Hasil untuk "Industrial directories"

F. Grasser, A. D'Arrigo, S. Colombi et al.

Jinbo Xiong, Rong Ma, Lei Chen et al.

With the rapid digitalization of various industries, mobile crowdsensing (MCS), an intelligent data collection and processing paradigm of the industrial Internet of Things, has provided a promising opportunity to construct powerful industrial systems and provide industrial services. The existing unified privacy strategy for all sensing data results in excessive or insufficient protection and low quality of crowdsensing services (QoCS) in MCS. To tackle this issue, in this article we propose a personalized privacy protection (PERIO) framework based on game theory and data encryption. Initially, we design a personalized privacy measurement algorithm to calculate users’ privacy level, which is then combined with game theory to construct a rational uploading strategy. Furthermore, we propose a privacy-preserving data aggregation scheme to ensure data confidentiality, integrity, and real-timeness. Theoretical analysis and ample simulations with real trajectory dataset indicate that the PERIO scheme is effective and makes a reasonable balance between retaining high QoCS and privacy.

Матвеев В.А., Ничкова Л.А.

Sayaka Ogawa, Natsu Sasaki, Norito Kawakami et al.

Objective: This study aimed to develop a Japanese version and a shortened version of the Online Social Support Scale and test their reliability and validity. Method: A 40-item scale was developed with the permission of the original developer to measure online and other social support, depressive symptoms, and self-esteem. Study participants were recruited online using snowball sampling. Internal and test-retest reliability were tested; confirmatory factor analysis was used to test for structural validity, and correlation analysis was used to test for convergent validity. A follow-up survey was conducted 2 weeks later to examine the test-retest reliability of the scale. A shortened 12-item version was also developed and tested. Result: A total of 288 people participated in the survey, of whom 254 (88.2%) responded to the follow-up survey. The Cronbach’s α coefficient was 0.98 for the overall scale and ranged from 0.94-0.96 for the subscales. The intraclass correlation coefficient was 0.90 for the scale as a whole and ranged from 0.85–0.88 for the subscales. Confirmatory factor analysis confirmed a four-factor structure with an acceptable model fit. The scale showed a significant positive correlation with social support and a significant negative correlation with depressive symptoms but no significant correlation with self-esteem. The shortened version demonstrated similar reliability and validity. Conclusion: The Japanese version of the Online Social Support Scale showed adequate reliability and some validity, and the short version showed adequate reliability and validity, making them useful tools for measuring online social support in various contexts, such as peer support groups or remote work environments.

Mustafa Çakir, M. Güvenç, S. Mıstıkoğlu

Abstract With the fourth industrial revolution, which has become increasingly widespread in the manufacturing industry, traditional maintenance has been replaced by the industrial internet of things (IIoT) based on condition monitoring system (CMS). The IIoT concept provides easier and reliable maintenance. Unlike traditional maintenance, IIoT systems that perform real-time monitoring can provide great advantages to the company by notifying the related maintenance team members of the factory before a serious failure occurs. It is very important to detect faulty bearings before they reach the critical level during the rotation. In this study, an industry 4.0 compatible, IIoT based and low-cost CMS was created and it consists of three main parts. Firstly experimental setup, secondly IIoT based condition monitoring application (CMA) and finally machine learning (ML) models and their evaluation. The experimental setup contains mechanical and electronic materials. Although the most common method used in the classification of bearing damage is vibration data, it observed that characteristics such as sound level, current, rotational speed, and temperature should be included in the data set in order to increase the success of the classification. All these data were collected from the setup, which is 6203 type bearing connected to the universal motor shaft. The designed CMA provides real-time monitoring and recording of the data, which comes wirelessly from the setup, on a mobile device that has an Android operating system. The CMA can also send SMS and e-mail notifications to maintenance team supervisors over mobile devices in case critical thresholds are exceeded. Lastly, the data collected from the experimental setup was modeled for classification with popular ML algorithms such as support vector machine (SVM), linear discrimination analysis (LDA), random forest (RF), decision tree (DT), and k-nearest neighbor (kNN). The models were evaluated with accuracy, precision, TPR, TNR, FPR, FNR, F1 score and, Kappa metrics. During the evaluation of all models, it was observed that with the increase in the number of features in the data set, the accuracy, sensitivity, TPR, TNR, F1 score and Kappa metrics increased above 99% at 95% confidence interval, and FPR and FNR metrics fell below 1%. Although ML models gave successful results, LDA and DT models gave results much faster than others did. On the other hand, the classification success of the LDA model is relatively low. However, DT model is the optimum choice for CMS due to its convenience in determining threshold values, and its ability to give fast and acceptable classification rates.

L. Ren, Zihao Meng, Xiaokang Wang et al.

Andrea Castellani, Sebastian Schmitt, S. Squartini

The continuously growing amount of monitored data in the Industry 4.0 context requires strong and reliable anomaly detection techniques. The advancement of Digital Twin technologies allows for realistic simulations of complex machinery; therefore, it is ideally suited to generate synthetic datasets for the use in anomaly detection approaches when compared to actual measurement data. In this article, we present novel weakly supervised approaches to anomaly detection for industrial settings. The approaches make use of a Digital Twin to generate a training dataset, which simulates the normal operation of the machinery, along with a small set of labeled anomalous measurement from the real machinery. In particular, we introduce a clustering-based approach, called cluster centers (CC), and a neural architecture based on the Siamese Autoencoders (SAE), which are tailored for weakly supervised settings with very few labeled data samples. The performance of the proposed methods is compared against various state-of-the-art anomaly detection algorithms on an application to a real-world dataset from a facility monitoring system, by using a multitude of performance measures. Also, the influence of hyperparameters related to feature extraction and network architecture is investigated. We find that the proposed SAE-based solutions outperform state-of-the-art anomaly detection approaches very robustly for many different hyperparameter settings on all performance measures.

Patrick K. Hyland, R. Lee, Maura J. Mills

M. Bortolini, M. Faccio, M. Gamberi et al.

Abstract Nowadays the Smart Factories operating within the Industry 4.0 revolution, require more and more reliable, fast and automatic tools for production analysis and improvement. Manufacturing companies, in which the human labour has a crucial role, need instruments able to manage complex production systems in terms of resource utilization, product mix, component allocation and material handling optimization. In this context, this work presents an original hardware/software architecture, Motion Analysis System (MAS), aimed at the human body digitalization and analysis during the execution of manufacturing/assembly tasks within the common industrial workstation. MAS is based on the integration of the Motion Capture (MOCAP) technology with an ad hoc software developed for productive and ergonomic analysis of the operator during his work. MAS hardware integrates a network of depth cameras initially developed for gaming (Microsoft Kinect v2™, conceived for markerless MOCAP) and now used for industrial analysis, while an original software infrastructure is programmed to automatically and quantitatively provide productive information (human task analysis in terms of time execution and used space within the workplace, movements of hands and locations visited by the operator) and ergonomic information (full body analysis implementing all the internationally adopted indexes OWAS, REBA, NIOSH and EAWS). This double perspective makes MAS a unique and valuable tool for industrial managers oriented to the workplace analysis and design (in terms of productivity) without neglecting the operator health. This proposed contribution ends with a real industrial application analysing a water pump assembly station: the system setup is discussed and the key results obtained adopting MAS are presented and analysed.

Saite Fan, Xinmin Zhang, Zhihuan Song

An imbalanced number of faulty and normal samples causes serious damage to the performance of the conventional diagnosis methods. To settle the data-imbalance fault diagnosis problem, this article presents a novel general imbalanced sample selection strategy (DiagSelect) based on deep reinforcement learning. In DiagSelect, the problem of imbalanced sample selection from the training set is formulated as a multiarmed bandit problem of deep reinforcement learning. The nondifferentiable optimization problem of imbalanced sample selection can be solved by the Markov decision process. The parameters of DiagSelect can be optimized by REINFORCE with the feedback of the validation set. DiagSelect performs intelligent imbalanced sample selection to obtain better diagnosis performance autonomously. As a data-level technique, DiagSelect can be easily used in conjunction with the conventional diagnosis models. DiagSelect is validated in a synthetic dataset and an industrial process dataset. The results have shown the effectiveness, stability, and transferability of DiagSelect.

Oleksii Zahorka , Serhii Polishchuk , Iryna Zahorka et al.

Під час створення або розвитку організації ключовим аспектом є визначення її потрібного складу, тобто сукупності елементів, здатних виконати визначені завдання з максимальною ефективністю за раціонального використання ресурсів. Проте функціонування організації здійснюється в умовах невизначеності. Це ускладнює обґрунтування складу її організаційної структури. Метою статті є розроблення методичних положень обґрунтування складу організаційної структури окресленого типу на основі принципів системного аналізу для виконання завдань за призначенням. Під час проведення дослідження застосовано: метод безпосереднього оцінювання – для визначення експертами ступеня взаємозв’язку згрупованих за визначеними ознаками завдань під час визначення необхідних типів підрозділів (елементів) організаційної структури; метод аналізу ієрархій – для оцінювання важливості завдань, які повинні виконуватися організацією; метод планування експерименту – для формування варіантів складу підрозділів-виконавців завдань; експертний метод ранжирування – для оцінювання важливості показників, що характеризують створення і функціонування організації; метод таксономії – для визначення раціонального варіанта складу організаційної структури. Застосування зазначених методів дало змогу забезпечити визначення складу організаційної структури відповідно до цілей діяльності організації та завдань, що мають виконуватися для їх досягнення, а також необхідних типів підрозділів (елементів) організаційної структури із врахуванням ступеня взаємозв’язку згрупованих завдань. Науковою новизною розроблених методичних положень є сумісне застосування методів планування експериментів і таксономії, що дає можливість визначити збалансований склад підрозділів за ефективністю і вартістю для виконання завдань організації. Особливістю розроблених методичних положень є комплексне застосування вказаних вище методів, що дасть змогу досягнути поставлену мету, яка визначає теоретичну значущість розроблених методичних положень. Розроблені у статті методичні положення можуть використовуватися органами державного і військового управління для обґрунтування збалансованого складу організаційних структур, що створюються або удосконалюються, за ефективністю і часом виконання завдань підрозділами та витратами на їх створення і застосування. Це визначає практичну значущість розроблених методичних положень. Застосування означених положень показано на прикладі.

Alessio Silvetti, Tiwana Varrecchia, Giorgia Chini et al.

In the Industry 4.0 scenario, human–robot collaboration (HRC) plays a key role in factories to reduce costs, increase production, and help aged and/or sick workers maintain their job. The approaches of the ISO 11228 series commonly used for biomechanical risk assessments cannot be applied in Industry 4.0, as they do not involve interactions between workers and HRC technologies. The use of wearable sensor networks and software for biomechanical risk assessments could help us develop a more reliable idea about the effectiveness of collaborative robots (coBots) in reducing the biomechanical load for workers. The aim of the present study was to investigate some biomechanical parameters with the 3D Static Strength Prediction Program (3DSSPP) software v.7.1.3, on workers executing a practical manual material-handling task, by comparing a dual-arm coBot-assisted scenario with a no-coBot scenario. In this study, we calculated the mean and the standard deviation (SD) values from eleven participants for some 3DSSPP parameters. We considered the following parameters: the percentage of maximum voluntary contraction (%MVC), the maximum allowed static exertion time (MaxST), the low-back spine compression forces at the L4/L5 level (L4Ort), and the strength percent capable value (SPC). The advantages of introducing the coBot, according to our statistics, concerned trunk flexion (SPC from 85.8% without coBot to 95.2%; %MVC from 63.5% without coBot to 43.4%; MaxST from 33.9 s without coBot to 86.2 s), left shoulder abdo-adduction (%MVC from 46.1% without coBot to 32.6%; MaxST from 32.7 s without coBot to 65 s), and right shoulder abdo-adduction (%MVC from 43.9% without coBot to 30.0%; MaxST from 37.2 s without coBot to 70.7 s) in Phase 1, and right shoulder humeral rotation (%MVC from 68.4% without coBot to 7.4%; MaxST from 873.0 s without coBot to 125.2 s), right shoulder abdo-adduction (%MVC from 31.0% without coBot to 18.3%; MaxST from 60.3 s without coBot to 183.6 s), and right wrist flexion/extension rotation (%MVC from 50.2% without coBot to 3.0%; MaxST from 58.8 s without coBot to 1200.0 s) in Phase 2. Moreover, Phase 3, which consisted of another manual handling task, would be removed by using a coBot. In summary, using a coBot in this industrial scenario would reduce the biomechanical risk for workers, particularly for the trunk, both shoulders, and the right wrist. Finally, the 3DSSPP software could be an easy, fast, and costless tool for biomechanical risk assessments in an Industry 4.0 scenario where ISO 11228 series cannot be applied; it could be used by occupational medicine physicians and health and safety technicians, and could also help employers to justify a long-term investment.

Alex de Voogt, Kayla Louteiro

Safety in General Aviation has been a continuous concern. About 12% of all airplane accidents in General Aviation involve nose-overs and nose-down events. A total of 134 accidents reported by the National Transportation Safety Board that include nose-overs and nose-downs were analyzed for their main causes. It was found that 35% of the defining events involved a loss of control on the ground while 58% of the total dataset involved tailwheel-type aircraft. A relatively high proportion of aircraft built before 1950 were found, which are also aircraft that have tailwheel-type landing gear, and thereby a higher propensity for ground loops and nose-overs. It is shown that the high accident rate in General Aviation, especially for accidents that did not result in a fatality, was, to an important extent, explained by tailwheel and older aircraft in the US General Aviation airplane fleet struggling with controlling the aircraft on the ground. Attention to this group of aircraft in future studies may help to more effectively address the relatively high accident rates in General Aviation.

Stephane Gille, Isabelle Clerc-Urmès

Test methods that use pushing forces to evaluate the maximal load capacities of carts in design standards require a flat, smooth and horizontal steel plate and thus do not take into account the real conditions of work. Resistive forces of a single wheel of a cart in a uniform rectilinear motion were measured using a unique test bench with five loads. Forty-four wheels were tested (varying diameters, treads and bearings) with one steel plate and four resilient floor coverings. Based on a linear mixed model, all the following results were significant (<i>p</i> < 0.05). Resistive forces were increased linearly with the load and depended on the characteristics of both the wheel and floor. These forces decreased as the diameter increased. They were important for wheels with sleeve bearings but decreased for cone ball bearings and precision ball bearings. Resistive forces depended on the material of the tread and were higher for solid rubber treads. In contrast, the hardness of the tread had little effect. Resistive forces strongly depended on the hardness of the base foam of resilient floor coverings: the softer the base foam, the higher the resistive forces. Test methods in design standards should be reviewed, using corrective forces based on these present results, to prevent musculoskeletal disorders.

Jan Zenisek, Florian Holzinger, M. Affenzeller

Abstract In this work we present a machine learning based approach for detecting drifting behavior – so-called concept drifts – in continuous data streams. The motivation for this contribution originates from the currently intensively investigated topic Predictive Maintenance (PdM), which refers to a proactive way of triggering servicing actions for industrial machinery. The aim of this maintenance strategy is to identify wear and tear, and consequent malfunctioning by analyzing condition monitoring data, recorded by sensor equipped machinery, in real-time. Recent developments in this area have shown potential to save time and material by preventing breakdowns and improving the overall predictability of industrial processes. However, due to the lack of high quality monitoring data and only little experience concerning the applicability of analysis methods, real-world implementations of Predictive Maintenance are still rare. Within this contribution, we present a method, to detect concept drift in data streams as potential indication for defective system behavior and depict initial tests on synthetic data sets. Further on, we present a real-world case study with industrial radial fans and discuss promising results gained from applying the detailed approach in this scope.

Dongxiao Liu, Amal Alahmadi, Jianbing Ni et al.

Industrial Internet of Things (IIoT) is revolutionizing the retail industry for manufacturers, suppliers, and retailers to improve operational efficiency and consumer experience. In IIoT-enabled retail marketing, reputation systems play a critical role to boost mutual trust among industrial entities and build consumer confidence. In this paper, we focus on reputation management in the consumer–retailer channel, where retailers can accumulate reputations from consumer feedbacks. To encourage consumers to post feedbacks without worrying about being tracked or retaliated, we propose an anonymous reputation system that preserves consumer identities and individual review confidentialities. To increase system transparency and reliability, we further exploit the tamper-proof nature and the distributed consensus mechanism of the blockchain technology. With system designs based on various cryptographic primitives and a Proof-of-Stake consensus protocol, our blockchain-based reputation system is more efficient to offer high levels of privacy guarantees compared with existing ones. Finally, we explore the implementation challenges of the blockchain-based architecture and present a proof-of-concept prototype system by Parity Ethereum. We measure the on/off-chain performance with the scalability discussion to demonstrate the feasibility of the proposed system.

Flávia Pires, A. Cachada, José Barbosa et al.

The digital transformation that is on-going worldwide, and triggered by the Industry 4.0 initiative, has brought to the surface new concepts and emergent technologies. One of these new concepts is the Digital Twin, which recently started gaining momentum, and is related to creating a virtual copy of the physical system, providing a connection between the real and virtual systems to collect and analyze and simulate data in the virtual model to improve the performance of the real system. The benefits of using the digital twin approach is attracting significant attention and interest from research and industry communities in the last few years, and its importance will increase in the upcoming years. Having this in mind, this paper surveys and discusses the digital twin concept in the context of the 4th industrial revolution, particularly focusing the concept and functionalities, the associated technologies, the industrial applications and the research challenges. The applicability of the digital concept is illustrated by the virtualisation of an UR3 collaborative robot which used the V-REP simulation environment and the Modbus communication protocol.

Peng Li, Zhikui Chen, Laurence T. Yang et al.

Matti Yli-Ojanperä, S. Sierla, N. Papakonstantinou et al.

Abstract Industry 4.0 architecture has been studied in a large number of publications in the fields of Industrial Internet of Things, Cyber Physical Production Systems, Enterprise Architectures, Enterprise Integration and Cloud Manufacturing. A large number of architectures have been proposed, but none of them has been adopted by a large number of research groups. Two major Industry 4.0 reference architectures have been developed by industry-driven initiatives, namely the German Industry 4.0 and the US-led Industrial Internet Consortium. These are the Reference Architecture Model Industry 4.0 and Industrial Internet Reference Architecture, which are being standardized by the International Electrotechnical Commission and the Object Management Group, respectively. The first research goal of this article is to survey the literature on Industry 4.0 architectures in a factory context and assess awareness and compatibility with Reference Architecture Model Industry 4.0 and Industrial Internet Reference Architecture. The second research goal is to adapt a previously proposed advanced manufacturing concept to Reference Architecture Model Industry 4.0. With respect to the first research goal, it was discovered that only a minority of researchers were aware of the said reference architectures and that in general authors offered no discussion about the compatibility of their proposals with any internationally standardized reference architecture for Industry 4.0. With respect to the second research goal, it was discovered that Reference Architecture Model Industry 4.0 was mature with respect to communication and information sharing in the scope of the connected world, that further standardization enabling interoperability of different vendors’ technology is still under development and that technology standardization enabling executable business processes between networked enterprises was lacking.

Arijit Karati, S. H. Islam, Marimuthu Karuppiah

In recent years, two technologies, the cloud computing and the Internet of Things (IoT), have a synergistic effect in the modern organizations as digitization is a new business trend for various industries. Therefore, many organizations outsource their crowdsourced industrial-IoT (IIoT) data in the cloud system to reduce data management overhead. However, data authentication is one of the fundamental security/trust requirements in such IIoT network. The certificateless signature (CLS) scheme is a cryptographic primitive that provides data authenticity in IIoT systems. Recently, CLS has become a prime research focus due to its ability to solve the key-escrow problem in a very recent identity-based signature technique. Many CLS schemes have already been developed using map-to-point (MTP) hash function and random oracle model (ROM). However, due to the implementation difficulty and probabilistic nature of MTP function and ROM, those CLSs are impractical. Hence, the development of a CLS for lightweight devices mounted in IIoT has become one of the most focused research trends. This paper presents a new pairing-based CLS scheme without MTP function and ROM. The new CLS scheme is secure against both the Type-I and Type-II adversaries under the hardness of extended bilinear strong Diffie–Hellman (BSDH) and BSDH assumptions, respectively. Performance evaluation and comparison proves that our scheme outperforms other CLS schemes.