Decomposition Of Vibration Signals Research Articles

Amplitude-cyclic frequency decomposition of vibration signals for bearing fault diagnosis based on phase editing

Abstract In rotating machine diagnosis different spectral tools are used to analyse vibration signals. Despite the good diagnostic performance such tools are usually refined, computationally complex to implement and require oversight of an expert user. This paper introduces an intuitive and easy to implement method for vibration analysis: amplitude cyclic frequency decomposition. This method firstly separates vibration signals accordingly to their spectral amplitudes and secondly uses the squared envelope spectrum to reveal the presence of cyclostationarity in each amplitude level. The intuitive idea is that in a rotating machine different components contribute vibrations at different amplitudes, for instance defective bearings contribute a very weak signal in contrast to gears. This paper also introduces a new quantity, the decomposition squared envelope spectrum, which enables separation between the components of a rotating machine. The amplitude cyclic frequency decomposition and the decomposition squared envelope spectrum are tested on real word signals, both at stationary and varying speeds, using data from a wind turbine gearbox and an aircraft engine. In addition a benchmark comparison to the spectral correlation method is presented.

A new fault detection strategy using the enhancement ensemble empirical mode decomposition

The vibration signals of mechanical components are non-linear and non-stationary and the feature frequencies of faulty bearings will be difficult extracted. This paper presents a new approach that combines the ensemble empirical mode decomposition (EEMD), the random decrement technique (RDT), and envelope spectrum for the fast detection of faults in bearings. The proposed approach uses the optimized and fast EEMD algorithm to extract intrinsic mode functions (IMFs) from vibration signals able to tack the feature frequency of bearings. If the Impulse response signal of the first IMF is unclear, it is further extracted by the RDT, and the feature frequencies are determined by analyzing the signals using envelope spectrum. The advantages of this method are its computational efficiency, and the strong non-stationary vibration signal decomposition and impulse signal extraction abilities. Numerical simulations and experimental data collected from faulty bearings are used to validate the proposed approach. The results show that the use of the EEMD, the RDT, and the envelope spectrum is a suitable and fast on-line strategy to detect faults of mechanical components.

An Optimal Ensemble Empirical Mode Decomposition Method for Vibration Signal Decomposition

The vibration signal decomposition is a critical step in the assessment of machine health condition. Though ensemble empirical mode decomposition (EEMD) method outperforms fast Fourier transform (FFT), wavelet transform, and empirical mode decomposition (EMD) on nonstationary signal decomposition, there exists a mode mixing problem if the two critical parameters (i.e., the amplitude of added white noise and the number of ensemble trials) are not selected appropriately. A novel EEMD method with optimized two parameters is proposed to solve the mode mixing problem in vibration signal decomposition in this paper. In the proposed optimal EEMD, the initial values of the two critical parameters are selected based on an adaptive algorithm. Then, a multimode search algorithm is explored to optimize the critical two parameters by its good performance in global and local search. The performances of the proposed method are demonstrated by means of a simulated signal, two bearing vibration signals, and a vibration signal in a milling process. The results show that compared with the traditional EEMD method and other improved EEMD method, the proposed optimal EEMD method automatically obtains the appropriate parameters of EEMD and achieves higher decomposition accuracy and faster computational efficiency.

Fault diagnosis of valve clearance in diesel engine based on BP neural network and support vector machine

Based on wavelet packet transformation(WPT), genetic algorithm(GA), back propagation neural network(BPNN)and support vector machine(SVM), a fault diagnosis method of diesel engine valve clearance is presented. With power spectral density analysis, the characteristic frequency related to the engine running conditions can be extracted from vibration signals. The biggest singular values(BSV)of wavelet coefficients and root mean square (RMS)values of vibration in characteristic frequency sub-bands are extracted at the end of third level decomposition of vibration signals, and they are used as input vectors of BPNN or SVM. To avoid being trapped in local minima, GA is adopted. The normal and fault vibration signals measured in different valve clearance conditions are analyzed. BPNN, GA back propagation neural network (GA-BPNN), SVM and GA-SVM are applied to the training and testing for the extraction of different features, and the classification accuracies and training time are compared to determine the optimum fault classifier and feature selection. Experimental results demonstrate that the proposed features and classification algorithms give classification accuracy of 100%.

A data-driven method to enhance vibration signal decomposition for rolling bearing fault analysis

Health condition analysis and diagnostics of rotating machinery requires the capability of properly characterizing the information content of sensor signals in order to detect and identify possible fault features. Time–frequency analysis plays a fundamental role, as it allows determining both the existence and the causes of a fault. The separation of components belonging to different time–frequency scales, either associated to healthy or faulty conditions, represents a challenge that motivates the development of effective methodologies for multi-scale signal decomposition. In this framework, the Empirical Mode Decomposition (EMD) is a flexible tool, thanks to its data-driven and adaptive nature. However, the EMD usually yields an over-decomposition of the original signals into a large number of intrinsic mode functions (IMFs). The selection of most relevant IMFs is a challenging task, and the reference literature lacks automated methods to achieve a synthetic decomposition into few physically meaningful modes by avoiding the generation of spurious or meaningless modes. The paper proposes a novel automated approach aimed at generating a decomposition into a minimal number of relevant modes, called Combined Mode Functions (CMFs), each consisting in a sum of adjacent IMFs that share similar properties. The final number of CMFs is selected in a fully data driven way, leading to an enhanced characterization of the signal content without any information loss. A novel criterion to assess the dissimilarity between adjacent CMFs is proposed, based on probability density functions of frequency spectra. The method is suitable to analyze vibration signals that may be periodically acquired within the operating life of rotating machineries. A rolling element bearing fault analysis based on experimental data is presented to demonstrate the performances of the method and the provided benefits.

Online Condition Monitoring of Bearings to Support Total Productive Maintenance in the Packaging Materials Industry.

The packaging materials industry has already recognized the importance of Total Productive Maintenance as a system of proactive techniques for improving equipment reliability. Bearing faults, which often occur gradually, represent one of the foremost causes of failures in the industry. Therefore, detection of their faults in an early stage is quite important to assure reliable and efficient operation. We present a new automated technique for early fault detection and diagnosis in rolling-element bearings based on vibration signal analysis. Following the wavelet decomposition of vibration signals into a few sub-bands of interest, the standard deviation of obtained wavelet coefficients is extracted as a representative feature. Then, the feature space dimension is optimally reduced to two using scatter matrices. In the reduced two-dimensional feature space the fault detection and diagnosis is carried out by quadratic classifiers. Accuracy of the technique has been tested on four classes of the recorded vibrations signals, i.e., normal, with the fault of inner race, outer race, and ball operation. The overall accuracy of 98.9% has been achieved. The new technique can be used to support maintenance decision-making processes and, thus, to increase reliability and efficiency in the industry by preventing unexpected faulty operation of bearings.

Study on Performance of Remanufactured Engine Based on EMD

According to the non-stationary property of vibration signal, the vibration signal is decomposed by empirical mode decomposition (EMD). And this method is applied to study the remanufacturing level of engine. Based on the decomposition of vibration signal, correlation coefficient is introduced to study the correlation between IMF (Intrinsic Mode Function) components and original signal, and dynamic structure of IMF components is analyzed by correlation dimension. In order to study the remanufacturing level of engine, correlation coefficient and correlation dimension are considered in the function of IMF, and an index of vibration intensity is proposed. A corresponding relationship is established between remanufacturing level and vibration intensity. This method has been proved that the running state of engine can be reflected by vibration intensity, and this index can be used to evaluate the remanufacturing level of engine. DOI : http://dx.doi.org/10.11591/telkomnika.v12i1.3896

Empirical Mode Decomposition of Vibration Signal for Detection of Local Disturbances in Planetary Gearbox Used in Heavy Machinery System

In rotating machinery, the detection of local damage is one of the most important issues. This kind of change of technical condition produce local disturbance according to temporal (local) change of stiffness of kinematic pair (tooth-tooth contact, rolling element-outer/inner race etc). In many practical, i.e. industrial cases, vibration signature of such change is weak in sense of produced energy, so consequently, completely masked by other vibration sources in machine. The general concept of signal processing for local damage detection is to use so called signal enhancement, i.e. a kind of tool that may improve signal to noise ratio. One may find many approaches used in the literature. Most of them use signal filtering (classical, adaptive and optimal filters), decomposition (wavelets) or extraction (blind source separation). Empirical Mode Decomposition (EMD) is one of such techniques that can be used with signal decomposition problem. In this paper, EMD will be used for vibration signal decomposition in order to extract information about local perturbation of arm (carrier) in planetary gearbox used in heavy mining machine, i.e. bucket wheel excavator. As a result of application of EMD, one may obtain several time series with different properties of sub-signal. Due to predefined task, namely local disturbance detection, several criteria have been investigated in order to select the most informative empirical mode. First criterion was kurtosis calculated for every mode with very simple decision rule (max kurtosis is the best). It was found that such approach is not optimal due to some random impulses that are not related to damage. To improve results, it is proposed to combine envelope spectrum and kurtosis. If envelope spectrum contains family of components related to arm (carrier) shaft frequency and signal is spiky (kurtosis is high) result of EMD for given mode is optimal in sense of carried information. However, in this approach decision was made based on visual inspection of the envelope spectra of each mode, which is non-effective way. Finally two parameters have been proposed: 1) Pearson correlation coefficient of an empirical mode and the empirically determined local mean of original signal; 2) a relative power of an empirical mode.

Improved Complexity Based on Time-Frequency Analysis in Bearing Quantitative Diagnosis

This paper compares and analyzes the quantitative diagnosis methods based on Lempel-Ziv complexity for bearing fault, using continuous wavelet transform (CWT), Empirical Mode Decomposition (EMD) method, and wavelet packet method for decomposition of vibration signal measured by acceleration sensors, respectively. The kurtosis and entropy indices are also analyzed in order to select the optimum analysis area from the vibration signal. The variation trend of vibration signal Lempel-Ziv complexity of bearing inner race and outer race with the varying fault severity is analyzed and predicted based on the generation mechanism and characteristics of fault vibration signals. Experimental results show that it is suitable for observing the fault growing trend and severity by examining the value based on improved Lempel-Ziv complexity algorithm using continuous wavelet transform (CWT), Empirical Mode Decomposition (EMD) method, and wavelet packet method for signal decomposition, using kurtosis indices for optimal analysis area selection, and using the optimal weight coefficients for complexity calculation.

Analysis of Blind Source Separation for Single Channel Vibration Signal Decomposition with a Composite Method

Analysis of Blind Source Separation for Single Channel Vibration Signal Decomposition with a Composite Method

Multilevel decomposition of vibration signals from motorcycles using the wavelet–Hilbert transformation

To maximize comfort and safety it is important to control motorcycle vibration. In order to assess the vibration of motorcycles, measurements need to be taken from these machines. Vibration signals acquired using sensors mounted on the machine always include the excitation components from the engine, the road surface, and the structure of the motorcycle frame. Generally, these vibration signals are coupled together. Presently, there is no ideal method to separate these signal components to allow better analysis of the different contributing factors. Contributions to the total vibration signal from the main vibration sources (the engine and the road surface) cannot be determined exactly. To solve this problem, a multilevel decomposition algorithm based on the wavelet–Hilbert transform is proposed in this paper. The paper formulates the vibration signal model for a motorcycle and discusses the performance of the multilevel decomposition algorithm using simulation and experimental methods. The simulation and experimental results show that the proposed new algorithm is an effective method for the identification of vibration sources on a motorcycle.

Sparse signal decomposition method based on multi-scale chirplet and its application to the fault diagnosis of gearboxes

Based on the chirplet path pursuit and the sparse signal decomposition method, a new sparse signal decomposition method based on multi-scale chirplet is proposed and applied to the decomposition of vibration signals from gearboxes in fault diagnosis. An over-complete dictionary with multi-scale chirplets as its atoms is constructed using the method. Because of the multi-scale character, this method is superior to the traditional sparse signal decomposition method wherein only a single scale is adopted, and is more applicable to the decomposition of non-stationary signals with multi-components whose frequencies are time-varying. When there are faults in a gearbox, the vibration signals collected are usually AM–FM signals with multiple components whose frequencies vary with the rotational speed of the shaft. The meshing frequency and modulating frequency, which vary with time, can be derived by the proposed method and can be used in gearbox fault diagnosis under time-varying shaft-rotation speed conditions, where the traditional signal processing methods are always blocked. Both simulations and experiments validate the effectiveness of the proposed method.

Mechanical failure classification for spherical roller bearing ofhydraulic injection molding machine using DWT–SVM

This paper presents a combined discrete wavelet transform (DWT) and support vector machine (SVM) technique for mechanical failure classification of spherical roller bearing application in high performance hydraulic injection molding machine. The proposed technique consists of preprocessing the mechanical failure vibration signal samples using Db2 discrete wavelet transform at the fourth level of decomposition of vibration signal for mechanical failure classification. After feature extraction from vibration signal, support vector machine is used for decision of mechanical failure types of the spherical roller bearing. The classification results indicate the effectiveness of the combined DWT and SVM based technique for mechanical failure classification of hydraulic injection molding machine.

Derivation of a Damage Sensitive Feature Using the Haar Wavelet Transform

In this paper, a damage sensitive feature based on the wavelet transform of the vibration signal is derived. The theoretical aspects of wavelet decomposition of vibration signals are presented. It is shown that the energies of the wavelet coefficients at appropriate scales can be used as damage sensitive features. Expressions for the energies of wavelet coefficients using the Haar wavelet basis function are derived for a single degree and a multidegree of freedom system. It is shown that the energies of the wavelet coefficients extracted at higher scales are functions of the physical parameters of the system and the loading function. Finally, the migration of damage sensitive feature vectors with damage is illustrated for the ASCE Benchmark Structure.

Decomposition of Vibration Signals into Deterministic and Nondeterministic Components and its Capabilities of Fault Detection and Identification

Decomposition of Vibration Signals into Deterministic and Nondeterministic Components and its Capabilities of Fault Detection and IdentificationThe paper investigates the possibility of decomposing vibration signals into deterministic and nondeterministic parts, based on the Wold theorem. A short description of the theory of adaptive filters is presented. When an adaptive filter uses the delayed version of the input signal as the reference signal, it is possible to divide the signal into a deterministic (gear and shaft related) part and a nondeterministic (noise and rolling bearings) part. The idea of the self-adaptive filter (in the literature referred to as SANC or ALE) is presented and its most important features are discussed. The flowchart of the Matlab-based SANC algorithm is also presented. In practice, bearing fault signals are in fact nondeterministic components, due to a little jitter in their fundamental period. This phenomenon is illustrated using a simple example. The paper proposes a simulation of a signal containing deterministic and nondeterministic components. The self-adaptive filter is then applied—first to the simulated data. Next, the filter is applied to a real vibration signal from a wind turbine with an outer race fault. The necessity of resampling the real signal is discussed. The signal from an actual source has a more complex structure and contains a significant noise component, which requires additional demodulation of the decomposed signal. For both types of signals the proposed SANC filter shows a very good ability to decompose the signal.

Surface roughness prediction based on the correlation between surface roughness and cutting vibrations in dry turning with TiN-coated carbide tools

This paper presents an approach to improving surface roughness prediction that is based on analysing cutting tool vibrations by a novel signal processing technique known as singular spectrum analysis (SSA). Each eigenvalue of the SSA decomposition of each vibration (radial, tangential, and feed vibration) and its corresponding principal component are studied and analysed to select only those eigenvalues of each vibration signal decomposition that contain valuable information for the development of a surface roughness prediction system (SRPS). Also, the influence of tool geometry and tool flank wear on the SSA decomposition of the vibrations is studied. Finally, only the information most correlated with surface quality, extracted by means of SSA of each vibration, is used to develop an SRPS. Experimental results provide conclusive support for the proposed SRPS, and justify the use of the SSA technique in the design of these systems. The ability of the SSA technique to extract only the information correlated with surface roughness of each cutting vibration and the manner in which tool geometry and tool flank wear influence the final Ra constitute the main contributions of this work.

Rolling element bearing fault diagnosis using wavelet packets

A method is proposed for the analysis of vibration signals resulting from bearings with localized defects using the wavelet packet transform (WPT) as a systematic tool. A time–frequency decomposition of vibration signals is provided and the components carrying the important diagnostic information are selected for further processing. The proposed method is designed in such a way that it can exploit the underlying physical concepts of the modulation mechanism, present in the vibration response of faulty bearings. The flexibility of the WPT and the systematic parameter selection criteria, help in the minimization of interventions by the end user. The method is evaluated using simulated and experimental signals.

DECOMPOSITION OF GEAR VIBRATION SIGNALS BY THE GENERALISED S TRANSFORM

This paper describes the generalised S transform, a variant of the wavelet transform which allows calculation of the instantaneous phase of a signal, and its application to the decomposition of vibration signals from mechanical systems such as gearboxes for the early detection of failure. It is shown that the original S transform is in essence the same as existing forms of the wavelet transform, except that it may be implemented in a way which allows the easy recovery of information about the phase of components of a signal. However, the definition of the original S transform places unnecessary restrictions on the form of the window function used. A new distribution, called the generalised S transform, is proposed which avoids these restrictions. A procedure is described for the automatic identification of the amplitude, phase, frequency, time and shape of components within a signal and for their step-by-step removal. Provided the components are sufficiently well separated so that significant interference does not occur in time or in frequency, perfect or near-perfect decomposition can be achieved. By considering the energy of the component, the window parameters can be optimised for each component to obtain the best match. Monitoring the total energy of the removed components and the remaining signal helps ensure the integrity of the decomposition. Several window functions are considered, including two forms of exponential functions, amplitude modulation by a cosine function and phase modulation by a cosine function. Decomposition using the generalised S transform and the new window functions is demonstrated using a numerically generated test signal and experimentally measured gear vibration data.