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Condition monitoring of rotating machines has become a more important strategy in structural health monitoring (SHM) research. For fault recognition, the analysis is categorized in two essential main parts: Feature extraction and classification; the first one is used for extracting the information from the signal and the other for decision-making based on these features. A higher accuracy is needed for sensitive places to avoid all kinds of damages that can lead to economic losses and it may affect the human safety as well. In this paper, we propose a new hybrid and automatic approach for bearing faults diagnosis. This method uses a combination between Empirical wavelet Transform (EWT) and Fuzzy logic System (FLS), in order to detect and localize the early degradation of bearing state under different working conditions. EWT build a wavelet filter bank to extract amplitude modulated-frequency modulated component of signal. Modes presenting a high impulsiveness is then selected using the kurtosis indicator. Thereafter, time domain features (TDFs) are applied for the reconstructed signal to extract the fault features which are finally used as an inputs of FLS in order to identify and classify the bearing states. The experimental results shows that the proposed method can accurately extract and classify the bearing fault under variable conditions. Moreover, performance of EWT and empirical mode decomposition (EMD) are studied and shows the superiority of the proposed method.

Many civil engineering infrastructures including bridges and buildings have been constructed several years ago. They are facing increasing challenges due to climate change and exposed to various external loads such as earthquakes. Extensive research works have been carried out to enable structural and health monitoring (SHM). Modal identification is a crucial part of SHM. Conventionally, it has been accomplished by accelerometers mounted on the structure. Their use may be extremely accurate. However, only a few sensors is usually set up on the structure which may limit modal identification and SHM performance. Vision-based techniques gained increased acceptance as cheaper and easier solution to perform long-range vibration measurements. Video cameras offer the capacity to collect high-spatial resolution data from a distant scene of interest. Various image processing techniques have been developed to extract motion from subtle time changes in the image brightness. Commonly, these motions are then used for modal identification. Instead, this paper explores the use of pixels intensity variations only to perform SHM. Our new approach can be divided in two parallel steps. The first one deals with processing video image flow to effectively selecting the 'active pixels' or pixels belonging to the structure edges. The pixel selection process relies on the power spectral density and an energy criterion. Second, stochastic subspace identification based method that takes into account uncertainty bounds for modal parameters is adopted. So, vision modal parameters from the vision data are recovered. Comparison of identification results with motion signals is finally studied. For that, we have assessed a methodology for extracting motions based on cross-correlation analysis and Taylor-based subpixel refinement. Experiments in a laboratory setting on a cantilever beam are performed to verify the approach. The beam was imaged by a high speed camera and excited on a shaking table.

In this article, a new feature extraction method is proposed for gear fault diagnosis by combining the empirical wavelet transform, Hilbert transform, and cosine similarity metric. In the first place, a number of empirical mode components acquisitions are done, using empirical wavelet transform. Since different empirical modes have different sensitivities to fault, not all of them are needed for further analysis. Therefore, the most sensitive empirical modes are selected using the cosine similarity metric method. Hilbert transform was then used to obtain the envelope for amplitude modulation. Finally, spectral analysis using fast Fourier transform is applied on the obtained envelope. Gear test rig with gears under different fault states has revealed an effective outcome and a solid stability of this new approach. The obtained results show that our approach is efficiently able to detect and expose the gear faults signatures, that is, it highlights their frequencies and the corresponding harmonics with respect to the rotary frequency. Furthermore, this proposed method demonstrates more satisfactory and advantageous performances compared to those of fast kurtogram, or the autogram.

Rolling-element bearing is one of the crucial mechanical components in induction motors. Since, its fault may produce huge damage; the way to efficiently diagnose the bearing faults is a high issue in signal processing, and its fault detection draw an important significance. In this paper, a hybrid method based on Empirical Wavelet Transform and S Transform has been proposed in order to detect the outer race bearing fault in an induction motor using vibration signals. As the collected signals are disturbed by noise, EWT is used to filter the raw signals in conjunction with isolating the region containing fault characteristic frequencies. Then ST is used to represent an Amplitude-Frequency and a Time-Frequency contour of the filtered signals, which allow to detect the bearing fault. Finally, experimental vibration data have been investigated to assess the reliability of the developed method. The results obtained show a good performance.

There are growing demands for condition monitoring and fault diagnosis of rotating machinery to lower unscheduled breakdown. Gearboxes are one of the fundamental components of rotating machinery; their faults identification and classification always draw a lot of attention. However, non-stationary vibration signals and low energy of weak faults makes this task challenging in many cases. Thus, a new fault diagnosis method which combines the Hilbert empirical wavelet transform (HEWT), singular value decomposition (SVD), and self-organizing feature map (SOM) neural network is proposed in this paper. HEWT, a new self-adaptive time-frequency analysis was applied to the vibration signals to obtain the instantaneous amplitude matrices. Then, the singular value vectors, as the fault feature vectors were acquired by applying the SVD. Last, the SOM was used for automatic gearbox fault identification and classification. An electromechanical model comprising an induction motor coupled with a single stage spur gearbox is considered where the vibration signals of four typical operation modes were simulated. The conditions include the healthy gearbox, input shaft slant crack, tooth cracking, and tooth surface pitting. Obtained results show that the proposed method effectively identifies the gearbox faults at an early stage and realizes automatic fault diagnosis. Moreover, performance evaluation and comparison between the proposed HEWT–SVD method and Hilbert–Huang transform (HHT)–SVD approach show that the HEWT–SVD is better for feature extraction.

Nowadays, fault detection, identification, and classification seem to be the most difficult challenge for gear systems. It is a complex procedure because the defects affecting gears have the same frequency signature. Thus, the variation in load and speed of the rotating machine will, inevitably, lead to erroneous detection results. Moreover, it is important to discern the nature of the anomaly because each gear defect has several consequences on the mechanism’s performance. In this article, a new intelligent fault diagnosis approach consisting of Autogram combined with radial basis function neural network is proposed. Autogram is a new sophisticated enhancement of the conventional Kurtogram, while radial basis function is used for classification purposes of the gear state. According to this approach, the data signal is decomposed by maximal overlap discrete wavelet packet transform into frequency bands and central frequencies called nodes. Thereafter, the unbiased autocorrelation of the squared envelope for each node is computed in order to calculate the kurtosis for each one at every decomposition level. Finally, the feature matrix obtained from the previous step will be the input of the radial basis function neural network to provide a new automatic gear fault diagnosis technique. Experimental results from the gearbox with healthy state and five different types of gear defects under variable speeds and loads indicate that the proposed method can successfully detect, identify, and classify the gear faults in all cases.

Achieving an accurate fault diagnosis of rolling bearings under variable working conditions is relatively difficult and challenging topic. Thus, a hybrid fault diagnosis method is proposed. The method combines the Hilbert empirical wavelet transform (HEWT) and the singular value decomposition (SVD). HEWT, a new self-adaptive time-frequency analysis was applied to the vibration signals to obtain the instantaneous amplitude matrices. Then, the singular value vectors, as the fault feature vectors were acquired by applying the SVD. The bearing fault classifications are displayed through the information that got from the first three singular values. Through experimental results, it was concluded, that the proposed method can accurately extract and classify the bearing fault features under variable conditions.

Gear fault diagnosis using vibration signals has become the subject of intensive studies to detect any sudden failure. However, these signals exhibit nonlinear and nonstationary behaviors when the rotating machine operates under multiple working conditions. Furthermore, fault features extraction and classification of multiple gear states are always unsatisfactory and considered as a huge task. This is the main reason that motivates us to develop a new intelligent gear fault diagnosis method in order to automatically identify and classify several kinds of gear defects under different work conditions. So in this article, we propose a combination between the maximal overlap discrete wavelet packet transform (MODWPT), entropy indicator, and a multilayer perceptron (MLP) neural network as a new automatic fault diagnosis approach. MODWPT decomposes the data signal into several components using a uniform frequency bandwidth. Each decomposed component is selected to extract feature vector using entropy indicator. Finally, MLP provides a powerful automatic tool for identifying and classifying the aforementioned extracted features. Experimental vibration signals of healthy gear; gear with general surface wear; gear with chipped tooth in length; gear with chipped tooth in width; gear with missing tooth; and gear with tooth root crack are recorded under fifteen different work conditions to test the effectiveness of the suggested technique. Experimental results affirm that our proposed approach can successfully detect, identify, and classify the gear fault pattern in all cases.

Owing to the relevance and severity of damages caused by rolling bearing faults, the development and application of a robust fault detection methods that offer a high reliable diagnosis in terms of processing and performance are still demanding tasks. In this paper, an application of the empirical wavelet transform (EWT) method is proposed for the vibration signal analysis and fault diagnosis of rolling bearing. This method first detects the Fourier supports of the analyzed signal, build the corresponding wavelet accordingly to those supports, and then filter the signal with the obtained filter bank. The effectiveness of the method is validated using practical vibration signals. The results show that the EWT provides a good performance in the detection of outer and inner race faults.