Computers in Industry最新文献

{"title":"Streamlining Assembly Instruction Design (S-AID): A comprehensive systematic framework","authors":"Mirco Bartolomei, Federico Barravecchia, Luca Mastrogiacomo, Davide Maria Gatta, Fiorenzo Franceschini","doi":"10.1016/j.compind.2024.104232","DOIUrl":"https://doi.org/10.1016/j.compind.2024.104232","url":null,"abstract":"Assembly instructions are detailed directives used to guide the assembly of products across various manufacturing sectors. As production processes evolve to become more flexible, the significance of assembly instructions in meeting rigorous efficiency and quality standards becomes increasingly pronounced. Nevertheless, the development of assembly instructions often remains unstructured and predominantly dependent on the experience or personal skills of the designer. This paper aims to address these issues by pursuing three main goals: (i) deciphering the assembly process and the information that characterizes it, thereby providing a taxonomy of instruction constituents; (ii) presenting a framework to assess the various formats in which such information can be communicated; and (iii) introducing a step-by-step method, named <ce:italic>S-AID</ce:italic>, which offers a consistent methodology for designers during the instruction design phase. Overall, this research provides a rigorous taxonomy of the building blocks of assembly instructions and defines their relationships with various instruction formats. Furthermore, by proposing a systematic design method, this works aims to address the redundancy and inconsistency commonly encountered in traditional instruction design processes. The proposed methodology is illustrated using a real-world case study involving the assembly of a mechanical equipment. Finally, the effectiveness of the <ce:italic>S-AID</ce:italic> method was evaluated quantitatively through comparative analysis with other instruction sets, focusing on metrics such as process failures, assembly completion time, and perceived cognitive load.","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"53 1","pages":""},"PeriodicalIF":10.0,"publicationDate":"2024-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142884277","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":"Meta-task interpolation-based data augmentation for imbalanced health status recognition of complex equipment","authors":"Jinyuan Li, Wenqing Wan, Yong Feng, Jinglong Chen","doi":"10.1016/j.compind.2024.104226","DOIUrl":"https://doi.org/10.1016/j.compind.2024.104226","url":null,"abstract":"In the research of health status detection technology for complex equipment such as liquid rocket engines, the extreme working environment hinders the widespread conduct of fault experimental simulations, leading to data scarcity and imbalance. Consequently, the performance of intelligent models deteriorates rapidly with direct training. To address this issue, this paper proposes a meta-task feature space interpolation network model. Firstly, the model uses an encoder to map randomly selected task pairs to a more discriminative feature space, and then interpolates corresponding features and labels within this latent feature space to generate additional tasks, increasing the distribution density of tasks and alleviating the problem of insufficient training tasks. Furthermore, the model leverages self-distillation to improve the learning of label information. By integrating soft labels with supervised labels, it captures the hidden category information of newly interpolated tasks, thereby reducing the impact of class imbalance on model performance. The effectiveness of the proposed method is validated through a series of experiments conducted across three different scenarios. The results demonstrate that the proposed method achieves an average accuracy of 97.91% on the turbopump bearing dataset, which is a significant improvement over the comparative methods.","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"80 1","pages":""},"PeriodicalIF":10.0,"publicationDate":"2024-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142884279","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":"Non-contact rPPG-based human status assessment via feature fusion embedding anti-aliasing in industry","authors":"Qiwei Xue, Xi Zhang, Yuchong Zhang, Amin Hekmatmanesh, Huapeng Wu, Yuntao Song, Yong Cheng","doi":"10.1016/j.compind.2024.104227","DOIUrl":"https://doi.org/10.1016/j.compind.2024.104227","url":null,"abstract":"Remote Photoplethysmography (rPPG) is a cost-effective, and non-contact technology that enables real-time monitoring of physiological status by extracting vital information such as heart rate (HR). This capability enables the assessment of fatigue and stress, helping to prevent accidents by identifying risky conditions early. Continuous monitoring with rPPG reduces operational risks, contributing to safer industrial and medical environments. However, the performance of rPPG is challenged by complex backgrounds and facial motions in industrial environments, which complicates feature extraction. To address these challenges, this paper proposes a spatial–temporal attention feature fusion network with anti-aliasing (ST-ASENet) for human status assessment. The ST-ASENet encodes spatial–temporal facial signals from multiple regions of interest (ROI) and enhances feature extraction through the attention mechanism. The network integrates anti-aliasing by low-pass filtering during the downsampling process to improve the accuracy of rPPG signals in complex environments. It calculates HR, respiratory rate (RR), and heart rate variability (HRV) for status evaluation. Additionally, the Robotics Operator Factors Assessment (ROFA) dataset is introduced, featuring diverse individuals and environments to improve the robustness of ST-ASENet. Experimental results demonstrate that ST-ASENet outperforms state-of-the-art methods in HR estimation and shows effectiveness across various industrial scenarios. The proposed method fosters operational efficiency and a data-driven approach to human-centric safety, making rPPG invaluable in modern, health-focused workplaces.","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"25 1","pages":""},"PeriodicalIF":10.0,"publicationDate":"2024-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142884278","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":"Deep hierarchical sorting networks for fault diagnosis of aero-engines","authors":"Jinlei Wu, Lin Lin, Dan Liu, Song Fu, Shiwei Suo, Sihao Zhang","doi":"10.1016/j.compind.2024.104229","DOIUrl":"https://doi.org/10.1016/j.compind.2024.104229","url":null,"abstract":"In modern industry, timely health assessments of aero-engines are crucial for ensuring their proper functionality and the safety of aviation operations. However, during the collection of operating data for aero-engines, influential fault features may exhibit hysteresis or even overwhelmed due to transmission delays in some sensors. Furthermore, these features in the data at interval points are difficult to extract using traditional deep neural networks. Moreover, in aero-engine fault diagnosis, the number of normal samples is significantly higher than that of fault samples. As a result, traditional deep neural networks tend to focus on normal samples while fault samples are neglected, increasing the risk of missed diagnoses or misdiagnoses. To address these problems, this paper proposes a parallel convolutional neural network based on hierarchical sorting of state points (FSHSM-PCNN), to improve the synergistic effect between state point data at different hierarchical levels via the hierarchical sorting module, and to efficiently extract fault information via the parallel convolutional neural network. First, the state point data in the original samples is internally sorted along the time dimension by the fault significance-based hierarchical sorting module (FSHSM), and the different levels of state point data obtained after sorting reveal a reinforced synergistic effect. Second, a parallel convolutional neural network is developed to extract temporal status features and reinforced synergistic features, and the fused information is used for fault diagnosis. Finally, the performance of the proposed FSHSM-PCNN is evaluated using actual monitoring data from aero-engines. The experimental results show that the proposed method is effective in extracting fault features from the monitoring data. Compared to other methods in the ablation study, the proposed method improves average performance in aero-engine fault diagnosis by 12.46 %, 7.07 %, and 12.62 %, respectively. In diagnosis tasks with imbalanced datasets, its accuracy exceeds that of other methods by at least 5.01 %.","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"132 1","pages":""},"PeriodicalIF":10.0,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142884280","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":"YOLOv10-pose and YOLOv9-pose: Real-time strawberry stalk pose detection models","authors":"Zhichao Meng, Xiaoqiang Du, Ranjan Sapkota, Zenghong Ma, Hongchao Cheng","doi":"10.1016/j.compind.2024.104231","DOIUrl":"https://doi.org/10.1016/j.compind.2024.104231","url":null,"abstract":"In the computer-aided industry, particularly within the domain of agricultural automation, fruit pose detection is critical for optimizing efficiency across various applications such as robotic harvesting, aerial crop surveillance, precision pruning, and automated sorting. These technologies enhance productivity and precision, addressing challenges posed by an aging labor force and the increasing demand for sophisticated robotic applications in agriculture. This is particularly crucial for strawberries, which are globally recognized for their high nutritional value. The strawberry pickting robots generally cut the stems, so knowing the pose of the strawberry stalks before cutting can effectively adjust the pose of the end effector, thereby improving the success rate of picking. This paper referred to the keypoint detection branch and loss function of the YOLOv8-pose model, and combined the latest YOLOv9 and YOLOv10 object detection models to propose YOLOv9-pose and YOLOv10-pose. The experimental results showed that YOLOv9-base-pose had the best comprehensive performance, reaching 0.962 in Box_mAP50 and 0.914 in Pose_mAP50, and the speed met the real-time requirement of FPS 51. The entire YOLOv10-pose series did not achieve satisfactory accuracy, but not using non-maximum suppression did indeed speed up the post-processing. In the YOLOv10-pose series, YOLOv10m-pose achieved the best comprehensive performance with Box_mAP50 of 0.954, Pose_ mAP50 of 0.903, and a speed of 61 FPS. Comparing YOLOv9-base-pose with the entire series of YOLOv8-pose and YOLOv5-pose also demonstrated the superior performance of YOLOv9-base-pose. YOLOv9-pose and YOLOv10-pose can provide a theoretical basis for pose detection and a reference for other similar fruit pose detection.","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"29 1","pages":""},"PeriodicalIF":10.0,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142884318","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":"Integration of industry 4.0 technologies for agri-food supply chain resilience","authors":"Rohit Sharma, Balan Sundarakani, Ioannis Manikas","doi":"10.1016/j.compind.2024.104225","DOIUrl":"https://doi.org/10.1016/j.compind.2024.104225","url":null,"abstract":"The agri-food supply chain (AFSC) operations are becoming challenging due to globalization, constantly shifting consumer demands, and intensive disruptions leading to inefficient production and distribution of safe and high-quality food. Technological advancements are the most promising ways to ensure firms’ survival and supply chains. To enhance the resilience of AFSCs, the present study aims to identify and model the challenges associated with AFSC operations in the context of the United Arab Emirates (UAE) food processing industry. An integrated methodology using the Grey Influence Analysis (GINA) and Fuzzy Linguistic Quantifier Ordered Weighted Aggregation (FLQOWA) methodology is applied to analyze resilience enablers and assess industry 4.0 technologies (I4Ts) that can enhance resilience in AFSCs. The GINA technique helps identify the most influential resilience enablers, and the FLQOWA helps assess and prioritize I4Ts to enhance resilient enablers. The findings reveal that out of thirteen sub-enablers, four are the most influential resilient enablers, viz., real-time information sharing, enhanced product traceability, improved risk management, and planning and network design; and out of ten I4Ts, three are the most influential technologies viz., big data analytics, Internet of things, and cloud computing can further enhance resilience enablers. The findings from the study can help AFSC organizations and the government formulate appropriate strategies based on the integrated matrix developed by selecting the best combination of technologies for strengthening the required resilient enablers among the AFSC stakeholders.","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"44 1","pages":""},"PeriodicalIF":10.0,"publicationDate":"2024-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142841247","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":"Intelligent prediction and soft-sensing of comprehensive production indicators for iron ore sintering: A review","authors":"Sheng Du, Xian Ma, Haipeng Fan, Jie Hu, Weihua Cao, Min Wu, Witold Pedrycz","doi":"10.1016/j.compind.2024.104215","DOIUrl":"https://doi.org/10.1016/j.compind.2024.104215","url":null,"abstract":"Iron ore sintering is a critical process in iron and steel production, with a substantial impact on overall energy consumption and the emission of various environmental pollutants. Enhancing the efficiency of this process is crucial for achieving sustainability in the iron and steel industry. Accurate prediction and real-time monitoring of comprehensive production indicators are essential for optimizing production and improving energy efficiency. This paper provides a systematic review of intelligent prediction and soft-sensing techniques applied to the iron ore sintering process. It details the mechanisms and operational principles of these technologies, with a focus on key indicators such as quality, thermal state, yield, and energy consumption. This paper explores the current state-of-the-art in four prediction methodologies: mechanism analysis-based methods, data feature analysis-based methods, multi-model fusion-based methods, and operating mode recognition-based methods. Finally, the challenges to the current comprehensive production indicator prediction of the sintering process are pointed out, including the difficulty of dealing with the changing operating mode, the incomplete analysis of image features, and the insufficient consideration of the differences in data distribution. In the future, operating mode recognition approaches, deep learning approaches, transfer learning approaches, and computer vision techniques will have a broad prospect in the comprehensive production indicator prediction of the sintering process.","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"117 1","pages":""},"PeriodicalIF":10.0,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142804460","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 robotic skill transfer learning framework of dynamic manipulation for fabric placement","authors":"Tianyu Fu, Cheng Li, Yunfeng Bai, Fengming Li, Jiang Wu, Chaoqun Wang, Rui Song","doi":"10.1016/j.compind.2024.104216","DOIUrl":"https://doi.org/10.1016/j.compind.2024.104216","url":null,"abstract":"Placing fabric poses a challenge to robots since fabric with high dimensional configuration space can deform during manipulation. Existing methods for placing fabric mostly rely on static operations, which are inefficient and require a large workspace. Therefore, this study applies dynamic manipulation (manipulating uncontrollable parts of the fabric by swinging) to fabric placement, proposing a novel learning framework for robotic dynamic fabric placement skill learning and generalization. The proposed framework integrates reinforcement learning with imitation learning, leveraging expert demonstration data to guide and accelerate skill acquisition. Additionally, fabric characteristics are combined with imitation learning to enable the transfer and generalization of the learned policy to real-world environments The experiments suggest that the proposed framework is capable of achieving the placement tasks for a range of positions and fabrics. For success rate, the policy of the proposed framework ultimately achieves a flatness of exceeding 95% and a placement distance error of less than 2 mm. Moreover, the proposed approach is similar in operation time to the fastest method, while it can reduce the space required for manipulating the fabric by over 15%. Compared with other placement policies, it is promising because of its high accuracy, flexibility, efficiency, as well as adaptability.","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"85 1","pages":""},"PeriodicalIF":10.0,"publicationDate":"2024-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142763009","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 approach for adaptive filtering with reinforcement learning for multi-sensor fusion in condition monitoring of gearboxes","authors":"Shahis Hashim,&nbsp;Sitesh Kumar Mishra,&nbsp;Piyush Shakya","doi":"10.1016/j.compind.2024.104214","DOIUrl":"10.1016/j.compind.2024.104214","url":null,"abstract":"<div><div>Condition monitoring of gearboxes is integral to maintaining floor safety, system stability, and inventory management. Capturing vibration response using sensors and subsequent response analysis is the standard procedure for gearbox fault detection. However, the sensors are susceptible to non-constant reliability due to the convolution of vibration responses from multiple sources, background noise interference, and transfer-path effect. The problem is multi-fold when ideal sensor attachment locations are unavailable due to spatial constraints of industrial floors. The response component reflective of the fault information must be enhanced for adequate fault severity estimations. The present study addresses this hurdle by proposing a multi-sensor framework with available sensor attachment locations for gearbox condition monitoring. Adaptive filtering is done in the framework with parameters optimised to enhance fault information. A proximal policy optimisation agent is trained with a reinforcement learning environment for parameter refinement. Further, fault severity estimation is achieved by a weighted fusion of spectral features reflective of the side-band excitation effect caused by gear fault. The proposed method is applied to datasets acquired from an in-house seeded fault test bed. The proposed method underscores superior performance compared to conventional single-sensor-based fault severity analysis and alternate fusion approaches.</div></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"164 ","pages":"Article 104214"},"PeriodicalIF":8.2,"publicationDate":"2024-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142718530","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":"Wasserstein distributionally robust learning for predicting the cycle time of printed circuit board production","authors":"Feng Liu ,&nbsp;Yingjie Lu ,&nbsp;Debiao Li ,&nbsp;Raymond Chiong","doi":"10.1016/j.compind.2024.104213","DOIUrl":"10.1016/j.compind.2024.104213","url":null,"abstract":"<div><div>This paper proposes a Wasserstein distributionally robust learning (WDRL) model to predict the production cycle time of highly mixed printed circuit board (PCB) orders on multiple production lines. The PCB production cycle time is essential for optimizing production plans. However, the design of the PCB, production line configuration, order combinations, and personnel factors make the prediction of the PCB production cycle time difficult. In addition, practical production situations with significant disturbances in feature data make traditional prediction models inaccurate, especially when there is new data. Therefore, we establishe a WDRL model, derive a tight upper bound for the expected loss function, and reformulate a tractable equivalent model based on the bound. To demonstrate the effectiveness of this method, we collected data related to surface mounted technology (SMT) production lines from a leading global display manufacturer for our computational experiments. In addition, we also designed experiments with perturbations in the training and testing datasets to verify the WDRL model’s ability to handle perturbations. This proposed method has also been compared with other machine learning methods, such as the support vector regression combined with symbiotic organism search, decision tree, and kernel extreme learning machine, among others. Experimental results indicate that the WDRL model can make robust predictions of PCB cycle time, which helps to effectively plan production capacity in uncertain situations and avoid overproduction or underproduction. Finally, we implement the WDRL model for the actual SMT production to predict the production cycle time and set it as the target for production. We observed a 98–103 % achievement rate in the last 20 months since the implementation in September 2022.</div></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"164 ","pages":"Article 104213"},"PeriodicalIF":8.2,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142673318","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}