Youness Trachi

Bio: Youness Trachi is an academic researcher from Centre national de la recherche scientifique. The author has contributed to research in topics: Fault detection and isolation & Fault (power engineering). The author has an hindex of 4, co-authored 5 publications receiving 87 citations.

Induction Machines Fault Detection Based on Subspace Spectral Estimation

Abstract: The main objective of this paper is to detect faults in induction machines using a condition monitoring architecture based on stator current measurements. Two types of fault are considered: bearing and broken rotor bars faults. The proposed architecture is based on high-resolution spectral analysis techniques also known as subspace techniques. These frequency estimation techniques allow to separate frequency components including frequencies close to the fundamental one. These frequencies correspond to fault sensitive frequencies. Once frequencies are estimated, their corresponding amplitudes are obtained by using the least squares estimator. Then, a fault severity criterion is derived from the amplitude estimates. The proposed methods were tested using experimental stator current signals issued from two induction motors with the considered faults. The experimental results show that the proposed architecture has the ability to efficiently and cost-effectively detect faults and identify their severity.

A Novel Induction Machine Fault Detector Based on Hypothesis Testing

Abstract: This paper investigates a new fault detection method for induction machines diagnosis. The proposed detection method is based on hypothesis testing. The decision is made between two hypotheses: the machine is healthy and the machine is faulty. The generalized likelihood ratio test is used to address this issue with unknown signal and noise parameters. To implement this detector, the unknown parameters are replaced by their estimates. Specifically, four estimations are required, which are model order, frequency, phase, and amplitude estimations. The model order is obtained using the Bayesian information criterion. Total least-squares estimation of signal parameters via rotational invariance techniques is used to estimate frequencies. Then, phases and amplitudes are obtained using the least-squares estimator. The proposed approach performance is assessed using simulation data by plotting the receiver operating characteristic curves. Two faults are considered: bearing and broken rotor bar faults. Experimental tests clearly show the effectiveness of the proposed detector.

Induction machine bearing faults detection based on Hilbert-Huang transform

Abstract: This paper focuses on rolling elements bearing faults detection in induction machine based on stator currents monitoring Specifically, it proposes to process the stator currents using Hilbert-Huang transform This approach is composed of two steps First, the empirical mode decomposition is used in order to estimate the intrinsic mode functions (IMFs), then the Hilbert transform is employed to compute the instantaneous amplitude (IA) and instantaneous frequency (IF) The energy of the instantaneous amplitude of the IMFs is used as fault indicator The proposed approach is used for bearing fault detection in induction machine at several fault degrees The effectiveness of the proposed Hilbert-Huang Transform technique is verified by a series of experimental tests corresponding to different bearing fault conditions

Stator current analysis by subspace methods for fault detection in induction machines

Abstract: This paper aims to develop a condition monitoring architecture for induction machines, with focus on bearing faults. The main objective of this paper is to identify fault signatures at an early stage by using high-resolution frequency estimation techniques. In particular, we present two subspace methods, which are Root-MUSIC and ESPRIT. Once the frequencies are determined, the amplitude estimation is obtained by using the Least Squares Estimator (LSE). Finally, the amplitude estimation is used to derive a fault severity criterion. The experimental results show that the proposed architecture has the ability to measure the faults severity.

Induction machine faults detection based on a constant false alarm rate detector

Abstract: This paper presents a novel approach for induction machine condition monitoring using stator current measurements. The proposed method, based on hypothesis testing, specifically investigates a binary detection problem: the machine is healthy or faulty. The Generalized Likelihood Ratio Test (GLRT) is used to address this statistical detection problem with unknown signal and noise parameters. It is indeed a Constant False Alarm Rate (CFAR) detector. Decision is obtained according to a threshold, which is set to reach a desired false alarm probability. The proposed detector implementation needs estimations that are based on the Maximum Likelihood Estimator (MLE). In particular, Total Least Squares-Estimation of Signal Parameters via Rotational Invariance Techniques (TLS-ESPRIT) estimates frequencies. The proposed CFAR detector is tested on experimental data of bearings faults and broken rotor bars that clearly show it effectiveness.

An Efficient Hilbert–Huang Transform-Based Bearing Faults Detection in Induction Machines

Abstract: This paper focuses on rolling elements bearing fault detection in induction machines based on stator currents analysis. Specifically, it proposes to process the stator currents using the Hilbert–Huang transform. This approach relies on two steps: empirical mode decomposition and Hilbert transform. The empirical mode decomposition is used in order to estimate the intrinsic mode functions (IMFs). These IMFs are assumed to be mono-component signals and can be processed using demodulation technique. Afterward, the Hilbert transform is used to compute the instantaneous amplitude (IA) and instantaneous frequency (IF) of these IMFs. The analysis of the IA and IF allows identifying fault signature that can be used for more accurate diagnosis. The proposed approach is used for bearing fault detection in induction machines at several fault degrees. The effectiveness of the proposed approach is verified by a series of simulation and experimental tests corresponding to different bearing fault conditions. The fault severity is assessed based on the IMFs energy and the variance of the IA and IF of each IMF.

Step-by-Step Fuzzy Diagnosis Method for Equipment Based on Symptom Extraction and Trivalent Logic Fuzzy Diagnosis Theory

Abstract: A step-by-step fuzzy diagnostic method based on frequency-domain symptom extraction and trivalent logic fuzzy diagnosis theory (TLFD), which is established by combining the trivalent logic inference theory with the possibility and fuzzy theories, is proposed herein. The features for diagnosing a number of abnormal states are extracted sequentially from the measured signals using statistical tests in the frequency domain. The symptom parameters (SPs) that can sensitively reflect symptoms of abnormal states are then selected to provide effective information for the discrimination of each state. The membership function of each state is then generated based on the possibility theory using the probability functions of the SPs. The step-by-step fuzzy diagnoses are performed based on the TLFD. This method can be used extensively to diagnose anomalies in various equipment. In this study, the diagnosis of structure faults of a rotating machine is cited as an example to demonstrate the effectiveness and universality of this method.

A Review of Artificial Intelligence Methods for Condition Monitoring and Fault Diagnosis of Rolling Element Bearings for Induction Motor

Abstract: The fault detection and diagnosis (FDD) along with condition monitoring (CM) and of rotating machinery (RM) have critical importance for early diagnosis to prevent severe damage of infrastructure in industrial environments. Importantly, valuable industrial equipment needs continuous monitoring to enhance the safety, reliability, and availability and to decrease the cost of maintenance of modern industrial systems and applications. However, induction motor (IM) has been extensively used in several industrial processes because it is cheap, reliable, and robust. Rolling bearings are considered to be the main component of IM. Undoubtedly, any failure of this basic component can lead to a serious breakdown of IM and for whole industrial system. Thus, many current methods based on different techniques are employed as a fault prognosis and diagnosis of rolling elements bearing of IM. Moreover, these techniques include signal/image processing, intelligent diagnostics, data fusion, data mining, and expert systems for time and frequency as well as time-frequency domains. Artificial intelligence (AI) techniques have proven their significance in every field of digital technology. Industrial machines, automation, and processes are the net frontiers of AI adaptation. There are quite developed literatures that have been approaching the issues using signals and data processing techniques. However, the key contribution of this work is to present an extensive review of CM and FDD of the IM, especially for rolling elements bearings, based on artificial intelligent (AI) methods. This study highlights the advantages and performance limitations of each method. Finally, challenges and future trends are also highlighted.

Induction Machines Fault Detection Based on Subspace Spectral Estimation

Abstract: The main objective of this paper is to detect faults in induction machines using a condition monitoring architecture based on stator current measurements. Two types of fault are considered: bearing and broken rotor bars faults. The proposed architecture is based on high-resolution spectral analysis techniques also known as subspace techniques. These frequency estimation techniques allow to separate frequency components including frequencies close to the fundamental one. These frequencies correspond to fault sensitive frequencies. Once frequencies are estimated, their corresponding amplitudes are obtained by using the least squares estimator. Then, a fault severity criterion is derived from the amplitude estimates. The proposed methods were tested using experimental stator current signals issued from two induction motors with the considered faults. The experimental results show that the proposed architecture has the ability to efficiently and cost-effectively detect faults and identify their severity.

Incipient Broken Rotor Bar Detection in Induction Motors Using Vibration Signals and the Orthogonal Matching Pursuit Algorithm

Abstract: A methodology for automatic incipient broken rotor bar detection in induction motors (IMs) is presented. Sparse representations of signals are applied as a diagnosis technique. The novelty of this technique is that it can analyze the frequency spectra from vibration signals even when the differences among signals are small. This representation allows decomposing or reconstructing signals through a trained dictionary that has learned the features of one specific group/class. The main feature of this paper is the use of overcomplete dictionaries trained from sets of signals with faults to be detected. In this way, trained dictionaries perform the decomposition of signals using the orthogonal matching pursuit (OMP) algorithm. The decomposition is evaluated and classified by error-based criteria and a majority decision classifier, allowing the detection of early damage, ranging from 1 mm to one broken bar. The detection is performed by the decomposition of vibration signals from three axes ( ${x}$ , ${y}$ , and ${z}$ ) of IMs under three load conditions (unloaded, half loaded, and three-fourths loaded) and different levels of damage (healthy or 0 mm, 1–9 mm, and one broken bar). These signals are processed by the Fourier transform and the spectrum obtained is evaluated by the OMP algorithm. Finally, the retrieved information is evaluated and the diagnosis is given. All algorithms are developed in MATLAB software and the detection accuracy is higher than 90% for damages as small as 1 mm.