Since the early 1980s, wind power technology has experienced an immense growth with respect to both the turbine size and market share. As the demand for large-scale wind turbines and lor operation & maintenance cost continues to raise, the interest on condition monitoring system has increased rapidly. The main components of wind turbines are the focus of all CMS since they frequently cause high repair costs and equipment downtime. However, vast quantities of their failures are caused due to a bearing failure. Therefore, bearing condition monitoring becomes crucial. This paper aims at providing a state-of-the-art review on wind turbine bearing condition monitoring techniques such as acoustic measurement, electrical effects monitoring, power quality, temperature monitoring, wear debris analysis and vibration analysis. Furthermore, this paper will present a literature review and discuss several technical, financial and operational challenges from the purchase of the CMS to the wind farm monitoring stage.

A review of wind turbine bearing condition monitoring: State of the art and challenges

Rotating machines have been widely used in industrial engineering. The fault diagnosis of rotating machines plays a vital important role to reduce the catastrophic failures and heavy economic loss. However, the measured vibration signal of rotating machinery often represents non-linear and non-stationary characteristics, resulting in difficulty in the fault feature extraction. As a statistical measure, entropy can quantify the complexity and detect dynamic change through taking into account the non-linear behavior of time series. Therefore, entropy can be served as a promising tool to extract the dynamic characteristics of rotating machines. Recently, many studies have applied entropy in fault diagnosis of rotating machinery. This paper aims to investigate the applications of entropy for the fault characteristics extraction of rotating machines. First, various entropy methods are briefly introduced. Its foundation, application, and some improvements are described and discussed. The review is divided into eight parts: Shannon entropy, Renyi entropy, approximate entropy, sample entropy, fuzzy entropy, permutation entropy, and other entropy methods. In each part, we will review the applications using the original entropy method and the improved entropy methods, respectively. In the end, a summary and some research prospects are given.

The Entropy Algorithm and Its Variants in the Fault Diagnosis of Rotating Machinery: A Review

Rotating machinery is widely applied in various types of industrial applications. As a promising field for reliability of modern industrial systems, early fault diagnosis (EFD) techniques have attracted increasing attention from both academia and industry. EFD is critical to provide appropriate information for taking necessary maintenance actions and thereby prevent severe failures and reduce financial losses. A massive amounts of research work has been conducted in last two decades to develop EFD techniques. This paper reviews and summarizes the research works on EFD of gears, rotors, and bearings. The main purpose of this paper is to serve as a guidemap for researchers in the field of early fault diagnosis. After a brief introduction of early fault diagnosis techniques, the applications of EFD of rotating machine are reviewed in two aspects: fault frequency-based methods and artificial intelligence-based methods. Finally, a summary and some new research prospects are discussed.

https://www.mdpi.com/1099-4300/21/4/409/pdf

A Review of Early Fault Diagnosis Approaches and Their Applications in Rotating Machinery.

Incipient fault diagnosis of rolling bearings based on adaptive variational mode decomposition and Teager energy operator

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.

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

Aiming at the problems that the incipient fault of rolling bearings is difficult to recognize and the number of intrinsic mode functions (IMFs) decomposed by variational mode decomposition (VMD) must be set in advance and can not be adaptively selected, taking full advantages of the adaptive segmentation of scale spectrum and Teager energy operator (TEO) demodulation, a new method for early fault feature extraction of rolling bearings based on the modified VMD and Teager energy operator (MVMD-TEO) is proposed. Firstly, the vibration signal of rolling bearings is analyzed by adaptive scale space spectrum segmentation to obtain the spectrum segmentation support boundary, and then the number K of IMFs decomposed by VMD is adaptively determined. Secondly, the original vibration signal is adaptively decomposed into K IMFs, and the effective IMF components are extracted based on the correlation coefficient criterion. Finally, the Teager energy spectrum of the reconstructed signal of the effective IMF components is calculated by the TEO, and then the early fault features of rolling bearings are extracted to realize the fault identification and location. Comparative experiments of the proposed method and the existing fault feature extraction method based on Local Mean Decomposition and Teager energy operator (LMD-TEO) have been implemented using experimental data-sets and a measured data-set. The results of comparative experiments in three application cases show that the presented method can achieve a fairly or slightly better performance than LMD-TEO method, and the validity and feasibility of the proposed method are proved.

Incipient fault feature extraction of rolling bearings based on the MVMD and Teager energy operator.

In order to overcome the influence of non-adaptive selection of non-stationary coefficient threshold of compound interpolation envelope (CIE) method on decomposition performance of local mean decomposition (LMD), a LMD method based on improved compound interpolation envelope (ICIE) is proposed in this paper. Firstly, combining the CIE with fractal box dimension, an improved envelope processing method, named ICIE, is proposed. Secondly, an improved LMD-based ICIE is presented and abbreviated as ICIELMD. Finally, three different data-sets, including simulation signal, rolling bearing data-sets from Case Western Reserve University (CWRU) and National Aeronautics and Space Administration (NASA), are used to complete the comparative experiments between the proposed ICIELMD and state-of-the-art methods (CIELMD) and demonstrate the effectiveness of the ICIELMD method. The experimental results show that the proposed method achieves comparable or slightly better than the other methods, and provides a new solution for complex signal analysis of rolling bearing faults.

An improved local mean decomposition method based on improved composite interpolation envelope and its application in bearing fault feature extraction.

Aiming at the problem of poor prediction performance of rolling bearing remaining useful life (RUL) with single performance degradation indicator, a novel based-performance degradation indicator RUL prediction model is established. Firstly, the vibration signal of rolling bearing is decomposed into some intrinsic scale components (ISCs) by piecewise cubic Hermite interpolating polynomial-local characteristic-scale decomposition (PCHIP-LCD), and the effective ISCs are selected to reconstruct signals based on kurtosis-correlation coefficient (K-C) criteria. Secondly, the multi-dimensional degradation feature set of reconstructed signals is extracted, and then the sensitive degradation indicator IICAMD is calculated by fusing the improved independent component analysis (IICA) and Mahalanobis Distance (MD). Thirdly, the false fluctuation of the IICAMD is repaired by using the gray regression model (GM) to obtain the health indicator (HI) of the rolling bearing, and the start prediction time (SPT) of the rolling bearing is determined according to the time mutation point of HI. Finally, generalized regression neural network (GRNN) model based on HI is constructed to predict the RUL of rolling bearing. The experimental results of two groups of different rolling bearing data-sets show that the proposed method achieves better performance in prediction accuracy and reliability.

A novel based-performance degradation indicator RUL prediction model and its application in rolling bearing.

Aiming at connatural limitations of extreme learning machine in practice, a new fault diagnosis method based on wavelet packet-energy entropy and fuzzy kernel extreme learning machine is proposed. ...

Fault diagnosis method based on wavelet packet-energy entropy and fuzzy kernel extreme learning machine:

Rolling bearing is one of the most crucial components in rotating machinery and due to their critical role, it is of great importance to monitor their operation conditions. However, due to the back...

Xiaodong Wang

Papers

Incipient fault feature extraction of rolling bearings based on the MVMD and Teager energy operator.

An improved local mean decomposition method based on improved composite interpolation envelope and its application in bearing fault feature extraction.

A novel based-performance degradation indicator RUL prediction model and its application in rolling bearing.

Fault diagnosis method based on wavelet packet-energy entropy and fuzzy kernel extreme learning machine:

A hybrid fault diagnosis method based on singular value difference spectrum denoising and local mean decomposition for rolling bearing