531 research outputs found

    Research on fault law of rolling bearing under different fault levels and loads with HHT method

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    Bearing is one of the most important components of rotating machinery. The vibration signals are generally nonlinear and nonstationary while operating. The failed rolling bearing will damage to the machine, or cause a serious loss of property. There are a lot of methods about fault diagnosis of bearing, such as shock pulse method, resonance demodulation. Especially the HHT (Hilbert-Huang Transform) method with the adaptive advantage has gradually become a very promising method to extract the characteristics of nonlinear, nonstationary signal. In this paper the variant energy method was introduced in HHT to reduce the computation of the decomposed signal, which effectively improved the computation, and then an experimental platform was designed and established. The bearing fault categories can be diagnosed correctly in dealing with the vibration signals using this method and the fault law is discovered that the trend of the vibration signal fault characteristic frequency amplitude changes with the load increasing. The bearing failure mechanism provides beneficial reference for further research of nonlinear signal analysis

    Identification of multi-fault in rotor-bearing system using spectral kurtosis and EEMD

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    Condition monitoring and fault diagnosis via vibration signal processing play an important role to avoid serious accidents. Aiming at the complexity of multiple faults in a rotor-bearing system and drawback, the characteristic frequency of relevant fault could not be determined effectively with traditional method. The Spectral Kurtosis (SK) is useful for the bearing fault detection. Nevertheless, the simulation of experiment in this paper shows that the SK is unable to identify multi-fault of rotor-bearing system fully when different faults excite different resonance frequencies. A new multi-fault detection method based on EEMD and spectral kurtosis (SK) is proposed in order to overcoming the shortcoming. The proposed method is applied to multi-faults of rotor imbalance and faulty bearings. The superiority of the proposed method based on spectral kurtosis (SK) and EEMD is demonstrated in extracting fault characteristic information of rotating machinery

    Characteristic analysis on the rubbing of rotor blade-casing of aero-engine based on Hilbert transform

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    A characteristic analytical method for the rubbing of aero-engine rotor blade-casing based on Hilbert Transform is proposed. The rotor experiment rig of aero-engine was used to simulate rubbing faults including single-point rub and local rub in the conditions of different casing thickness, different rubbing intensities, different rotational speeds and different rubbing positions. The casing acceleration signal was collected and subjected to the analysis by Hilbert envelope spectrum, and the result was compared with traditional spectrum analysis. The result indicates that the Hilbert envelope spectrum can effectively monitor the aero-engine running state in low frequency, and the method is more insensitive in sensor position, rotational speed, casing thickness and rubbing position. But the spectrum cannot efficiently monitor the aero-engine running state in low frequency

    Bearing Health Monitoring Based on the Orthogonal Empirical Mode Decomposition

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    Bearing is a crucial component of industrial equipment, since any fault occurring in this system usually affects the functionality of the whole machine. To manage this problem, some currently available technologies enable the remote prognosis and diagnosis of bearings, before that faults compromise the system function and safety, respectively. A system for the in-service monitoring of bearing, to detect any inner fault or damage of components and material, allows preventing undesired machine stops. Moreover, it even helps in performing an out-monitoring action, aimed at revealing any anomalous behaviour of the system hosting bearings, through their dynamic response. The in-monitoring can be based on the vibration signal measurement and exploited to detect the presence of defects in material. In this paper, the orthogonal empirical mode decomposition is analysed and tested to investigate how it could be effectively exploited in a lean in-service monitoring operation and remote diagnosis. The proposed approach is validated on a test rig, where an elementary power transmission line was set up. The activity highlights some main properties and practical issues of the technological implementation, as well as the precision of the Orthogonal Empirical Mode Decomposition, as a compact approach for an effective detection of bearing faults in operation

    Fault diagnosis of rotor using EMD thresholding-based de-noising combined with probabilistic neural network

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    De-noising of signal processing is crucial for fault diagnosis in order to successfully conduct feature extraction and is an efficient method for accurate determination of cause. In this paper, the empirical mode decomposition (EMD) thresholding-based de-noising method and probabilistic neural network (PNN) are respectively used in the de-noising of the vibration signal and rotor fault diagnosis and compared with wavelet thresholding-based de-noising technology and back propagation neural network (BPNN). The results show that the clear iterative EMD interval thresholding performs better than wavelet thresholding in the de-noising of the vibration signal, and avoids the determination of wavelet basis and decomposition level. In addition, the PNN created by feature samples does not require training and has a higher accuracy than BPNN

    Automatic condition monitoring system for crack detection in rotating machinery

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    Maintenance is essential to prevent catastrophic failures in rotating machinery. A crack can cause a failure with costly processes of reparation, especially in a rotating shaft. In this study, the Wavelet Packets transform energy combined with Artificial Neural Networks with Radial Basis Function architecture (RBF-ANN) are applied to vibration signals to detect cracks in a rotating shaft. Data were obtained from a rig where the shaft rotates under its own weight, at steady state at different crack conditions. Nine defect conditions were induced in the shaft (with depths from 4% to 50% of the shaft diameter). The parameters for Wavelet Packets transform and RBF-ANN are selected to optimize its success rates results. Moreover, ‘Probability of Detection’ curves were calculated showing probabilities of detection close to 100% of the cases tested from the smallest crack size with a 1.77% of false alarms.The authors would like to thank the Spanish Government for financing through the CDTI project RANKINE21 IDI-20101560

    An improved spline-local mean decomposition and its application to vibration analysis of rotating machinery with rub-impact fault

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    A troublesome problem in application of local mean decomposition (LMD) is that the moving averaging process is time-consuming and inaccurate in processing the mechanical vibration signals. An improved spline-LMD (SLMD) method is proposed to solve this problem. The proposed method uses the cubic spline interpolation to compute the upper and lower envelopes of a signal, and then the local mean and envelope estimate functions can be derived using the envelopes. Meanwhile, a signal extending approach based on self-adaptive waveform matching technique is applied to extend the raw signal and overcome the boundary distortion resulting from the process of computing the upper and lower envelopes. Subsequently, this paper compares SLMD with LMD in four aspects through a simulative signal. The comparative results illustrate that SLMD consumes less computation time and produces more accurate decomposed results than LMD. In the experimental part, SLMD and LMD are respectively applied to analyze the vibration signals resulting from a rotor-bearing system with rub-impact fault. The results show that SLMD can more efficiently and accurately extract the important fault features, which demonstrates that SLMD performs better than LMD in analyzing the mechanical vibration signals

    The Recovery of Weak Impulsive Signals Based on Stochastic Resonance and Moving Least Squares Fitting

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    In this paper a stochastic resonance (SR)-based method for recovering weak impulsive signals is developed for quantitative diagnosis of faults in rotating machinery. It was shown in theory that weak impulsive signals follow the mechanism of SR, but the SR produces a nonlinear distortion of the shape of the impulsive signal. To eliminate the distortion a moving least squares fitting method is introduced to reconstruct the signal from the output of the SR process. This proposed method is verified by comparing its detection results with that of a morphological filter based on both simulated and experimental signals. The experimental results show that the background noise is suppressed effectively and the key features of impulsive signals are reconstructed with a good degree of accuracy, which leads to an accurate diagnosis of faults in roller bearings in a run-to failure test

    Advances in Electrical Machine, Power Electronic, and Drive Condition Monitoring and Fault Detection: State of the Art

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    © 2015 IEEE. Personal use of this material is permitted. Permissíon from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertisíng or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.[EN] Recently, research concerning electrical machines and drives condition monitoring and fault diagnosis has experienced extraordinarily dynamic activity. The increasing importance of these energy conversion devices and their widespread use in uncountable applications have motivated significant research efforts. This paper presents an analysis of the state of the art in this field. The analyzed contributions were published in most relevant journals and magazines or presented in either specific conferences in the area or more broadly scoped events.Riera-Guasp, M.; Antonino-Daviu, J.; Capolino, G. (2015). Advances in Electrical Machine, Power Electronic, and Drive Condition Monitoring and Fault Detection: State of the Art. IEEE Transactions on Industrial Electronics. 62(3):1746-1759. doi:10.1109/TIE.2014.2375853S1746175962
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