scholarly journals Condition Monitoring and Predictive Maintenance of Process Equipments

2021 ◽  
Vol 40 ◽  
pp. 01003
Author(s):  
Manthan Deshmukh ◽  
Rohan Dumbre ◽  
Shubham Anekar ◽  
Heramb Kulkarni ◽  
Sushant Pawar
Keyword(s):  
Heat Exchanger ◽  
Semiconductor Manufacturing ◽  
Industrial Revolution ◽  
Predictive Maintenance ◽  
Support Vector ◽  
Heat Exchanger Plate ◽  
Historic Data ◽  
Modelling Techniques ◽  
The Moment ◽  
Available Information

Industry 4.0 the proclaimed fourth industrial revolution is unfolding at the moment. It is characterized by interconnectedness and vast amounts of available information. Industrial production has evolved enormously over the last centuries due to modern instruments. Hence issue of the instrument failure is very paramount in any industry. Even if one machine fails it halts the whole production. Overall, it may cost us with more man-hours, project delay, process latency and all this sums up as a huge loss. The life of the instruments should be taken care by continuously monitoring its health. Any faulty or unnatural disturbance in usage of the instrument may lead to its failure. Every instrument needs proper maintenance, even with the slight negligence towards the anomaly it may lead to instrument failure. In, predictive maintenance historic data is utilized and analyzed with the help of advance analytics and modelling techniques using Machine learning, moreover we can predict failures and can schedule the maintenance beforehand and predict failure in advance. With the help of relevant sensor dataset, we can estimate the remaining runtime of the instruments. This maintenance approach helps to lower the costs which are incurred due to system shut downs. It also ease the scheduling and maintenance activities.In this work, three different industrial case studies are considered like shell and tube type heat exchanger, plate type heat exchanger, and semiconductor manufacturing process.Here the predictive maintenance is carried out for heat exchanger by utilizing the concept of multi linear regression and time series analysis. For the semiconductor manufacturing dataset, support vector machine algorithm is implemented to find out the good and bad quality of semiconductor production slots.

Fungi on leaves, branches and stems of trees and shrubs of Saint Petersburg suburban parks

2017 ◽  
Author(s):  
М.В. Сидельникова ◽  
А.В. Тобиас ◽  
Д.Ю. Власов
Keyword(s):  
Pathogenic Fungi ◽  
Dutch Elm Disease ◽  
City Center ◽  
Saint Petersburg ◽  
Xylotrophic Fungi ◽  
Fungi Infection ◽  
Mycological Examination ◽  
The Moment ◽  
Available Information ◽  
Trees And Shrubs

Проведены микологические обследования древесной и кустарниковой растительности на территории парковой зоны Санкт-Петербурга и пригородов. Сбор материала проводился в парках южных пригородов Санкт-Петербурга (Павловский парк, Екатерининский парк, Нижний сад и Верхний парк Ораниенбаума, Верхний сад и Нижний парк ГМЗ «Петергоф»). В сравнительных целях был обследован парк при Обуховской больнице в центре Санкт-Петербурга. На древесно-кустарниковых породах парковой зоны нами выявлено 230 видов грибов (микро- и макромицетов). На листьях выявлено 28 видов микромицетов, в числе которых возбудители мучнистой росы, ржавчины и пятнистостей. На ветвях и стволах древесных пород выявлено 150 видов микромицетов, среди которых есть как часто встречающиеся, так и редкие виды грибов. Большинство из них обнаруживается в анаморфной стадии. Наибольшее разнообразие и развитие микромицетов отмечено на сухих ветвях. Высокой вредоносностью характеризуются тиростромоз липы и голландская болезнь вязов. Выявлены устойчивые патогенные комплексы грибов, развитие которых приводит к заметному ухудшению состояния растений. На стволах живых и усыхающих деревьев, а также растительных остатках отмечено 52 вида макромицетов. Среди них выявлены доминирующие и редкие виды. Среди источников заражения древесных растений ксилотрофными грибами выделяются отмершие вязы, усохшие стволы которых можно наблюдать как в пригородных парках, так и в центральной части Санкт-Петербурга. Полученные данные существенно расширяют имеющиеся сведения по микобиоте парков Санкт-Петербурга. Mycological examination of tree and shrub vegetation on the territory of Saint Petersburg park zone and its suburbs was conducted. Material was collected in the parks of southern suburbs of Saint Petersburg (Pavlovsk Park, Catherine Park, Lower Garden and Upper Park in Oranienbaum, Upper Garden and Lower Park in Peterhof). For comparative purposes Park of Obukhov Hospital in Saint Petersburg city center was also examined. At the moment, 230 fungi species (micro- and macrofungi) were identified on trees and shrubs of the park zone. Among them, 28 species of microfungi, including powdery mildew, rust and blights pathogens were found on leaves. Also, 150 species of microfungi, including both common and rare fungi species, were found on branches and trunks. Most of them were found in the anamorphic stage. The greatest diversity and microfungi development were noted on dry branches. Thyrostromose of linden and Dutch elm disease are the most harmful. Stable complexes of pathogenic fungi, which development leads to clear decline of plants' condition, were identified. In addition, 52 species of macrofungi, including dominant and rare species, were observed on trunks of living and drying trees and vegetation residues. Among the sources of xylotrophic fungi infection of woody plants, dead elms are the most distinguished. Their dead trunks can be found in both suburban parks and the central part of Saint Petersburg. The presented data significantly expand available information on mycobiota Saint Petersburg parks.

scholarly journals AVERAGING OF AN INCREASING NUMBER OF MOMENT CONDITION ESTIMATORS

2014 ◽  
Vol 32 (1) ◽  
pp. 30-70 ◽  
Author(s):  
Xiaohong Chen ◽  
David T. Jacho-Chávez ◽  
Oliver Linton
Keyword(s):  
Moment Condition ◽  
Moment Conditions ◽  
Parameter Heterogeneity ◽  
Class Of Estimators ◽  
Optimal Weighting ◽  
The Mean ◽  
The Moment ◽  
Image Position ◽  
Nonlinear Estimators ◽  
Available Information

We establish the consistency and asymptotic normality for a class of estimators that are linear combinations of a set of$\sqrt n$-consistent nonlinear estimators whose cardinality increases with sample size. The method can be compared with the usual approaches of combining the moment conditions (GMM) and combining the instruments (IV), and achieves similar objectives of aggregating the available information. One advantage of aggregating the estimators rather than the moment conditions is that it yields robustness to certain types of parameter heterogeneity in the sense that it delivers consistent estimates of the mean effect in that case. We discuss the question of optimal weighting of the estimators.

A Machine Learning Based Approach of Performance Estimation for High-Pressure Compressor Airfoils

2018 ◽  
Author(s):  
Jonas Marx ◽  
Stefan Gantner ◽  
Jörn Städing ◽  
Jens Friedrichs
Keyword(s):  
Machine Learning ◽  
High Pressure ◽  
Machine Learning Algorithms ◽  
Performance Estimation ◽  
Predictive Maintenance ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Operating Characteristics ◽  
High Pressure Compressor ◽  
Pressure Compressor

In recent years, the demands of Maintenance, Repair and Overhaul (MRO) customers to provide resource-efficient after market services have grown increasingly. One way to meet these requirements is by making use of predictive maintenance methods. These are ideas that involve the derivation of workscoping guidance by assessing and processing previously unused or undocumented service data. In this context a novel approach on predictive maintenance is presented in form of a performance-based classification method for high pressure compressor (HPC) airfoils. The procedure features machine learning algorithms that establish a relation between the airfoil geometry and the associated aerodynamic behavior and is hereby able to divide individual operating characteristics into a finite number of distinct aero-classes. By this means the introduced method not only provides a fast and simple way to assess piece part performance through geometrical data, but also facilitates the consideration of stage matching (axial as well as circumferential) in a simplified manner. It thus serves as prerequisite for an improved customary HPC performance workscope as well as for an automated optimization process for compressor buildup with used or repaired material that would be applicable in an MRO environment. The methods of machine learning that are used in the present work enable the formation of distinct groups of similar aero-performance by unsupervised (step 1) and supervised learning (step 2). The application of the overall classification procedure is shown exemplary on an artificially generated dataset based on real characteristics of a front and a rear rotor of a 10-stage axial compressor that contains both geometry as well as aerodynamic information. In step 1 of the investigation only the aerodynamic quantities in terms of multivariate functional data are used in order to benchmark different clustering algorithms and generate a foundation for a geometry-based aero-classification. Corresponding classifiers are created in step 2 by means of both, the k Nearest Neighbor and the linear Support Vector Machine algorithms. The methods’ fidelities are brought to the test with the attempt to recover the aero-based similarity classes solely by using normalized and reduced geometry data. This results in high classification probabilities of up to 96 % which is proven by using stratified k-fold cross-validation.

scholarly journals Smart Mechanical Systems for Manufacturing in the Era of Industry 4.0: Condition-Based Predictive Maintenance and Dynamic System Modification for Small and Medium-Sized Enterprises

2021 ◽  
Vol 20 (2) ◽  
Author(s):  
Giovanni Carabin ◽  
Erich Wehrle ◽  
Renato Vidoni
Keyword(s):  
Dynamic System ◽  
Industry 4.0 ◽  
Industrial Revolution ◽  
Mechanical Systems ◽  
Predictive Maintenance ◽  
Condition Based Maintenance ◽  
Specific Target ◽  
New Class ◽  
Fourth Industrial Revolution ◽  
Potential Benefits

We are in the era of the fourth industrial revolution. Which highlights adaptability, monitoring, digitisation and efficiency in manufacturing as a result of the design of new smart mechanical systems. A central role in Industry 4.0 is played by maintenance and, within this framework, we define and review condition-based predictive maintenance. Thereafter, we propose a new class of smart mechanical systems that self-optimise utilising both condition-based maintenance and dynamic system modification. Akin to smart structures, smart mechanical systems will recognise and predict faults or malfunctions and, subsequently, self-optimise to restore desirable system behaviour. Potential benefits include increased reliability and efficiency while reducing cost without the requirement of highly skilled technicians. Thus, small and medium-sized enterprises are a specific target of such technology due to their increasing level of automatisation within Industry 4.0.

Artificial Intelligence Assisted Learning (AIAL) Blueprint

2020 ◽  
pp. 317-338
Author(s):  
Alexiei Dingli ◽  
Lara Caruana Montalto
Keyword(s):  
Artificial Intelligence ◽  
Educational System ◽  
Assembly Line ◽  
Industrial Revolution ◽  
Quality Of Teaching ◽  
The Moment ◽  
The Individual ◽  
Case Basis

Education is facing various challenges at the moment and needs to be reinvented. Some of the methods used have been inspired by the industrial revolution when an assembly line one-size-fits-all approach was setup in schools. Today, teachers are struggling to manage the number of students in a class thus making the quality of teaching inconsistent. Furthermore, they have to deal with students having different abilities in the same class which makes it impossible to give each and every student the individual attention they deserve. Through the artificial intelligence assisted learning (AIAL) system, the authors believe that they can personalise the learning and thus free a lot of time for the teacher which can be used to focus on those students that are really in need. This will be done on a case by case basis, thus creating a fairer educational system which is personalized for the needs of each and every student which guarantees equity.

Experience on Transient Thermal Modeling of Sodium to Sodium Heat Exchanger Using FDM and FVM

2010 ◽  
Author(s):  
Sourabh Agarwal ◽  
K. Revathy ◽  
I. Banerjee ◽  
G. Padma Kumar ◽  
C. A. Babu ◽  
...  
Keyword(s):  
Heat Exchanger ◽  
Finite Difference Method ◽  
Heat Balance ◽  
Operating Conditions ◽  
Outlet Temperature ◽  
Intermediate Heat Exchanger ◽  
Modelling Techniques ◽  
The Finite Difference Method ◽  
Transient Thermal ◽  
Volume Method

Several intermediate heat exchanger (IHX) modelling techniques were examined, in order to predict the outlet temperature of primary and secondary sodium at different operating conditions. In the present study, two different approaches namely the Finite Difference Method (FDM) with nodal heat balance and modified nodal heat balance schemes; and Finite Volume Method (FVM) using simple upwind, exponential extrapolation and QUICK schemes have been attempted.

scholarly journals Automatic Emotion-Based Music Classification for Supporting Intelligent IoT Applications

Electronics ◽  
2019 ◽  
Vol 8 (2) ◽  
pp. 164 ◽  
Author(s):  
Yeong-Seok Seo ◽  
Jun-Ho Huh
Keyword(s):  
Support Vector Machine ◽  
Industrial Revolution ◽  
Nearest Neighbor ◽  
New Technologies ◽  
Support Vector ◽  
Emotion Classification ◽  
K Nearest Neighbor ◽  
Music Classification ◽  
Iot Applications ◽  
Emotional Model

With the arrival of the fourth industrial revolution, new technologies that integrate emotional intelligence into existing IoT applications are being studied. Of these technologies, emotional analysis research for providing various music services has received increasing attention in recent years. In this paper, we propose an emotion-based automatic music classification method to classify music with high accuracy according to the emotional range of people. In particular, when the new (unlearned) songs are added to a music-related IoT application, it is necessary to build mechanisms to classify them automatically based on the emotion of humans. This point is one of the practical issues for developing the applications. A survey for collecting emotional data is conducted based on the emotional model. In addition, music features are derived by discussing with the working group in a small and medium-sized enterprise. Emotion classification is carried out using multiple regression analysis and support vector machine. The experimental results show that the proposed method identifies most of induced emotions felt by music listeners and accordingly classifies music successfully. In addition, comparative analysis is performed with different classification algorithms, such as random forest, deep neural network and K-nearest neighbor, as well as support vector machine.

scholarly journals Modelling and analysis the effect of unbalanced moment directions on behaviour of edge column-slab connections

2018 ◽  
Vol 159 ◽  
pp. 01007 ◽  
Author(s):  
I Ketut Sudarsana ◽  
I Gede Gegiranang Wiryadi ◽  
Gede Adi Susila
Keyword(s):  
Experimental Data ◽  
Shear Force ◽  
Free Edge ◽  
Experimental Result ◽  
Element Analysis ◽  
Concrete Damage ◽  
Modelling Techniques ◽  
Concrete Damage Plasticity ◽  
The Moment ◽  
Punching Failure

This research investigates the effect of unbalanced moment directions on the behaviour of edge column slab connections using a finite element analysis. The analyses were done on subassembly edge column slab connections that were designed according to Indonesian Concrete Standard (SNI 2847:2013). Three unbalanced moment directions were considered namely perpendicular, parallel and inclined 45° to the slab free edge. The concrete damage plasticity (CDP) and truss elements in Abaqus were utilized to model and analyse the behaviour of concrete and reinforcement bars, respectively. The modelling techniques were first validated using an experimental result available in the literature. There are five parameters in the CDP model need to be validated to get convergent results with the experimental data. Using the CDP validated parameters, then seven specimen models were analysed under combined shear force and an unbalanced moment in three directions. The ratio of M/V was kept constant of 0.3. The results show that the punching failure capacity of connections having an unbalanced moment inclined 45° is smaller than that of an unbalanced moment perpendicular to the slab free edge, but higher than that of an unbalanced moment parallel to the slab free edge. The patterns of concrete strain are consistent with the moment directions. All tension rebars passing through column sections yield at the connection failures.

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