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The objective of this research is to investigate the feasibility of utilizing the hybrid method of ensemble empirical mode decomposition (EEMD) and pure empirical mode decomposition (EMD) to efficiently decompose the complicated vibration signals of rotating machinery into a finite number of intrinsic mode functions (IMFs), so that the fault characteristics of the misaligned shaft can be examined in the time–frequency Hilbert spectrum as well as the marginal Hilbert spectrum. The intrawave frequency modulation (FM) phenomenon, which indicates the nonlinear vibration behavior of a misaligned shaft, can be observed in the time–frequency Hilbert spectrum through the Hilbert–Huang transform (HHT) technique. The fault characteristic of shaft misalignment is also featured in terms of the amplitude modulation (AM) phenomenon in the information-containing IMF components that are extracted by the significance test. Through performing the envelope analysis on the information-containing IMF, the marginal Hilbert spectrum of the envelope signal of this IMF component exhibits that the level of shaft misalignment is presented by the level of AM in the IMF. A test bed of a rotor-bearing system is performed experimentally to illustrate both the parallel and angular shaft misalignment conditions as well as the healthy condition. The analysis results show that the proposed approach is capable of diagnosing the misaligned fault of the shaft in rotating machinery and providing a more meaningful physical insight compared with the conventional methods.

The purpose of this research is to investigate the feasibility of utilizing the post-processing of Ensemble Empirical Mode Decomposition (EEMD) and Autoregressive (AR) modeling to identify the looseness faults at different mechanical components of rotating machinery. The post-processing of EEMD is employed to decompose the complicated vibration signals of rotating machinery into a finite number of Intrinsic Mode Functions (IMFs) which represent the mono-oscillated components of different frequency bands. The AR modeling process reveals the fault features of the information-contained IMF components. The characteristic vectors derived from the AR modeling process can be utilized for identifying the locations of looseness faults in the operating machinery. A rotor-bearing test rig is performed to illustrate the looseness faults at different mechanical components. The analysis results show that the proposed approach is capable of identifying the locations of looseness faults at the rotating machinery.

The objective of this research in this paper is to investigate the feasibility of utilizing the Hilbert–Huang transform method for diagnosing the looseness faults of rotating machinery. The complicated vibration signals of rotating machinery are decomposed into finite number of intrinsic mode functions (IMFs) by integrated ensemble empirical mode decomposition technique. Through the significance test, the information-contained IMFs are selected to form the neat time-frequency Hilbert spectra and the corresponding marginal Hilbert spectra. The looseness faults at different components of the rotating machinery can be diagnosed by measuring the similarities among the information-contained marginal Hilbert spectra. The fault indicator index is defined to measure the similarities among the information-contained marginal Hilbert spectra of vibration signals. By combining the statistical concept of Mahalanobis distance and cosine index, the fault indicator indices can render the similarities among the marginal Hilbert spectra to enhanced and distinguishable quantities. A test bed of rotor-bearing system is performed to illustrate the looseness faults at different mechanical components. The effectiveness of the proposed approach is evaluated by measuring the fault indicator indices among the marginal Hilbert spectra of different looseness types. The results show that the proposed diagnosis method is capable of classifying the distinction among the marginal Hilbert spectra distributions and thus identify the type of looseness fault at machinery.

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The purpose of this research is to investigate the feasibility of utilizing the adaptive sandwich algorithm to find the optimal left and right eigenvectors for active structural noise reduction. As depicted in the previous studies, the structural acoustic radiation depends on the structural vibration behavior, which is strongly related to both the left eigenvectors (concept of disturbance rejection capability) and right eigenvectors (concept of mode shape distributions) of the system, respectively. In this research, a novel adaptive sandwich algorithm is developed for determining the optimal combination of left and right eigenvectors of the structural system. The sound suppression performance index (SSPI) is defined by combining the orthogonality index of left eigenvectors and the modal radiation index of right eigenvectors. Through the proposed adaptive sandwich algorithm, both the left and right eigenvectors are adjusted such that the SSPI decreases, and therefore one can find the optimal combination of left and right eigenvectors of the closed-loop system for structural noise reduction purpose. The optimal combination of left-right eigenvectors is then synthesized to determine the feedback gain matrix of the closed-loop system. The result of the active noise control shows that the proposed method can significantly suppress the sound pressure radiated from the vibrating structure.

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