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All Journal ELKHA : Jurnal Teknik Elektro Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI)
Pebriani, Sovi
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Industrial & Manufacturing Engineering

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Design and Implementation of a Kalman-Bucy Filter for Fault Detection in DC Motor Systems Mursyitah, Dian; Faizal, Ahmad; Safitri, Elfira; Pebriani, Sovi; Alfadri, Ramadhan
ELKHA : Jurnal Teknik Elektro Vol. 17 No.2 October 2025
Publisher : Faculty of Engineering, Universitas Tanjungpura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/elkha.v17i2.96853

Abstract

This study presents the design and implementation of a Kalman-Bucy filter for fault detection in DC motor systems, which are widely used in industrial drives and automation. Accurate state estimation is essential for ensuring reliable operation, particularly in the presence of measurement noise and parameter uncertainties. The proposed observer exhibits rapid convergence in speed estimation (less than one second) and strong robustness to measurement noise, achieving a Root Mean Square Error (RMSE) of 24.38 rad/s, closely matching the noise standard deviation (σᵥ = 23.01 rad/s). This close agreement indicates that the Kalman-Bucy filter operates near its theoretical optimal performance under Gaussian noise assumptions. Fault detection is carried out through residual analysis under three fault scenarios: ramp, inverse ramp, and square wave. Each scenario generates distinct residual patterns, providing clear indicators of both gradual and abrupt anomalies. Quantitative evaluation demonstrates high sensitivity (97.0% for ramp and inverse ramp, 94.1% for square), perfect specificity (100%), and a zero false alarm rate across all scenarios. These findings highlight the potential of the Kalman-Bucy filter as a reliable and computationally efficient approach for state estimation and fault indication using data representative of a real DC motor system. The results provide a valuable basis for developing predictive maintenance strategies and improving system reliability. Future work will focus on experimental implementation and validation to confirm its performance under real-world operating conditions.
Fault Detection in Continuous Stirred Tank Reactor (CSTR) System Using Extended Luenberger Observer Mursyitah, Dian; Son Maria, Putut; Pebriani, Sovi; Delouche, David; Zhang, Tingting; Kratz, Frédéric
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 10 No. 4 (2024): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v10i4.29797

Abstract

This research proposes fault detection in a Continuous Stirred Tank Reactor (CSTR) system using an Extended Luenberger Observer (ELO). The ELO is chosen due to the non-linearity of the CSTR system. Accurate state estimation is critical for effective fault diagnosis; therefore, the performance of the ELO is initially tested using two indicators: robustness and sensitivity in estimating the level and concentration within the CSTR system. The sensitivity test yields promising results, with the ELO accurately estimating the actual system despite variations in input and initial conditions, and with a fast convergence time of 1 seconds. The robustness test also demonstrates positive outcomes, as the ELO continues to estimate the system accurately even in the presence of noise with standard deviation 2.5% of measurements. Furthermore, faults that can be related to sensor malfunctions or the disturbances in the CSTR process were successfully detected using the ELO. Performance analysis and fault detection in the CSTR system are presented through simulation. The contributions of this research include development of ELO for non-linear dynamics CSTR system and evidence of its effectiveness in detecting fault within the in CSTR system.
Co-Authors Ahmad Faizal Alfadri, Ramadhan Delouche, David Dian Mursyitah Elfira Safitri Kratz, Frederic Son Maria, Putut Zhang, Tingting