MULTI-SENSOR EMBEDDED DIAGNOSTIC ARCHITECTURE FOR REAL-TIME CONDITION MONITORING OF COTTON HARVESTER PICKING ASSEMBLIES
Keywords:
cotton harvester, spindle picking head, condition monitoring, multi-sensor telemetry, MEMS vibration analysis, ISO 20816-1, ISO 11783 (ISOBUS).Abstract
In modern mechanical cotton harvesting, the vertical spindle picking head operates under harsh field conditions characterized by abrasive mineral dust, plant waxes, and severe dynamic loading. Continuous monitoring of critical operating parameters is essential to prevent premature bearing seizure, drive belt slip, and drivetrain breakdown. This paper presents a multi-sensor embedded diagnostic architecture for real-time condition monitoring of cotton harvester picking heads. The system integrates Hall-effect speed transducers, embedded PT100 RTD probes, industrial tri-axial MEMS accelerometers, piezoresistive fluid pressure transmitters, and high-frequency capacitive moisture sensors interfaced with a 32-bit ARM Cortex-M4 microcontroller. Digital signal processing routines execute real-time vibration velocity Root-Mean-Square () extraction pursuant to ISO 20816-1, complemented by Crest Factor (CF) and Kurtosis to detect incipient raceway spalling. A dual-threshold decision engine classifies machine health into Normal, Warning, and Critical states with temporal persistence filtering. Diagnostic telemetry is broadcast via an SAE J1939 / ISO 11783 (ISOBUS) CAN network to an in-cab graphical Human–Machine Interface (HMI). Field-validated thresholds project a 15–25% reduction in unscheduled harvesting downtime.
References
1.ISO 20816-1:2016, Mechanical vibration — Measurement and evaluation of machine vibration — Part 1: General guidelines. Geneva: International Organization for Standardization, 2016.
2.ISO 11783-1:2017, Tractors and machinery for agriculture and forestry — Serial control and communications data network (ISOBUS) — Part 1: General standard for mobile data communication. Geneva: ISO, 2017.
3.ISO 13373-1:2002, Condition monitoring and diagnostics of machines — Vibration condition monitoring — Part 1: General procedures. Geneva: International Organization for Standardization, 2002.
4.J. Dong, G. Wang, H. Lin, X. Bi, Z. Li, P. Zhao, T. Pei, and F. Tan, "Vibration characteristic analysis and structural optimization of the frame of a triplex row-baling cotton picker," Agriculture, vol. 13, no. 7, art. 1440, pp. 1–19, 2023. DOI: 10.3390/agriculture13071440.
5.H. Jiang, P. Zhao, X. Bi, T. Pei, J. Yang, J. Su, J. Dong, and Y. Bao, "Design and vibration analysis of the frame structure in a six-row self-propelled packaging cotton picker," Machines, vol. 13, no. 12, art. 1086, pp. 1–18, 2025. DOI: 10.3390/machines13121086.
6.G. Sharma, T. Kaur, S. K. Mangal, N. K. Dhiman, and G. L. Jat, "MEMS approach for rolling bearing fault diagnosis using vibration analysis," Measurement, vol. 195, art. 111234, pp. 1–12, 2022. DOI: 10.1016/j.measurement.2022.111234.
7.M. Fidali, D. Augustyn, J. Ochmann, and W. Uchman, "Evaluation of the diagnostic sensitivity of digital vibration sensors for condition monitoring," Sensors, vol. 20, no. 21, art. 6120, pp. 1–18, 2020. DOI: 10.3390/s20216120.
8.A. A. Rizaev, R. R. Tabakov, and Sh. A. Yusupov, "Dynamic interaction and vibration modeling of vertical spindle drums in cotton harvesting apparatus," IOP Conf. Ser.: Earth Environ. Sci., vol. 868, art. 012045, pp. 1–8, 2021. DOI: 10.1088/1755-1315/868/1/012045.
