Adaptive Mode Decomposition Research Articles

Generalized adaptive mode decomposition for nonstationary signal analysis of rotating machinery: Principle and applications

Abstract Rotating machinery signals are usually intricate and often nonstationary. The analysis of such signals is further complicated because of the spectral overlaps between constituent frequency components under time-varying running conditions. Most conventional signal processing methods are unable to effectively extract the embedded meaningful information. Adaptive mode decomposition (AMD) methods are flexible to describe complex signals, free from the limitations inherent in conventional basis expansion and thus more suited to unveiling the underlying physical nature. However, as those methods work in a way similar to filter banks, i.e. via rectangular partition of time-frequency plane, they are effective only for signals with constituent frequency components that are almost constant and parallel to each other. As such, it would be inappropriate to apply such methods when signal constituent frequency components vary considerably over time and particularly overlap in frequency domain. To address this issue, this paper proposes a general framework by exploiting the unique capability of generalized demodulation to transform an arbitrary time-varying instantaneous frequency into a constant frequency. In doing so, many state-of-the-art AMD methods (including empirical mode composition, local mean decomposition, intrinsic time-scale decomposition, local characteristic scale decomposition, Hilbert vibration decomposition, empirical wavelet transform, variational mode decomposition, and adaptive local iterative filtering) can be extended to analyze highly nonstationary complex signals with spectral overlaps. The principle and advantage of generalized adaptive mode decomposition (GAMD) are illustrated through numerical simulation. The GAMD’s performance in monitoring different rotating machinery components (including a planetary gearbox, an aircraft engine rolling bearing and a hydraulic turbine rotor) is demonstrated and validated under time-varying conditions.

Fractional iterative variational mode decomposition and its application in fault diagnosis of rotating machinery

Variational mode decomposition (VMD), a recently developed adaptive mode decomposition technique, has attracted much attention in various fields. However, due to the assumption that the obtained intrinsic mode functions should be band-limited and separable in the Fourier domain, VMD has experienced many obstacles when processing wideband nonstationary signals. In this paper, a new method named fractional iterative variational mode decomposition (FrIVMD) is proposed for the decomposition of a multicomponent linear frequency modulation signal. By accurately estimating the chirp rate of the linear frequency modulation (LFM) component, the original signal is mapped to the fractional Fourier domain by the fractional Fourier transform (FRFT), where the corresponding LFM component is narrowly banded. Then, the conventional VMD is applied to separate the components. Finally, the signal mode in the time domain is obtained by the inverse FRFT. Numerical and real-world vibration signals are employed to validate the effectiveness of the FrIVMD technique. The results prove that the proposed method performs well for noisy signals and even signals containing weak components.

A Generalized Framework of Adaptive Mode Decomposition

As an effective tool for nonlinear and non-stationary signal separation method, empirical mode decomposition (EMD) has attracted a lot of attention of many scholars and has been successfully applied to many engineering areas. Since the kernel of EMD is to define a baseline and then implement the sift process, it is important to select an appropriate baseline to improve the performance of EMD for an accurate decomposition. However, it is difficult to choose the most suitable method for dealing with a given arbitrary signal. Besides, the baseline constructed through the existing methods generally does not equal the expected one. To address these problems, we propose a new generalized framework for adaptive mode decomposition (GF-AMD) based on EMD, in which the optimal baseline is firstly chosen from different ones and a weighted factor is introduced to adjust the baseline for getting the optimal one. Then the optimal baseline is implemented to the sift process of EMD. Two simulation signals are used to verify the effectiveness and superiority to EMD. Finally, the proposed GF-AMD method is applied to the vibration signal analysis of faulty rotary machinery by comparing with EMD method and the analysis results indicate its effect and superiority.

Review of local mean decomposition and its application in fault diagnosis of rotating machinery

Rotating machinery is widely used in the industry. They are vulnerable to many kinds of damages especially for those working under tough and time-varying operation conditions. Early detection of these damages is important, otherwise, they may lead to large economic loss even a catastrophe. Many signal processing methods have been developed for fault diagnosis of the rotating machinery. Local mean decomposition (LMD) is an adaptive mode decomposition method that can decompose a complicated signal into a series of mono-components, namely product functions (PFs). In recent years, many researchers have adopted LMD in fault detection and diagnosis of rotating machines. We give a comprehensive review of LMD in fault detection and diagnosis of rotating machines. First, the LMD is described. The advantages, disadvantages and some improved LMD methods are presented. Then, a comprehensive review on applications of LMD in fault diagnosis of the rotating machinery is given. The review is divided into four parts: fault diagnosis of gears, fault diagnosis of rotors, fault diagnosis of bearings, and other LMD applications. In each of these four parts, a review is given to applications applying the LMD, improved LMD, and LMD-based combination methods, respectively. We give a summary of this review and some future potential topics at the end.

Machinery Fault Diagnosis Using Signal Analysis

Abstract Signal analysis methods are important for machinery fault diagnosis. Signals provided by faulty manufacturing systems are usually arbitrary and nonlinear. These detected changes carry information about system functioning. Compared to conventional signal analysis methods, adaptive mode decomposition based procedures have good versatility and high adaptability in nonlinear signal analysis. This paper presents an instantaneous frequency estimation based on energy separation approach which has effectiveness in estimating both instantaneous frequency and instantaneous amplitude of arbitrary time-varying signals. The proposed algorithm is tested on synthetic signals created for this purpose. This paper presents the principles of this fault diagnosis method, results indicate that the proposed procedure can handle fault detection issues even in noisy environment.

Local maximum synchrosqueezing transform: An energy-concentrated time-frequency analysis tool

Abstract Time-frequency (TF) analysis (TFA) is an effective tool to analyze time-varying signals. The reassignment method (RM) and synchrosqueezing transform (SST) are high-resolution TFA methods that have attracted much attention recently in the signal-processing field. However, the RM and SST methods suffer from some drawbacks, such as unavailable signal reconstruction and poor energy concentration that hinder their applications in real-world data analysis. To overcome these drawbacks, we propose a novel SST-based technique that can achieve more concentrated representations than RM and SST. Meanwhile, we prove that it allows for perfect signal reconstruction. Furthermore, the ridge detection method and synchroextracting operator are combined to form an adaptive mode decomposition algorithm. The numerical validation shows that the proposed method can be used to sensitively discover the amplitude-weak modes and effectively address signals with heavy noise. In the experimental validation, we first analyze the gravitational-wave (GW) signal. The analysis results show that the proposed method can provide a better time-varying description for the generation procedure of the GW signal. Moreover, the reconstructed signal has a high consistency with the general relativity proposed by Einstein. The experimental analysis on fault bearing shows that the proposed method can discover more detailed features that are helpful for diagnosing bearing faults. Comparisons with other TFA methods demonstrate the superiority and effectiveness of the proposed method.

Adaptive Mode Decomposition Methods and Their Applications in Signal Analysis for Machinery Fault Diagnosis: A Review With Examples

Effective signal processing methods are essential for machinery fault diagnosis. Most conventional signal processing methods lack adaptability, thus being unable to well extract the embedded meaningful information. Adaptive mode decomposition methods have excellent adaptability and high flexibility in describing arbitrary complicated signals, and are free from the limitations imposed by conventional basis expansion, thus being able to adapt to the signal characteristics, extract rich characteristic information, and therefore reveal the underlying physical nature. This paper presents a systematic and up-to-date review on adaptive mode decomposition in two major topics, i.e., mono-component decomposition algorithms (such as empirical mode composition, local mean decomposition, intrinsic time-scale decomposition, local characteristic scale decomposition, Hilbert vibration decomposition, empirical wavelet transform, variational mode decomposition, nonlinear mode decomposition, and adaptive local iterative filtering) and instantaneous frequency estimation approaches (including Hilbert-transform-based analytic signal, direct quadrature, and normalized Hilbert transform based on empirical AM-FM decomposition, as well as generalized zero-crossing and energy separation) reported in more than 80 representative articles published since 1998. Their fundamental principles, advantages and disadvantages, and applications to signal analysis in machinery fault diagnosis, are examined. Examples are provided to illustrate their performance.