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Role of Empirical Mode Decomposition in Fault Diagnosis of Industrial Components
(2026-02-01)
Chauhan, Sumika
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Vashishtha, Govind
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Singh, Riya
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Shakya, Paramjeet
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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
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Khunkitti, Pirat
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Siritaratiwat, Apirat
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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
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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.
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Heat pipe inspection system for thermal management in electronic circuit
(2011-07-26)
Nilas, P.
This research paper proposes the inspection system of heat pipe cooling for electronic devices, such as CPU in personal computer, laptop computer and so on. The inspection system is an essential part in production process of heat pipes CPU cooler. To ensure the high quality product, a 100% quality checking is taken into account. Generally, heat pipe inspection system is conducted by human ears that may cause errors. Therefore, this work proposes the automatic inspection system using the concept on the basis of sensing Acoustic Emission (AE) signals. In the experiment, the AE sensor was mounted on the heat pipe surface to measure the vibration degree sent to computer. The signal of acoustic vibration was analyzed by windows operating system using LABVIEW and NI USB-9233 data acquisition module. Threshold level of noise vibration shown in the program could identify quality or defect of the heat pipe cooling operation. The performances evaluation is satisfactorily achieved with the proposed system illustrated by the testing of the known condition product. This research could potentially help improving the quality assurance operation of heat pipe cooling inspection system in any industries.
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An open circuit fault diagnostic technique in IGBTS for ac to dc converters applied in microgrid applications
(2011-01-01)
Khomfoi, Surin
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Sae-Kok, Warachart
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Ngamroo, Issarachai
An open circuit fault diagnostic method in IGBTs for the ac to dc converters used in microgrid applications is developed in this paper. An ac to dc converter is a key technology for microgrids in order to interface both distributed generation (DG) and renewable energy resources (RES). Also, highly reliable ac to dc converters are necessary to keep converters in continuous operation as long as possible during power switch fault conditions. Therefore, the proposed fault diagnostic method is developed to reduce the fault detection time and to avoid any other fault alarms because continuous operation is desired. The proposed diagnostic method is a combination of the absolute normalized dc current technique and the false alarm suppression algorithm to overcome the long fault detection time and fault alarm problems. The simulation and experimental results show that the developed fault diagnostic method can perform fault detection within about one cycle. The results illustrate that the reliability of an ac to dc converter interfaced with a microgrid can be improved by using the proposed fault diagnostic method.
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Fault diagnosis and reconfiguration for multilevel inverter drive using AI-based techniques
(2007-12-01)
Khomfoi, Surin
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Tolbert, Leon M.
A fault diagnostic and reconfiguration method for a cascaded H-bridge multilevel inverter drive (MLID) using artificial-intelligence-based techniques is proposed in this paper. Output phase voltages of the MLID are used as diagnostic signals to detect faults and their locations. It is difficult to diagnose an MLID system using a mathematical model because MLID systems consist of many switching devices and their system complexity has a nonlinear factor. Therefore, a neural network (NN) classification is applied to the fault diagnosis of an MLID system. Multilayer perceptron networks are used to identify the type and location of occurring faults. The principal component analysis is utilized in the feature extraction process to reduce the NN input size. A lower dimensional input space will also usually reduce the time necessary to train an NN, and the reduced noise can improve the mapping performance. The genetic algorithm is also applied to select the valuable principal components. The proposed network is evaluated with simulation test set and experimental test set. The overall classification performance of the proposed network is more than 95%. A reconfiguration technique is also proposed. The proposed fault diagnostic system requires about six cycles to clear an open-circuit or short-circuit fault. The experimental results show that the proposed system performs satisfactorily to detect the fault type, fault location, and reconfiguration. © 2007 IEEE.
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Fault detection and reconfiguration technique for cascaded H-bridge 11-level inverter drives operating under faulty condition
(2007-12-01)
Khomfoi, Surin
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Tolbert, Leon M.
A fault detection and reconfiguration technique for a cascaded H-bridge 11-level inverter drives during faulty condition is proposed in this paper. The ability of cascaded H-bridge multilevel inverter drives (MLID) to operate under faulty condition is also discussed. Output phase voltages of a MLID can be used as a diagnostic signal to detect faults and their locations. AI-based techniques are used to perform the fault classification. A neural network (NN) classification is applied to the fault diagnosis of a MLID system. Multilayer perceptron (MLP) networks are used to identify the type and location of occurring faults. The principal component analysis (PCA) is utilized in the feature extraction process to reduce the NN input size. The genetic algorithm (GA) is also applied to select the valuable principal components to train the NN. A reconfiguration technique is also developed. The developed system is validated with simulation and experimental results. The developed fault diagnostic system requires about 6 cycles (∼100 ms at 60 Hz) to clear an open circuit and about 9 cycles (∼150 ms at 60 Hz) to clear a short circuit fault. The experiment and simulation results are in good agreement with each other, and the results show that the developed system performs satisfactorily to detect the fault type, fault location, and reconfiguration. © 2007 IEEE.
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A low cost prototype for testing stator winding shorted turns in 3-phase induction motors
(2005-12-01)
Manop, Chalermchat
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Senavongse, Wongwit
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Kinnares, Vijit
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Tangsiriworakul, Chanwit
This paper presents a prototype of stator winding shorted turns detector for three-phase induction motors. The focus condition is turn-to-turn short circuit, because this condition is the initial problem. The diagnosis technique is detectable from the voltage sum of the three instantaneous line to star-point voltage. In the testing, the supply is sinusoidal waveform and the motor must be star connected with floating neutral. Before diagnosis, this instrument will also determine the level of supply voltage imbalance, if it is over the limit, the test instrument will not diagnose shorted turns. From experimental results, 3-hp and 5-hp induction motors were used for testing this equipment which is capable of accurately identifying percentage of stator winding shorted turns. © 2005 IEEE.
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Fault diagnosis in transmission lines using wavelet transform analysis
(2002-12-01)
Makming, Pongsak
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Bunjongjit, Sulee
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Kunakorn, Anantawat
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Jiriwibhakorn, Somchart
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Kando, M.
This paper presents a new method to diagnose faults in a transmission system, This is based on detecting high frequency components contained in a fault signal spectrum. The Discrete Wavelet Transform (DWT) is used in the analysis in order to classify fault types and to locate fault positions. Simulations are performed using ATP/EMTP. It is found that the proposed method gives satisfactory results, and will be very useful in the development of a power system protection scheme.

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