Lotfi Saidi

Application of higher order spectral features and support vector machines for bearing faults classification

Condition monitoring and fault diagnosis of rolling element bearings timely and accurately are very important to ensure the reliability of rotating machinery. This paper presents a novel pattern classification approach for bearings diagnostics, which combines the higher order spectra analysis features and support vector machine classifier. The use of non-linear features motivated by the higher order spectra has been reported to be a promising approach to analyze the non-linear and non-Gaussian characteristics of the mechanical vibration signals. The vibration bi-spectrum (third order spectrum) patterns are extracted as the feature vectors presenting different bearing faults. The extracted bi-spectrum features are subjected to principal component analysis for dimensionality reduction. These principal components were fed to support vector machine to distinguish four kinds of bearing faults covering different levels of severity for each fault type, which were measured in the experimental test bench running under different working conditions. In order to find the optimal parameters for the multi-class support vector machine model, a grid-search method in combination with 10-fold cross-validation has been used. Based on the correct classification of bearing patterns in the test set, in each fold the performance measures are computed. The average of these performance measures is computed to report the overall performance of the support vector machine classifier. In addition, in fault detection problems, the performance of a detection algorithm usually depends on the trade-off between robustness and sensitivity. The sensitivity and robustness of the proposed method are explored by running a series of experiments. A receiver operating characteristic (ROC) curve made the results more convincing. The results indicated that the proposed method can reliably identify different fault patterns of rolling element bearings based on vibration signals.

Linear feature selection and classification using PNN and SFAM neural networks for a nearly online diagnosis of bearing naturally progressing degradations

In this work, an effort is made to characterize seven bearing states depending on the energy entropy of Intrinsic Mode Functions (IMFs) resulted from the Empirical Modes Decomposition (EMD). Three run-to-failure bearing vibration signals representing different defects either degraded or different failing components (roller, inner race and outer race) with healthy state lead to seven bearing states under study. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) are used for feature reduction. Then, six classification scenarios are processed via a Probabilistic Neural Network (PNN) and a Simplified Fuzzy Adaptive Resonance Theory Map (SFAM) neural network. In other words, the three extracted feature data bases (EMD, PCA and LDA features) are processed firstly with SFAM and secondly with a combination of PNN-SFAM. The computation of classification accuracy and scattering criterion for each scenario shows that the EMD-LDA-PNN-SFAM combination is the suitable strategy for online bearing fault diagnosis. The proposed methodology reveals better generalization capability compared to previous works and it is validated by an online bearing fault diagnosis. The proposed strategy can be applied for the decision making of several assets. Display Omitted A new methodology proposed for a nearly online damage stage detection.The proposed strategy is based on neural networks and EMD method.The nearly online detection of bearing health status is done thanks to various damage stages.Experimental results show that this methodology is very effective.Unlike previous works, the proposed method is tested for the diagnosis of naturally progressing bearing degradations.

Application of higher order spectral analysis for rotor broken bar detection in induction machines

Detection and identification of induction machine faults through the stator current signal using higher order spectra analysis is presented. This paper proposes two higher order spectra techniques, namely the power spectrum and the slices of bi-spectrum used for the analysis of induction machine stator current leading to the detection of electrical failures within the rotor. Experimental signals have been analyzed highlighting that bi-spectrum results show their superiority in the accurate detection of rotor broken bars. Even when the induction machine is rotating at a low level of shaft load, the rotor fault detection is efficient.

Stator current bi-spectrum patterns for induction machines multiple-faults detection

Inspecting the literature, the most used techniques proposed for induction machines diagnosis are focused on detecting single faults. There is a lack of works dealing with the diagnosis and identification of multiple combined faults. In this framework, this paper presents the stator current bi-spectrum analysis to detect two types of faults mixed together which may appear in three phase induction motors. Based on real experimental data, the detection study concerns isolated rotor broken bars and damage in the bearings inner race rolling element. For multiple faults detection, and for lack of experimental data, only synthetic data are used. To deal with the frequency analysis, a mathematical model of the stator current has been derived and used into the bi-spectrum formulas. The main contribution of this paper is the development of a theoretical method which may help the user to assess the presence of fault frequencies in induction motors in both settings namely single or multiple combined faults. To highlight the superiority of the bi-spectrum tool over the spectrum, receiver operating characteristics (ROC) analysis has been carried out. Therefore, simulation results are performed in noisy environment showing that the bi-spectrum is able to detect frequency faults better.

Bearing defects decision making using higher order spectra features and support vector machines

This paper presents a novel pattern classification approach for bearing defects diagnostics, which combine the higher order spectra (HOS) analysis features and support vector machine classifier (SVM). The use of non-linear features motivated by the higher order spectra has been reported to be a promising approach to analyze the non-linear and non-Gaussian characteristics of the bearing mechanical vibration signals. The vibration bi-spectrum patterns are extracted as the feature vectors presenting different faults of the bearings. The extracted bi-spectrum features are subjected to principal component analysis (PCA) for dimensionality reduction. These principal components were fed to support vector machine to distinguish four kinds of bearing conditions namely: normal, inner race fault, outer race fault and ball fault, which were measured in the experimental test bench running under different working conditions. The results indicated that the proposed method can reliably identify different fault patterns of rolling element bearings based on mechanical vibration signals.

The use of SESK as a trend parameter for localized bearing fault diagnosis in induction machines.

A critical work of bearing fault diagnosis is locating the optimum frequency band that contains faulty bearing signal, which is usually buried in the noise background. Now, envelope analysis is commonly used to obtain the bearing defect harmonics from the envelope signal spectrum analysis and has shown fine results in identifying incipient failures occurring in the different parts of a bearing. However, the main step in implementing envelope analysis is to determine a frequency band that contains faulty bearing signal component with the highest signal noise level. Conventionally, the choice of the band is made by manual spectrum comparison via identifying the resonance frequency where the largest change occurred. In this paper, we present a squared envelope based spectral kurtosis method to determine optimum envelope analysis parameters including the filtering band and center frequency through a short time Fourier transform. We have verified the potential of the spectral kurtosis diagnostic strategy in performance improvements for single-defect diagnosis using real laboratory-collected vibration data sets.

The use of spectral kurtosis as a trend parameter for bearing faults diagnosis

Vibration signals are widely used in the health monitoring of rolling element bearings. A critical work of the bearing fault diagnosis is locating the optimum frequency band that contains faulty bearing signal, which is usually buried in the noise background. Now, envelope analysis is commonly used to obtain the bearing defect harmonics from the envelope signal spectrum analysis and has shown fine results in identifying incipient failures occurring in the different parts of a bearing (inner race, outer race, cage, as well as balls). However, a main step in implementing envelope analysis is to determine a frequency band that contains faulty bearing signal component with highest signal noise level. Conventionally, the choice of the band is made by manual spectrum comparison via identifying the resonance frequency where the largest change occurred. In This paper, we present a spectral kurtosis based method to determine optimum envelope analysis parameters including the filtering band and centre frequency through a short time Fourier transform. In the literature, spectral kurtosis is mainly presented as a tool used to detect non-stationary components in a signal. The results show that the maximum amplitude of the kurtogram (ways to compute the spectral kurtosis) provides the optimal parameters for band pass filter which allows both small outer race fault and large inner race fault to be determined from optimized envelope spectrum.

Bi-spectrum based-EMD applied to the non-stationary vibration signals for bearing faults diagnosis

Empirical mode decomposition (EMD) has been widely applied to analyze vibration signals behavior for bearing failures detection. Vibration signals are almost always nonstationary since bearings are inherently dynamic (e.g., speed and load condition change over time). By using EMD, the complicated non-stationary vibration signal is decomposed into a number of stationary intrinsic mode functions (IMFs) based on the local characteristic time scale of the signal. Bi-spectrum, a third-order statistic, helps to identify phase coupling effects, the bi-spectrum is theoretically zero for Gaussian noise and its flat for non-Gaussian white noise, consequently the bi-spectrum analysis is insensitive to random noise, which are useful for detecting faults in induction machines. Utilizing the advantages of EMD and bi-spectrum, this article proposes a joint method for detecting such faults, called bi-spectrum based EMD (BSEMD). First, original vibration signals collected from accelerometers are decomposed by EMD and a set of intrinsic mode functions (IMFs) is produced. Then, the IMF signals are analyzed via bi-spectrum to detect outer race bearing defects. The procedure is illustrated with the experimental bearing vibration data. The experimental results show that BSEMD techniques can effectively diagnosis bearing failures.

The use of nonlinear future reduction techniques as a trend parameter for state of health estimation of lithium-ion batteries

Remaining Useful Life (RUL) prediction accurately is an imperative industrial challenge. In this sense, the monitoring of lithium-ion battery is very significant for planning repair work and minimizing unexpected electricity outage. As the RUL estimation is essentially a problem of pattern recognition, the most valuable feature extraction techniques and more accurate classifier are needed to obtain higher prognostic effectiveness. Consequently, this paper discusses the importance of non linear feature reduction techniques for more adequate prognosis feature data base. For more convenience, the isometric feature mapping technique (ISOMAP) is used to reduce some features extracted from lithium-ion batteries, with different health states, in both modes of charge and discharge. Experimental results show that non linear feature reduction techniques are very promising to provide some trend parameters for industrial prognostic.

Application of feature reduction techniques for automatic bearing degradation assessment

Bearings are important assets for most industrial applications. The non-destructive diagnosis of these elements needs an accurate and reliable acquisition of its dynamic vibration signals affected by noise and the other part of system such as gears, shafts, etc. Empirical mode decomposition is an advanced signal processing tool for bearing fault feature extraction. In this paper, empirical mode decomposition is used to decompose non-linear and non-stationary bearing vibration signals into several stationary intrinsic mode functions and the empirical mode decomposition energy entropy is computed for each intrinsic mode function. Moreover, principal component analysis and linear discriminant analysis are used for feature reduction. Based on the Fishers criterion, experimental results show that linear discriminant analysis features are highlighted compared to principal component analysis features and original empirical mode decomposition features for bearing fault diagnosis as type (inner race, outer race, rolling element) and severity (normal, degraded, faulting).