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The paper has brought in the Hjorth Complexity parameter and combined it with Intrinsic time decomposition (ITD) algorithm as characteristic parameter index in order to implement accurate identification of multiple faults of rolling bearings. Firstly, concerning about the much uncertainty in manual setting of decomposition layer number in ITD, overdoing of automatic decomposition and the fact that a larger correlation coefficient of signal relates with the greater correlation of signals before and after decomposition and vice versa, the paper has carried out self-adaptive determination of the number of ITD decomposition layers. Secondly, regarding the insensitivity of Hjorth Complexity parameter to noise and the fact that with larger Complexity parameter, signals are simpler and it becomes more available to dig out characteristic information of fault from signals. With Complexity parameter as the index of characteristic parameter, option of optimal Proper rotation component (PRC) is made after ITD. Finally, through the comparison with other methods and the analysis of multiple faults of bearings, it indicates that correlation coefficient can self-adaptively determine the number of ITD decomposition layers and prevent from overdoing and underdoing of decomposition. The Hjorth Complexity parameter can be treated as index parameter to implement optimal PRC option, based on which multiple fault characteristics of bearings can be effectively extracted and the type precisely determined. • The decomposition layers of ITD are determined based on the correlation coefficient adaptive. • Hjorth parameter is introduced into the feature extraction of rolling bearing compound faults. • Complexity parameter is used as the index parameter of the optimal PRC selection. • Cross-correlation function is brought in to make mixed study on the acceleration signals from different sensors. • ITD is combined with Hjorth parameter. [ABSTRACT FROM AUTHOR]

• Sensibility of variance to variation of signal intensity is used to construct Hankel matrix to highlight fault feature. • Optimal scale of the variance series was determined by sample entropy to create a multi-scale Hankel matrix. • Proposed multiscale singular value method can effectively reduce noise and highlight fault feature information. • Proposed method is insensitive to installation positions of sensors and the fault type of rolling bearings. To solve the vulnerability of singular value denoising to the effective number of singular value orders and influence of Hankel matrix structure, the paper has proposed a multiscale singular value denoising algorithm (M-SVD). Meanwhile, in concerning that when a fault of bearing occurs, the fault feature information contained in vibration signal tends to be weak and complex, the M-SVD method is blended with variational mode decomposition (VMD) algorithm in identification. Firstly, when bearing faults occur, raw vibration acceleration signals are decomposed using the superiority of VMD algorithm for non-stationary signal; in addition, considering that with the larger difference between singular values of signal, feature information will be more obvious, the method takes advantage of sensibility of variance to signal intensity and proposes to constitute Hankel matrix based on variance feature of signal. During the construction of Hankel matrix, the method to determine effective length of variance sequence (optimal scale) has been brought forward by sample entropy which is able to precisely measure the regularity of signal and detect the weak change of signal. Furthermore, component signals are denoised in line with reconstructed Hankel matrix and the spectrum of signals denoised are selected to describe fault characteristic information and judge the failure types. For the sake of verification of the accuracy and effectiveness of proposed method, the paper has analyzed and verified the data of rolling bearing in 3 different failure types and 2 different installation positions of accelerometers. The comparative analysis with classical singular value difference spectrum (SVDS) and singular value median decomposition (SVMD) denoising has further proved that presented VMD combined M-SVD method can effectively restrain noise while retaining fault characteristic information of signal. Therefore, the identification for rolling bearing fault types can be implemented, accurately. [ABSTRACT FROM AUTHOR]

• Take advantage of sensibility of Activity parameter to vibration intensity to describe the intensity of signals and identify different rub-impact states. • Make use of the sensitivity of Mobility parameter to signal state to determine the occurrence of rub-impact fault. • The sensitivity of Complexity parameter to signal complexity will be relied on to identify the different rub-impact states of equipment. • Hjorth parameters can be treated as the characteristic parameters for identifying of rub-impact fault and location. Hjorth parameters reveals Activity, Mobility and Complexity of signals. From this, the paper has proposed the scheme to identify a rotor–stator rub-impact fault and occurrence location by Hjorth parameters. Firstly, the approach has given an analysis to the difference of Hjorth parameters of signals and verified the sensibility of Hjorth parameters to running state of equipment; secondly, in the same running state of equipment, the consistency and robustness of Hjorth parameters of signals from the same sensor have been analyzed and proved; thirdly, for verifying the sensitivity of sensors installation locations to rub-impact fault and occurrence locations, Hjorth parameters of signals from different sensors have been analyzed; finally, the calculated Hjorth parameters are used as feature vector, and K-nearest neighbor (KNN) classification algorithm is employed for identifying a rub-impact fault and determining the rub-impact locations. The results indicate that proposed method can accurately identify the rub-impact fault and occurrence locations. [ABSTRACT FROM AUTHOR]