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• A novel multi-extracting velocity-synchronous chirplet transform (MEVCT) for efficient and effective time–frequency analysis. • An adaptive chirp angle search method for improving computational efficiency and velocity synchronous basis accuracy. • A higher-order iterative extraction operator for eliminating the non-reassigned time–frequency points. • The improved IF accuracy and anti-noise capability of MEVCT provide ideal TFR for synchronous multi-component signals. • Demonstrated advantages of MEVCT by numerical simulations as well as experimental tests. In many engineering applications, the instantaneous frequency (IF) needs to be identified rapidly and precisely, especially in rotating machinery condition monitoring, where quasi-real-time analysis is a favorable option, and any error in shaft speed (identified from the instantaneous frequency) needs to be reduced to prevent it from accumulating with time. The modern time–frequency analysis (TFA) method provides a way for IF identification; however, sometimes, it still suffers from energy smearing and closely spaced frequency interference. In addition, many TFA methods based on the chirplet transform also present the problem of excessive computation when searching for the optimal parameters. To overcome these limitations, we propose the multi-extracting velocity-synchronous chirplet transform (MEVCT), an enhanced TFA method, in this paper. In the MEVCT, a refined chirp parameter search is developed first to reduce the computational cost while increasing time–frequency accuracy. Then, an iterative high-order IF estimator is constructed to allow the IF to achieve or approach the actual values even in highly nonlinear situations. After that, the obtained IF estimate is substituted into the simultaneous extraction operator to reduce energy dispersion further and eliminate non-reassigned points (NRPs). Simulated and experimental signal analyses demonstrate that MEVCT is able to eliminate NRPs completely. Furthermore, when handling synchronous multicomponent signals, the proposed method has higher IF accuracy with less computational cost and better anti-noise capability. [ABSTRACT FROM AUTHOR]

Variational Mode Decomposition (VMD) has attracted much attention and been used to analyze different kinds of signals, such as mechanical signals, medical data, and financial time series, etc. However, the VMD is still confronted with some dilemmas during the applications, including the determination of the number of the decomposed modes, the selection of the balance parameter, and so on. To address these problems of the VMD, a coarse-to-fine decomposing strategy is proposed for weak fault detection of rotating machines in this paper. Firstly, through extensive numerical simulations, the characteristics of the relative bandwidths of the decomposed modes are given with the change of the balance parameter and the number of the decomposed modes. Then, motivated by the bandwidth characteristics, the rationalities and advantages of iterative decomposition of the VMD and the fine adjustment of the balance parameter are discussed in detail, respectively. Subsequently, the new coarse-to-fine decomposing strategy of the VMD is developed to obtain the optimal mode and extract the weak repetitive transients of rotating machines. The analysis results of the simulated signals and the experimental signals measured from two run-to-failure cases show that the proposed method can well-detect the weak repetitive transients in the signals with heavy noise and overcome the drawbacks of the original VMD. The superiority of the proposed method for faint repetitive transient detection is also demonstrated by comparing with the existing methods. [ABSTRACT FROM AUTHOR]

The effectiveness of common algorithms for feedforward compensation of narrowband disturbance depends mainly on the model quality. To avoid this dependency several direct adaptive control algorithms without explicitly identified secondary path models have been developed over the last years. However an overview of their properties and a comparison of their performances in a standardized benchmark is still lacking. In this paper the three most promising algorithms are modified for narrowband feedforward vibration control for the use in rotating machinery. As in this application the reference signal is generated using the frequency measurement from a speed sensor it can be assumed that there is no coupling between reference measurement and the secondary path. First the algorithms are tested in simulation, then they are implemented on a test rig for active vibration control of unbalance induced rotor vibration. In simulation as well as for the test rig the performances of the algorithms are compared to each other. Advantages and drawbacks of the algorithms are discussed and practical instructions for implementation are given. The work is intended to serve as starting point and motivation for future research in this field of study. [ABSTRACT FROM AUTHOR]

Nowadays, the vibration analysis of rotating machine signals is a well-established methodology, rooted on powerful tools offered, in particular, by the theory of cyclostationary (CS) processes. Among them, the squared envelope spectrum (SES) is probably the most popular to detect random CS components which are typical symptoms, for instance, of rolling element bearing faults. Recent researches are shifted towards the extension of existing CS tools – originally devised in constant speed conditions – to the case of variable speed conditions. Many of these works combine the SES with computed order tracking after some preprocessing steps. The principal object of this paper is to organize these dispersed researches into a structured comprehensive framework. Three original features are furnished. First, a model of rotating machine signals is introduced which sheds light on the various components to be expected in the SES. Second, a critical comparison is made of three sophisticated methods, namely, the improved synchronous average, the cepstrum prewhitening, and the generalized synchronous average, used for suppressing the deterministic part. Also, a general envelope enhancement methodology which combines the latter two techniques with a time-domain filtering operation is revisited. All theoretical findings are experimentally validated on simulated and real-world vibration signals. [ABSTRACT FROM AUTHOR]

This paper presents a novel Sinusoidal Synthesis Based Adaptive Tracking (SSBAT) technique for vibration-based rotating machinery fault detection. The proposed SSBAT algorithm is an adaptive time series technique that makes use of both frequency and time domain information of vibration signals. Such information is incorporated in a time varying dynamic model. Signal tracking is then realized by applying adaptive sinusoidal synthesis to the vibration signal. A modified Least-Squares (LS) method is adopted to estimate the model parameters. In addition to tracking, the proposed vibration synthesis model is mainly used as a linear time-varying predictor. The health condition of the rotating machine is monitored by checking the residual between the predicted and measured signal. The SSBAT method takes advantage of the sinusoidal nature of vibration signals and transfers the nonlinear problem into a linear adaptive problem in the time domain based on a state-space realization. It has low computation burden and does not need a priori knowledge of the machine under the no-fault condition which makes the algorithm ideal for on-line fault detection. The method is validated using both numerical simulation and practical application data. Meanwhile, the fault detection results are compared with the commonly adopted autoregressive (AR) and autoregressive Minimum Entropy Deconvolution (ARMED) method to verify the feasibility and performance of the SSBAT method. [ABSTRACT FROM AUTHOR]

Abstract: The vibration or acoustic signal from rotating machinery with localized fault usually behaves as the form of amplitude modulation (AM) and/or frequency modulation (FM). The demodulation techniques are conventional ways to reveal the fault characteristics from the analyzed signals. One of these techniques is the time-scale manifold (TSM) ridge demodulation method with the merits of good time–frequency localization and in-band noise suppression properties. However, due to the essential attribute of wavelet ridge, the survived in-band noise on the achieved TSM will still disturb the envelope extraction of fault-induced impulses. This paper presents an improved TSM ridge demodulation method, called exchanged ridge demodulation of TSM, by combining the benefits of the first two TSMs: the noise suppression of the first TSM and the noise separation of the second TSM. Specifically, the ridge on the second TSM can capture the fault-induced impulses precisely while avoiding the in-band noise smartly. By putting this ridge on the first TSM, the corresponding instantaneous amplitude (IA) waveform can represent the real envelope of pure faulty impulses. Moreover, an adaptive selection method for Morlet wavelet parameters is also proposed based on the smoothness index (SI) in the time-scale domain for an optimal time-scale representation of analyzed signal. The effectiveness of the proposed method is verified by means of a simulation study and applications to diagnosis of bearing defects and gear fault. [Copyright &y& Elsevier]

Abstract: This paper presents a hybrid technique to enhance health diagnosis of rotating machines under varying speed conditions. This is achieved through the integration of complex wavelet transform-based envelope extraction of speed-varying vibration signals with computed order tracking for eliminating speed dependence in the sampled data. By performing a post-spectral analysis of the resampled signal, a multi-scale enveloping order spectrogram is realized that has shown to enable enhanced defect feature extraction. Numerical simulation and experimental studies have demonstrated the effectiveness of the hybrid technique in identifying bearing structural defects under varying operating conditions. [Copyright &y& Elsevier]

Abstract: In large scale rotating machinery the resonance amplitude during the passage through resonance is a matter of consideration because of its influence in the surrounding environment of the rotational system and foundation. In this paper, a variable geometry journal bearing (VGJB), recently patented, is applied for the mounting of a large scale rotor bearing system operating at the range of medium speed. The simulation of the rotor-bearing system incorporates a recent method for simulation of a multi-segment continuous rotor in combination with nonlinear bearing forces. The use of the current bearing gives results that encourage the use of such a bearing in rotating machinery since the vibration amplitude during the passage through the critical speed can be reduced up to 60–70%. In the presented study, the developed amplitude and the rotor stresses are severely reduced compared to those of the system with normal cylindrical journal bearings during a virtual start up of the system. [Copyright &y& Elsevier]

Abstract: A simple and effective down-sample algorithm, Peak-Hold-Down-Sample (PHDS) algorithm is developed in this paper to enable a rapid and efficient data transfer in remote condition monitoring applications. The algorithm is particularly useful for high frequency Condition Monitoring (CM) techniques, and for low speed machine applications since the combination of the high sampling frequency and low rotating speed will generally lead to large unwieldy data size. The effectiveness of the algorithm was evaluated and tested on four sets of data in the study. One set of the data was extracted from the condition monitoring signal of a practical industry application. Another set of data was acquired from a low speed machine test rig in the laboratory. The other two sets of data were computer simulated bearing defect signals having either a single or multiple bearing defects. The results disclose that the PHDS algorithm can substantially reduce the size of data while preserving the critical bearing defect information for all the data sets used in this work even when a large down-sample ratio was used (i.e., 500 times down-sampled). In contrast, the down-sample process using the existing normal down-sample technique in signal processing eliminates the useful and critical information such as bearing defect frequencies in a signal when the same down-sample ratio was employed. Noise and artificial frequency components were also induced by the normal down-sample technique, thus limits its usefulness for machine condition monitoring applications. [Copyright &y& Elsevier]

Abstract: This paper presents a novel wavelet decomposition scheme, named adaptive morphological gradient lifting wavelet (AMGLW), for detecting bearing defects. The adaptability of the AMGLW consists in that the scheme can select between two filters, mean the average filter and morphological gradient filter, to update the approximation signal based on the local gradient of the analyzed signal. Both a simulated signal and vibration signals acquired from bearing are employed to evaluate and compare the proposed AMGLW scheme with the traditional linear wavelet transform (LWT) and another adaptive lifting wavelet (ALW) developed in literature. Experimental results reveal that the AMGLW outperforms the LW and ALW obviously for detecting bearing defects. The impulsive components can be enhanced and the noise can be depressed simultaneously by the presented AMGLW scheme. Thus the fault characteristic frequencies of bearing can be clearly identified. Furthermore, the AMGLW gets an advantage over LW in computation efficiency. It is quite suitable for online condition monitoring of bearings and other rotating machineries. [Copyright &y& Elsevier]

Abstract: This paper deals with some experimental results where rub-impact of rotor systems are reproduced. A dedicated experimental set-up was installed. In particular partial light, medium and severe rubs are considered. Then a model, suitable to be employed for real rotating machinery, is presented and the corresponding theoretical predictions are compared with the experimental results. It was found that the model is able to reproduce the behaviour of the test rig. [Copyright &y& Elsevier]

Abstract: This paper presents the results of computer simulation of bearing misalignment defects in a power turbogenerator. This malfunction is typical for great multi-rotor and multi-bearing rotating machines and very common in power turbo-sets. Necessary calculations were carried out by the computer code system MESWIR, developed and used at the IFFM in Gdansk for calculating dynamics of rotors supported on oil bearings. The results are presented in the form of a set of journal and bush trajectories of all turbo-set bearings. Our analysis focuses on the vibrational effects of displacing the two most vulnerable machine bearings in horizontal and vertical directions by the maximum acceptable range calculated with regard to bearing vibration criterion. This assumption required preliminary assessment of the maximum values for the permissible bearing dislocations. We show the relations between the attributes of the particular bearing trajectories and the bearing displacements in relation to their base design position. The shape and dimensions of bearing trajectories are interpreted based on the theory of hydrodynamic lubrication of oil bearings. It was shown that the relative journal trajectories and absolute bush trajectories carry much important information about the dynamic state of the machine, indicating also the way in which bearings are loaded. Therefore, trajectories can be a source of information about the position and direction of bearing misalignments. This article indicates the potential of using trajectory patterns for diagnosing misalignment defects in rotating machines and suggests including sets of trajectory patterns to the knowledge base of a machine diagnostic system. [ABSTRACT FROM AUTHOR]

Abstract: In order to enhance the desired features related to some special type of machine fault, a technique based on the dual-tree complex wavelet transform (DTCWT) is proposed in this paper. It is demonstrated that DTCWT enjoys better shift invariance and reduced spectral aliasing than second-generation wavelet transform (SGWT) and empirical mode decomposition by means of numerical simulations. These advantages of the DTCWT arise from the relationship between the two dual-tree wavelet basis functions, instead of the matching of the used single wavelet basis function to the signal being analyzed. Since noise inevitably exists in the measured signals, an enhanced vibration signals denoising algorithm incorporating DTCWT with NeighCoeff shrinkage is also developed. Denoising results of vibration signals resulting from a crack gear indicate the proposed denoising method can effectively remove noise and retain the valuable information as much as possible compared to those DWT- and SGWT-based NeighCoeff shrinkage denoising methods. As is well known, excavation of comprehensive signatures embedded in the vibration signals is of practical importance to clearly clarify the roots of the fault, especially the combined faults. In the case of multiple features detection, diagnosis results of rolling element bearings with combined faults and an actual industrial equipment confirm that the proposed DTCWT-based method is a powerful and versatile tool and consistently outperforms SGWT and fast kurtogram, which are widely used recently. Moreover, it must be noted, the proposed method is completely suitable for on-line surveillance and diagnosis due to its good robustness and efficient algorithm. [Copyright &y& Elsevier]

Abstract: A new technique of diagnosing data for broken rotor bars in induction motors derived from two of the three stator currents, the Beirut diagnostic procedure (BDP) is presented in this paper. The theoretical principles directly related to the application of this diagnostic technique are described, emphasizing the use of a severity factor in order to evaluate the extension of the fault. Defining the severity factor as the normalized amplitude of the fault characteristic frequency enables us to draw up a table of comparison of several usual electric diagnostic methods. Besides the traditional one-phase current spectrum analysis, values of the severity factor related to electrical signals like the instantaneous powers, the current space vector modulus and finally related to the new Beirut diagnostic method are analyzed with respect to the variation of the power factor angle and of the sum of the two current side-band angular displacement. The BDP offers several advantages over the usual motor current signature analyses (MCSA) methods: it is shown how the proposed severity factor applied to the new diagnostic technique is not dependent on motor parameters such as the power factor angle and the fault type which is not the case of the instantaneous powers. In addition, the BDP has the advantage of detecting easily fault characteristic frequencies, which is not possible via diagnostic methods that use the detection of two side-band components as in the simple current spectrum. By theoretical analysis, computer simulations, and laboratory experiments, it is shown that the new method enhances the reliability of diagnostics of broken rotor bars in induction motor. [Copyright &y& Elsevier]