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RETRACTED ARTICLE: Economic IoT strategy: the future technology for health monitoring and diagnostic of agriculture vehicles

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This article was retracted on 27 September 2022

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Abstract

Today’s Agriculture vehicles (AgV)s are expected to encompass mainly the three requirements of customers; economy, the use of High technology and reliability. In this manuscript, we investigate the technology solution for efficient health monitoring and diagnostic (HM&D) strategy to maximize the field efficiency and minimize the machine cost. Based on the data captured by various IoT sensors, we demonstrate the facts to shift the HM&D technology from costly sensor to economic microphone based mechanism. The adopted strategy is capable to reduce the bulky data transmission on the internet, and to increase the up-time of AgVs. We experimented on the essential red hot chili peppers system of the AgV’s backbone hydraulic system—the hydraulic filter and pump. The measurement system analysis is adopted to determine the preciseness of data captured near the considered components. The envision of the correlation between the collected data extracts significant information to draw the facts to embrace the future HM&D technology shift. Correlation between the signals captured from costly sensors and Microphone for the generated faults in hydraulic components demonstrates the effectiveness of audio to replace existing HM&D technology.

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Notes

  1. In LMS SCADA is equipped with multiple channels, where one channel can be assigned for a particular data type (Vimal et al. 2020a, b).

References

  • Ali, A. H., Atia, A., & Mostafa, M. S. M. (2017). Recognizing driving behavior and road anomaly using smartphone sensors. International Journal of Ambient Computing and Intelligence (IJACI), 8(3), 22–37.

    Article  Google Scholar 

  • Aydn, I., Karakose, M., & Akin, E. (2015). Combined intelligent methods based on wireless sensor networks for condition monitoring and fault diagnosis. Journal of Intelligent Manufacturing, 26(4), 717–729.

    Article  Google Scholar 

  • Boudiaf, A., Moussaoui, A., Dahane, A., & Atoui, I. (2016). A comparative study of various methods of bearing faults diagnosis using the case Western Reserve University data. Journal of Failure Analysis and Prevention, 16(2), 271–284.

    Article  Google Scholar 

  • Burdick, R. K., Borror, C. M., & Montgomery, D. C. (2005). Design and analysis of gauge R&R studies: Making decisions with confidence intervals in random and mixed ANOVA models (Vol. 17). SIAM.

  • Chenghu, Z., Haiyan, W., & Dexing, S. (2011, January). Design principle of hydraulic and continuous filter regeneration equipment. In 2011 Third international conference on measuring technology and mechatronics automation (vol. 1, pp. 1022–1025). IEEE.

  • Cintas, P. G., Marco-Almagro, L., & Llabres, J. T. M. (2012). Industrial statistics with Minitab. Hoboken: Wiley.

    Book  Google Scholar 

  • Datta, S. K., Da Costa, R. P. F., Harri, J., & Bonnet, C. (2016). Integrating connected vehicles in Internet of Things ecosystems: Challenges and solutions. In 2016 IEEE 17th international symposium on a world of wireless, mobile and multimedia networks (WoWMoM) (pp. 1–6). IEEE.

  • Dey, N., Hassanien, A. E., Bhatt, C., Ashour, A., & Satapathy, S. C. (Eds.). (2018). Internet of things and big data analytics toward next-generation intelligence (pp. 3–549). Berlin: Springer.

    Google Scholar 

  • digitalcommons.unl.edu, last on-line: April 12th 2020.

  • Frigieri, E. P., Ynoguti, C. A., & Paiva, A. P. (2019). Correlation analysis among audible sound emissions and machining parameters in hardened steel turning. Journal of Intelligent Manufacturing, 30(4), 1753–1764.

    Article  Google Scholar 

  • Gao, Z., Cecati, C., & Ding, S. X. (2015). A survey of fault diagnosis and fault-tolerant techniquesPart I: Fault diagnosis with model-based and signal-based approaches. IEEE Transactions on Industrial Electronics, 62(6), 3757–3767.

    Article  Google Scholar 

  • Garcia, C. G., Valdez, E. R. N., Diaz, V. G., Bustelo, B. C. P. G., & Lovelle, J. M. C. (2019). A review of artificial intelligence in the Internet of Things. IJIMAI, 5(4), 9–20.

    Article  Google Scholar 

  • Gebraeel, N., Lawley, M., Liu, R., & Parmeshwaran, V. (2004). Residual life predictions from vibration-based degradation signals: A neural network approach. IEEE Transactions on Industrial Electronics, 51(3), 694–700.

    Article  Google Scholar 

  • Gupta, S., Khosravy, M., Gupta, N., Tiwari, B. N., Senzio-Savino, B., Asharif, F., & Asharif, M. R. (2016) Tractor oil pump fault diagnosis by pseudo-spectrum analysis of vehicle sound records. In Proceedings of the 31st international technical conference on circuits/systems, computers and communications.

  • Gupta, N., Khosravy, M., Patel, N., Dey, N., Gupta, S., Darbari, H., & Crespo R.G. (2020). Economic data analytic AI technique on IoT edge devices for health monitoring of agriculture machines. Applied Intelligence. (in press)

  • Gupta, N., Khosravy, M., Patel, N., Gupta, S., & Varshney, G. (2020). Evolutionary artificial neural networks: Comparative study on state-of-the-art optimizers. In Frontier Applications of Nature Inspired Computation (pp. 302–318). Springer, Singapore.

  • Gupta, S., Khosravy, M., Gupta, N., & Darbari, H. (2019a). In-field failure assessment of tractor hydraulic system operation via pseudospectrum of acoustic measurements. Turkish Journal of Electrical Engineering & Computer Sciences, 27(4), 2718–2729.

  • Gupta, S., Khosravy, M., Gupta, N., Darbari, H., & Patel, N. (2019b). Hydraulic system onboard monitoring and fault diagnostic in agricultural machine. Brazilian Archives of Biology and Technology,. https://doi.org/10.1590/1678-4324-2019180363.

    Article  Google Scholar 

  • Gupta, N., Khosravy, M., Patel, N., & Senjyu, T. (2018). A bi-level evolutionary optimization for coordinated transmission expansion planning. IEEE Access, 6, 48455–48477.

    Article  Google Scholar 

  • Gupta, N., Shekhar, R., & Kalra, P. K. (2014). Computationally efficient composite transmission expansion planning: A Pareto optimal approach for techno-economic solution. International Journal of Electrical Power & Energy Systems, 63, 917–926.

    Article  Google Scholar 

  • Henssler, D., Schneider, L., Gassmann, S., & Felix, T. (2015). Qualification and optimization of solid polymer tilting pad bearings for subsea pump applications. In Proceedings of the 31st international pump users symposium. Turbomachinery Laboratories, Texas A&M Engineering Experiment Station.

  • https://www.minitab.com/uploadedFiles/Documents/sample-materials/FuelInjectorNozzles-EN.pdf.

  • https://www.plm.automation.siemens.com/fr_fr/Images/Siemens-PLM-LMS-SCADAS-br_tcm68-221355.pdf

  • James, E. P., Tudor, M. J., Beeby, S. P., Harris, N. R., Glynne-Jones, P., Ross, J. N., et al. (2004). An investigation of self-powered systems for condition monitoring applications. Sensors and Actuators A: Physical, 110(1–3), 171–176.

    Article  Google Scholar 

  • Khosravy, M., Gupta, N., Patel, N., Dey, N., Nitta, N., & Babaguchi, N. (2020). Probabilistic Stones Blind Source Separation with application to channel estimation and multi-node identification in MIMO IoT green communication and multimedia systems. Computer Communications., 157, 423.

    Article  Google Scholar 

  • Khosravy, M., Gupta, N., Patel, N., & Senjyu, T. (2020). Frontier Applications of Nature Inspired Computation. Berlin: Springer.

    Book  Google Scholar 

  • Kong, X. T., Luo, H., Huang, G. Q., & Yang, X. (2019). Industrial wearable system: The human-centric empowering technology in Industry 4.0. Journal of Intelligent Manufacturing, 30(8), 2853–2869.

    Article  Google Scholar 

  • Kumar, S., Solanki, V. K., Choudhary, S. K., Selamat, A., & Gonz-lez Crespo, R. (2020). Comparative Study on Ant Colony Optimization (ACO) and K-Means Clustering Approaches for Jobs Scheduling and Energy Optimization Model in Internet of Things (IoT). International Journal of Interactive Multimedia & Artificial Intelligence,. https://doi.org/10.9781/ijimai.2020.01.003.

    Article  Google Scholar 

  • Li, S. Y., Gu, K. R., & Chen, C. S. (2019). A novel smart fault-diagnosis method with procedures of feature productions and extractions. In 2019 IEEE 4th international conference on advanced robotics and mechatronics (ICARM) (pp. 268–273). IEEE.

  • Measurement System Analysis Reference Manual. (2019). 4th Edition. Chrysler, Ford, General Motors supplier Quality Requirements task Force.

  • Michael, P., Wanke, T., & McCambridge, M. (2008). Additive and base oil effects in automatic particle counters. In Automotive lubricant testing and advanced additive development. ASTM International

  • Minitab - Minitab: Data Analysis, Statistical & Process Improvement Tools. (2020). https://support.minitab.com/.

  • Mohamed, E., Al-Attar, M., & Mitani, Y. (2019). Genetic Algorithm-Moth swarm for optimal placement and capacity of renewable sources in distribution systems. International Journal of Interactive Multimedia and Artificial Intelligence, 5(7), 105–117.

    Article  Google Scholar 

  • Montgomery, D. C. (2007). Introduction to statistical quality control. Hoboken: Wiley.

    Google Scholar 

  • Patil, V. C., Al-Gaadi, K. A., Biradar, D. P., & Rangaswamy, M. (2012). Internet of things (Iot) and cloud computing for agriculture: An overview. In Proceedings of agro-informatics and precision agriculture (AIPA 2012), India (pp. 292–296).

  • Rao, B. K. N. (Ed.). (1996). Handbook of condition monitoring. Amsterdam: Elsevier.

    Google Scholar 

  • Relf-Eckstein, J. E., Ballantyne, A. T., & Phillips, P. W. (2019). Farming Reimagined: A case study of autonomous farm equipment and creating an innovation opportunity space for broadacre smart farming. NJAS-Wageningen Journal of Life Sciences, 90, 100307.

    Google Scholar 

  • Samanta, B., Al-Balushi, K. R., & Al-Araimi, S. A. (2001, September). Use of genetic algorithm and artificial neural network for gear condition diagnostics. In Proceedings of COMADEM (pp. 449–456). Elsevier Science Ltd.

  • Sarkar, M., Banerjee, S., Badr, Y., & Sangaiah, A. K. (2017). Configuring a trusted cloud service model for smart city exploration using hybrid intelligence. International Journal of Ambient Computing and Intelligence (IJACI), 8(3), 1–21.

    Article  Google Scholar 

  • Sarowar, M. G., Kamal, M. S., & Dey, N. (2019). Internet of Things and its impacts in computing intelligence: A comprehensive ReviewIoT application for big data. In Big data analytics for smart and connected cities(pp. 103–136). IGI Global.

  • Scanlon, P., Kavanagh, D. F., & Boland, F. M. (2012). Residual life prediction of rotating machines using acoustic noise signals. IEEE Transactions on Instrumentation and Measurement, 62(1), 95–108.

    Article  Google Scholar 

  • Tavner, P., Ran, L., Penman, J., & Sedding, H. (2008). Condition monitoring of rotating electrical machines (Vol. 56). London: IET.

    Book  Google Scholar 

  • TechCrunch report. (2020). https://techcrunch.com/2018/03/08/major-trends-in-agtech-for-2018/.

  • Vimal, S., Khari, M., Crespo, R. G., Kalaivani, L., Dey, N., & Kaliappan, M. (2020a). Energy enhancement using Multiobjective Ant colony optimisation with Double Q learning algorithm for IoT based cognitive radio networks. Computer Communications, 154, 481.

  • Vimal, S., Khari, M., Dey, N., Crespo, R. G., & Robinson, Y. H. (2020b). Enhanced resource allocation in mobile edge computing using reinforcement learning based MOACO algorithm for IIOT. Computer Communications, 151, 355–364.

  • Wang, J., Zhang, L., Duan, L., & Gao, R. X. (2017). A new paradigm of cloud-based predictive maintenance for intelligent manufacturing. Journal of Intelligent Manufacturing, 28(5), 1125–1137.

    Article  Google Scholar 

  • Wu, B., & Wang, H. (2019). A lane identifying approach of the intelligent vehicle in complex condition: Intelligent vehicle in complex condition. International Journal of Ambient Computing and Intelligence (IJACI), 10(4), 25–44.

    Article  Google Scholar 

  • www.homieshydraulics.net, last online: April 2nd 2020.

  • Yadav, A. (2016). Measurement systems analysis and a study of anova method.

  • Zhang, Q., & Pierce, F. J. (2016). Agricultural automation: Fundamentals and practices. Boca Taton: CRC Press.

    Book  Google Scholar 

  • Zhao, F., Tian, Z., & Zeng, Y. (2013). Uncertainty quantification in gear remaining useful life prediction through an integrated prognostics method. IEEE Transactions on Reliability, 62(1), 146–159.

    Article  Google Scholar 

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Authors and Affiliations

  1. School of Engineering and Computer Science, Oakland University, Rochester, USA

    Neeraj Gupta & Nilesh Patel

  2. Department of Computer Science, Banasthali Vidyapith, Rajasthan, India

    Saurabh Gupta & Nisheeth Joshi

  3. John Deere Co. Ltd, Pune, India

    Saurabh Gupta

  4. Graduate School of Engineering, Osaka University, Suita, Japan

    Mahdi Khosravy

  5. Techno International New Town, Kolkata, India

    Nilanjan Dey

  6. Department of Computer Science, Faculty of Engineering, International University of La Rioja, La Rioja, Spain

    Rubén González Crespo

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  1. Neeraj Gupta
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  2. Saurabh Gupta
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Correspondence to Rubén González Crespo.

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This article has been retracted. Please see the retraction notice for more detail: https://doi.org/10.1007/s10845-022-02035-7

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Gupta, N., Gupta, S., Khosravy, M. et al. RETRACTED ARTICLE: Economic IoT strategy: the future technology for health monitoring and diagnostic of agriculture vehicles. J Intell Manuf 32, 1117–1128 (2021). https://doi.org/10.1007/s10845-020-01610-0

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