IJAT Vol.14 No.6 pp. 1005-1012
doi: 10.20965/ijat.2020.p1005


Model-Based Deterioration Estimation with Cyber Physical System

Tomoaki Hiruta*,† and Yasushi Umeda**

*Research & Development Group, Hitachi Ltd.
7-1-1 Omika-cho, Hitachi, Ibaraki 319-1292, Japan

Corresponding author

**Research into Artifacts, Center for Engineering, School of Engineering, The University of Tokyo, Tokyo, Japan

April 26, 2020
June 23, 2020
November 5, 2020
maintenance, deterioration, cyber physical system

A key aspect of life cycle management for pursuing sustainability is machine condition prognosis, which requires a condition monitoring system that estimates machine system deterioration to assist engineers in determining which maintenance actions to take. Conventional data-driven methods such as machine learning, have two issues. One is data dependency. The accuracy of a data-driven method depends on the data volume because a data-driven method builds a classification model on the basis of historical data as training data. However, it is difficult to acquire enough data on all deterioration modes, which requires a long time, because deterioration modes are diverse, and some of them rarely happen. The other issue is interpretability. When a condition monitoring system using a data-driven method sends the degree of deterioration (DoD) of the machine system to maintenance engineers, they have difficulty in understanding the results because the method is a black box. The objective of this paper is to address these two issues. We propose a model-based method that simulates machine system deterioration with a cyber physical system (CPS). Model-based methods address these issues in the following manner. First, the methods can simulate the progress of deterioration from an initial condition to failure to estimate the DoD. Second, the methods employ mathematical models that represent machine systems. Engineers create such mathematical models (which we call “physical models”) by referring to various kinds of knowledge like design information and the result of failure mode and effects analysis. A physical model allows us to reason about a machine system to address interpretability. For dealing with machinery that has multiple operation modes, we introduce a state space to clarify the relationship among input, observable state variables, and DoD in a physical model. The CPS estimates DoD by comparing observed data with simulated data in the state space. In our case study, we evaluated our proposed method with a hydraulic pump of a mining machine. First we created a physical model with Modelica, which is a multi-domain modeling language. Then, the method constructed the state space by simulating deterioration with the physical model given all combinations of inputs and DoD. After that, we showed that the estimated DoD tended to increase until the hydraulic pump was replaced, using the observed data from an actual mining machine. As a result, the experimental results revealed that the proposed method succeeded in identifying the DoD with observed data of the hydraulic pump of a mining machine.

Cite this article as:
T. Hiruta and Y. Umeda, “Model-Based Deterioration Estimation with Cyber Physical System,” Int. J. Automation Technol., Vol.14 No.6, pp. 1005-1012, 2020.
Data files:
  1. [1] Y. Umeda, S. Takata, F. Kimura, T. Tomiyama, J. W. Sutherland, S. Kara, C. Herrmann, and J. R. Duflou, “Toward Integrated Product and Process Life Cycle Planning – An Environmental Perspective,” CIRP Annals-Manufacturing Technology, Vol,61, No.2, pp. 681-702, 2012.
  2. [2] E. Kunii, T. Matsuura, S. Fukushige, and Y. Umeda, “Proposal of Consistency Management Method Between Product and its Life Cycle for Supporting Life Cycle Design,” Int. J. Automation Technol., Vol.6, No.3, pp. 272-278, 2012.
  3. [3] A. Bousdekis, B. Magoutas, D. Apostolou, and G. Mentzas, “Review, Analysis and Synthesis of Prognostic-based Decision Support Methods for Condition Based Maintenance,” J. of Intelligent Manufacturing, Vol.29, pp. 1303-1316, 2018.
  4. [4] E. Kharlamov, T. Mailis, G. Mehdi, C. Neuenstadt, Ö. Özçep, M. Roshchin, N. Solomakhina, A. Soylu, C. Svingos, S. Brandt, M. Giese, Y. Ioannidis, S. Lamparter, R. Möller, Y. Kotidis, and A. Waaler, “Semantic Access to Streaming and Static Data at Siemens,” J. of Web Semantics, Vol.44, pp. 54-74, 2017.
  5. [5] R. Roy, R. Stark, K. Tracht, S. Takata, and M. Mori, “Continuous Maintenance and the Future – Foundations and Technological Challenges,” CIRP Annals-Manufacturing Technology, Vol.65, No.2, pp. 667-688, 2016.
  6. [6] L. Tang, T. Li, L. Shwartz, F. Pinel, and G. Grabarnik, “An Integrated Framework for Optimizing Automatic Monitoring Systems in Large IT Infrastructures,” KDD’13, pp. 1249-1257, 2013.
  7. [7] T. Hiruta, T. Uchida, S. Yuda, and Y. Umeda, “A Design Method of Data Analytics Process for Condition Based Maintenance,” CIRP Annals-Manufacturing Technology, Vol.68, No.1, pp. 145-148, 2019.
  8. [8] D. Djurdjanovic, J. Lee, and J. Ni, “Watchdog Agent – An Infotronics-based Prognostics Approach for Product Performance Degradation Assessment and Prediction,” Advanced Engineering Informatics, Vol.17, Issues 3-4, pp. 109-125, 2003.
  9. [9] S. Wegerich, “Similarity-based Modeling of Vibration Features for Fault Detection and Identification,” Sensor Review, Vol.25, No.2, pp. 114-122, 2005.
  10. [10] F. Xue and W. Yan, “Parametric Model-based Anomaly Detection for Locomotive Subsystems,” Proc. of Int. Joint Conf. on Neural Networks, 2007.
  11. [11] R. Sipos, D. Fradkin, F. Moerchen, and Z. Wang, “Log-based Predictive Maintenance,” KDD’14, pp. 1867-1876, 2014.
  12. [12] C. Sobie, C. Freitas, and M. Nicolai, “Simulation-driven Machine Learning: Bearing Fault Classification,” Mechanical Systems and Signal Processing, Vol.99, pp. 403-419, 2018.
  13. [13] M. Yu, D. Wang, M. Luo, and L. Huang, “Prognosis of Hybrid Systems with Multiple Incipient Faults: Augmented Global Analytical Redundancy Relations Approach,” IEEE Trans. on Systems Man and Cybernetics, Part A: Systems and Humans, Vol.41, No.3, pp. 540-551, 2011.
  14. [14] A. Saxena, K. Goebel, D. Simon, and N. Eklund, “Damage Propagation Modeling for Aircraft Engine Run-to-Failure Simulation,” Int. Conf. on Prognostics and Health Management, 2008.
  15. [15] J. Lee, B. Bagheri, and H. Kao, “A Cyber-physical Systems Architecture for Industry 4.0-based Manufacturing Systems,” Manufacturing Letters, Vol.3, pp. 18-23, 2015.
  16. [16] S. Vetra-Carvalho, P. J. v. Leeuwen, L. Nerger, A. Barth, M. U. Altaf, P. Brasseur, P. Kirchgessner, and J. Beckers, “State-of-the-art Stochastic Data Assimilation Methods for High-dimensional Non-Gaussian Problems,” Tellus A: Dynamic Meteorology and Oceanography, Vol.70, Issue 1, pp. 1-43, 2018.
  17. [17] T. Tsuyuki and T. Miyoshi, “Recent Progress of Data Assimilation Methods in Meteorology,” J. of the Meteorological Society of Japan, Vol.85B, pp. 331-361, 2007.
  18. [18] H. Arabian-Hoseynabadi, H. Oraee, and P. J. Tavner, “Failure Modes and Effects Analysis (FMEA) for Wind Turbines,” Electrical Power and Energy Systems, Vol.32, Issue 7, pp. 817-824, 2010.
  19. [19] T. Kato, “Characteristics and Applications of Controlled Volume Pumps,” Turbomachinery, Vol.17, Issue 9, pp. 576-582, 1989 (in Japanese).
  20. [20] M. E. Klenk, J. de Kleer, D. Bobrow, and B. Janssen, “Qualitative Reasoning with Modelica Models,” AAAI’14 Proc. of the Twenty-Eighth AAAI Conf. on Artificial Intelligence, pp. 1084-1090, 2014.
  21. [21] [Accessed June 1, 2020]

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Apr. 22, 2024