145020157","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}