UML diagrams for dynamical monitoring of rail vehicles

UML diagrams for dynamical monitoring of rail vehicles

Physica A 531 (2019) 121169 Contents lists available at ScienceDirect Physica A journal homepage: www.elsevier.com/locate/physa UML diagrams for dy...

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Physica A 531 (2019) 121169

Contents lists available at ScienceDirect

Physica A journal homepage: www.elsevier.com/locate/physa

UML diagrams for dynamical monitoring of rail vehicles Miloš Milovančević a , Jelena Stefanović Marinović a , Jovana Nikolić b , Ana Kitić a , ∗ Mahdi Shariati c , Nguyen Thoi Trung d,e , , Karzan Wakil f , Majid Khorami g a

University of Niš, Faculty of Mechanical Engineering, Niš, Serbia Faculty of Electronic Engineering, University of Nis, Serbia Institute of Research and Development, Duy Tan University, Da Nang 550000, Viet Nam d Division of Computational Mathematics and Engineering, Institute for Computational Science, Ton Duc Thang University, Ho Chi Minh City, Viet Nam e Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City, Viet Nam f Research Center, Sulaimani Polytechnic University, Sulaimani 46001, Kurdistan Region, Iraq g Universidad UTE, Facultad de Arquitectura y Urbanismo, Calle Rumipamba s/n y Bourgeois, Quito, Ecuador b c

highlights • • • • •

Rail vehicles diagnostics is very important task. To secure regular work of railcar and to obtain list of maintenances measures. To analyze and model an information system for e-diagnostics for rail vehicles. The e-diagnostics system was designed based on object-oriented approach. Frequent analyses was used to dissolve vibrations on separated frequents components.

article

info

Article history: Received 26 March 2019 Received in revised form 12 April 2019 Available online 11 June 2019 Keywords: e-diagnostics Train Vibration Dynamical behavior Object-oriented

a b s t r a c t Rail vehicles diagnostics is very important task since it is necessary to secure regular work of railcar and to obtain list of maintenances measures. Therefore in this study an attempt was made to analyze and model an information system for e-diagnostics for rail vehicles. The e-diagnostics system was designed based on object-oriented approach. The system should be enabled to identify and to analyze vibrations on shaft assembly. The outcomes from the system could give also conclusion about other assemblies of rail cars. The main purpose of the system us to determine the dynamic behavior and running behavior of the rail cars. Frequent analyses are main roll in early failure determining. Frequent analyses was used to dissolve vibrations on separated frequents components. © 2019 Elsevier B.V. All rights reserved.

1. Introduction Diagnostics of rail vehicles or railcars is very sensitive and important test in order to have safe travel and transport. One of the most difficult indicator for diagnostics is acceleration due to contacts between railcar wheels and track of the railway. The acceleration could occur because of irregularity in track manufacturing. Therefore it is essential to make a system for real-time monitoring of acceleration of railcars due to the contacts with tracks. There are several investigations which investigate different aspects of rail vehicles dynamical behavior. ∗ Corresponding author at: Division of Computational Mathematics and Engineering, Institute for Computational Science, Ton Duc Thang University, Ho Chi Minh City, Viet Nam. E-mail address: [email protected] (N.T. Trung). https://doi.org/10.1016/j.physa.2019.121169 0378-4371/© 2019 Elsevier B.V. All rights reserved.

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M. Milovančević, J.S. Marinović, J. Nikolić et al. / Physica A 531 (2019) 121169

Fig. 1. Railcar TMD 22DC.

The calculation results in article [1] were shown that rail weld irregularities have fractal characteristics due to the fractal dimension of measured geometries mostly larger than 1.1. The geometry evolution of rail welded irregularity has great effects on the wheel–rail dynamic interactions in the time domain, but has little effects in the frequency domain [1]. An iterative method based on prediction of wheel–rail forces was presented in article [2] to determine the dynamic response of railway vehicle–track coupled systems. A multiaxial fatigue criterion was applied in article [3] to predict fatigue damage in rail welded joints with the help of an explicit finite element model. 0.1 mm, fatigue damage is not relevant, regardless of the length. A full nonlinear physical ‘in-service’ model was built in article [4] for a rail vehicle secondary suspension hydraulic damper with shim-pack-type valves. Taking a continuous rigid segmental prefabrication and assembly bridge on Guangzhou Metro No.14 as example, railway vehicle induced vibration energy harvesting and saving was studied in this paper [5]. Condition monitoring of railway tracks is essential in ensuring the safety of railway systems [6] and the track condition monitoring based on in-service vehicle has been paid more attention in the recent years. In paper [7] was presented an autonomous health management scheme on rail vehicles driven by permanent magnet synchronous motors (PMSMs). In paper [8] was dealt with the robust safety design optimization of a rail vehicle system moving in short radius curved tracks. To model the dynamic response of a train running over a given length of rail, the input conditions must be adequately defined [9]. As technology advances in railway systems, one theoretically challenging and practically significant problem is how to use the automatic train operation system to make the current railway network more efficient with higher carrying capacity, lower cost and improved quality of service by optimized railway traffic management and train operation [10]. The main goal of the study is to establish an architecture for an information system for dynamical monitoring of railcars. Object-oriented principle is used for such a purpose [11,12]. Experimental results are presented afterwards. 2. Methodology 2.1. Experimental diagnostics of dynamical behavior of railcars Fig. 1 shows the railcar which is used for the experimental diagnostics of the locomotion composure. The railcar could be implemented into the railway traffic after the confirmation of the experimental diagnostics. In order to diagnose the locomotion composure of the railcar TMD 22DC, measurements and analysis of acceleration at specific places during different speeds and directions of the railcar. The railcar is a self-driven railcar with two shafts. The main purpose of the railcar is pulling of the height wagons, easy maneuvers, transport of bulky-cargo, transport of people and etc. The railcar bottom support is a welded construction in the shape of grid with standard steel profiles and sheets. The support construction is made for easy building of other parts of the railcar. The shafts of the railcar have standard housing of bearings. Operative table is composed of instruments and light indicators which enables assertive indication of pressure, temperature, revolution number, speed, traveled distance, time, current intensity, voltage, fuel level and etc. These indicators are important for stable control of the railcar which leads to the safe and confided travel. Fig. 2 shows the basic dimensions of the railcar. The basic data of the railcar and track are listed below:

• Track width 1435 mm • Minimal radius of curvature:

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Fig. 2. Main dimensions of the railcar TMD 22DC.

(a) Open tracks 150 m (b) Industry tracks 80 m

• • • • • • • •

The largest upward slope 30% Climatic conditions continental Air temperature range: -30◦ C - 40◦ C Relative humidity 60% Shafts distance 3900 mm Railcar width 2800 mm Railcar height 4065 mm Wheel diameter 840/740 mm (a) New wheel 840 mm (b) Used up wheel 740 mm

• • • • • •

Maximal speed of vehicle 60 km/h Maximal projected speed of vehicle 70 km/h Railcar mass per shaft 9t Total mass of empty railcar 14t Total mass of full railcar 18t Number of sitting places 8+1

Measurement of dynamical behavior of the railcar is performed during 16 tours for four railcar speeds (40, 50, 60 i 70 km/h). For the each speed the locomotion composure is measure with and without cargo for the both traveling directions. Acceleration measurement is performed at housing of bearing of the shaft as it shown in Fig. 3.

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Fig. 3. Measurement place at housing of bearing of the shaft.

Fig. 4. Use case model.

The acceleration is measured in three directions, transversal, vertical and longitude direction. The measurement is performed based on inductive principle with units with range of 200 Hz. Relevant values of acceleration for locomotion composure are from 0.5 up to 30 Hz. 2.2. E-diagnostics modeling for dynamical monitoring of railcar Modeling of e-diagnostics system for dynamical monitoring of railcar is very complicated and nonlinear process. Therefore in this article the e-diagnostics system is modeled in early stage in order to simplify the process. Rational unified process (RUP) [12] is used for the modeling purpose. This is standard methodology which are used for different aspects of the system modeling. RUP is an interactive and iterative methodology which are based on modeling of system architecture and use cases. RUP methodology is based on Unified Modeling Language (UML). RUP methodology has many elements which can be used during information system modeling. There is no need to use all elements of the methodology but it is enough to select only the elements which are appropriate for the specific process. Each phase of the RUP methodology has iteration. In the each iterations some disciplines are considered. The disciplines are described by process flow. The process shows activity and roles of subjects in the project. Finally there are artifacts which presents documentation, models and model elements of the information system. The use case presents one sequence of action which system should to do in order to obtain some results (Fig. 4). The use case one complete operation of the system and it is used to define system behavior. Therefore one could presents desired behavior of system. However the desired behavior might not be achieved in the final product. There are different types of UML diagrams which can be used in the system development. The UML diagrams could be divided in two main groups, structural and behavioral diagrams. Structural diagrams represent the system structure and behavioral diagrams represent the active behavior of objects in the system or dynamical state. In this article use case models are used which present dynamic behavior of the system. 3. Results In this section the e-diagnostics system is presented based on object-oriented approach methodology. For such a process there is need to identify the main users and the main use cases of the system. E-diagnostics system is able to track the dynamical behavior of the railcar based on geometrical characteristics of the railways. The analyses represents the nonlinear vibrations of the railcar because of contacts between railcar and tracks. The tracks are not ideal smooth and therefore the vibration of the railcar occurs. The main sources of the vibrations are between wheels and tracks. There are many parameters of the railcar, contacts between wheel and tracks and tracks and wheel geometry as well, which have effect on the dynamical behavior of the railcar. Analysis of the transient response data represents the most complicated task since it includes in calculation also double contact between wheel and track.

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Fig. 5. Structure of e-diagnostics system.

Fig. 6. Use case: Importing of header data.

Fig. 7. Use case: Importing of transient response data.

3.1. E-diagnostics system Fig. 5 shows the main use case diagram of the e-diagnostics system for the dynamical monitoring of railcars. As can be seen there are two subjects in the system: users and module. The users could import of header data, import of transient response data and import of creep data. The module is responsible for acceleration monitoring of the housing of bearing of railcars. Fig. 6 shows the use case of importing of header data. The use case has only one activity and it is loading of vehicle file. The file defines geometry and physical characteristics of the vehicle. The file is written in notepad. Fig. 7 shows the use case of importing of transient response data. The use case has two activities. The first activity is importing of desired vehicle speeds and the second activity is importing of track distance. In this investigation desired speeds are 50 km/h and 60 km/h. Also there is possible to define speed range for the investigation. There is need to define track distances where investigations will be performed. Fig. 8 shows the use case of importing creep data. The use case has seven activities. These activities are: importing wheelset and track profiles, importing of wheelset geometry, importing of coefficient of friction, importing of wheelset misalignments, importing of track stiffness, setting of independent wheelsets and importing of truck damping. Fig. 9 shows the use case of acceleration monitoring by module. The use case has one activity and it is monitoring vertical acceleration at wheel bearing. The module should perform nonlinear calculations based on the input data in order to monitor the dynamical behavior of the railcar.

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Fig. 8. Use case: Importing of creep data.

Fig. 9. Use case: Acceleration monitoring.

Fig. 10. Vertical acceleration in time domain.

3.2. Results of vertical acceleration Vertical accelerations are very important for the stable locomotion of railcar. Here in the study time domain id used for the presentation of the results. Fig. 10 shows the vertical acceleration in time domain where one can see that in range of 0–60 s there is maximal acceleration amplitude. In the time range of 80–230 s there is stabilization of locomotion and maximal acceleration amplitudes are in range of −10–10 m/s2 . In the time range of 230–270 s the maximal acceleration amplitudes are in range of −20–20 m/s2 . Afterwards the locomotion is stabilized again. 4. Conclusion Investigations and development of new information systems for dynamical monitoring of railcars is very important task in regard to travel safety. There is need for constant online monitoring since railways is very sensitive to any errors. Therefore in this study an attempt was made to analyze and model an information system for e-diagnostics for the railcars. The e-diagnostics system was designed based on object-oriented approach. The system should be enabled to identify and to analyze vibrations on shaft assembly of the railcars. The outcomes from the system could give also conclusion about other assemblies of rail cars. For future investigation it is need to add use more advanced methods for rail car diagnostics [13–28].

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Acknowledgment This paper presents the results of the research conducted within the project "Research and development of new generation machine systems in the function of the technological development of Serbia" funded by the Faculty of Mechanical Engineering, University of Niš, Serbia. References [1] J. Xu, P. Wang, Y. Gao, J. Chen, R. Chen, Geometry evolution of rail weld irregularity and the effect on wheel-rail dynamic interaction in heavy haul railways, Eng. Fail. Anal. 81 (2017) 31–44. [2] W. Wang, Y. Zhang, H. Ouyang, An iterative method for solving the dynamic response of railway vehicle-track coupled systems based on prediction of wheel-rail forces, Eng. Struct. 151 (2017) 297–311. [3] C. Lu, J. Nieto, I. Puy, J. Melendez, J.M. Martínez-Esnaola, Fatigue prediction of rail welded joints, Int. J. Fatigue (2018). [4] W.L. Wang, Z.R. Zhou, D.S. Yu, Q.H. Qin, S. Iwnicki, Rail vehicle dynamic response to a nonlinear physical ‘in-service’model of its secondary suspension hydraulic dampers, Mech. Syst. Signal Process. 95 (2017) 138–157. [5] W. Hou, Y. Li, W. Guo, J. Li, Y. Chen, X. Duan, Railway vehicle induced vibration energy harvesting and saving of rail transit segmental prefabricated and assembling bridges, J. Clean. Prod. 182 (1) (2018) 946–959. [6] X. Wei, F. Liu, L. Jia, Urban rail track condition monitoring based on in-service vehicle acceleration measurements, Measurement 80 (2016) 217–228. [7] G. Niu, J. Jiang, B.D. Youn, M. Pecht, Autonomous health management for PMSM rail vehicles through demagnetization monitoring and prognosis control, ISA Trans. (2017). [8] M. Nejlaoui, A. Houidi, Z. Affi, L. Romdhane, A hybrid multi-objective imperialist competitive algorithm and Monte Carlo method for robust safety design of a rail vehicle, C. R. Mec. 345 (10) (2017) 712–723. [9] T.L. Chong, M.N. Awad, N. Nadarajah, W.K. Chiu, S.N. Lingamanaik, G. Hardie, et al., Defining rail track input conditions using an instrumented revenue vehicle, Procedia Eng. 188 (2017) 479–485. [10] J. Yin, T. Tang, L. Yang, J. Xun, Y. Huang, Z. Gao, Research and development of automatic train operation for railway transportation systems: A survey, Transp. Res. C 85 (2017) 548–572. [11] T.C. Lethbridge, R. Laganiere, Object-Oriented Software Engineering, McGraw-Hill, New York, 2005. [12] I. Jacobson, Object-Oriented Software Engineering: A Use Case Driven Approach, Pearson Education India, 1993. [13] Y. Zandi, M. Shariati, A. Marto, X. Wei, Z. Karaca, D.K. Dao, et al., Computational investigation of the comparative analysis of cylindrical barns subjected to earthquake, Steel Compos. Struct. 28 (4) (2018) 439–447. [14] M. Safa, M. Shariati, Z. Ibrahim, A. Toghroli, S.B. Baharom, N.M. Nor, et al., Potential of adaptive neuro fuzzy inference system for evaluating the factors affecting steel-concrete composite beam’s shear strength, Steel Compos. Struct. Int. J. 21 (3) (2016) 679–688. [15] A. Toghroli, M. Suhatril, Z. Ibrahim, M. Safa, M. Shariati, S. Shamshirband, Potential of soft computing approach for evaluating the factors affecting the capacity of steel–concrete composite beam, J. Intell. Manuf. (2016) 1–9. [16] Toghroli Ali, Mohammadhassani Mohammad, Shariati Mahdi, Suhatril Meldi, Ibrahim Zainah, Ramli Sulong Nor Hafizah, Prediction of shear capacity of channel shear connectors using the ANFIS model, Steel Compos. Struct. 17 (5) (2014) 623–639. [17] I. Mansouri, M. Shariati, M. Safa, Z. Ibrahim, M. Tahir, D. Petković, Analysis of influential factors for predicting the shear strength of a V-shaped angle shear connector in composite beams using an adaptive neuro-fuzzy technique, J. Intell. Manuf. (2017) 1–11. [18] I. Mansouri, M. Safa, Z. Ibrahim, O. Kisi, M. Tahir, S. Baharom, et al., Strength prediction of rotary brace damper using MLR and MARS, Struct. Eng. Mech. 60 (3) (2016) 471–488. [19] M. Mohammadhassani, A. Saleh, M. Suhatril, M. Safa, Fuzzy modelling approach for shear strength prediction of RC deep beams, Smart Struct. Syst. 16 (3) (2015) 497–519. [20] E. Sadeghipour Chahnasir, Y. Zandi, M. Shariati, E. Dehghani, A. Toghroli, E. Tonnizam Mohamad, et al., Application of support vector machine with firefly algorithm for investigation of the factors affecting the shear strength of angle shear connectors, Smart Struct. Syst. 22 (4) (2018) 413–424. [21] A. Toghroli, E. Darvishmoghaddam, Y. Zandi, M. Parvan, M. Safa, M. Abdullahi, et al., Evaluation of the parameters affecting the schmidt rebound hammer reading using ANFIS method, Comput. Concr. 21 (5) (2018) 525–530. [22] Y. Sedghi, Y. Zandi, M. Shariati, E. Ahmadi, V. Moghimi Azar, A. Toghroli, et al., Application of ANFIS technique on performance of c and l shaped angle shear connectors, Smart Struct. Syst. 22 (3) (2018) 335–340. [23] M. Safa, M. Shariati, Z. Ibrahim, A. Toghroli, S.B. Baharom, N.M. Nor, et al., Potential of adaptive neuro fuzzy inference system for evaluating the factors affecting steel-concrete composite beam’s shear strength, Steel Compos. Struct. 21 (3) (2016) 679–688. [24] A. Toghroli, Applications of the ANFIS and LR Models in the Prediction of Shear Connection in Composite Beams: Jabatan Kejuruteraan Awam, Fakulti Kejuruteraan, Universiti Malaya, 2015. [25] M. Aghakhani, M. Suhatril, M. Mohammadhassani, M. Daie, A. Toghroli, A simple modification of homotopy perturbation method for the solution of blasius equation in semi-infinite domains, Math. Probl. Eng. 2015 (2015) 7. [26] M. Hamidian, A. Shariati, M.M.A. Khanouki, H. Sinaei, A. Toghroli, K. Nouri, Application of schmidt rebound hammer and ultrasonic pulse velocity techniques for structural health monitoring, Sci. Res. Essays 7 (21) (2012) 1997–2001. [27] M. Khorami, M. Alvansazyazdi, M. Shariati, Y. Zandi, A. Jalali, M. Tahir, Seismic performance evaluation of buckling restrained braced frames (BRBF) using incremental nonlinear dynamic analysis method (IDA), Earthq. Struct. 13 (6) (2017) 531–538. [28] M. Shariat, M. Shariati, Computational Lagrangian multiplier method by using for optimization and sensitivity analysis of rectangular reinforced concrete beams, Steel Compos. Struct. 29 (2018) 243–256.