Computers in Industry最新文献

Novel digital on-demand manufacturing technologies provide a significant opportunity to support development of virtual warehousing and in turn improve supply chain performance. However, the implementation of virtual warehouse comes with a set of challenges, especially where the objective is to virtually warehouse standard or legacy parts that have been developed and verified initially for conventional (non-digital) manufacturing. In this paper, we explore the key elements required for successful implementation of a virtual warehouse for legacy parts based on a combination of part digitalization, on-demand manufacturing, and part validation. Our proposed framework for adoption of virtual warehouse includes development of a digital inventory which includes supply chain and manufacturability data, identification, and selection of suitable parts for on-demand manufacturing, selection of on-demand manufacturing technology, fit-for-purpose validation of the parts. Our framework is exemplified through a case study, and we conclude that the building of an effective virtual warehouse requires several enablers, including availability of digital data about technical and supply chain characteristics of parts, but also a suitable part identification tool. This part identification tool needs to be flexible to include comparison with reference parts already produced by different on-demand manufacturing technologies.

We propose a novel framework for detecting 3D human–object interactions (HOI) in construction sites and a toolkit for generating construction-related human–object interaction graphs. Computer vision methods have been adopted for construction site safety surveillance in recent years. The current computer vision methods rely on videos and images, with which safety verification is performed on common-sense knowledge, without considering 3D spatial relationships among the detected instances. We propose a new method to incorporate spatial understanding by directly inferring the interactions from 3D point cloud data. The proposed model is trained on a 3D construction site dataset generated from our crafted simulation toolkit. The model achieves 54.11% mean interaction over union (mIOU) and 72.98% average mean precision(mAP) for the worker–object interaction relationship recognition. The model is also validated on PiGraphs, a benchmarking dataset with 3D human–object interaction types, and compared against other existing 3D interaction detection frameworks. It was observed that it achieves superior performance from the state-of-the-art model, increasing the interaction detection mAP by 17.01%. Besides the 3D interaction model, we also simulate interactions from industrial surveillance footage using MoCap and physical constraints, which will be released to foster future studies in the domain.

In business processes, an operational problem refers to a deviation and an inefficiency that prohibits an organization from reaching its goals, e.g., a delay in approving a purchase order in a Procure-To-Pay (P2P) process. Operational process monitoring aims to assess the occurrence of such operational problems by analyzing event data that record the execution of business processes. Once the problems are detected, organizations can act upon the corresponding problems with viable actions, e.g., adding more resources, bypassing problematic activities, etc. A plethora of approaches have been proposed to implement operational process monitoring. The lion’s share of existing approaches assumes that a single case notion (e.g., a purchase order in a P2P process) exists in a business process and analyzes operational problems defined over the single case notion. However, most real-life business processes manifest the interplay of multiple interrelated objects. For instance, an execution of an omnipresent P2P process involves multiple objects of different types, e.g., purchase orders, goods receipts, invoices, etc. Applying the existing approaches in these object-centric business processes results in inaccurate or misleading results. In this study, we propose a novel approach to assessing operational problems within object-centric business processes. Our approach not only ensures an accurate assessment of existing problems but also facilitates the analysis of object-centric problems that consider the interaction among different objects. We evaluate this approach by applying it to both simulated business processes and real-life business processes.

Performance degradation and remaining useful life (RUL) prediction are of great significance in improving the reliability of mechanical equipment. Existing cross-domain RUL prediction methods usually reduce data distribution discrepancy by domain adaptation, to overcome domain shift under cross-domain conditions. However, the fine-grained information between cross-domain degradation features and the specific characteristics of the target domain are often ignored, which limits the prediction performance. Aiming at these issues, a RUL prediction method based on dynamic hybrid domain adaptation (DHDA) and attention contrastive learning (A-CL) is proposed for the cross-domain rolling bearings. In the DHDA module, the conditional distribution alignment is achieved by the designed pseudo-label-guided domain adversarial network, and is assigned with a dynamic penalty term to dynamically adjust the conditional distribution when aligning the joint distribution, for improving the fine-grainedness of domain adaptation. The A-CL module aims to help the prediction model actively extract the degradation information of the target domain, to generate the degradation features that match the characteristics of the target domain, for improving the robustness of RUL prediction. Then, the proposed method is verified by the ablation and comparison experiments conducted on PHM2012 and XJTU-SY datasets. The results show that the proposed method performs high accuracy for cross-domain RUL prediction with good generalization performance under three different cross-domain scenarios.

Cheese production, a globally cherished culinary tradition, faces challenges in ensuring consistent product quality and production efficiency. The critical phase of determining cutting time during curd formation significantly influences cheese quality and yield. Traditional methods often struggle to address variability in coagulation conditions, particularly in small-scale factories. In this paper, we present several key practical contributions to the field, including the introduction of CM-IDB, the first publicly available image dataset related to the cheese-making process. Also, we propose an innovative artificial intelligence-based approach to automate the detection of curd-firming time during cheese production using a combination of computer vision and machine learning techniques. The proposed method offers real-time insights into curd firmness, aiding in predicting optimal cutting times. Experimental results show the effectiveness of integrating sequence information with single image features, leading to improved classification performance. In particular, deep learning-based features demonstrate excellent classification capability when integrated with sequence information. The study suggests the suitability of the proposed approach for integration into real-time systems, especially within dairy production, to enhance product quality and production efficiency.

The proposed transfer learning-based fault diagnosis models have achieved good results in multi-source domain generalization (MDG) tasks. However, research on single-source domain generalization (SDG) is relatively scarce, and the limited availability of small training samples is seldom considered. Therefore, to address the insufficient feature extraction capability and poor generalization performance of existing models on single-source domain small sample data, a novel single-source domain generalization fault diagnosis (SDGFD) framework, the prior knowledge embedded convolutional autoencoder (PKECA), is proposed. During the training phase, first, single-source domain data are used to construct prior features based on the time domain, frequency domain, and time-frequency domain. Second, a prior knowledge embedding structure based on the convolutional autoencoder is built, which compresses the prior knowledge and original vibration data into a high-dimensional space of consistent dimensions, embedding the prior knowledge into the features corresponding to the vibration data using a mean squared error loss function. Subsequently, the proposed centroid-based self-supervised learning (CBSSL) strategy further constrains high-dimensional features, improving the generalization ability. The designed sparse regularized activation (SRA) function significantly enhances the regularization effect on features. During the testing phase, it is only necessary to input the data from the unknown domain to identify the fault types. The experimental results show that the proposed method achieves superior performance in fault diagnosis tasks involving cross-speed, time-varying speed, and small sample data in SDGFD, demonstrating that PKECA has strong generalizability. The code can be found here: https://github.com/John-520/PKECA. © 2024 Elsevier Science. All rights reserved

This preliminary research presents a comparative study between Text-to-Image AI models and Shape Grammars, one of the main generative approaches to Computer Aided Conceptual Design. The goal is to determine to which extent AI models can reproduce or complement the performance of grammar algorithms as creative support tools for shape exploration in conceptual product design. Workflows, advantages and limitations are identified through a comprehensive practical comparison example. The results show many similarities regarding generative capabilities and highlight several advantages of Text-to-Image AI models, including an easier way of capturing product grammars and a wider and more immediate range of further applications. In contrast, Shape Grammars approach proved more solid in aspects related to exploration workflows and cognitive stimulation. These results encourage the research on new ways to address the interaction between designers and AI generative models, combining the AI potential with well-established generative strategies.

This paper delves into the accurate detection of the early initial degradation point (IDP) in bearings, and proposes, for the first time, a comprehensive adaptive IDP detection framework for bearings under variable operating conditions, along with an outlier data repair strategy. First, this study introduces the adaptive early initial degradation point detection (AEIDPD) method, which incorporates least-squares fitting to compute the slope and intercept of health indicators, and t-tests are used to construct the “sum-of-slopes” indicator. An adaptive IDP threshold construction method that adapts to variable operating conditions is proposed, establishing a strategy for IDP detection based on sum-of-slopes and adaptive thresholds. To enhance the robustness of AEIDPD in variable operating conditions, this paper introduces synchronized wavelet transform to obtain the "synchronous pseudo-speed" signal of bearing vibration, and constructs a condition interference elimination strategy based on velocity and sliding window averaging to mitigate the effects of variable operating conditions. Additionally, the study constructs upper and lower bounds for the root mean square feature of vibration signals using empirical parameters to correct outliers, providing more accurate data to support bearing life predictions. Experimental results demonstrate the effectiveness and robustness of the proposed methods.

The crude oil price has been subject to periodic fluctuations because of seasonal changes in industrial demand and supply, weather, natural disasters and global political unrest. An accurate forecast of crude oil prices is of utmost importance for decision makers and industry players in the energy sector. Despite this, the volatility of crude oil prices contributes to the uncertainty of the energy industry, which was particularly challenging following the recent global spread of the COVID-19 epidemic and the Russia–Ukraine conflict. This paper proposes a hybrid deep learning (DL) modelling framework to deal with the volatility of crude oil prices, applying ensemble empirical mode decomposition (EEMD), convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM) integrated with quantile regression (QR); named EEMD-CNN-BiLSTM-QR. Two real-world datasets on crude oil prices from the West Texas Intermediate and Brent Crude Oil markets were employed to validate the EEMD-CNN-BiLSTM-QR hybrid modelling framework. Given that the probability density forecast can capture uncertainty, an in-depth analysis was carried out and prediction accuracy calculated. The findings of this study demonstrate that the proposed EEMD-CNN-BiLSTM-QR DL modelling framework, which uses the probability density forecast method, is superior to other tested models in terms of its ability to forecast crude oil prices. The novelty of this study stems mainly from its use of QR, which allows for the description of the conditional distribution of predicted variables and the extraction of more uncertain information for probability density forecasts.

The methods for unsupervised visual inspection use algorithms that are developed, trained and evaluated on publicly available datasets. However, these datasets do not reflect genuine industrial conditions, and thus current methods are not evaluated in real-world industrial production contexts. To answer this shortcoming, we introduce AutoVI, an industrial dataset of visual defects that can be encountered on automotive assembly lines. This dataset, comprising six inspection tasks, was designed as a benchmark to assess the performance of defect detection methods under realistic acquisition conditions. We analyze the performance of current state-of-the-art methods and discuss the difficulties specifically encountered in the industrial context. Our results show that current methods leave considerable room for improvement. We make AutoVI publicly available to develop unsupervised detection methods that will be better suited to real industrial tasks.