A review on data-driven fault severity assessment in rolling bearings

Vibration based condition monitoring and fault diagnosis of wind turbine planetary gearbox: A review

Application of an improved maximum correlated kurtosis deconvolution method for fault diagnosis of rolling element bearings

https://iopscience.iop.org/article/10.1088/1361-6501/aa8a57/pdf

Improvement of kurtosis-guided-grams via Gini index for bearing fault feature identification

Large wind farms are gaining prominence due to increasing dependence on renewable energy. In order to operate these wind farms reliably and efficiently, advanced maintenance strategies such as condition based maintenance are necessary. However, wind turbines pose unique challenges in terms of irregular load patterns, intermittent operation and harsh weather conditions, which have deterring effects on life of rotating machinery. This paper reviews the state-of-the-art in the area of diagnostics and prognostics pertaining to two critical failure prone components of wind turbines, namely, low-speed bearings and planetary gearboxes. The survey evaluates those methods that are applicable to wind turbine farm-level health management and compares these methods on criteria such as reliability, accuracy and implementation aspects. It concludes with a brief discussion of the challenges and future trends in health assessment for wind farms.

A review of diagnostics and prognostics of low-speed machinery towards wind turbine farm-level health management

A new improved Kurtogram was proposed in this paper Instead of Kurtosis, correlated Kurtosis of envelope signal extracted from the wavelet packet node was used as an indicator to determine the optimal frequency band Correlated Kurtosis helps to determine the fault related impulse signals not affected by other unrelated signal components Finally, two simulated and three experimental bearing fault cases are used to validate the effectiveness of proposed method and to compare with other similar methods The results demonstrate it can locate resonant frequency band with a high reliability than two previous developed methods by Lei et al and Wang et al especially for the incipient faults under low load

/pdf/a-new-improved-kurtogram-and-its-application-to-bearing-3o0mmpya2q.pdf

A New Improved Kurtogram and Its Application to Bearing Fault Diagnosis

The wear fault is one of the gear failure modes which most frequently happen in gearboxes. Tracking the development of wear, especially estimating the remaining usage life for early wear is a challenge in gearbox fault diagnosis and prognosis. A method to extract the feature of gearbox's signal is proposed and the remaining usage life is evaluated in this paper. The total life vibration signal of experimental study is taken as the research basis. Extracting features from vibration signals is carried out by Time-Frequency analysis. The rule of gear wear progression is studied using the vibration signal feature. The remaining usage life estimate is implemented by Hidden semi-Markov model. Through the total life test of gearbox, the feasibility and effectiveness of this method is verified.

Experimental study on gearbox prognosis using total life vibration analysis

Find optimum frequency band which contains strong impulsive signal is very critical for bearing fault diagnosis. Recently developed kurtogram method is an effective method to determine optimum frequency band for envelope analysis. However, most of kurtograms and its improvements are based on kurtosis criteria. A limitation of kurtosis is that high kurtosis value will be acquired even if the signal has only a single impulse. This will lead to error frequency band selection when the signal contains some impulse like noise. So, this paper uses correlated kurtosis as a criterion to construct kurtogram. Correlated kurtosis is superior to traditional kurtosis for detecting the periodic impulses produced by bearing fault. Finally, a real bearing outer race fault experiment is used to demonstrate the method’s effectiveness.

/pdf/rolling-element-bearings-fault-diagnosis-based-on-correlated-1kbtsqal0q.pdf

Rolling element bearings fault diagnosis based on correlated kurtosis kurtogram

This paper presents an intelligent method for gear fault diagnosis based on wavelet packet analysis and support vector machine (SVM). For this purpose, two experiments were selected to verify the proposed method. One is a spur gear of the motorcycle gearbox system. Slight-worn, medium-worn, and broken-tooth were selected as the faults. In fault simulating, two very similar models of worn gear have been considered with partial difference for evaluating the preciseness of the proposed method. The other one is a helical gear of a gearbox system. Broken-tooth and crack in root of gear were selected as the faults. Raw vibration signals were segmented into the signals recorded during one complete revolution of the input shaft using tachometer information and then synchronized using cubic spline interpolation to construct the sample signals with the same length. Next, standard deviations of wavelet packet coefficients of the vibration signals which have been normalized and dimension deducted using principal component analysis (PCA) were considered as the feature vector for training purposes of the SVM. The parameters of SVM are optimized using particle swarm optimization (PSO). Its effectiveness is verified by experimental results.

Gearbox fault diagnosis method based on wavelet packet analysis and support vector machine

In the condition based maintenance work of rotating machineries, bearings’ fault diagnosis and prognosis are an important content. Their faults can lead to many disasters. So, to detect the bearing faults earlier can benefit the remaining useful life (RUL) prediction and maintenance actions. In order to achieve this goal, narrowband interference cancellation (NIC) is used to extract the periodic impulsive signals which are indicative of a bearing fault. This method filters the narrowband signals not associated with the impulsive signal produced by bearing faults out. Therefore, the signal-to-noise will be improved and lead to the easier fault detection. However, only easier fault detection is not enough for RUL prediction. The features extracted from the vibration signals should reflect the bearings’ degradation well. This needs the features have good degradation trend (increase or decrease with time). In order to demonstrate the effectiveness of the proposed method, one implemented bearing fault test and one run-to-failure test are used to do the analysis. The results shows that bearing faults detection can be enhanced and root mean square (RMS) extracted from the NIC signal can track the bearing degradation well than the RMS extracted from the original vibration signal.

Hongzhi Teng

Papers

A New Improved Kurtogram and Its Application to Bearing Fault Diagnosis

Experimental study on gearbox prognosis using total life vibration analysis

Rolling element bearings fault diagnosis based on correlated kurtosis kurtogram

Gearbox fault diagnosis method based on wavelet packet analysis and support vector machine

Enhanced bearing fault detection and degradation analysis based on narrowband interference cancellation