Search results for: paper machine risk analysis

Authors: M. Barański

Abstract:

This article presents a new vibration diagnostic method designed to (PM) machines with permanent magnets. Those devices are commonly used in small wind and water systems or vehicles drives. The author’s method is very innovative and unique. Specific structural properties of PM machines are used in this method - electromotive force (EMF) generated due to vibrations. There was analysed number of publications which describe vibration diagnostic methods and tests of electrical PM machines and there was no method found to determine the technical condition of such machine basing on their own signals. In this article will be discussed: the method genesis, the similarity of machines with permanent magnet to vibration sensor and simulation and laboratory tests results. The method of determination the technical condition of electrical machine with permanent magnets basing on its own signals is the subject of patent application and it is the main thesis of author’s doctoral dissertation.

Keywords: Electrical vehicle, generator, permanent magnet, traction drive, vibrations.

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Authors: Sorayya Malek, Mogeeb Mosleh, Sharifah M. Syed

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In this study, Support Vector Machine (SVM) technique was applied to predict the dichotomized value of Dissolved oxygen (DO) from two freshwater lakes namely Chini and Bera Lake (Malaysia). Data sample contained 11 parameters for water quality features from year 2005 until 2009. All data parameters were used to predicate the dissolved oxygen concentration which was dichotomized into 3 different levels (High, Medium, and Low). The input parameters were ranked, and forward selection method was applied to determine the optimum parameters that yield the lowest errors, and highest accuracy. Initial results showed that pH, Water Temperature, and Conductivity are the most important parameters that significantly affect the predication of DO. Then, SVM model was applied using the Anova kernel with those parameters yielded 74% accuracy rate. We concluded that using SVM models to predicate the DO is feasible, and using dichotomized value of DO yields higher prediction accuracy than using precise DO value.

Keywords: Dissolved oxygen, Water quality, predication DO, Support Vector Machine.

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Authors: Chan-Sik Kim, Jong-Seong Kim, Se Won Lee, Hoo-Gon Choi

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In rapidly changing market environment, firms are investing a lot of time and resources into new product development (NPD) projects to make profit and to obtain competitive advantage. However, failure rate of NPD projects is becoming high due to various internal and external risks which hinder successful NPD projects. To reduce the failure rate, it is critical that risks have to be managed effectively and efficiently through good strategy, and treated by optimal responses to minimize risk cost. Four strategies are adopted to handle the risks in this study. The optimal responses are characterized by high reduction of risk costs with high efficiency. This study suggests a framework to decide the optimal responses considering the core risks, risk costs, response efficiency and response costs for successful NPD projects. Both binary particles warm optimization (BPSO) and multi-objective particle swarm optimization (MOPSO) methods are mainly used in the framework. Although several limitations exist in use for real industries, the frame work shows good strength for handling the risks with highly scientific ways through an example.

Keywords: NPD projects, risk cost, strategy, optimal responses, Particle Swarm Optimization.

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Authors: Chinlun Lai, Lunjyh Jiang

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Due to the heavy burden and pressure of caring for infants, an integrated automatic baby watching system based on IoT smart sensing and deep learning machine vision techniques is proposed in this paper. By monitoring infant body conditions such as heartbeat, breathing, body temperature, sleeping posture, as well as the surrounding conditions such as dangerous/sharp objects, light, noise, humidity and temperature, the proposed system can analyze and predict the obvious/potential dangerous conditions according to observed data and then adopt suitable actions in real time to protect the infant from harm. Thus, reducing the burden of the caregiver and improving safety efficiency of the caring work. The experimental results show that the proposed system works successfully for the infant care work and thus can be implemented in various life fields practically.

Keywords: Baby care system, internet of things, deep learning, machine vision.

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Authors: W. S. Hsu, Y. Chiang, H. C. Chen, J. R. Wang, S. W. Chen, J. H. Yang, C. Shih

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Not only radiation materials, but also the normal chemical material stored in the power plant can cause a risk to the residents. In this research, the ALOHA code was used to perform the concentration analysis under the CO₂ storage burst or leakage conditions for Kuosheng nuclear power plant (NPP). The Final Safety Analysis Report (FSAR) and data were used in this study. Additionally, the analysis results of ALOHA code were compared with the R.G. 1.78 failure criteria in order to confirm the control room habitability. The comparison results show that the ALOHA result for burst case was 0.923 g/m³ which was below the criteria. However, the ALOHA results for leakage case was 11.3 g/m³.

Keywords: BWR, ALOHA, habitability, Kuosheng.

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Authors: Zhaoan Wang

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Research in predictive maintenance modeling has improved in the recent years to predict failures and needed maintenance with high accuracy, saving cost and improving manufacturing efficiency. However, classic prediction models provide little valuable insight towards the most important features contributing to the failure. By analyzing and quantifying feature importance in predictive maintenance models, cost saving can be optimized based on business goals. First, multiple classifiers are evaluated with cross-validation to predict the multi-class of failures. Second, predictive performance with features provided by different feature selection algorithms are further analyzed. Third, features selected by different algorithms are ranked and combined based on their predictive power. Finally, linear explainer SHAP (SHapley Additive exPlanations) is applied to interpret classifier behavior and provide further insight towards the specific roles of features in both local predictions and global model behavior. The results of the experiments suggest that certain features play dominant roles in predictive models while others have significantly less impact on the overall performance. Moreover, for multi-class prediction of machine failures, the most important features vary with type of machine failures. The results may lead to improved productivity and cost saving by prioritizing sensor deployment, data collection, and data processing of more important features over less importance features.

Keywords: Automated supply chain, intelligent manufacturing, predictive maintenance machine learning, feature engineering, model interpretation.

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Authors: A. Chebak, N. Barka, A. Menou, J. Brousseau, D. S. Ramdenee

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This paper presents the study of hardness profile of spur gear heated by induction heating process in function of the machine parameters, such as the power (kW), the heating time (s) and the generator frequency (kHz). The global work is realized by 3D finite-element simulation applied to the process by coupling and resolving the electromagnetic field and the heat transfer problems, and it was performed in three distinguished steps. First, a Comsol 3D model was built using an adequate formulation and taking into account the material properties and the machine parameters. Second, the convergence study was conducted to optimize the mesh. Then, the surface temperatures and the case depths were deeply analyzed in function of the initial current density and the heating time in medium frequency (MF) and high frequency (HF) heating modes and the edge effect were studied. Finally, the simulations results are validated using experimental tests.

Keywords: Induction heating, simulation, experimental validation, 3D model, hardness profile.

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Authors: Mohd. Noor Md. Sap, A. Majid Awan

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Intelligent systems based on machine learning techniques, such as classification, clustering, are gaining wide spread popularity in real world applications. This paper presents work on developing a software system for predicting crop yield, for example oil-palm yield, from climate and plantation data. At the core of our system is a method for unsupervised partitioning of data for finding spatio-temporal patterns in climate data using kernel methods which offer strength to deal with complex data. This work gets inspiration from the notion that a non-linear data transformation into some high dimensional feature space increases the possibility of linear separability of the patterns in the transformed space. Therefore, it simplifies exploration of the associated structure in the data. Kernel methods implicitly perform a non-linear mapping of the input data into a high dimensional feature space by replacing the inner products with an appropriate positive definite function. In this paper we present a robust weighted kernel k-means algorithm incorporating spatial constraints for clustering the data. The proposed algorithm can effectively handle noise, outliers and auto-correlation in the spatial data, for effective and efficient data analysis by exploring patterns and structures in the data, and thus can be used for predicting oil-palm yield by analyzing various factors affecting the yield.

Keywords: Pattern analysis, clustering, kernel methods, spatial data, crop yield

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Authors: Sidharth Talan, Sanjay Kumar Sharma, Eoin Joseph Wallace, Nikita Agrawal

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Underground mining is at the core of rapidly evolving metals and minerals sector due to the increasing mineral consumption globally. Even though the surface mines are still more abundant on earth, the scales of industry are slowly tipping towards underground mining due to rising depth and complexities of orebodies. Thus, the efficient and productive functioning of underground operations depends significantly on the synchronized performance of key elements such as operating site, mining equipment, manpower and mine services. Production drilling is the process of conducting long hole drilling for the purpose of charging and blasting these holes for the production of ore in underground metal mines. Thus, production drilling is the crucial segment in the underground metal mining value chain. This paper presents the process mapping tool to evaluate the production drilling process in the underground metal mining operation by dividing the given process into three segments namely Input, Process and Output. The three segments are further segregated into factors and sub-factors. As per the study, the major input factors crucial for the efficient functioning of production drilling process are power, drilling water, geotechnical support of the drilling site, skilled drilling operators, services installation crew, oils and drill accessories for drilling machine, survey markings at drill site, proper housekeeping, regular maintenance of drill machine, suitable transportation for reaching the drilling site and finally proper ventilation. The major outputs for the production drilling process are ore, waste as a result of dilution, timely reporting and investigation of unsafe practices, optimized process time and finally well fragmented blasted material within specifications set by the mining company. The paper also exhibits the drilling loss matrix, which is utilized to appraise the loss in planned production meters per day in a mine on account of availability loss in the machine due to breakdowns, underutilization of the machine and productivity loss in the machine measured in drilling meters per unit of percussion hour with respect to its planned productivity for the day. The given three losses would be essential to detect the bottlenecks in the process map of production drilling operation so as to instigate the action plan to suppress or prevent the causes leading to the operational performance deficiency. The given tool is beneficial to mine management to focus on the critical factors negatively impacting the production drilling operation and design necessary operational and maintenance strategies to mitigate them. 

Keywords: Process map, drilling loss matrix, availability, utilization, productivity, percussion rate.

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Authors: Abiodun M. Lawal, Benjamin O. Olley

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Long Distance Truck Drivers (LDTDs) have been found to be a high risk group in the spread of HIV/AIDS globally; perhaps, due to their high Sexual Risk Behaviours (SRBs). Interventions for reducing SRBs in trucking population have not been fully exploited. A quasi-experimental control group pretest-posttest design was used to assess the efficacy of psycho-education and behavioural skills training in reducing SRBs among LDTDs. Sixteen drivers rivers were randomly assigned into either experimental or control groups using balloting technique. Questionnaire was used as an instrument for data collection. Repeated measures t-test and independent t-test were used to test hypotheses. Intervention had significant effect on the SRBs among LDTDs at post-test (t{7}= 6.01, p<.01) and at follow up (t{7} = 6.42, p<.01). No significant difference in sexual risk behaviour of LDTDs at post-test and at follow-up stage. Similarly, intervention had significant effects on sexual risk behaviour at post-test (t {14} = - 4.69, p<.05) and at follow-up (t {14} = -9.56, p<.05) respectively. At post-test and follow-up stages, drivers in experimental group reported reduced SRBs than those in control group. Drivers in experimental group reported lower sexual risk behaviour a week after intervention as well as at three months follow-up than those in control group. It is concluded that HIV/AIDS preventive intervention that provides the necessary informational and behavioural skills content can significantly impact long distance truck drivers’ sexual risk behaviours.

Keywords: HIV/AIDS interventions, Long distance truck drivers, Nigeria, Sexual risk behaviours.

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Authors: Jindřiška Šedová

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In the past 20 years the economy of the Czech Republic has experienced substantial changes. In the 1990s the development was affected by the transformation which sought to establish the right conditions for privatization and creation of elementary market relations. In the last decade the characteristic elements such as private ownership and corresponding institutional framework have been strengthened. This development was marked by the accession of the Czech Republic to the EU. The Czech Republic is striving to reduce the difference between its level of economic development and the quality of institutional framework in comparison with other developed countries. The process of finding the adequate solutions has been hampered by the negative impact of the world financial crisis on the Czech Republic and the standard of living of its inhabitants. This contribution seeks to address the question of whether and to which extent the economic development of the transitive Czech economy is affected by the change in behaviour of households and their tendency to consumption, i.e. in the sense of reduction or increase in demand for goods and services. It aims to verify whether the increasing trend of household indebtedness and decreasing trend of saving pose a significant risk in the Czech Republic. At a general level the analysis aims to contribute to finding an answer to the question of whether the debt increase of Czech households is connected to the risk of "eating through" the borrowed money and whether Czech households risk falling into a debt trap. In addition to household indebtedness risks in the Czech Republic the analysis will focus on identification of specifics of the transformation phase of the Czech economy in comparison with the EU countries, or selected OECD countries.

Keywords: household indebtedness, household consumption, credits, financial literacy

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Authors: M. H. Hairi, H. Zainuddin, M.H.N. Talib, A. Khamis, J. Y. Lichun

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Short circuit currents plays a vital role in influencing the design and operation of equipment and power system and could not be avoided despite careful planning and design, good maintenance and thorough operation of the system. This paper discusses the short circuit analysis conducted in KSO briefly comprising of its significances, methods and results. A result sample of the analysis based on a single transformer is detailed in this paper. Furthermore, the results of the analysis and its significances were also discussed and commented.

Keywords: Short circuit currents, Transformer fault current

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Authors: A. Asambe

Abstract:

A study was conducted to determine the level of compliance with sanitary measures in piggeries, and awareness and risk factors of African swine fever in Benue State, Nigeria. Questionnaires were distributed to 74 respondents consisting of piggery owners and attendants in different piggeries across 12 LGAs to collect data for this study. Sanitary measures in piggeries were observed to be generally very poor, though respondents admitted being aware of ASF. Piggeries located within a 1 km radius of a slaughter slab (OR=9.2, 95% CI - 3.0-28.8), piggeries near refuse dump sites (OR=3.0, 95% CI - 1.0-9.5) and piggeries where farm workers wear their work clothes outside of the piggery premises (OR=0.2, 95% CI - 0.1-0.7) showed higher chances of ASFV infection and were significantly associated (p < 0.0001), (p < 0.05) and (p < 0.01), and were identified as potential risk factors. The study concluded that pigs in Benue State are still at risk of an ASF outbreak. Proper sanitary and hygienic practices is advocated and emphasized in piggeries, while routine surveillance for ASFV antibodies in pigs in Benue State is strongly recommended to provide a reliable reference data base to plan for the prevention of any devastating ASF outbreak.

Keywords: African swine fever, awareness, piggery, risk factors, sanitary measures.

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Authors: Zhou Xiong, Kang Shao Bo, Yang Bo

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To date, high-performance structural steel has been widely used for columns in construction practices due to its significant advantages over conventional steel. However, the same design approach with conventional steel columns is still adopted in the design of high-performance steel columns. As a result, its superior properties cannot be fully considered in design. This paper conducts a test and finite element analysis on the overall stability behaviour of welded Q460GJ steel box columns. In the test, four steel columns with different slenderness and width-to-thickness ratio were compressed under an axial compression testing machine. And finite element models were established in which material nonlinearity and residual stress distributions of test columns were included. Then, comparisons were made between test results and finite element result, it showed that finite element analysis results are agree well with the test result. It means that the test and finite element model are reliable. Then, we compared the test result with the design value calculated by current code, the result showed that Q460GJ steel box columns have the higher overall buckling capacity than the design value. It is necessary to update the design curves for Q460GJ steel columns so that the overall stability capacity of Q460GJ box columns can be designed appropriately.

Keywords: Axial compression, Finite element analysis, Overall stability, Q460GJ steel, Welded box columns.

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Authors: Seung Woo Lee, So Jeong Nam, Jai-Kyung Lee

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It can be said that the business sector is faced with a range of challenges–a rapidly changing business environment, an increase and diversification of customers- demands and the consequent need for quick response–for having in place flexible management and production info systems. As a matter of fact, many manufacturers have adopted production info management systems such as MES and ERP. Nevertheless, managers are having difficulties obtaining ever-changing production process information in real time, or responding quickly to any change in production related needs on the basis of such information. This is because they rely on poor production info systems which are not capable of providing real-time factory settings. If the manufacturer doesn-t have a capacity for collecting or digitalizing the 4 Ms (Man, Machine, Material, Method), which are resources for production, on a real time basis, it might to difficult to effectively maintain the information on production process. In this regard, this paper will introduce some new alternatives to the existing methods of collecting the 4 Ms in real time, which are currently comprise the production field.

Keywords: 4M, Acquisition of Data on shop-floor, Real-time machine interface

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Authors: Avadhoot Bhosale, Robin Davis P., Pradip Sarkar

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The seismic performance of buildings with irregular distribution of mass, stiffness and strength along the height may be significantly different from that of regular buildings with masonry infill. Masonry infilled reinforced concrete (RC) frames are very common structural forms used for multi-storey building construction. These structures are found to perform better in past earthquakes owing to additional strength, stiffness and energy dissipation in the infill walls. The seismic performance of a building depends on the variation of material, structural and geometrical properties. The sensitivity of these properties affects the seismic response of the building. The main objective of the sensitivity analysis is to found out the most sensitive parameter that affects the response of the building. This paper presents a sensitivity analysis by considering 5% and 95% probability value of random variable in the infills characteristics, trying to obtain a reasonable range of results representing a wide number of possible situations that can be met in practice by using pushover analysis. The results show that the strength-related variation values of concrete and masonry, with the exception of tensile strength of the concrete, have shown a significant effect on the structural performance and that this effect increases with the progress of damage condition for the concrete. The seismic risk assessments of the selected frames are expressed in terms of reliability index.

Keywords: Fragility curve, sensitivity analysis, reliability index, RC frames.

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Authors: Mohamed A. Mahfouz, M. A. Ismail

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Biclustering is a very useful data mining technique for identifying patterns where different genes are co-related based on a subset of conditions in gene expression analysis. Association rules mining is an efficient approach to achieve biclustering as in BIMODULE algorithm but it is sensitive to the value given to its input parameters and the discretization procedure used in the preprocessing step, also when noise is present, classical association rules miners discover multiple small fragments of the true bicluster, but miss the true bicluster itself. This paper formally presents a generalized noise tolerant bicluster model, termed as μBicluster. An iterative algorithm termed as BIDENS based on the proposed model is introduced that can discover a set of k possibly overlapping biclusters simultaneously. Our model uses a more flexible method to partition the dimensions to preserve meaningful and significant biclusters. The proposed algorithm allows discovering biclusters that hard to be discovered by BIMODULE. Experimental study on yeast, human gene expression data and several artificial datasets shows that our algorithm offers substantial improvements over several previously proposed biclustering algorithms.

Keywords: Machine learning, biclustering, bi-dimensional clustering, gene expression analysis, data mining.

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Authors: S. Golmohammadi, M. Noorian Bidgoli

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Tunneling progress is a key parameter in the blasting method of tunneling. Taking measures to enhance tunneling advance can limit the progress distance without a supporting system, subsequently reducing or eliminating the risk of damage. This paper focuses on modeling tunneling progress using three main groups of parameters (tunneling geometry, blasting pattern, and rock mass specifications) based on the Rock Engineering Systems (RES) methodology. In the proposed models, four main effective parameters on tunneling progress are considered as inputs (RMR, Q-system, Specific charge of blasting, Area), with progress as the output. Data from 86 blasts conducted at the tailrace tunnel in the Azad Dam, western Iran, were used to evaluate the progress value for each blast. The results indicated that, for the 86 blasts, the progress of the estimated model aligns mostly with the measured progress. This paper presents a method for building the interaction matrix (statistical base) of the RES model. Additionally, a comparison was made between the results of the new RES-based model and a Multi-Linear Regression (MLR) analysis model. In the RES-based model, the effective parameters are RMR (35.62%), Q (28.6%), q (specific charge of blasting) (20.35%), and A (15.42%), respectively, whereas for MLR analysis, the main parameters are RMR, Q (system), q, and A. These findings confirm the superior performance of the RES-based model over the other proposed models.

Keywords: Rock Engineering Systems, tunneling progress, Multi Linear Regression, Specific charge of blasting.

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Authors: O. O. Olawusi-Peters, K. I. Adejugbagbe

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The concentration of heavy metal (Cd, Pb, Fe, Zn, Cu) in Clarias gariepinus collected from fish markets; Fanibi (Station I) and Fiwasaye (Station II) in Akure metropolis, Ondo state, Nigeria were investigated to ascertain the safety for the consumers. 60 samples were collected from the two markets in three batches (I, II, III) for a period of six months and analyzed for heavy metals in the gills and muscles of the fish. Also, the Health Risk Index (HRI) was used to determine the health risk of these metals to the consumer. The results showed that the investigated metal concentration was higher in station I than station II, except Pb having higher concentration in station II than station I. In both stations, the highest concentration of Fe was recorded in the gills (12.60 ± 1.51; 6.94 ± 1.38) and muscles (3.72 ± 0.09; 3.86 ± 0.33) of samples in batch I. Also, the HRI revealed that consumption of Clarias gariepinus from these study areas did not pose any health risk (HRI < 1). In addition, concentrations of the heavy metals were all below the permissible limits recommended by FAO/WHO.

Keywords: Health risk index, heavy metals, Clarias gariepinus, Akure metropolis, fish monger.

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Authors: Ronit Chakraborty, Sugata Banerji

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There has been a marked increase in the reported prevalence of Autism Spectrum Disorder (ASD) among children in the US over the past two decades. This research has analyzed the growth in state-level ASD prevalence against 45 different potentially explanatory factors including socio-economic, demographic, healthcare, public policy and political factors. The goal was to understand if these factors have adequate predictive power in modeling the differential growth in ASD prevalence across various states, and, if they do, which factors are the most influential. The key findings of this study include (1) there is a confirmation that the chosen feature set has considerable power in predicting the growth in ASD prevalence, (2) the most influential predictive factors are identified, (3) given the nature of the most influential predictive variables, an indication that a considerable portion of the reported ASD prevalence differentials across states could be attributable to over and under diagnosis, and (4) Florida is identified as a key outlier state pointing to a potential under-diagnosis of ASD.

Keywords: Autism Spectrum Disorder, ASD, clustering, Machine Learning, predictive modeling.

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Authors: Marcus Banda

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In this article the influence of higher frequency effects in addition to a special damper design on the electrical behavior of a synchronous generator main exciter machine is investigated. On the one hand these machines are often highly stressed by harmonics from the bridge rectifier thus facing additional eddy current losses. On the other hand the switching may cause the excitation of dangerous voltage peaks in resonant circuits formed by the diodes of the rectifier and the commutation reactance of the machine. Therefore modern rotating exciters are treated like synchronous generators usually modeled with a second order equivalent circuit. Hence the well known Standstill Frequency Response Test (SSFR) method is applied to a test machine in order to determine parameters for the simulation. With these results it is clearly shown that higher frequencies have a strong impact on the conventional equivalent circuit model. Because of increasing field displacement effects in the stranded armature winding the sub-transient reactance is even smaller than the armature leakage at high frequencies. As a matter of fact this prevents the algorithm to find an equivalent scheme. This issue is finally solved using Laplace transfer functions fully describing the transient behavior at the model ports.

Keywords: Synchronous exciter machine, Linear transfer function, SSFR, Equivalent Circuit

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Authors: M. Hoxha, V. Capra, C. Buccellati, A. Sala, C. Cena, R. Fruttero, M. Bertinaria, G. E. Rovati

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Today COXIBs are used in the treatment of arthritis and many other painful conditions in selected patients with high gastrointestinal risk and low cardiovascular (CV) risk. Previously, we have identified an unexpected mechanism of action of a traditional non-steroidal anti-inflammatory drug (NSAID) (diclofenac) and a specific inhibitor of cyclooxygenase-2 (COXIB) (lumiracoxib) demonstrating that they possess weak competitive antagonism at the thromboxane receptor (TP). We hypothesize that modifying the structure of a known COXIB so that it becomes also a more potent TP antagonist will preserve the anti-inflammatory and gastrointestinal safety typical of COXIBs and prevent the CV risk associated with long term therapy.

Keywords: Cyclooxygenase, inflammation, lumiracoxib, thromboxane A2.

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Authors: Guo Xiuhua, Sun Tao, Wu Haifeng, He Wen, Liang Zhigang, Zhang Mengxia, Guo Aimin, Wang Wei

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Purpose: To explore the use of Curvelet transform to extract texture features of pulmonary nodules in CT image and support vector machine to establish prediction model of small solitary pulmonary nodules in order to promote the ratio of detection and diagnosis of early-stage lung cancer. Methods: 2461 benign or malignant small solitary pulmonary nodules in CT image from 129 patients were collected. Fourteen Curvelet transform textural features were as parameters to establish support vector machine prediction model. Results: Compared with other methods, using 252 texture features as parameters to establish prediction model is more proper. And the classification consistency, sensitivity and specificity for the model are 81.5%, 93.8% and 38.0% respectively. Conclusion: Based on texture features extracted from Curvelet transform, support vector machine prediction model is sensitive to lung cancer, which can promote the rate of diagnosis for early-stage lung cancer to some extent.

Keywords: CT image, Curvelet transform, Small pulmonary nodules, Support vector machines, Texture extraction.

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Authors: Sidharth Talan, Devendra Kumar Yadav, Yuvraj Singh Rajput, Subhajit Bhattacharjee

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Mining industry is known for its appetite to spend sizeable capital on mine equipment. However, in the current scenario, the mining industry is challenged by daunting factors of non-uniform geological conditions, uneven ore grade, uncontrollable and volatile mineral commodity prices and the ever increasing quest to optimize the capital and operational costs. Thus, the role of equipment reliability and maintenance planning inherits a significant role in augmenting the equipment availability for the operation and in turn boosting the mine productivity. This paper presents the Risk Based Maintenance (RBM) planning conducted on mine loading equipment namely Load Haul Dumpers (LHDs) at Vedanta Resources Ltd subsidiary Hindustan Zinc Limited operated Sindesar Khurd Mines, an underground zinc and lead mine situated in Dariba, Rajasthan, India. The mining equipment at the location is maintained by the Original Equipment Manufacturers (OEMs) namely Sandvik and Atlas Copco, who carry out the maintenance and inspection operations for the equipment. Based on the downtime data extracted for the equipment fleet over the period of 6 months spanning from 1^st January 2017 until 30^th June 2017, it was revealed that significant contribution of three downtime issues related to namely Engine, Hydraulics, and Transmission to be common among all the loading equipment fleet and substantiated by Pareto Analysis. Further scrutiny through Bubble Matrix Analysis of the given factors revealed the major influence of selective factors namely Overheating, No Load Taken (NTL) issues, Gear Changing issues and Hose Puncture and leakage issues. Utilizing the equipment wise analysis of all the downtime factors obtained, spares consumed, and the alarm logs extracted from the machines, technical design changes in the equipment and pre shift critical alarms checklist were proposed for the equipment maintenance. The given analysis is beneficial to allow OEMs or mine management to focus on the critical issues hampering the reliability of mine equipment and design necessary maintenance strategies to mitigate them.

Keywords: Bubble matrix analysis, LHDs, OEMs, pareto chart analysis, spares consumption matrix, critical alarms checklist.

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Authors: Jacqueline R. T. Alipo-on, Francesca I. F. Escobar, Myles J. T. Tan, Hezerul Abdul Karim, Nouar AlDahoul

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Electrocardiogram (ECG) signal analysis and processing are crucial in the diagnosis of cardiovascular diseases which are considered as one of the leading causes of mortality worldwide. However, the traditional rule-based analysis of large volumes of ECG data is time-consuming, labor-intensive, and prone to human errors. With the advancement of the programming paradigm, algorithms such as machine learning have been increasingly used to perform an analysis on the ECG signals. In this paper, various deep learning algorithms were adapted to classify five classes of heart beat types. The dataset used in this work is the synthetic MIT-Beth Israel Hospital (MIT-BIH) Arrhythmia dataset produced from generative adversarial networks (GANs). Various deep learning models such as ResNet-50 convolutional neural network (CNN), 1-D CNN, and long short-term memory (LSTM) were evaluated and compared. ResNet-50 was found to outperform other models in terms of recall and F1 score using a five-fold average score of 98.88% and 98.87%, respectively. 1-D CNN, on the other hand, was found to have the highest average precision of 98.93%.

Keywords: Heartbeat classification, convolutional neural network, electrocardiogram signals, ECG signals, generative adversarial networks, long short-term memory, LSTM, ResNet-50.

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Authors: Ewa Dostatni, Jacek Diakun, Damian Grajewski, Radosław Wichniarek, Anna Karwasz

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In the paper the method of product analysis from recycling point of view has been described. The analysis bases on set of measures that assess a product from the point of view of final stages of its lifecycle. It was assumed that such analysis will be performed at the design phase – in order to conduct such analysis the computer system that aids the designer during the design process has been developed. The structure of the computer tool, based on agent technology, and example results has been also included in the paper.

Keywords: Agent technology, PLM, design for environment, ecodesign.

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Authors: Milton Montejano Castillo, Tarsicio Pastrana Salcedo

Abstract:

Based on the hypothesis that disaster risk is constructed socially and historically, this article shows the importance of keeping alive the historical memory of disaster by means of architectural and urban heritage conservation. This is illustrated with three examples of Latin American World Heritage cities, where disasters like floods and earthquakes have shaped urban form. Therefore, the study of urban form or "Urban Morphology" is proposed as a tool to understand and analyze urban transformations with the documentation of the occurrence of disasters. Lessons learned from such cities may be useful to reduce disasters risk in contemporary built environments.

Keywords: Conservation, disaster risk reduction, urban morphology, world heritage.

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Authors: Sheng-Tse Wu, Wu-Sung Yao

Abstract:

In this paper, a drift assist control system is proposed for remote control (RC) cars to get the perfect drift angle. A steering servo control scheme is given powerfully to assist the drift driving. A gyroscope sensor is included to detect the machine's tail sliding and to achieve a better automatic counter-steering to prevent RC car from spinning. To analysis tire traction and vehicle dynamics is used to obtain the dynamic track of RC cars. It comes with a control gain to adjust counter-steering amount according to the sensor condition. An illustrated example of 1:10 RC drift car is given and the real-time control algorithm is realized by Arduino Uno.

Keywords: Drift assist control system, remote control cars, gyroscope, vehicle dynamics.

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Authors: Reimund Neugebauer, Welf-Guntram Drossel, Andre Bucht, Christoph Ohsenbrügge

Abstract:

In this paper the authors propose and verify an approach to control heat flow in machine tool components. Thermal deformations are a main aspect that affects the accuracy of machining. Due to goals of energy efficiency, thermal basic loads should be reduced. This leads to inhomogeneous and time variant temperature profiles. To counteract these negative consequences, material with high melting enthalpy is used as a method for thermal stabilization. The increased thermal capacity slows down the transient thermal behavior. To account for the delayed thermal equilibrium, a control mechanism for thermal flow is introduced. By varying a gap in a heat flow path the thermal resistance of an assembly can be controlled. This mechanism is evaluated in two experimental setups. First to validate the ability to control the thermal resistance and second to prove the possibility of a self-sufficient option based on the selfsensing abilities of thermal shape memory alloys.

Keywords: energy-efficiency, heat transfer path, MT thermal stability, thermal shape memory alloy

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Authors: Jasna Prester, Ivana Rašić Bakarić, Božidar Matijević

Abstract:

Innovations not only contribute to competitiveness of the company but have also positive effects on revenues. On average, product innovations account to 14 percent of companies’ sales. Innovation management has substantially changed during the last decade, because of growing reliance on external partners. As a consequence, a new task for purchasing arises, as firms need to understand which suppliers actually do have high potential contributing to the innovativeness of the firm and which do not. Proper organization of the purchasing function is important since for the majority of manufacturing companies deal with substantial material costs which pass through the purchasing function. In the past the purchasing function was largely seen as a transaction-oriented, clerical function but today purchasing is the intermediate with supply chain partners contributing to innovations, be it product or process innovations. Therefore, purchasing function has to be organized differently to enable firm innovation potential. However, innovations are inherently risky. There are behavioral risk (that some partner will take advantage of the other party), technological risk in terms of complexity of products and processes of manufacturing and incoming materials and finally market risks, which in fact judge the value of the innovation. These risks are investigated in this work. Specifically, technological risks which deal with complexity of the products, and processes will be investigated more thoroughly. Buying components or such high edge technologies necessities careful investigation of technical features and therefore is usually conducted by a team of experts. Therefore it is hypothesized that higher the technological risk, higher will be the centralization of the purchasing function as an interface with other supply chain members. Main contribution of this research lies is in the fact that analysis was performed on a large data set of 1493 companies, from 25 countries collected in the GMRG 4 survey. Most analyses of purchasing function are done by case study analysis of innovative firms. Therefore this study contributes with empirical evaluations that can be generalized.

Keywords: Purchasing function organization, innovation, technological risk, GMRG 4 survey.

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