Development of new fault detection methods for rotating machines (roller bearings)

Abstract Early fault diagnosis of roller bearings is extremely important for rotating machines, especially for high speed, automatic and precise machines. Many research efforts have been focused on fault diagnosis and detection of roller bearings, since they constitute one the most important elements of rotating machinery. In this study a combination method is proposed for early damage detection of roller bearing. Wavelet packet transform (WPT) is applied to the collected data for denoising and the resulting clean data are break-down into some elementary components called Intrinsic mode functions (IMFs) using Ensemble empirical mode decomposition (EEMD) method. The normalized energy of three first IMFs are used as input for Support vector machine (SVM) to recognize whether signals are sorting out from healthy or faulty bearings. Then, since there is no robust guide to determine amplitude of added noise in EEMD technique, a new Performance improved EEMD (PIEEMD) is proposed to determine the appropriate value of added noise. A novel feature extraction method is also proposed for detecting small size defect using Teager-Kaiser energy operator (TKEO). TKEO is applied to IMFs obtained to create new feature vectors as input data for one-class SVM. The results of applying the method to acceleration signals collected from an experimental bearing test rig demonstrated that the method can be successfully used for early damage detection of roller bearings. Most of the diagnostic methods that have been developed up to now can be applied for the case stationary working conditions only (constant speed and load). However, bearings often work at time-varying conditions such as wind turbine supporting bearings, mining excavator bearings, vehicles, robots and all processes with run-up and run-down transients. Damage identification for bearings working under non-stationary operating conditions, especially for early/small defects, requires the use of appropriate techniques, which are generally different from those used for the case of stationary conditions, in order to extract fault-sensitive features which are at the same time insensitive to operational condition variations. Some methods have been proposed for damage detection of bearings working under time-varying speed conditions. However, their application might increase the instrumentation cost because of providing a phase reference signal. Furthermore, some methods such as order tracking methods still can be applied when the speed variation is limited. In this study, a novel combined method based on cointegration is proposed for the development of fault features which are sensitive to the presence of defects while in the same time they are insensitive to changes in the operational conditions. It does not require any additional measurements and can identify defects even for considerable speed variations. The signals acquired during run-up condition are decomposed into IMFs using the performance improved EEMD method. Then, the cointegration method is applied to the intrinsic mode functions to extract stationary residuals. The feature vectors are created by applying the Teager-Kaiser energy operator to the obtained stationary residuals. Finally, the feature vectors of the healthy bearing signals are utilized to construct a separating hyperplane using one-class support vector machine. Eventually the proposed method was applied to vibration signals measured on an experimental bearing test rig. The results verified that the method can successfully distinguish between healthy and faulty bearings even if the shaft speed changes dramatically

On the Use of Wavelet Transform for Practical Condition Monitoring Issues

The aim of this study is to assess the effectiveness of the Wavelet Transform (WT) for machine condition monitoring purposes. In this chapter the WT is set up specifically for vibration signals captured from real life complex case studies having a poor extent in literature: marine couplings and i.c. engines tested in cold conditions. Both Continuous (CWT) and Discrete Wavelet Transform (DWT) are applied. The former has been used for faulty event identification and impulse event characterization through the analysis of the three-dimensional representation of the CWT coefficients. The latter has been applied for filtering and feature extraction purposes and for detecting impulsive events strongly masked by noise. Comparing the results from both the CWT and DWT analyses it has been clearly demonstrated the ability of the WT in satisfying both the condition monitoring and fault detection requirements for all tested cases. This means that the application of the WT not only permit to recognize the change of the state of the tested machine but it is also able to localise the source of the alteration

PHM survey: implementation of signal processing methods for monitoring bearings and gearboxes

The reliability and safety of industrial equipments are one of the main objectives of companies to remain competitive in sectors that are more and more exigent in terms of cost and security. Thus, an unexpected shutdown can lead to physical injury as well as economic consequences. This paper aims to show the emergence of the Prognostics and Health Management (PHM) concept in the industry and to describe how it comes to complement the different maintenance strategies. It describes the benefits to be expected by the implementation of signal processing, diagnostic and prognostic methods in health-monitoring. More specifically, this paper provides a state of the art of existing signal processing techniques that can be used in the PHM strategy. This paper allows showing the diversity of possible techniques and choosing among them the one that will define a framework for industrials to monitor sensitive components like bearings and gearboxes

Blade fault diagnosis using artificial intelligence technique

Blade fault diagnosis is conventionally based on interpretation of vibration spectrum and wavelet map. These methods are however found to be difficult and subjective as it requires visual interpretation of chart and wavelet color map. To overcome this problem, important features for blade fault diagnosis in a multi row of rotor blade system was selected to develop a novel blade fault diagnosis method based on artificial intelligence techniques to reduce subjective interpretation. Three artificial neural network models were developed to detect blade fault, classify the type of blade fault, and locate the blade fault location. An experimental study was conducted to simulate different types of blade faults involving blade rubbing, loss of blade part, and twisted blade. Vibration signals for all blade fault conditions were measured with a sampling rate of 5 kHz under steady-state conditions at a constant rotating speed. Continuous wavelet transform was used to analyse the vibration signals and its results were used subsequently for feature extraction. Statistical features were extracted from the continuous wavelet coefficients of the rotor operating frequency and its corresponding blade passing frequencies. The extracted statistical features were grouped into three different feature sets. In addition, two new feature sets were proposed: blade statistical curve area and blade statistical summation. The effectiveness of the five different feature sets for blade fault detection, classification, and localisation was investigated. Classification results showed that the statistical features extracted from the operating frequency to be more effective for blade fault detection, classification, and localisation than the statistical features from blade passing frequencies. Feature sets of blade statistical curve area was found to be more effective for blade fault classification, while feature sets of blade statistical summation were more effective for blade fault localisation. The application of feature selection using genetic algorithm showed good accuracy performance with fewer features achieved. The neural network developed for blade fault detection, classification, and localisation achieved accuracy of 100%, 98.15% and 83.47% respectively. With the developed blade fault diagnosis methods, manual interpretation solely dependent on knowledge and the experience of individuals can be reduced. The novel methods can therefore be used as an alternative method for blade fault diagnosis

The comparison of the methods based on Wavelet Transform and Hilbert-Huang Transform in fault diagnosis of rotating machinery

As the most common mechanisms for transmitting power and motion, gears and bearings have been widely used in various mechanical equipment. Many accidents have happened because of failing to detect and replace the faulty gears or bearings in time. Hence it is very important to perform accurate fault diagnosis of gears and bearings. When a gear or bearing has a fault, the vibration signal collected from the mechanical equipment will become non-stationary and contain a series of periodic impulses that are caused by the fault. Many theories and techniques have been developed to extract the faulty information based on analyzing periodic impulses contained in the vibration signals. It has been reported that neither time-domain analysis nor frequency-domain analysis can do well in analyzing non-stationary signals. Hence time-frequency analysis methods based on wavelet transform and Hilbert-Huang Transform (HHT) have been investigated. Wavelet transform includes continuous wavelet transform (CWT) and discrete wavelet transform (DWT). Research was reported to compare the performance of these methods in fault diagnosis of mechanical components. However, the previous works either only compared HHT based methods with CWT based methods, or only compared HHT based methods with DWT based methods, for certain applications. There are no reported comprehensive comparisons of the three methods for fault diagnosis of gears and bearings. Cepstrum analysis can detect the periodicity and reduce the influence of the noise for the low energy signals. However, previous research usually only applied Cepstrum analysis directly to extract fault features from the entire original signal. Some other existing methods first applied DWT to decompose the original signal to obtain the detail signals, and then used Hilbert spectral analysis to analyze the periodic impulses contained in the detail signals through identifying faulty characteristic frequency with frequency domain analysis and plotting the instantaneous amplitude with time domain analysis. Hilbert spectral analysis has the advantage in frequency domain analysis. However, sometimes it is not very sensitive in time domain analysis when the energy of the periodic impulses is not strong enough. In this thesis, we investigate fault diagnosis of gears and bearings using two sets of vibration monitoring data collected in the lab environment: one set for gear condition monitoring and the other for bearing condition monitoring. We propose an improved DWT method that integrates Cepstrum analysis to analyze the periodic impulses contained in the data. With the proposed method, the vibration signals are first decomposed using DWT, and Cepstrum analysis is used to analyze the resulting detail signals. The results show that the proposed method performs better than the existing methods of applying Cepstrum analysis directly. Furthermore, with the help of Cepstrum analysis, the proposed method has better performance in time domain analysis than Hilbert spectral analysis in analyzing the periodic impulses contained in the detail signals. A comprehensive study is conducted in this thesis to compare the following three methods in fault diagnosis of the gears and bearings: (1) The CWT method using time-wavelet energy spectrum, (2) the improved DWT method using Cepstrum analysis, and (3) the HHT method. The results show that in fault diagnosis of gears, the HHT method has better noise immunity and is more sensitive in frequency domain analysis than the other two methods. The proposed method shows its advantage in analyzing the periodic impulses in time domain than the other two methods. In fault diagnosis of the bearings, the fault can be more clearly detected using the CWT and DWT methods in analyzing the periodic impulse that caused by the outer race fault of the bearing. The results obtained in this thesis can assist researchers and practitioners to select suitable methods for fault diagnosis of gears and bearings