IJAT Vol.16 No.2 pp. 175-181
doi: 10.20965/ijat.2022.p0175


A Study on Anomaly Detection of Water-Soluble Coolant Using Internal-Sensors

Yasuo Kondo and Youji Miyake

Graduate School of Engineering, Yamagata University
4-3-16 Jonan, Yonezawa, Yamagata 992-8510, Japan

Corresponding author

July 29, 2021
November 9, 2021
March 5, 2022
condition monitoring, water-soluble coolant, spindle motor, NC data, internal sensor

The quality of water-soluble coolant is managed based on the maintenance schedules provided by a manager including periodic replacement of coolant. Post-maintenance is adopted when an anomaly is detected in the daily measurements. However, the reliability of management is dependent upon on the competence and experience of an operator. Condition monitoring allows users to detect critical changes in a water-soluble coolant. In contrast to the conventional method, condition monitoring can be assumed to be continuous and remote using ICT technologies. In this study, the spindle motor and NC data were utilized as internal sensors to monitor the quality of water-soluble coolant. The signal obtained from this sensor system can be easily broadcasted to the Internet as digital data and extended to an automatic data analysis using AI and machine learning in the future. It can be stated that this study enhances continuous and remote monitoring of water-soluble coolant and has the possibility of monitoring the changes in sludge concentration and Brix%. However, the sensor data cannot be used as an absolute index to estimate the quality of water-soluble coolant. It is a valuable indicator only when it is analyzed in combination with other sensor data such as pH and Brix%. The method proposed in this study can be widely extended to monitor the condition of water-soluble coolant and cutting tools.

Cite this article as:
Yasuo Kondo and Youji Miyake, “A Study on Anomaly Detection of Water-Soluble Coolant Using Internal-Sensors,” Int. J. Automation Technol., Vol.16, No.2, pp. 175-181, 2022.
Data files:
  1. [1] Y. Kondo, “Technical Feature of Recycling System for water-soluble Coolant,” J of Japanese Society for Abrasive Technology, Vol.55, pp. 82-85, 2011 (in Japanese).
  2. [2] Y. Kondo, M. Yamaguchi, Y. Yamaguchi, and S. Sakamoto, “A Pramatic Approach to Reduce Environmental Load in Machining Process,” Applied Mechanics and Materials, Vol.163, pp. 12-17, 2012.
  3. [3] Y. Kondo, M. Yamaguchi, S. Sakamoto, and K. Yanaguchi, “A Dynamic Observation Concept to Keep Water-soluble Coolant in Normal Condition for Long-time,” Advanced Materials Research, Vol.652-654, pp. 2119-2122, 2013.
  4. [4] A. Rabenstein, T. Koch, M. Remesch, E. Brinksmeier, and J. Kuever, “Microbial degradation of water miscible metal working fluids,” Int. Biodeterior. Biodegradation, Vol.63, pp. 1023-1029, 2009.
  5. [5] D. C. Seo, H. J. Lee, H. N. Hwang, M. R. Park, N. W. Kwak, I. J. Cho, J. S. Cho, J. Y. Seo, W. H. Joo, K. H. Park, and J. S. Heo, “Treatment of non-biodegradable cutting oilwastewater by ultrasonication-Fenton oxidation process,” Water Sci. Technol., Vol.55, pp. 251-259, 2007.
  6. [6] C. J. van der Gast, A. S. Whiteley, A. K. Lilley, C. J. Knowles, and I. P. Thompson, “Bacterialcommunity structure and function in a metalworking fluid,” Environ. Microbiol., Vol.5, pp. 453-461, 2003.
  7. [7] M. Hermann, T. Pentek, and B. Otto, “Design Principles for Industrie 4.0 Scenarios,” Proc. of 2016 49th Hawaii Int. Conf. on System Sciences (HICSS), pp. 3928-3937, 2016.
  8. [8] N. Yamada, Y. Takahashi, and Y. Kimura, “Towards realizing “super smart society” (Society 5.0): CPS/IoT and its future,” J. of Information Processing and Management, Vol.60, pp. 325-334, 2017 (in Japanese).
  9. [9] M. Syafrudin, G. Alfian, N. L. Fitriyani, and J. Rhee, “Performance Analysis of IoT-based Sensor, Big Data Processing, and Machine Learning Model for Real-time Monitoring System in Automotive Manufacturing,” Sensors, Vol.18, Article No.2946, 2018.
  10. [10] F. Civerchia, S. Bocchino, C. Salvadori, E. Rossi, L. Maggiani, and M. Petracca, “Industrial Internet of Things Monitoring Solution for Advanced Predictive Maintenance Applications,” J. of Industrial Information Integration, Vol.7, pp. 4-12, 2017.
  11. [11] K. S. Adu-Manu, C. Tapparello, W. Heinzelman, F. A. Katsriku, and J.-D. Abdulai, “Water quality monitoring using wireless sensor networks: Current trends and future research directions,” ACM Trans. on Sensor Networks (TOSN), Vol.13, Article No.4, 2017.
  12. [12] B. Paul, “Sensor based water quality monitoring system,” BRAC University, B.Sc. Thesis, 2018.
  13. [13] S. Thombre, R. U. Islam, K. Andersson, and M. S. Hossain, “IP based Wireless Sensor Networks: performance Analysis using Simulations and Experiments,” J. of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications, Vol.7, No.3, pp. 53-76, 2016.
  14. [14] Z. Abedin, A. S. Chowdhury, M. S. Hossain, K. Andersson, and R. Karim, “An Interoperable IP based WSN for Smart Irrigation Systems,” Proc. of the 14th Annual IEEE Consumer Communications & Networking Conf. (CCNC), pp. 1-5, 2017.
  15. [15] R. Ul Islam, K. Andersson, and M. S. Hossain, “Heterogeneous Wireless Sensor Networks Using CoAP and SMS to Predict Natural Disasters,” Proc. of the 2017 IEEE Conf. on Computer Communications Workshops (INFOCOM WKSHPS): The 8th IEEE INFOCOM Int. Workshop on Mobility Management in the Networks of the Future World (MobiWorld’17), pp. 30-35, 2017.
  16. [16] P. W. Rundel, E. A. Graham, M. F. Allen, J. C. Fisher, and T. C. Harmon, “Environmental sensor networks in ecological research,” New Phytologist, Vol.182, pp. 589-607, 2009.
  17. [17] H. R. Maier and G. C. Dandy, “The use of artificial neural networks for the prediction of water quality parameters,” Water Resources Research, Vol.32, pp. 1013-1022, 1996.
  18. [18] N. Vijayakumar and R. Ramya, “The real time monitoring of water quality in IoT environment,” Proc. of the 2015 Int. Conf. on Innovations in Information, Embedded and Communication Systems (ICIIECS), pp. 1-5, 2015.
  19. [19] X. Zhang, J. Zhang, L. Li, Y. Zhang, and G. Yang, “Monitoring Citrus Soil Moisture and Nutrients Using an IoT Based System,” Sensors, Vol.17, pp. 447-455, 2017.
  20. [20] W.-F. Cheung, T.-H. Lin, and Y.-C. Lin, “A Real-Time Construction Safety Monitoring System for Hazardous Gas Integrating Wireless Sensor Network and Building Information Modeling Technologies,” Sensors, Vol.18, pp. 436-444, 2018.
  21. [21] Y. S. Moon, H. R. Choi, J. J. Kim, D. W. Kim, J. H. Cho, J. W. Kim, and J. W. Jeong, “Development of IoT-Based Sensor Tag for Smart Factory,” Int. Res. J. Electron. Comput. Eng., Vol.3, pp. 28-51, 2017.
  22. [22] F. Salamone, L. Danza, I. Meroni, and M. C. Pollastro, “A Low-Cost Environmental Monitoring System: How to Prevent Systematic Errors in the Design Phase through the Combined Use of Additive Manufacturing and Thermographic Techniques,” Sensors, Vol.17, pp. 828-835, 2017.
  23. [23] A. J. C. Godoy and I. G. Pérez, “Integration of Sensor and Actuator Networks and the SCADA System to Promote the Migration of the Legacy Flexible Manufacturing System towards the Industry 4.0 Concept,” J. Sens. Actuator Netw., Vol.7, pp. 23-19, 2018.
  24. [24] A. P. Plageras, K. E. Psannis, C. Stergiou, H. Wang, and B. B. Gupta, “ Efficient IoT-based sensor BIG Data collection–processing and analysis in smart buildings,” Future Gener. Comput. Syst., Vol.82, pp. 349-357, 2018.
  25. [25] M. Benammar, A. Abdaoui, S. H. M. Ahmad, F. Touati, and A. Kadri, “A Modular IoT Platform for Real-Time Indoor Air Quality Monitoring,” Sensors, Vol.18, pp. 581-587, 2018.
  26. [26] Y. Kondo, Y. Higashimoto, S. Sakamoto, T. Fujita, and K. Yamaguchi, “Feature Extraction from Sensor Data Streams for Optimizing Grinding Condition,” IOP Conf. Series: Materials Science and Engineering, Vol.229, pp. 2-16, 2017.
  27. [27] Y. Kondo, S. Mizunoya, S. Sakamoto, K. Yamaguchi, T. Fujita, and M. Yamaguchi, “A Utilization Method of Big Sensor Data to Detect Tool Anomaly in Machining Process,” Key Engineering Materials, Vol.719, pp. 122-126, 2016.
  28. [28] Y. Kondo, M. Yamaguchi, and S. Sakamoto, “A Big Data Analysis Technology for Catching Usual/unusual State of Cutting Tool,” Proc. of the 3rd Int. Conf. on Machining, Materials and Mechanical Technologies (IC3MT), CD-ROM, pp. 208-211, 2018.
  29. [29] F. Draganescu, M. Gheorghe, and C. V. Doicin, “Models of Machine Tool Efficiency and Specific Consumed Energy,” J. of Materials Processing Technology, Vol.141, pp. 9-15, 2003.
  30. [30] S. Kara and W. Li, “Unit Process Energy Consumption Models for Material Removal Processes,” CIRP Annals, Vol.60, pp. 37-40, 2011.

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

Last updated on May. 20, 2022