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Low-Complexity Monitoring of DC Motor Speed Sensor Additive Faults Using a Discrete Kalman Filter Observer

Author

Listed:
  • Rossy Uscamaita-Quispetupa

    (TESLA Laboratory, Universidad Nacional de San Antonio Abad del Cusco (UNSAAC), Cusco 08003, Peru)

  • Erwin J. Sacoto-Cabrera

    (GIHP4C, Universidad Politécnica Salesiana, Cuenca 010102, Ecuador)

  • Roger Jesus Coaquira-Castillo

    (LIECAR Laboratory, Universidad Nacional de San Antonio Abad del Cusco (UNSAAC), Cusco 08003, Peru)

  • L. Walter Utrilla Mego

    (TESLA Laboratory, Universidad Nacional de San Antonio Abad del Cusco (UNSAAC), Cusco 08003, Peru)

  • Julio Cesar Herrera-Levano

    (TESLA Laboratory, Universidad Nacional de San Antonio Abad del Cusco (UNSAAC), Cusco 08003, Peru)

  • Yesenia Concha-Ramos

    (School of Systems and Computer Engineering, Universidad Continental, Cusco 08000, Peru)

  • Edison Moreno-Cardenas

    (TESLA Laboratory, Universidad Nacional de San Antonio Abad del Cusco (UNSAAC), Cusco 08003, Peru
    Technology and Engineering Group, EM Research & Tech, Cusco 08003, Peru)

Abstract

This article presents an online additive fault-detection system for the speed sensor of a 200 W shunt-type direct current (DC) motor, integrated into a power module controlled by an Insulated Gate Bipolar Transistor (IGBT). The system is designed to trigger an alarm signal when an additive fault occurs by comparing the Kalman Filter (KF) residual against a predefined detection threshold. Three specific fault types in the speed sensor were analyzed: offset, disconnection, and sinusoidal noise. Experimental results demonstrate effective fault detection across a speed range of 80 to 690 rpm under no-load conditions. However, when a constant torque of 0.5 Nm is applied, both the detection threshold and the subset of reliably identifiable faults must be adjusted. The main contribution of this study is the development of a customized real-time fault detection framework and the characterization of residual variations caused by unmodeled load disturbances in actual hardware. This approach improves the monitoring and fault-diagnosis capabilities of sensor systems in DC motors by quantifying the stochastic behavior of residuals under different operating constraints.

Suggested Citation

  • Rossy Uscamaita-Quispetupa & Erwin J. Sacoto-Cabrera & Roger Jesus Coaquira-Castillo & L. Walter Utrilla Mego & Julio Cesar Herrera-Levano & Yesenia Concha-Ramos & Edison Moreno-Cardenas, 2026. "Low-Complexity Monitoring of DC Motor Speed Sensor Additive Faults Using a Discrete Kalman Filter Observer," Energies, MDPI, vol. 19(6), pages 1-21, March.
    • Handle: RePEc:gam:jeners:v:19:y:2026:i:6:p:1485-:d:1895938
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