Computers in Industry最新文献

{"title":"Enhanced cross-domain signal and physics-based interpretation for fault diagnosis of aircraft brake control valve under limited onboard signal acquisition","authors":"Lin Lin ,&nbsp;Yin Chen ,&nbsp;Wenhui He ,&nbsp;Song Fu ,&nbsp;Shiwei Suo","doi":"10.1016/j.compind.2025.104378","DOIUrl":"10.1016/j.compind.2025.104378","url":null,"abstract":"<div><div>Sensor data collection from commercial aircraft faces challenges such as incomplete datasets, difficulty in assessing sensor significance, and inability to detect anomalous time points, leading to issues like ambiguous brake control valve faults categories. To address these, a new modeling framework is proposed to improve fault mode distinguishability through high-dimensional mapping. This framework uses Variational Autoencoders for training, combining reconstruction error and latent space similarity. It trains low-dimensional sensor data in two rounds, gradually approximating the target domain and synthesizing high-dimensional samples, enhancing cross-domain feature representation. Additionally, a time-adaptive weight allocation mechanism in a Bidirectional Long Short-Term Memory highlights critical signals, while a multi-head spatial attention mechanism reduces irrelevant signals. Experimental results show that the proposed fault diagnosis approach for brake control valves, utilizing aircraft onboard sensor data, achieves over 96 % in accuracy, precision, recall, and F1-score, outperforming the best performance of six classical network models by approximately 5 %.</div></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"173 ","pages":"Article 104378"},"PeriodicalIF":9.1,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145158581","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":"SmartARW: A text-aware smart mobile industrial augmented reality (AR) wiring assembly system","authors":"Tienong Zhang ,&nbsp;Wei Fang ,&nbsp;Lixi Chen ,&nbsp;Qiankun Zhang ,&nbsp;Hao Hu ,&nbsp;Jiapeng Bi","doi":"10.1016/j.compind.2025.104379","DOIUrl":"10.1016/j.compind.2025.104379","url":null,"abstract":"<div><div>Augmented reality (AR) has demonstrated its potential by delivering intuitive guidance on the workbench directly, alleviating the operators’ mental load for traditional paper-based wiring assembly tasks while ensuring procedural correctness. Nevertheless, current industrial AR wiring applications mainly focus on superimposing the virtual models with the commercial AR glass, lacking practical adaptability due to ergonomic issues derived from their weight, and human intervention is also necessary to monitor the current wiring harness and activate ongoing procedural progress. To bridge this gap for real-world applications, this article proposes human-centric SmartARW: a text-aware smart mobile industrial AR wiring assembly system. Firstly, a mobile AR wiring assembly system is established with the off-the-shelf tablet and sensor module, followed by accurate system calibration among different components and self-contained markerless motion tracking. Then, a closed-loop confirming strategy enabled lightweight text-aware network is proposed and integrated into the mobile AR system, as well as lots of shop-floor datasets are prepared and augmented to improve the context-aware performance, thus the status of the ongoing wiring activity can be perceived accurately even encountered challenge scenarios while activating the corresponding AR instructions automatically. Finally, extensive quantitative and qualitative experiments are carried out to illustrate that the proposed SmartARW has strong usability and can provide superior performance for human-centric smart wiring assembly action.</div></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"173 ","pages":"Article 104379"},"PeriodicalIF":9.1,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145158583","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 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}