447 research outputs found

    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

    Experimental diagnosis of multiple faults on a rotor-stator system by fast Fourier transform and wavelet scalogram

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    This paper presents the recent application of the scalogram of Continuous Wavelet Transform (CWT) as a vibration monitoring and signal processing tool for a rotor dynamic response under parametric excitation. The experimental test data of coupled lateral-torsional vibrations of a rotor-stator system with transverse crack was obtained through a data acquisition set-up interfaced with Rotor-Kit-4 (RK-4). Analysis was executed on rotor deflection, orbit, frequency and time-frequency spectrum of the RK-4 experimental data. The scalograms of CWT were used experimentally to represent the aperiodic occurrence of rub between the rotor-stator and crack features. Variation in 3-D scalogram peaks in the presence of rub and crack were unique and were used to distinguish quasi-periodic motion from other types of motion. An unbalanced cracked rotor gave a higher frequency amplitude response compared to an unbalanced rotor with rub under the same conditions. Irregularities in orbit orientation near sub-harmonic resonances were observed in the test data. Multiple rebounds inside the orbit loop were unique rub indicators. Conspicuous horizontal components of the higher harmonics were observed near the critical speed when a crack existed. CWT established inherent feature patterns that discriminated unbalance, rub and a crack

    Advanced Fault Diagnosis and Health Monitoring Techniques for Complex Engineering Systems

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    Over the last few decades, the field of fault diagnostics and structural health management has been experiencing rapid developments. The reliability, availability, and safety of engineering systems can be significantly improved by implementing multifaceted strategies of in situ diagnostics and prognostics. With the development of intelligence algorithms, smart sensors, and advanced data collection and modeling techniques, this challenging research area has been receiving ever-increasing attention in both fundamental research and engineering applications. This has been strongly supported by the extensive applications ranging from aerospace, automotive, transport, manufacturing, and processing industries to defense and infrastructure industries

    Signal Denoising Method Based on Adaptive Redundant Second-Generation Wavelet for Rotating Machinery Fault Diagnosis

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    Vibration signal of rotating machinery is often submerged in a large amount of noise, leading to the decrease of fault diagnosis accuracy. In order to improve the denoising effect of the vibration signal, an adaptive redundant second-generation wavelet (ARSGW) denoising method is proposed. In this method, a new index for denoising result evaluation (IDRE) is constructed first. Then, the maximum value of IDRE and the genetic algorithm are taken as the optimization objective and the optimization algorithm, respectively, to search for the optimal parameters of the ARSGW. The obtained optimal redundant second-generation wavelet (RSGW) is used for vibration signal denoising. After that, features are extracted from the denoised signal and then input into the support vector machine method for fault recognition. The application result indicates that the proposed ARSGW denoising method can effectively improve the accuracy of rotating machinery fault diagnosis

    Parameters Optimisation in the Vibration-based Machine Learning Model for Accurate and Reliable Faults Diagnosis in Rotating Machines

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    Artificial intelligence (AI)-based machine learning (ML) models seem to be the future for most of the applications. Recent research effort has also been made on the application of these AI and ML methods in the vibration-based faults diagnosis (VFD) in rotating machines. Several research studies have been published over the last decade on this topic. However, most of the studies are data driven, and the vibration-based ML (VML) model is generally developed on a typical machine. The developed VML model may not predict faults accurately if applied on other identical machines or a machine with different operation conditions or both. Therefore, the current research is on the development of a VML model by optimising the vibration parameters based on the dynamics of the machine. The developed model is then blindly tested at different machine operation conditions to show the robustness and reliability of the proposed VML model

    Application of fuzzy random finite element method on rotor dynamics

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    Fuzzy and stochastic characteristics of parameters exist widely in rotating machinery. To research the parameters characteristics is of great significance in rotor dynamics. Dynamic characteristics of rotor system are analyzed taking into account uncertain properties of fuzzy and stochastic coexisting. Fuzzy variables are transformed into stochastic variables based on information entropy theory. The Neumann stochastic finite element method based on Neumann expansion combined with Newmark-β method is used in linear and nonlinear rotor system within the frame work of Monte Carlo simulation. Critical speed and dynamic response of fuzzy stochastic rotor systems are described by the proposed method. The results show that the Neumann stochastic finite element method has good applicability and efficiency in rotor dynamics

    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

    Blade Crack Detection of Centrifugal Fan Using Adaptive Stochastic Resonance

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