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In order to explore the effect of accounting computer information processing technology on enterprise internal control, this paper puts forward the method of simultaneous equation of panel data. This paper constructs an accounting computer index to measure the accounting computer level of manufacturing enterprises in Shanghai stock market, and studies the relationship between enterprise accounting computer and internal control by using panel data simultaneous equations model. The empirical results show that: first, although the average accounting computer level of manufacturing industry increases year by year, the overall level is low, and there are great differences among sub industries, even within the same industry; Second, the level of accounting computer has a negative impact on the internal control of enterprises, and the internal control of enterprises has a positive impact on the level of accounting computer; Third, enterprise scale and operating years have a significant positive impact on the level of accounting computer. The experimental results show that: from the estimation results of accounting computer equation, firstly, ROA has a significant positive impact on the level of accounting computer; Secondly, the scale and operating age of enterprises positively affect the level of accounting computer, while the nature of ownership and listing age are not related to the level of accounting computer; Finally, document disclosure has a high level of accounting computer, which is consistent with the conclusion of the above descriptive statistics of EMI, which may be related to the mandatory provisions of national laws and regulations on the industry of document disclosure.

The vibration waves induced by marine power and gearing systems travel to the hull structure through various supporting bases, which is one of the important factors that cause ship vibration and underwater sound radiation and also directly affects the acoustic performance of ships and ocean acoustic environment. First, a comprehensive review on the theoretical research and engineering application of the vibration control technologies for marine power and gearing systems is presented, and the state of the art of the vibration control technologies is introduced from the perspectives of dynamic optimal design, vibration source control, passive and active control. Next, the technologies that need to be developed, such as the low-frequency active/passive vibration isolation for power flow control, adaptive system and broadband vibration control, metamaterial/metastructure-based vibration and noise control, and intelligent vibration sensing and control for sound radiation, are proposed. The challenges of vibration control technologies for marine power and gearing systems are analyzed subsequently, and countermeasures are further proposed. To promote the vibration control technologies for marine power and gearing systems in China, research on the vibration control theories need to be strengthened and the research and application of key technologies needs to be accelerated. Furthermore, the development of various active vibration control devices and the related technical standards system should be encouraged and established. National research platforms and talent training bases should be consolidated and the integration of industry, research institutes, universities and customers should be promoted.

Dynamic mode decomposition (DMD) is widely used in extracting the main features of high-dimensional nonlinear systems in computational fluid dynamics. However, it cannot be directly applied to real compressors due to the limitations of data acquisition over the entire flow field. In order to overcome this limitation, our paper proposes a time-delay-locked DMD algorithm. It combines the idea of phase locking, Hankel time-delay method, and DMD method. Based on it, the flow patterns are successfully extracted and the unsteady flow phenomena including rotating stall and rotating instability are explained as modal waves. Multi-peak spectrum nearly 1/2 BPF observed under off-design RI conditions is explained. In addition, multi-scale of rotating stall is analyzed and shock wave is captured. [ABSTRACT FROM AUTHOR]

Bolted joints are widely used in industrial applications and joint failure can cause a disastrous accident if loosening happens. Bolt loosening detection can be made by regular manual inspection or by using sensors based on different physical principles, such as acoustoelastic effect, piezoelectric active sensing, and electromechanical impedance. Compared with the above methods, vibration based bolt looseness monitoring using accelerometers is appealing for its economy and convenience for measurement. In this paper, cargo bolts looseness monitoring under random excitation is studied based on vibration transmissibility, which overcomes the drawback of commonly used vibration methods in finding local bolt looseness. Vibration transmissibility analysis only uses two vibration transducers to monitor bolt group looseness, where the vibration signal below the cargo bolts is used as the "input" and the other one above the cargo bolts is used as the "output." There are 12 bolts in the cargo bolts studied in this paper, providing an essential clamping force to fix cargo during transportation. Six kinds of bolt group looseness with an increasing degree are simulated in the experiment. The experimental analysis shows that variation of the spectral moment can be used to monitor the global variation of the torque wrench exerted on the cargo bolts. The early stage of the bolt group looseness is that some one or two bolts begin to loose; however, the spectrum moment factor is insensitive to the local bolt looseness in the bolt group. To address this issue, the eigensystem realization algorithm (ERA) based on random input and output is utilized to find the subtle eigenvalue variation of the system matrix, which is neglected by the frequency transmissibility function. The experimental results show the effectiveness of the proposed method for detecting local bolt looseness. [ABSTRACT FROM AUTHOR]

A novel bearing fault diagnosis method based on improved locality-constrained linear coding (LLC) and adaptive PSO-optimized support vector machine (SVM) is proposed. In traditional LLC, each feature is encoded by using a fixed number of bases without considering the distribution of the features and the weight of the bases. To address these problems, an improved LLC algorithm based on adaptive and weighted bases is proposed. Firstly, preliminary features are obtained by wavelet packet node energy. Then, dictionary learning with class-wise K-SVD algorithm is implemented. Subsequently, based on the learned dictionary the LLC codes can be solved using the improved LLC algorithm. Finally, SVM optimized by adaptive particle swarm optimization (PSO) is utilized to classify the discriminative LLC codes and thus bearing fault diagnosis is realized. In the dictionary leaning stage, other methods such as selecting the samples themselves as dictionary and K-means are also conducted for comparison. The experiment results show that the LLC codes can effectively extract the bearing fault characteristics and the improved LLC outperforms traditional LLC. The dictionary learned by class-wise K-SVD achieves the best performance. Additionally, adaptive PSO-optimized SVM can greatly enhance the classification accuracy comparing with SVM using default parameters and linear SVM. [ABSTRACT FROM AUTHOR]

It is important to diagnose the bearing fault to prevent the serious accident of equipment. This paper introduces a bearing fault identification scheme based on envelope power spectrum analysis, linear discriminate analysis and continuous hidden Markov model. First the envelope power spectrum features are extracted from amplitude demodulated vibration signals from fault bearings. Then, linear discriminate analysis is employed to reduce the feature dimensions, which are helpful for improving the computing speed and diagnosing accuracy. At last, the new linear discriminate analysis features are input into continuous hidden Markov model to train the models under different conditions, respectively. In bearing fault identification, test data are input into the pretrained continuous hidden Markov models, and the bearing state can be detected by the output of continuous hidden Markov model. To validate the effectiveness of the proposed method, experimental samples of four bearing conditions at different fault sizes and loads are utilized to test the continuous hidden Markov model and back-propagation neural network. The result shows that continuous hidden Markov model and linear discriminate analysis-based method have higher accuracy and efficiency than back-propagation neural network. [ABSTRACT FROM AUTHOR]

The rolling bearing’s early stage fault feature is very weak for reasons of the signal attenuation phenomenon between the fault source and the sensor collecting the fault signal and the interference of environment noise such as the rotor rotating frequency and its harmonics and so on. The feature extraction of rolling bearing’s early weak fault is not only very important but also very hard. The minimum entropy de-convolution and Fast Kurtogram algorithm are combined in the paper for rolling bearing’s early stage weak fault feature extraction. The effect of transmission path is de-convolved effectively, and the impulses are clarified using minimum entropy de-convolution technique firstly. Then the obtained signal by minimum entropy de-convolution is handled by the Fast Kurtogram algorithm and an optimal filter is established. At last the envelope de-modulation is applied on the filtered signal and better feature extraction result is obtained compared with the other methods such as wavelet transform, frequency slice wavelet transformation and ensemble empirical mode decomposition. The effectiveness and advantages of the proposed method are verified through simulation signal and experiment. [ABSTRACT FROM AUTHOR]

Rolling element bearings are vital components in rotating machines, and it is important to diagnose bearing faults to avoid serious accidents in equipment. In this paper, singular spectrum analysis (SSA) is utilized to extract the bearing fault features. SSA is a non-parametric technique of time series analysis which decomposes the acquired vibration signals into an additive set of time series. Based on the selected singular features from SSA, a continuous hidden Markov model (CHMM) is introduced to diagnose the bearing fault. The detailed description and identification results of applying the proposed method to rolling element bearing fault diagnosis are shown in experiment 1. In experiment 2, a rolling element bearing accelerated life test is performed to simulate the performance variation of the bearing. The result demonstrates that the singular features and CHMM can reflect the performance degradation of the bearing from health to failure. A conclusion can be made that SSA and CHMM are feasible and effective in bearing fault diagnosis and performance assessment. [ABSTRACT FROM PUBLISHER]

Although various methods have been presented in the literature for rotating machine fault detection, it still remains a huge challenge to accurately extract features from non-stationary vibration signals with a high noise level, typically in the case of rolling element bearings faults diagnosis. Due to its random and non-stationary nature, fault related features are difficult to extract by common techniques and are usually overwhelmed by noise and macro-structural vibrations. In this paper, a new time series decomposition method based on a dynamic linear model is proposed, which provides time domain decomposition of a non-stationary signal into collections of latent components. The signal of a damaged bearing consists of exponentially decaying ringing that occurs periodically at the bearing characteristic defect frequency. Dynamic linear models with time-varying cyclical components provide a more generalized and adaptive description on rolling element bearings’ vibration signal with time-varying cyclical behavior. This allows the precise isolation of latent, quasi-cyclical bearing fault components via inferences on the time-varying parameters which characterize these components. An accelerated whole lifetime test of bearing has been performed to collect vibration data, which is utilized to validate the effectiveness of the proposed method. [ABSTRACT FROM PUBLISHER]

The bearing is the key component in a rotating machine. It is important to assess the performance degradation of bearings for realizing proactive maintenance and near-zero downtime. In this paper, a methodology based on the zero crossing characteristic features and a coupled hidden Markov model is introduced for estimating bearing performance degradation. Zero crossing features are time domain representations of the vibration signature in the spectrum domain. They discover the change of bearing performance. When zero crossing features are extracted, a coupled hidden Markov model is employed to assess the performance degradation quantitatively. Results from a bearing accelerated life experiment validate the feasibility and effectiveness of the proposed method. [ABSTRACT FROM PUBLISHER]

The rolling bearing fault signal under strong background noise is very weak because of environmental noise impaction and the attenuation of signal. The feature extraction of rolling bearings’ weak fault is very important in avoiding serious disaster, but it is also very difficult. Sparse decomposition has been used in the fault feature extraction of rolling bearings. But its performance is very poor when the background noise is very strong. This text combines the minimum entropy de-convolution (MED) and sparse decomposition to extract the feature of a rolling bearing’s weak fault. Firstly, the rolling bearing weak fault signal with strong background noise is de-noised using the MED method, and subsequently the de-noised signal is handled by sparse decomposition. Finally, the fault feature extraction method-envelope demodulation is carried on the last given signal and better results are obtained. In conclusion, through simulation and experiment the effectiveness and the feasibility of the proposed method are verified. [ABSTRACT FROM PUBLISHER]

In condition monitoring of mechanical equipment, the collected signals are mostly mixtures of several different sources. It is very important to recover the individual effects of each source for exact feature extraction and further fault diagnosis. A blind separation method based on second-order cyclic statistics is presented for convolved cyclostationary processes such as those observed in rotating machinery. The convolutive mixtures in time domain are transformed into instantaneous mixtures in frequency domain by discrete Fourier transform (DFT). Then the aim is to find a separating matrix that simultaneously and jointly diagonalizes the set of cyclic spectral density (CSD) matrices of the observed signals. Examples of successful separation are provided on both synthetic convolutive mixed bearing signals and real data tested from a gearbox. [ABSTRACT FROM PUBLISHER]

Fast Fourier transform-based near-field acoustic holography requires that the acoustic field should be sampled with a uniform spacing which is less than a half wave length of the highest frequency of interest to avoid spatial aliasing. Therefore, a large number of microphones are demanded for high frequencies. To overcome this difficulty and enhance the reconstruction accuracy, a data interpolation method based on the wave superposition algorithm (WSA) is proposed. At first, WSA is employed to regenerate the acoustic pressures between the measurement points. Then, the accuracy of the regenerated data is improved through an iterative process. In the end, taking the interpolated pressures as input data, the acoustic field can be reconstructed precisely with relatively few measurements. To solve the inherent ill-posed problem, Tikhonov regularization method together with generalized cross validation parameter choice method is utilized in the data interpolation method. Numerical simulation and experimental results demonstrate that this method can enhance the reconstruction accuracy and efficiency. [ABSTRACT FROM AUTHOR]

In vibration analysis, weak fault detect and diagnosis is of great importance. A method based on minimum entropy deconvolution and envelop spectrum analysis is proposed in this article. Minimum entropy deconvolution technique searches for an optimal set of filter coefficients to enhance the impulse making the filtered signal to contain clearer fault information. When there is a fault in bearing, it can be obviously reflected by the power spectral density estimation of the filtered signal even if the power spectral density estimation of original signal denies the information. The filtered signals are then analyzed by performing envelop spectrum analysis, where the bearing characteristic frequencies are quite clear for further diagnosis. The feasibility and validity of utilizing the minimum entropy deconvolution in weak fault diagnosis and condition monitoring is demonstrated by both simulation and experiments. The analysis of actual data from accelerated life test of rolling bearing shows that it can detect and diagnose fault 7 min earlier than RMS can reflect while the entire life takes 1962 min. This is considered to be significant in condition monitoring and early incipient fault diagnosis. The results of gearbox test also demonstrates the effective bearing fault diagnosis under strong noise and gear vibration interactions. [ABSTRACT FROM PUBLISHER]

In vibration analysis, fault feature extraction from strong background noises is of great importance. Frequency band entropy based on short-time Fourier transform illustrates the complexity of every frequency component in the frequency domain, and it can be used to detect the periodical components hidden in the signal. This article shows how the frequency band entropy offers a robust way in detecting faults even when the signal is under strong masking noises. Furthermore, frequency band entropy provides a way of blindly designing optimal band-pass filters. The filtering signal combined with envelope analysis is helpful in fault diagnosis. The effectiveness of the proposed method is demonstrated on both simulated and actual data from rolling bearings. [ABSTRACT FROM AUTHOR]

Abstract: Rolling element bearings are frequently used in rotary machinery, but they are also fragile mechanical parts. Hence, exact condition monitoring and fault diagnosis for them plays an important role in ensuring machinery''s reliable running. Timely diagnosis of early bearing faults is desirable, but the early fault signatures are easily submerged in noise. In this paper, Wigner–Ville spectrum based on cyclic spectral density (CSWVS for a brief notation) is studied, which is able to represent the cyclostationary signals while reducing the masking effect of additive stationary noise. Both simulations and experiments show that CSWVS is a noise resistant time frequency analysis technique for extracting bearing fault patterns, when bearing signals are under influences of random noise and gear vibrations. The 3-D feature of the CSWVS is proved useful in extracting bearing fault pattern from gearbox vibration signals, where bearing signals are affected by gear meshing vibration and noise. Besides, CSWVS utilizes the second order cyclostationary property of the vibration signals produced by bearing distributed fault, and clearly extracts its fault features, which cannot be extracted by envelope analysis. To quantitatively describe the extent of bearing fault, Renyi information encoded in the time frequency diagram of CSWVS is studied. It is shown to be a more sensitive index to reflect bearing performance degradation, compared with the spectral entropy (SE), squared envelope spectrum entropy (SESE) and Renyi informations for WVD, PWVD, especially when SNR is low. [Copyright &y& Elsevier]

An energy analyzing method called the Impact Energy Model (IEM) is introduced to assess and diagnose the rub–impact fault of a rotor system. The main purpose of the model is to indicate the probability of rub–impact or the severity level of the fault. Utilizing the nonlinear predictive energy, an effective quantitative evaluation index IEM is obtained from the model. Some work has been done elsewhere to partly demonstrate the IEM through experiments. In this paper we mainly discuss the variation of the IEM index with the speed ratio Ω at different levels of damping ratio ξ. The stagnation point is discussed in detail which is important to the assessment for the probability or severity of a rub–impact fault. The influence of dynamic stiffness is discussed which may affect the assessment result of the IEM. Furthermore, a sensitive area of the rotor operating system is presented. The result shows that this sensitive area provides a reference for the operation and design of a rotor system. [ABSTRACT FROM PUBLISHER]

Various techniques have been presented to detect rolling element bearing faults, however, it is still a huge challenge to accurately extract features under high noise level. In this paper, a new approach based on matching pursuit for analyzing the signals of rolling element bearings is proposed. Different from most of the matching pursuit related works focusing on the time-frequency plane, complexity spectrum and complexity spectrum entropy are proposed in this research to accurately extract the fault feature of a rolling element bearing signal under a low signal to noise ratio. Both simulation and experiment show that complexity spectrum works better than envelope spectrum in distinguishing characteristic frequencies of fault bearings submerged in noise. An accelerated whole lifetime test of bearing has been performed to collect vibration data, which is analyzed by complexity spectrum entropy and other normal approaches. Results show that complexity spectrum entropy has some unique characteristics. [ABSTRACT FROM PUBLISHER]

Hidden Markov model (HMM) has been widely applied in bearing performance degradation assessment. As a machine learning-based model, its accuracy, subsequently, is dependent on the sensitivity of the features used to estimate the degradation performance of bearings. It׳s a big challenge to extract effective features which are not influenced by other qualities or attributes uncorrelated with the bearing degradation condition. In this paper, a bearing performance degradation assessment method based on HMM and nuisance attribute projection (NAP) is proposed. NAP can filter out the effect of nuisance attributes in feature space through projection. The new feature space projected by NAP is more sensitive to bearing health changes and barely influenced by other interferences occurring in operation condition. To verify the effectiveness of the proposed method, two different experimental databases are utilized. The results show that the combination of HMM and NAP can effectively improve the accuracy and robustness of the bearing performance degradation assessment system. [ABSTRACT FROM AUTHOR]

Many existing signal processing methods usually select a predefined basis function in advance. This basis functions selection relies on a priori knowledge about the target signal, which is always infeasible in engineering applications. Dictionary learning method provides an ambitious direction to learn basis atoms from data itself with the objective of finding the underlying structure embedded in signal. As a special case of dictionary learning methods, shift-invariant dictionary learning (SIDL) reconstructs an input signal using basis atoms in all possible time shifts. The property of shift-invariance is very suitable to extract periodic impulses, which are typical symptom of mechanical fault signal. After learning basis atoms, a signal can be decomposed into a collection of latent components, each is reconstructed by one basis atom and its corresponding time-shifts. In this paper, SIDL method is introduced as an adaptive feature extraction technique. Then an effective approach based on SIDL and hidden Markov model (HMM) is addressed for machinery fault diagnosis. The SIDL-based feature extraction is applied to analyze both simulated and experiment signal with specific notch size. This experiment shows that SIDL can successfully extract double impulses in bearing signal. The second experiment presents an artificial fault experiment with different bearing fault type. Feature extraction based on SIDL method is performed on each signal, and then HMM is used to identify its fault type. This experiment results show that the proposed SIDL-HMM has a good performance in bearing fault diagnosis. [ABSTRACT FROM AUTHOR]

Due to the important role rolling element bearings play in rotating machines, condition monitoring and fault diagnosis system should be established to avoid abrupt breakage during operation. Various features from time, frequency and time–frequency domain are usually used for bearing or machinery condition monitoring. In this study, NCA-based feature extraction (FE) approach is proposed to reduce the dimensionality of original feature set and avoid the “curse of dimensionality”. Furthermore, coupled hidden Markov model (CHMM) based on multichannel data acquisition is applied to diagnose bearing or machinery fault. Two case studies are presented to validate the proposed approach both in bearing fault diagnosis and fault severity classification. The experiment results show that the proposed NCA-CHMM can remove redundant information, fuse data from different channels and improve the diagnosis results. [ABSTRACT FROM AUTHOR]

Based on the traditional theory of singular value decomposition (SVD), singular values (SVs) and ratios of neighboring singular values (NSVRs) are introduced to the feature extraction of vibration signals. The proposed feature extraction method is called SV–NSVR. Combined with selected SV–NSVR features, continuous hidden Markov model (CHMM) is used to realize the automatic classification. Then the SV–NSVR and CHMM based method is applied in fault diagnosis and performance assessment of rolling element bearings. The simulation and experimental results show that this method has a higher accuracy for the bearing fault diagnosis compared with those using other SVD features, and it is effective for the performance assessment of rolling element bearings. [ABSTRACT FROM AUTHOR]

Abstract: When early weak fault emerges in rolling bearing the fault feature is too weak to extract using the traditional fault diagnosis methods such as Fast Fourier Transform (FFT) and envelope demodulation. The tunable Q-factor wavelet transform (TQWT) is the improvement of traditional one single Q-factor wavelet transform, and it is very fit for separating the low Q-factor transient impact component from the high Q-factor sustained oscillation components when fault emerges in rolling bearing. However, it is hard to extract the rolling bearing’ early weak fault feature perfectly using the TQWT directly. Ensemble empirical mode decomposition (EEMD) is the improvement of empirical mode decomposition (EMD) which not only has the virtue of self-adaptability of EMD but also overcomes the mode mixing problem of EMD. The original signal of rolling bearing’ early weak fault is decomposed by EEMD and several intrinsic mode functions (IMFs) are obtained. Then the IMF with biggest kurtosis index value is selected and handled by the TQWT subsequently. At last, the envelope demodulation method is applied on the low Q-factor transient impact component and satisfactory extraction result is obtained. [Copyright &y& Elsevier]

Abstract: Weak machinery fault detection is a difficult task because of two main reasons (1) At the early stage of fault development, signature of fault related component performs incompletely and is quite different from that at the apparent failure stage. In most instances, it seems almost identical with the normal operating state. (2) The fault feature is always submerged and distorted by relatively strong background noise and macro-structural vibrations even if the fault component already performs completely, especially when the structure of fault components and interference are close. To solve these problems, we should penetrate into the underlying structure of the signal. Sparse representation provides a class of algorithms for finding succinct representations of signal that capture higher-level features in the data. With the purpose of extracting incomplete or seriously overwhelmed fault components, a sparse representation based latent components decomposition method is proposed in this paper. As a special case of sparse representation, shift-invariant sparse coding algorithm provides an effective basis functions learning scheme for capturing the underlying structure of machinery fault signal by iteratively solving two convex optimization problems: an L1-regularized least squares problem and an L2-constrained least squares problem. Among these basis functions, fault feature can be probably contained and extracted if optimal latent component is filtered. The proposed scheme is applied to analyze vibration signals of both rolling bearings and gears. Experiment of accelerated lifetime test of bearings validates the proposed method׳s ability of detecting early fault. Besides, experiments of fault bearings and gears with heavy noise and interference show the approach can effectively distinguish subtle differences between defect and interference. All the experimental data are analyzed by wavelet shrinkage and basis pursuit de-noising (BPDN) method for comparison. [Copyright &y& Elsevier]

Abstract: Rolling element bearing faults are among the main causes of rotating machines breakdown. It is important to distinguish the incipient fault before the bearings step into serious failure. Based on the traditional singular value decomposition (SVD) theory, short-time matrix series (STMS) and singular value ratio (SVR) are introduced to the vibration signal processing. The proposed signal processing method is called S-SVDR (STMS based SVD method using SVR) and it has been proved to have a good local identification capability in the rolling bearing fault diagnosis. The detailed description of applying S-SVDR methods to rolling bearing fault diagnosis is given through the artificial fault signal processing in experiment 1. In experiment 2, rolling element bearing accelerated life test is performed in Hangzhou Bearing Test & Research Center (HBRC). The experimental result shows that the incipient fault can be well detected through S-SVDR processing method. However, the envelope analysis of original signal cannot detect the fault due to the existence of signal interference. A conclusion can be made that the proposed S-SVDR method has a good effect on de-noising and eliminating the signal interference of rolling bearing for the fault diagnosis. [Copyright &y& Elsevier]

Abstract: An experimental setup which can simulate the rotor-to-stator rub in a rotor system is installed. A rub screw is used to simulate the condition of local rub–impact fault. Based on the theory of elastic collision and energy conservation, an Impact Energy Model (IEM) is proposed to evaluate the probability or severity of rub–impact fault. To prove this model, the paper conducts the experiment in two steps i.e. hammer test and rub–impact fault validation. The wave signal, spectrum and axis orbit are used to analyze the severity of the rub–impact fault when it occurs. The analysis result shows that the proposed Impact Energy Model (IEM) is effective in the assessment of rub–impact fault. Furthermore, the proposed IEM can also provide a reference for the design and operation of a rotor system. [Copyright &y& Elsevier]

Abstract: For machine fault diagnosis the signals from working machine are always numerous, even uncountable, but there contains only a little useful information. Hence how to find out the useful signal from numerous signals, including noises, that is, how to only extract the desired fault signal is very attractive. This paper shows that the constrained independent component analysis (cICA) can solely extract desired faulty signal using some prior mechanical information. The methods of creating reference of cICA for machine diagnostics are discussed, and the effectiveness of the method is successfully verified by simulations and experiments. [Copyright &y& Elsevier]

Abstract: In vibration analysis, weak fault feature extraction under strong background noise is of great importance. A method based on cyclic Wiener filter and envelope spectrum analysis is proposed. Cyclic Wiener filter exploits the spectral coherence theory induced by the second-order cyclostationary signal. The original signal is duplicated and shifted in the frequency domain by amounts corresponding to the cyclic frequencies. The noise component is optimally filtered by a filter-bank. The filtered signal is analyzed by performing envelope spectrum. In the envelope spectrum, characteristic frequencies are quite clear. Then the most impactive part is effectively extracted for further fault diagnosis. The effectiveness of the method is demonstrated on both simulated signal and actual data from rolling bearing accelerated life test. [Copyright &y& Elsevier]

Abstract: A new model for vibration analysis of a crankshaft with a slant crack in crankpin is proposed, and the influence of crack depth on the transient response of a cracked crankshaft is investigated. A slant cracked shaft element is developed by deducing the local flexibility due to a slant crack. The frequently occurred slant crack in crankpin is studied, and a new finite element model of crankshaft including the slant crack in crankpin, which combines the slant cracked shaft element and Timoshenko beam elements, is derived. The support of engine block and the switching behaviour of the crack are considered, and the non-linear equation of motion for cracked crankshaft-bearing system is set up in a rotating coordinate system. The motion of a crankshaft of a four in-line cylinder engine with and without an initial crack is simulated. The influence of the crack depth on the transient response is investigated. The numerical simulation demonstrates that the current model is valid for simulating the motion of cracked crankshaft system. The results show that a useful foundation is laid for crack detection of crankshaft. [Copyright &y& Elsevier]