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

Fault diagnosis of roller bearings using ensemble empirical mode decomposition (EEMD) and support vector machine (SVM)

Rolling bearings are widely used in rotating machinery and their fault is one of the most common causes of industrial machinery failure. Damage identification of roller bearings has been deeply developed to detect faults using vibration-based signal processing. There exist different signal processing techniques to decompose a signal and extract informative features such as EMD and Wavelet transform. EMD is a method for decomposing a multi- component signal into several elementary Intrinsic Mode Functions (IMFs) and has been widely applied to fault diagnosis of rotating machines. However, there are some drawbacks such as stopping criterion for sifting process, mode mixing and border effect problem. Ensemble empirical mode decomposition (EEMD) is a newly developed noise assisted method to solve mode mixing problem exists in empirical mode decomposition (EMD) method. Since the white noise is added throughout the entire signal decomposition process, mode mixing is effectively eliminated. However, there is still a great challenge: identifying two effective parameters (the amplitude of added noise and the number of ensemble trials) which may affect the performance of EEMD. Using low amplitude (relative to the signal), mode mixing cannot be prevented. On the other hand, too large amplitude achieves some redundant IMFs. Although some algorithm or values have been proposed, there is no robust guide to select optimal amplitude yet, especially for early damage detection (very small defects). In this study a reliable method is investigated to determine suitable amplitude and numerous real vibration signals (various operating conditions and two damage locations) are analysed to verify effectiveness and robustness of the proposed method. Vibration signals for healthy and defective bearings were acquired using the test rig assembled by Dynamics & Identification Research Group (DIRG) at Department of Mechanical and Aerospace Engineering, Politecnico di Torino

Performance Improvement of Ensemble Empirical Mode Decomposition for Roller Bearings Damage Detection

Ensemble empirical mode decomposition (EEMD) is a noise assisted method widely used for roller bearing damage detection. However, to successfully handle this technique still remains a great challenge: identification of two effective parameters (the amplitude of added noise and the number of ensemble trials), which affect the performances of the EEMD. Although a number of algorithms or values have been proposed, there is no robust guide to select optimal amplitude and the ensemble trial number yet, especially for early damage detection. In this study, a reliable method is proposed to determine the suitable amplitude and the proper number of trials is investigated as well. It is shown that the proposed method (performance improved EEMD) achieves higher damage detection success rate and creates larger Margin than the original algorithm. It leads to a substantially low trial numbers required to achieve perfect labelling of samples; in turn this fact leads to considerably less computational cost. The number of real vibration signals is analysed to verify effectiveness and robustness of the proposed method in discriminating and separating the faulty conditions

A novel feature extraction for anomaly detection of roller bearings based on performance improved Ensemble Empirical Mode Decomposition and Teager-Kaiser energy operator

Although Ensemble empirical mode decomposition (EEMD) method has been successfully applied to various applications, features extracted using EEMD could not detect anomalies for roller bearings, especially when anomalies includes small defects. In this study a novel feature extraction method is proposed to detect the state of roller bearings. Performance improved EEMD, which is a reliable adaptive method to calculate an appropriate noise amplitude is applied to decompose the acceleration signals into zer0-mean components called intrinsic mode functions (IMFs). Then, three dimensional feature vectors are created by applying the Teager-Kaiser energy operator (TKEO) to the first three IMFs. The novel features obtained from the healthy bearing signals are utilized to construct the separating hyperplane using one-class support vector machine (SVM). In order to validate the method proposed, a number of operating conditions (shaft speed and load) are considered to generate the data (vibration signals) by means of an assembled test rig. It is shown that the proposed method can successfully identify the states of the new samples (healthy and faulty). The uncertainty of the model prediction is investigated computing Margin and the number of support vectors. It create less complex (less fraction of support vectors) and more reliable (higher Margin) hyperplane than the EEMD method

Influence of stopping criterion for sifting process of Empirical Mode Decomposition technique (EMD) on roller bearing fault diagnosis

Empirical mode decomposition (EMD) is a self-adaptive data driven technique for analyzing nonlinear and non-stationary signals and decompose them into some elementary Intrinsic Mode Functions (IMFs). Although EMD method has been applied in various applications successfully, this method has some drawbacks, i.e. lack of a mathematical base, no robust stopping criterion for sifting process, mode mixing and border effect problem. Under the practical point of view, the most relevant is possibly the sifting stop criterion. Although sifting as many times as possible is needed to decompose the signal, too many sifting steps will reduce the physical meaning of IMFs. To preserve the natural amplitude variations of the oscillations, sifting must be limited to as few steps as possible. The proposed criteria so far are: Cauchy-type convergence, three-threshold, energy difference tracking, resolution factor, bandwidths, and orthogonality criterion. There is not a thorough study yet regarding the fault diagnosis application, to determine the effects of stopping criteria on the fault detection performance. In this paper the influence of different criteria to this purpose is investigated

Ensemble empirical mode decomposition (EEMD) and Teager-Kaiser energy operator (TKEO) based damage identification of roller bearings using one-class support vector machine.

Ensemble empirical mode decomposition (EEMD) is a newly developed noise assisted method aimed to solve mode mixing problem exists in empirical mode decomposition (EMD) method. Although EEMD has been utilized in various applications successfully, small defects of bearings are not able to be detected, especially in automatic defect detection, when only healthy samples are available for training. Teager-Kaiser energy operator (TKEO) technique is a non-linear operator that can track the energy and identify the instantaneous frequencies and instantaneous amplitudes of signals at any instant. As Teager-Kaiser energy operator (TKEO) technique detects a sudden change of the energy stream without any priori assumption of the data structure, it can be utilized for vibration based condition monitoring (non-stationary signals). In this study it is investigated whether an automatic method is able to diagnose a small defect level of roller bearings through processing of the acquired signals. After applying TKEO on IMFs decomposed by means of EEMD, the extracted informative feature vectors of the healthy bearing are used to construct the separating hyperplane using one-class support vector machine (SVM). Then, success rates of state identification of both samples (healthy and faulty) are examined by labelling the samples. The data were generated by means of a test rig assembled in the labs of the Dynamics & Identification Research Group (DIRG) at mechanical and aerospace engineering department, Politecnico di Torino. Various operating conditions (three shaft speeds, three external loads and one small size damage on a roller) were considered to obtain reliable results