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Start date: 2020Subject: search.filters.subject.Fault diagnosis

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    Role of Empirical Mode Decomposition in Fault Diagnosis of Industrial Components
    (2026-02-01)
    Chauhan, Sumika
    ;
    Vashishtha, Govind
    ;
    Singh, Riya
    ;
    Shakya, Paramjeet
    ;
    Chaiyaput, Salisa
    Background: Rotating machinery is critical in industrial applications but prone to malfunctions due to harsh operating conditions. Signal processing techniques, especially Empirical Mode Decomposition (EMD), are effective for fault detection and diagnosis. EMD has been widely researched and applied. Purpose: This study aims to provide a comprehensive review of the latest developments in EMD applications for rotating machinery fault diagnosis. It serves as a valuable resource for researchers in this area and helps to identify potential future research directions. Methods: The review begins with a brief introduction to EMD, highlighting its benefits, and addressing common issues with the proposed solutions. It then summarizes recent EMD applications in fault diagnosis for critical components, such as bearings, gears, and rotors. Conclusion: The paper concludes by discussing unresolved challenges and suggesting avenues for future research. This review is designed to serve as both an introduction to EMD for newcomers and a state-of-the-art summary for experienced researchers in the field of fault diagnosis.
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    Label-Consistent Input Structuring Based on Second-Stage Sliding Window for PMSM Fault Diagnosis
    (2026-01-01)
    Nguyen, Thanh Son
    ;
    Khunkitti, Pirat
    ;
    Siritaratiwat, Apirat
    ;
    Seangwong, Pattasad
    Accurate fault diagnosis of electrical machines is essential for operational reliability and safety. Handcrafted features remain attractive in practice because of their interpretability and computational efficiency. However, this traditional approach relies on individual feature vectors and is limited in preserving inherent temporal dynamics. To address this issue, a label-consistent input structuring based on the second-stage sliding window (SSSW) method is proposed. This approach retains the benefits of handcrafted features and arranges feature vectors into temporally coherent sequences while ensuring label consistency. A hyperparameter optimization scheme is incorporated with a long short-term memory classifier to reduce manual tuning. Performance evaluations demonstrate the robustness of the proposed SSSW method across diverse operating conditions and input signal configurations. These include single-phase currents, multiphase currents, vibration, and fused current-vibration signals. Notably, the proposed approach achieves classification accuracy of up to 100% and demonstrates stable learning behavior. Experimental verification on a laboratory-scale permanent magnet synchronous motor testbed further validates the proposed method under realistic measurement noise and interference conditions. Finally, combining handcrafted features with the proposed SSSW input structuring method provides a practical, scalable solution for reliable electrical machine fault diagnosis.
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    Process safety enhancement of feedforward control using foundation fieldbus
    (2020-04-01)
    Julsereewong, Amphawan
    ;
    Kummool, Sart
    This paper presents a practical technique in engineering phase for enhancing safety of Foundation Fieldbus (FF)-based feedforward control through propagation of measurement validity and status information in the loop using function block language to prevent hazards in the presence of a transmitter failure. The proposed technique is based on fault diagnosis of FF devices to increase the safety beyond that found in basic control loops using traditional technologies. For hybrid architecture by assigning basic and advanced function blocks to execute in H1 field instruments and H1 interface module, respectively, all possible cases for configuring parameter options not only to shut the loop down when the failure occurs but also to resume the loop to normal when the failure disappears are described. The feedforward on temperature control of H1 segment configured and operated on the DeltaV integrated host is used for experimentally testing the correctness of the defined parameter options to provide function block interlocks and failsafe actions as well as fault recovery mechanisms. In addition, the Petri net model to represent the control loop behaviors for comparing the process safety enhancement in different scenarios from five cases of parameter configurations is also included.