Voltage source inverters VSIs are extensively used in several industrial applications, including the area of variable speed induction motor IM or permanent magnet synchronous motor PMSM drives. However, they can suffer some critical faults such as open-circuit faults OCFs in IGBT power switches. This paper aims to study a fault diagnosis FD and fault-tolerant system against IGBT OCFs in voltage-source inverter-fed PMSM. The fault tolerance strategies that will be discussed here are, the first one is to connect the faulted phase of the power converter to the DC-link capacitors midpoint by firing TRIACS; the second strategy consists of the connection of the faulty inverter phase to an extra-leg through TRIACS. The effectiveness of the studied FD method and fault-tolerant strategies is verified through computer simulation using MATLAB-SIMULINK software.

The paper’s primary focus is on the monitoring of vibration signals and introduces an innovative method for the detection of bearing faults in electric machines WTMP. While conventional techniques based on vibration signals are popular in identifying the characteristic frequencies associated with faults, they encounter difficulties when dealing with signals that vary over time (non-stationary signals). To tackle this challenge, the proposed approach combines three distinct techniques: Continuous Wavelet Transform (CWT), Wavelet Packet Transform (WPT), and Matrix Pencil (MP). This hybrid method has several objectives: It aims to reconstruct signals that exhibit non-stationary behavior, emphasize the frequency related to bearing faults, and ultimately enhance the accuracy of fault detection. By harnessing the unique strengths of CWT, WPT, and MP, this proposed approach significantly improves the effectiveness of condition monitoring in electric machines, particularly in the context of detecting bearing faults. To validate the method’s performance, an experimental setup has been established. This setup allows for testing under various load conditions, offering a comprehensive assessment of the capabilities of the proposed technique. This rigorous experimental testing ensures the method's reliability and practical applicability in real-world scenarios.

Non-stationary fault detection under bearing fault operation of induction motor is investigated in this paper. For this aim, the vibration signal is analyzed by wavelet method and pencil matrix method. The pencil matrix (PM) or (MP) method has been combined with wavelet transform (WT), in order to reconstruct the non-stationary signal and detect the bearing fault frequency. For validation of results, an experimental setup is used for an induction motor under different load operation and with failure on its inner race. The application of the proposed technique on vibration signal under non-stationary state show that fault can be characterized by a particular signature that it is not possible with fast Fourier transform (FFT).

PurposeBearings play a critical role in the reliable operation of induction machines, and their failure can lead to significant operational challenges and downtime. Detecting and diagnosing these defects is imperative to ensure the longevity of induction machines and preventing costly downtime. The purpose of this paper is to develop a novel approach for diagnosis of bearing faults in induction machine.Design/methodology/approachTo identify the different fault states of the bearing with accurately and efficiently in this paper, the original bearing vibration signal is first decomposed into several intrinsic mode functions (IMFs) using variational mode decomposition (VMD). The IMFs that contain more noise information are selected using the Pearson correlation coefficient. Subsequently, discrete wavelet transform (DWT) is used to filter the noisy IMFs. Second, the composite multiscale weighted permutation entropy (CMWPE) of each component is calculated to form the features vector. Finally, the features vector is reduced using the locality-sensitive discriminant analysis algorithm, to be fed into the support vector machine model for training and classification.FindingsThe obtained results showed the ability of the VMD_DWT algorithm to reduce the noise of raw vibration signals. It also demonstrated that the proposed method can effectively extract different fault features from vibration signals.Originality/valueThis study suggested a new VMD_DWT method to reduce the noise of the bearing vibration signal. The proposed approach for bearing fault diagnosis of induction machine based on VMD-DWT and CMWPE is highly effective. Its effectiveness has been verified using experimental data.

One of the problems with induction motors, say the experts, is voltage. Insulation deterioration is brought on by the winding overheating because the issue is frequently one that is defective, old, or damaged as a result of voltage surge, voltage drop, or overheating. In this study, we follow the stator winding insulation of an AC machine using leakage current insulation measures. Then the insulation life of the induction motor was determined under different loading scenarios, such as voltage fluctuations and varied modulation frequencies

Bearings are the most common components prone to failure in induction motors. To minimize the effects of failure, it is important to monitor the health of bearings, so in this paper we propose a new and advanced technique for predicting bearing failure using extraction and selection features and continuous wavelet transform (CWT) technique. In the proposed approach, vibration signals are represented by spectrograms, and various parameters such as time or frequency domain, (RMS, Power, Standard deviation (Std), Peak2peak, Mean, CrestFactor) are applied in order to process this data. Then CWT is used to directly extract TSP (prediction start time), RUL (remaining life) and threshold. In this study, run test data were used to validate the work.

This paper proposes a new fault-diagnosis method and fault-tolerance control (FTC) strategy applied to the field-oriented control (FOC) of induction motor drives. The proposed diagnosis method for open-switch faults (OSFs) relies solely on measured current behavior. The diagnostic variables are extracted from the average absolute values of the normalized phase currents. The simulation results show that the diagnosis method can detect the OSFs in less than 36

This paper introduces a novel approach for detecting and prognosing stator inter-turn faults in induction motors, addressing an important aspect of motor health monitoring.The most commonly employed method for fault detection in this context is Motor Current Signature Analysis (MCSA).By leveraging this method, the paper focuses on the generation of periodic Magneto Motive Force (MMF) waves in the balanced current signal as a result of inter-turn faults.These MMF waves serve as crucial indicators for identifying the presence of such faults.To achieve early detection and prognostic capability for inter-turn faults, the paper proposes a numerical model that relies on analyzing the forward and backward currents.This model offers a promising approach to effectively detect and prognose these faults before they escalate into more severe issues.The obtained results from applying the proposed method demonstrate its efficiency in fault detection and prognostic accuracy for stator inter-turn faults.To validate the effectiveness of the proposed approach, an experimental setup is implemented.This setup provides a real-world context for evaluating the performance and reliability of the method in detecting and prognosing inter-turn faults.Through this validation process, the paper strengthens the credibility and applicability of the proposed technique in practical motor maintenance and fault management scenarios.

The bearing fault diagnosis plays an important role to reduce catastrophic failures and ensure the continuity of running machines to avoid heavy economic loss. The vibration signals of rolling bearings are often nonlinear and nonstationary; it is difficult to extract sensitive features and diagnose faults by traditional signal processing methods. To solve this problem, a novel intelligent fault-diagnosis approach based on whale optimization algorithm grey wolf optimization-variational mode decomposition (WOAGWO-VMD) algorithm and the marine predators algorithm optimization-least squares support vector machine (MPA-LSSVM) is proposed in this paper. Firstly, hybrid algorithm WOAGWO is used to optimize the parameters of VMD and obtain the optimal combination (K; α). Then, the optimized VMD algorithm is utilized to decompose the vibration signal of the rolling bearing into several intrinsic mode functions, and a new sensitive indicator is created to select the components containing the most information. For these components, the dispersion entropy feature, permutation entropy feature, and singular value feature are extracted to form the multi-feature vector. Finally, the feature vectors obtained are input to the MPA-LSSVM for diagnosis and identification. The validity and strength of the proposed method is verified by experimental data under different bearing conditions. The results have shown that the proposed method can extract the fault feature information of 16 bearing signals of different fault types effectively and identify them accurately.

This paper presents high performances fault detection and diagnosis approach for broken rotor bar (BRB) and severity evaluation in squirrel cage induction motors. The proposed approach is based on combination of multiple features extraction techniques from the three-phase stator currents, features selection, and self-organising maps (SOM) as classifier in the BRB fault diagnosis process. For feature extraction, the envelope and the zero crossing times (ZCT) signals are extracted from stator currents, then, statistical parameters from time and frequency domains, in addition to fault-related frequencies are calculated from the current waveform, the envelope, and the ZCT signals. The most relevant features are then selected using the relief feature selection algorithm. Finally, the SOM is used for the decision-making step. Conducted experimental investigations on a healthy and faulty machines, have exposed the robustness and accuracy of the proposed BRB fault detection technique.

Background: Primary lymphoma of the thyroid is a relatively rare disease posing many times a diagnostic challenge. In this study we aim to investigate the clinicopathologic characteristics of primary thyroid lymphoma in a tunisian population

Among the upper aero-digestive tract (UADT) cancers, laryngeal carcinoma is reputed to have one of the best prognoses. Advanced stage tumors are still curable although the complex management strategy. Anterior spread of the tumor to the overlaying skin is a rare condition. Local control of the disease is still possible usually with a combination of extended surgery and adjuvant external beam radiotherapy (RT). Survival rates are reasonable as long as an intensive post-operative care and a strict follow up are provided. Our purpose was to investigate T4a patients with skin infiltration in terms of management and survival.

This work illustrates a method to detect and separate the broken rotor bars (BRBs) from load torque oscillations (LTOs) in motor’s line current signature. The LTOs (due to mechanical load condition abnormalities, load fluctuations like speed reduction couplings or a defective transmission) can introduce similar symptoms as the rotor cage breaks do. The proposed policy is based on the set of two rotating coordinates (same and inverse angular velocity as the current’s fundamental frequency ω) for the stator current vector, and its decomposition into positive and negative components. The extracted components of the positive sequence allow to separate the similar effects produced by rotor defects and the oscillating load . The detection and separation process is performed through the demodulation of the amplitude modulating signal due to BRBs and the phase modulating signal due to LTOs. An experimental test bench has been conducted to validate the simulation results and demonstrate the effectiveness of the proposed approach.

In this study, a new effective approach for detection and classification of stator winding faults in induction motors is presented. The approach is based on current analysis. It uses multiple features extraction techniques, where Park transform, zero crossing time signal, and the envelope are extracted from the three-phase stator currents. Then, statistical features are calculated from time and frequency domains of each extracted signal. The Features selection techniques (ReliefF, minimum redundancy and max relevancy, and support vector machine approach based on recursive feature elimination) are used to select from the extracted features the most relevant ones. As a classifier, the self-organising map neural network is used. The proposed procedure is experimentally studied using stator current signals obtained from various faulty cases and a healthy induction motor at different load variations. The experimental results verify that the proposed strategy is able to distinguish the faulty cases from the healthy ones. Also, it effectively identifies the faulty phase in addition to the extent of the fault.

This paper presents a new robust and high performances fault diagnosis scheme for broken bar fault detection and severity evaluation. The aim is to ensure an accurate condition monitoring and reduced false or missed alarms rate for induction motor operating in critical applications. It investigates the combination of features selection methods with the Self-Organizing Maps (SOM) neural network in a fault detection and severity evaluation system. This approach, based on the current analysis, uses multiple features extraction techniques, where the zero crossing times (ZCT) signal and the envelope are extracted from the three-phase stator currents. Then, statistical and frequency domains features are calculated from these extracted signals. The ReliefF feature selection technique is used to select from the extracted features the most sensitive and relevant ones. Next, the SOM neural network is used as a decision-making system. The experimental investigations, conducted using a healthy machine and a machine with broken bars, show the effectiveness of the proposed fault detection technique in terms of the classification accuracy.