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The wind power industry suffers from unexpectedly high failure rates in wind turbine high-speed shaft bearings (HSSBs). To reduce cost and improve availability, the industry needs an accurate fault prognostic and remaining useful life (RUL) capability. A reliable prognostic allows maintainers to better define when maintenance can be performed, improving availability or allowing for opportunistic maintenance. Wind turbines operate under harsh conditions and condition monitoring data shows the environment to be both non stationary behavior with high noise. This paper proposes a practical and effective data-driven methodology that can be applied for RUL prediction of HSSBs. A new health indicator (HI) is constructed based on the entropy measure of the so called “spectral shape factor” (SSF), after strengthening the original signal by Teager energy operator (TEO). An Elman neural network (ENN) is used for RUL estimation. Furthermore, prediction intervals of the RUL estimates are computed based on the trained ENN model in order to quantify the errors associated with the prediction. The methodology is validated using a real word data collected form 2 MW Suzlon S88 wind turbine.

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.

Hilbert-Huang Transform (HHT) has been renowned for its capacity to reveal fault indicating information issue from vibration signals. It uses Empirical Mode Decomposition (EMD) to decompose a signal accordingly to its contained information into a set of Intrinsic Mode Functions (IMFs). Then, the instantaneous frequencies are performed of each IMF using Hilbert Transform (HT). However, the HHT has some disadvantages which are caused by the EMD technique. The EMD has the mode mixing problem that may occur between IMFs, it causes the End Effect phenomenon, which leads to a wrong instantaneous values at both sides of the signal. Furthermore, its lack of mathematical basis. To overcome the HHT inherent problems, we propose the use of the Empirical Wavelet Transform (EWT) which designs an appropriate wavelet filter bank fully depends on the processed signal with HT in the early detection and condition monitoring of tooth crack fault. In this paper, we develop a dynamic model describing a single stage spur gear in normal and abnormal functioning. Results of analyzing the pinion’s vibration displacement show that the proposed approach denoted (HEWT) successfully detect the tooth crack at a much earlier stage of damage development even though in noisy environment. Performance evaluation and comparison between HEWT and HHT methods show that the HEWT is better sensitive to tooth crack fault detection in gearbox systems.

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.

Gears are one of the most common mechanisms used for transmitting power and motion in various mechanical applications. Tooth pitting fault is frequently failure modes encountered. An analytical model of one stage spur gearbox is presented where the effects of tooth pitting fault were simulated by magnitude and phase changes in the gearmesh stiffness. This paper deals with the problem by using the Empirical Wavelet Transform (EWT) and the Hilbert Transform (HT) techniques. First, the EWT is used to extract adaptive modes from the vibration signals by designing an appropriate wavelet Alter bank. Then, the instantaneous frequencies are performed for each mode using the HT. The proposed tooth pitting fault diagnosis method was tested on both clean and noisy signals to evaluate its performance. The results show that the proposed method can effectively detect the fault in an early stage of development.