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Detection of structural changes is critically important in operational monitoring of a rotating machine. This paper presents a novel framework for this purpose, where a graph model for data modeling is adopted to represent/capture statistical dynamics in machine operations. Meanwhile we develop a numerical method for computing temporal anomalies in the constructed graphs. The martingale-test method is employed for the change detection when making decisions on possible structural changes, where excellent performance is demonstrated outperforming exciting results such as the autoregressive-integrated-moving average (ARIMA) model. Comprehensive experimental results indicate good potentials of the proposed algorithm in various engineering applications. This work is an extension of a recent result (Lu et al., 2017). [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]

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]

This paper addresses the detection of periodic transients in vibration signals so as to detect faults in rotating machines. For this purpose, we present a method to estimate periodic-group-sparse signals in noise. The method is based on the formulation of a convex optimization problem. A fast iterative algorithm is given for its solution. A simulated signal is formulated to verify the performance of the proposed approach for periodic feature extraction. The detection performance of comparative methods is compared with that of the proposed approach via RMSE values and receiver operating characteristic (ROC) curves. Finally, the proposed approach is applied to single fault diagnosis of a locomotive bearing and compound faults diagnosis of motor bearings. The processed results show that the proposed approach can effectively detect and extract the useful features of bearing outer race and inner race defect. [ABSTRACT FROM AUTHOR]

Abstract: A turbo-generator system of the modern rotating machinery consists of the driver and driven shafts, which are coupled through flexible couplings and mounted on flexible bearings. Dynamic characterisation of vital machine elements of such rotating machinery is a challenging problem for reliable and accurate response predictions. In this paper, a key intention is to estimate the bearing and coupling dynamic parameters along with residual unbalances at predefined planes, and the misalignment forces and moments at the coupling based on the rundown vibration data. To tackle a practical difficulty of limited measurements and a numerical difficulty of the conventional dynamic condensation in the development of identification algorithm, a novel condensation technique has been implemented especially to overcome measurement of transverse rotational DOFs. Numerical examples are also presented to show the effectiveness of the proposed method. The measurement noise has been added in numerically simulated responses that are used in the present algorithm to identify the parameters and it is found to be robust. Modelling errors of few physical parameters are also considered and estimates are found to be very good. [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: Time–frequency analysis, including the wavelet transform, is one of the new and powerful tools in the important field of structural health monitoring, using vibration analysis. Commonly-used signal analysis techniques, based on spectral approaches such as the fast Fourier transform, are powerful in diagnosing a variety of vibration-related problems in rotating machinery. Although these techniques provide powerful diagnostic tools in stationary conditions, they fail to do so in several practical cases involving non-stationary data, which could result either from fast operational conditions, such as the fast start-up of an electrical motor, or from the presence of a fault causing a discontinuity in the vibration signal being monitored. Although the short-time Fourier transform compensates well for the loss of time information incurred by the fast Fourier transform, it fails to successfully resolve fast-changing signals (such as transient signals) resulting from non-stationary environments. To mitigate this situation, wavelet transform tools are considered in this paper as they are superior to both the fast and short-time Fourier transforms in effectively analyzing non-stationary signals. These wavelet tools are applied here, with a suitable choice of a mother wavelet function, to a vibration monitoring system to accurately detect and localize faults occurring in this system. Two cases producing non-stationary signals are considered: stator-to-blade rubbing, and fast start-up and coast-down of a rotor. Two powerful wavelet techniques, namely the continuous wavelet and wavelet packet transforms, are used for the analysis of the monitored vibration signals. In addition, a novel algorithm is proposed and implemented here, which combines these two techniques and the idea of windowing a signal into a number of shaft revolutions to localize faults. [Copyright &y& Elsevier]

Abstract: In industrial field, one of the most important practical problems of rotating machinery concerns rotor balancing. Many different methods are used for rotor balancing. Traditional influence coefficient method is often employed along with weighted least squares in order to reduce vibration amplitude, typically at selected rotating speeds like critical or operating ones. Usually the selection of the weights of the least-squares algorithm is manually made by a skilled operator that can decide in which speed range the vibration reduction is more effective. Several methods have been proposed in order to avoid operator''s arbitrariness and an automatic procedure based on robust regression is introduced in this paper. In particular, the analysis is focused on high breakdown-point and bounded-influence estimators. Theoretical aspects and properties of these methods are investigated. The effectiveness and robustness of the proposed balancing procedure are shown by means of an experimental case using a test-rig. [Copyright &y& Elsevier]

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]