Yanyang Zi

A new approach to intelligent fault diagnosis of rotating machinery

This paper presents a new approach to intelligent fault diagnosis based on statistics analysis, an improved distance evaluation technique and adaptive neuro-fuzzy inference system (ANFIS). The approach consists of three stages. First, different features, including time-domain statistical characteristics, frequency-domain statistical characteristics and empirical mode decomposition (EMD) energy entropies, are extracted to acquire more fault characteristic information. Second, an improved distance evaluation technique is proposed, and with it, the most superior features are selected from the original feature set. Finally, the most superior features are fed into ANFIS to identify different abnormal cases. The proposed approach is applied to fault diagnosis of rolling element bearings, and testing results show that the proposed approach can reliably recognise different fault categories and severities. Moreover, the effectiveness of the proposed feature selection method is also demonstrated by the testing results.

EEMD method and WNN for fault diagnosis of locomotive roller bearings

Research highlights? EEMD and WNN are combined to propose an automated fault diagnosis method. ? Features are extracted from the sensitive IMF of EEMD in this method. ? The features are fed into WNN to identify the bearing health conditions. ? The method can identify the fault severities and the compound faults. The ensemble empirical mode decomposition (EEMD) can overcome the mode mixing problem of the empirical mode decomposition (EMD) and therefore provide more precise decomposition results. Wavelet neural network (WNN) possesses the advantages of both wavelet transform and artificial neural networks. This paper combines the merits of EEMD and WNN to propose an automated and effective fault diagnosis method of locomotive roller bearings. First, the vibration signals captured from the locomotive roller bearings are preprocessed by EEMD method and intrinsic mode functions (IMFs) are produced. Second, a kurtosis based method is presented and used to select the sensitive IMF. Third, time- and frequency-domain features are extracted from the sensitive IMF, its frequency spectrum and its envelope spectrum. Finally, these features are fed into WNN to identify the bearing health conditions. The diagnosis results show that the proposed method enables the identification of the single faults in the bearings and at the same time the recognition of the fault severities and the compound faults.

Application of an intelligent classification method to mechanical fault diagnosis

A new method for intelligent fault diagnosis of rotating machinery based on wavelet packet transform (WPT), empirical mode decomposition (EMD), dimensionless parameters, a distance evaluation technique and radial basis function (RBF) network is proposed in this paper. In this method, WPT and EMD are, respectively, used to preprocess vibration signals to mine fault characteristic information more accurately. Then, dimensionless parameters in time domain are extracted from each of the original vibration signals and preprocessed signals to form a combined feature set. Moreover, the distance evaluation technique is utilised to calculate evaluation factors of the combined feature set. Finally, according to the evaluation factors, the corresponding sensitive features are selected and input into the RBF network to automatically identify different machine operation conditions. An experiment of rolling element bearings is carried out to test the performance of the proposed method. The experimental result demonstrates that the method combining WPT, EMD, the distance evaluation technique and the RBF network may accurately extract fault information and select sensitive features, and therefore it may correctly diagnose the different fault categories occurring in the bearings. Furthermore, this method is applied to slight rub fault diagnosis of a heavy oil catalytic cracking unit, the actual result shows the method may be applied to fault diagnosis of rotating machinery effectively.

A multidimensional hybrid intelligent method for gear fault diagnosis

Identifying gear damage categories, especially for early faults and combined faults, is a challenging task in gear fault diagnosis. This paper proposes a new multidimensional hybrid intelligent diagnosis method to identify different categories and levels of gear damage automatically. In this method, Hilbert transform, wavelet packet transform (WPT) and empirical mode decomposition (EMD) are performed on gear vibration signals to extract additional fault characteristic information. Then, multidimensional feature sets including time-domain, frequency-domain and time-frequency-domain features are generated to reveal gear health conditions. Multiple classifiers based on several classification algorithms and input features are combined with genetic algorithm (GA). Because of the use of multidimensional features and the combination of multiple classifiers, more accurate diagnosis results are expected with the proposed method. Experiments with different gear damage categories and damage levels were conducted, and the vibration signals were captured under different loads and motor speeds. The proposed method is applied to the collected signals to identify the gear damage categories and damage levels. The diagnosis results show it can reliably recognize single damage modes, combined damage modes, and damage levels.

A Comparative Study on the Local Mean Decomposition and Empirical Mode Decomposition and Their Applications to Rotating Machinery Health Diagnosis

Health diagnosis of the rotating machinery can identify potential failure at its early stage and reduce severe machine damage and costly machine downtime. In recent years, the adaptive decomposition methods have attracted many researchers’ attention, due to less influences of human operators in the practical application. This paper compares two adaptive methods: local mean decomposition (LMD) and empirical mode decomposition (EMD) from four aspects, i.e., local mean, decomposed components, instantaneous frequency, and the waveletlike filtering characteristic through numerical simulation. The comparative results manifest that more accurate instantaneous frequency and more meaningful interpretation of the signals can be acquired by LMD than by EMD. Then LMD and EMD are both exploited in the health diagnosis of two actual industrial rotating machines with rub-impact and steam-excited vibration faults, respectively. The results reveal that LMD seems to be more suitable and have better performance than EMD for the incipient fault detection. LMD is thus proved to have potential to become a powerful tool for the surveillance and diagnosis of rotating machinery.

A demodulation method based on improved local mean decomposition and its application in rub-impact fault diagnosis

Demodulation is an available method for mechanical diagnoses, and a demodulation technique based on improved local mean decomposition (LMD) is proposed in this paper. A method of boundary process and a strategy for determining the step size of moving average are presented to improve the LMD algorithm. Instantaneous amplitude (IA) and instantaneous frequency (IF) of the signal can be computed independently of Hilbert transform using the improved LMD method. A well-constructed description of the derived IA and IF is given in the form of instantaneous time–frequency spectrum (ITFS) which preserves both the time and frequency information simultaneously. Results of three synthetic signals indicate that this proposed method is the best demodulation approach to extracting the all-round carrier and modulated components as well as the accurate IF, compared with Hilbert–Huang transform and stationary wavelet transform. The validity of the technique is then demonstrated on a real rotor system of a gas turbine with rub-impact fault. Due to the opposite friction during operation, the transient fluctuations of the IF of the fundamental harmonic component are successfully identified in the ITFS. In addition, we find that the proposed technique is more effective and sensitive than other methods in detecting sub-harmonics and FM components contained in the rub-impact signals. Thus the present method is powerful in the analysis of modulated signals and is an effective tool for the detection of rub-impact faults.

A demodulating approach based on local mean decomposition and its applications in mechanical fault diagnosis

Since machinery fault vibration signals are usually multicomponent modulation signals, how to decompose complex signals into a set of mono-components whose instantaneous frequency (IF) has physical sense has become a key issue. Local mean decomposition (LMD) is a new kind of time–frequency analysis approach which can decompose a signal adaptively into a set of product function (PF) components. In this paper, a modulation feature extraction method-based LMD is proposed. The envelope of a PF is the instantaneous amplitude (IA) and the derivative of the unwrapped phase of a purely flat frequency demodulated (FM) signal is the IF. The computed IF and IA are displayed together in the form of time–frequency representation (TFR). Modulation features can be extracted from the spectrum analysis of the IA and IF. In order to make the IF have physical meaning, the phase-unwrapping algorithm and IF processing method of extrema are presented in detail along with a simulation FM signal example. Besides, the dependence of the LMD method on the signal-to-noise ratio (SNR) is also investigated by analyzing synthetic signals which are added with Gaussian noise. As a result, the recommended critical SNRs for PF decomposition and IF extraction are given according to the practical application. Successful fault diagnosis on a rolling bearing and gear of locomotive bogies shows that LMD has better identification capacity for modulation signal processing and is very suitable for failure detection in rotating machinery.

Multiwavelet construction via an adaptive symmetric lifting scheme and its applications for rotating machinery fault diagnosis

Multiwavelets and the lifting scheme are two important developments of wavelet theory. Multiwavelets outperform scalar wavelets in many applications due to their better properties. The lifting scheme is a method to construct a new wavelet with prescribed properties. In this paper, multiwavelets are integrated with the lifting scheme, synthesizing their advantages. Due to multiple wavelet bases, the lifting scheme of multiwavelets is more flexible than that of scalar wavelets. With supplement of a symmetric condition, a novel adaptive symmetric lifting scheme of multiwavelets is presented. Kurtosis is chosen to be the performance measurement of lifting coefficients, and the genetic algorithm is used to optimize the free parameters in the lifting scheme. The proposed method, constructing a new multiwavelet via an adaptive lifting scheme, is applied to analyze the simulation of a rolling bearing and gearbox vibration signals. The results demonstrate that the adaptive symmetric lifting of multiwavelets is more effective in extracting fault features of rotating machinery than conventional diagnosis techniques with scalar wavelets and non-adaptive multiwavelets.

Application of a Novel Hybrid Intelligent Method to Compound Fault Diagnosis of Locomotive Roller Bearings

To diagnose compound faults of locomotive roller bearings accurately, a novel hybrid intelligent diagnosis method is proposed in this paper. First of all, vibration signals are preprocessed to mine valid fault characteristic information. They are filtered and at the same time, they are decomposed by the empirical mode decomposition method and eight intrinsic mode functions (IMFs) are acquired. The filtered signals and IMFs are further demodulated to obtain their Hilbert envelope spectrums. Second, six feature sets are extracted, and they are time- and frequency-domain statistical features of the raw and preprocessed signals. Then, each feature set is evaluated and a few salient features are selected from it by applying the improved distance evaluation technique. Correspondingly, six salient feature sets are obtained. Finally, the six salient feature sets are, respectively, input into six classifiers based on adaptive neurofuzzy inference system (ANFIS), and genetic algorithm is employed to combine the outputs of the six ANFISs and to attain the final diagnosis result. The diagnosis results of the compound faults of the locomotive roller bearings verify that the proposed hybrid intelligent method may accurately recognize not only a single fault and fault severities but also compound faults.

Improved spectral kurtosis with adaptive redundant multiwavelet packet and its applications for rotating machinery fault detection

Rotating machinery fault detection is significant to avoid serious accidents and huge economic losses effectively. However, due to the vibration signal with the character of non-stationarity and nonlinearity, the detection and extraction of the fault feature turn into a challenging task. Therefore, a novel method called improved spectral kurtosis (ISK) with adaptive redundant multiwavelet packet (ARMP) is proposed for this task. Spectral kurtosis (SK) has been proved to be a powerful tool to detect and characterize the non-stationary signal. To improve the SK in filter limitation and enhance the resolution of spectral analysis as well as match fault feature optimally, the ARMP is introduced into the SK. Moreover, since kurtosis does not reflect the actual trend of periodic impulses, the SK is improved by incorporating an evaluation index called envelope spectrum entropy as supplement. The proposed method is applied to the rolling element bearing and gear fault detection to validate its reliability and effectiveness. Compared with the conventional frequency spectrum, envelope spectrum, original SK and some single wavelet methods, the results indicate that it could improve the accuracy of frequency-band selection and enhance the ability of rotating machinery fault detection.