323,642 research outputs found
Data-Driven Fault Diagnosis of Once-through Benson Boilers
No full textFault diagnosis (FD) of once-through Benson boilers, as a crucial equipment of many thermal power plants, is of paramount importance to guarantee continuous performance. In this study, a new fault diagnosis methodology based on data-driven methods is presented to diagnose faults in once-through Benson boilers. The present study tackles this issue by adopting a combination of data-driven methods to improve the robustness of FD blocks. For this purpose, one-class versions of minimum spanning tree and K-means algorithms are employed to handle the strong interaction between measurements and part load operation and also to reduce computation time and system training error. Furthermore, an adaptive neuro-fuzzy inference system algorithm is adopted to improve accuracy and robustness of the proposed fault diagnosing system by fusion of the output of minimum spanning tree (MST) and K-means algorithms. Performance of the presented scheme against six major faults is then assessed by analyzing several test scenario
Fault diagnosis in industrial rotating equipment based on permutation entropy, signal processing and multi-output neuro-fuzzy classifier
No full textRotating equipment is considered as a key component in several industrial sectors. In fact, the continuous operation of many industrial machines such as sub-sea pumps and gas turbines relies on the correct performance of their rotating equipment. In order to reduce the probability of malfunctions in this equipment, condition monitoring, and fault diagnosis systems are essential. In this work, a novel approach is proposed to perform fault diagnosis in rotating equipment based on permutation entropy, signal processing, and artificial intelligence. To that aim, vibration signals are employed for an indication of bearing performance. In order to facilitate fault diagnosis, fault detection and isolation are performed in two separate steps. As first, once a vibration signal is received, the faulty state of the bearing is determined by permutation entropy. In case a faulty state is detected, the fault type is determined using an approach based on signal processing and artificial intelligence. Wavelet packet transform and envelope analysis of the vibration signals are utilized to extract the frequency components of the fault. The proposed approach allows for the automatic selection of a frequency band that includes the characteristic resonance frequency of the fault, which is subject to change in different operational conditions. The method works by extracting the proper features of the signals that are used to decide about the faulty bearing’s condition by a multi-output adaptive neuro-fuzzy inference system classifier. The effectiveness of the approach is assessed by the Case Western Reserve University dataset: the analysis demonstrates the proposed method’s capabilities in accurately diagnosing faults in rotating equipment as compared to existing approaches
Self-adaptive fault diagnosis for unseen working conditions based on digital twins and domain generalization
No full textIn recent years, intelligent fault diagnosis based on domain adaptation has been used to address domain shifts in cyber–physical systems; however, the need for acquiring target data sufficiently limits their applicability to unseen working conditions. To overcome such limitations, domain generalization techniques have been introduced to enhance the capacity of fault diagnostic models to operate under unseen working conditions. Nevertheless, existing approaches assume access to extensive labeled training data from various source domains, posing challenges in real-world engineering scenarios due to resource constraints. Moreover, the absence of a mechanism for updating diagnostic models over time calls for the exploration of self-adaptive generalized diagnosis models that are capable of autonomous reconfiguration in response to new unseen working conditions. In such a context, this paper proposes a self-adaptive fault diagnosis system that combines several paradigms, namely Monitor-Analyze-Plan-Execute over a shared Knowledge (MAPE-K), Domain Generalization Network Models (DGNMs), and Digital Twins (DT). The MAPE-K loop enables run-time adaptation to dynamic industrial environments without human intervention. To address the scarcity of labeled training data, digital twins are used to generate supplementary data and continuously tune parameters to reflect the dynamics of new unseen working conditions. DGNM incorporates adversarial learning and a domain-based discrepancy metric to enhance feature diversity and generalization. The introduction of multi-domain data augmentation enhances feature diversity and facilitates learning correlations among multiple domains, ultimately improving the generalization of feature representations. The proposed fault diagnosis system has been evaluated on three publicly available rotating machinery datasets to demonstrate its higher performance in cross-work operation and cross-machine tasks compared to other state-of-the-art methods
Tephritis azari Mohamadzade Namin and S. Korneyev, sp. nov.
No full text<i>Tephritis azari</i> Mohamadzade Namin and S. Korneyev sp. nov. <p>(Figs. 1–12)</p> <p> <b>Type material</b>. Holotype (female): <b>Iran</b>: West Azerbaijan province, 10 km W Ziveh, 2700m, 37°08'N, 44°52'E, 24.vii.2012 (Mohamadzade & Najarpoor leg.) (JAZM).</p> <p> Paratypes: 1Ƥ: <b>Azerbaijan</b>: Talysh, vic. Lerik, Dzhoni vil. [=Çoni, 38°36.8'N 48°30.5'E, h= 1390 m], 5.06.1981 (V. Ermolenko leg.) (SIZK); 3Ƥ, 23, <b>Iran</b>: same collection data as in holotype; 1Ƥ, 13, East Azerbaijan Province, Sahand ski resort, 30 km of Tabriz, 37°45.850' N 46°30.754' E, 2900 m, 30.viii.2011 (Mohamadzade & Najarpoor leg.); 1Ƥ, 13, Ardabil Province, Sabalan Mountain, 2900m; 12.vii.2012, swept from <i>Senecio</i> sp. (Mohamadzade & Najarpoor leg.) (JAZM, some paratypes are deposited also in SIZK and SMN’s personal collection).</p> <p> <b>Description.</b></p> <p> <b>Head</b> (fig. 3): Yellow, whitish microtrichose, except ocellar triangle, distal 2/3 of arista and V-shaped mark on upper part of occiput blackish. Flagellomere 1 yellow. Frontal stripe and face less distinctly microtrichose; frons above lunule with 5–10 setulae. Setulae whitish-yellow, brownish on anterior part of gena. Postocular setae and setulae (both longer and shorter) whitish-yellow. Length: height: width ratio = 1: 1.24: 1.57. Frons subquadrate, twice as wide as eye, which is about 1.35 times as high as long. Gena 0.47 times as high as length of flagellomere 1. Flagellomere 1, 1.6 times as long as wide, pointed at apicodorsal angle.</p> <p> <b>Thorax</b>: Black (only postpronotal lobe narrowly yellow), densely white microtrichose (fig. 9). Setae yellowish brown; posterior notopleural and anepimeral seta dark yellow. Setulae white; scutellum with 15–17 white marginal setulae on each side. Calypteres white. Halter yellow.</p> <p> <b>Legs</b>: Yellow, fore femur with 2 rows of white posterodorsal and one row of yellowish brown posteroventral setae; mid and hind legs with brown setae and setulae.</p> <p> <b>Wing</b> (Figs. 1–2, 11): Base hyaline, including all of cells c, bm and bcu; apical portion with brown radiate mark, with few hyaline spots and indentations; pterostigma entirely brown, cell r 1 in females and 2 males with only one hyaline spot (In females this spot is smaller and reaching to vein R2+3 but in males is larger and penetrates to at most mid-width of cell r2+3) but in the remaining 2 males with 1 additional small hyaline spot on anterior margin (in one male the small hyaline spot is near the larger spot; in another it is closer to the apex of R2+3); cell r2+3 hyaline between radial fork and level of apex of vein Sc, distally brown, with 2 apical hyaline spots, the posterior fused with subapical spot in cell r4+5; br hyaline between basal part of cell and level of apex of Sc, in apical third completely dark, without hyaline spots; cell r4+5 with round hyaline spot touching vein M at or near level of dm-cu, and apical spot rather long, bordered by 2 narrow apical rays extending to apices of veins R4+5 and M; cell dm hyaline on proximal half, on apical half with anterodistal corner dark brown and with 2–3 brown rays (including 1 on dm-cu) and 1 pear-shaped or in some paratypes 2 smaller round subapical hyaline spots; the short brown ray in cell dm basal to level of r-m absent (two males) or usually reaching only mid-width of cell dm, rarely (one female) reaching to vein Cua1. Cell m with 3 large hyaline spots, the most distal extending into cell r4+5; cell cua1 mostly hyaline, with dark apex with small hyaline spot narrow brown bars in apical half. Vein R4+5 with 4–5 setulae ventrally.</p> <p> <b>Abdomen</b>: Black, tergites entirely microtrichose, with white setulae and yellow marginal setae. Oviscape shining black, with whitish setae on basal part; shorter than tergites 5 and 6 combined, its dorsum 1.36 times as long as tergite 6 (Fig. 11). Aculeus brown, 3 times as long as wide, rapidly tapered subapically to short, pointed apex (Figs. 4–5). Spermathecae teardrop shaped (Fig. 8) round with narrowed neck. Epandrium oval, like other <i>Tephritis</i> species (Fig. 6); glans (Fig. 7) moderately short, mostly membranous.</p> <p> <b>Measurements.</b> Female. Body length 3.7–4.4 mm, wing length 3.75–4 mm, oviscape length 0.75–0.87; Male. Body length 3.7–4 mm, wing length 3.5–4 mm.</p> <p> <b>Diagnosis.</b> The new species differs from most Palaearctic species of <i>Tephritis</i> by having only one marginal hyaline spot in cell r1 or if 2, the second spot very small. <i>T. azari</i> sp. nov. is closely related to <i>T. maccus</i> Hering 1937 (Fig. 13) in having similar body, wing size and wing pattern (hyaline basally, with brown radiate pattern on apical two-thirds, and usually with one hyaline spot in cell r1, as well as flagellomere 1 pointed, oviscape short, aculeus relatively blunt, with short acute apex and similar spermathecae shape. It differs from <i>T. maccus</i> by the short brown ray in cell dm basal to the level of r-m, absent or usually reaching only mid-width of cell dm, at most reaching Cu1 (long, reaching middle of cell cua 1 in <i>T. maccus</i>). In addition in both sexes of <i>T. azari</i> there is a large hyaline spot present in cell r4+5 at the level of dm-cu (in <i>T. maccus</i> only males have such a spot).</p> <p> <i>Tephritis azari</i> is also similar to <i>T. urelliosomima</i> (Figs. 14–15), in which the aculeus shape is similar (blunt, but pointed at very apex). It differs in wing pattern with a hyaline spot in cell r1 and the apical fork narrowly connected to the main pattern (in <i>T. urelliosomima</i> the large hyaline spot in r1 is lacking and the base of the apical fork is broadly connected to the main part of the pattern).</p> <p> <b>Etymology.</b> Azari, also known as Old Azeri (also spelled Adari, Adhari), is an ancient language of the Iranian group spoken in the Iranian Azerbaijan; the species name is considered a Latinized noun in apposition.</p>Published as part of <i>Namin, Saeed Mohamadzade & Korneyev, Severyn V., 2012, Tephritis azari, a New Fruit Fly (Diptera: Tephritidae) from Iran and Azerbaijan, with a Key to the Tephritis maccus Species Group, pp. 79-85 in Zootaxa 3590</i> on pages 80-82, DOI: <a href="http://zenodo.org/record/215220">10.5281/zenodo.215220</a>
Mosquito fauna (Diptera: Culicidae) of the Iranian islands in the Persian Gulf II. Greater Tonb, Lesser Tonb and Kish Islands
No full textKhoobdel, M., Azari-Hamidian, S., Hanafi-Bojd, A.A. (2012): Mosquito fauna (Diptera: Culicidae) of the Iranian islands in the Persian Gulf II. Greater Tonb, Lesser Tonb and Kish Islands. Journal of Natural History 46 (29-32): 1939-1945, DOI: 10.1080/00222933.2012.707238, URL: http://dx.doi.org/10.1080/00222933.2012.70723
A Systematic Literature Review on Transfer Learning for Predictive Maintenance in Industry 4.0
No full textThe advent of Industry 4.0 has resulted in the widespread usage of novel paradigms and digital technologies within industrial production and manufacturing systems. The objective of making industrial operations monitoring easier also implied the usage of more effective data-driven predictive maintenance approaches, including those based on machine learning. Although those approaches are becoming increasingly popular, most of the traditional machine learning and deep learning algorithms experience the following three major challenges: 1) lack of training data (especially faulty data), 2) incompatible computation power, and 3) discrepancy in data distribution. A new data-driven technique, such as transfer learning, can be developed to overcome the issues related to traditional machine learning and deep learning for predictive maintenance. Motivated by the recent big interest towards transfer learning within computer science and artificial intelligence, in this paper we provide a systematic literature review addressing related research with a focus on predictive maintenance. The review aims to define transfer learning in the context of predictive maintenance by introducing a specific taxonomy based on relevant perspectives. We also discuss current advances, challenges, open-source datasets, and future directions of transfer learning applications in predictive maintenance from both theoretical and practical viewpoints
Anticancer biomaterials: A special design for precision medicine
No full textDespite prominent progress in cancer detection and therapy, critical challenges still remain and need to be addressed. In this sense, several innovative biomaterials have been designed and produced using different types of biocompatible materials like metals, and polymers, as well as glasses and glass-ceramics. Currently, more precise treatments can be achieved based on the tissue type, the stage of the cancer, and other patient-specific factors, resulting in improved cancer diagnosis and treatment. Indeed, the integration of interdisciplinary research areas, including tissue engineering, regenerative medicine, biomaterials science, nanotechnology, and pharmacology has led to a move forward toward cancer precision medicine. In this sense, precision biomaterials have emerged as promising approaches for more efficient modeling, detection, and treatment of cancers. In this chapter, we will discuss the recent advances and potential applications of precision biomaterials for personalized cancer treatment and try to summarize future directions according to the available data in the literature
Diffusive author(s), cohesive author: Analysis of S/N (1994)
No full textThis study indicates the ways in which various aspects of the author(s) are brought forth in Dumb type’s performance art, the S/N production. Previous research has suggested a non-hierarchical organization of Dumb type and the absence of a “privileged author” in Dumb type’s collaborative work, S/N. However, the results that I have investigated from member’s interviews on the creative process of S/N along with my analysis of the recorded images of S/N, indicate a different aspect of the author(s). First, S/N was created through, so to speak, the collective ideas of the members of Dumb type. Further, S/N has at least nine quotations from previous performances, installations, and printed writings, besides the work-in-progress technique. Explicating one of the “author functions” as given by Michel Foucault, each text has plural subjects of the author. However, it has been revealed from members’ interviews that Teiji Furuhashi had a decision-making role in selecting the members’ ideas within the performance. Since then, S/N has had plural subjects of creation; however, Furuhashi is one of the subjects of creation along with the “privileged author.” S/N has plural authors (diffusive authors) yet at the same time, it has a “privileged author,” Teiji Furuhashi (cohesive author)
Going Beyond Counting First Authors in Author Co-citation Analysis
No full textThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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