Second-order Cyclostationarity Research Articles

Fault Feature Extraction of Rolling Bearing Based on Maximum Second-Order Cyclostationarity Blind Deconvolution and CEEMD-Teager

The operation of rolling bearings is directly related to the reliability of the whole rotating machinery. If the rolling bearing failure cannot be diagnosed and repaired in time, it will probably lead to equipment shutdown. The feature extraction process is an important part of bearing fault diagnosis. Some existing feature extraction methods are sensitive to noise and interference, and cannot fully explore and characterize useful information in nonlinear and non-stationary signals in complex scenes, and sometimes even lose important information contained in the data, resulting in low diagnostic accuracy. Therefore, a new method combining Maximum Second-order Cyclostationarity Blind Deconvolution (CYCBD) and Complementary Ensemble Empirical Mode Decomposition (CEEMD) was proposed for feature extraction of rolling bearing faults. Firstly, the cyclic frequency is set according to the failure frequency, and the original signal is filtered by CYCBD. Then, the filtered signal is decomposed into multiple Intrinsic Mode Function (IMF) by CEEMD, and the effective IMF components are selected by kurtosis criterion for reconstruction. Finally, the Teager energy operator is used to enhance the transient impact of the reconstructed signals. The results of simulation and comparison experiments show that the proposed method can extract bearing characteristics in different fault states more effectively, and the accuracy of network model diagnosis is improved compared with the traditional methods.

Fast computation of the spectral correlation via frequency-averaging

Spectral Correlation (SC) has become a critical analytical instrument for assessing the second-order cyclostationarity (CS) in signals, especially for mechanical vibration signals. Nonetheless, the computational demand for computing SC is considerable. Built upon the Smoothed Cyclic Periodogram (SCP), a consistent estimator named Fast Smoothed Cyclic Periodogram (FastSCP) is proposed to address this issue. FastSCP represents the computational process of SCP through the convolution of spectral components and incorporates the Overlap-Save algorithm for accelerating. Comparison results with other fast SC estimators, including Fast Spectral Correlation (FastSC) and Faster Spectral Correlation (FasterSC), indicate that the proposed method is competitive in terms of both computational cost and memory usage, particularly when dealing with wide cyclic frequency ranges.

Cyclostationarity blind deconvolution via eigenvector screening and its applications to the condition monitoring of rotating machinery

Maximum second-order cyclostationarity blind deconvolution (CYCBD) is accomplished by maximizing the second-order cyclostationarity of signals through the indicator of second-order cyclostationarity (ICS2). It is a significant method for extracting weak periodic pulses related to bearing faults. However, since the interference spectral lines generated by the interference signals in squared envelope spectrum do not be considered by ICS2, the method can be invalid for certain applications. In addition, when the detected signal contains compound faults, only one fault can be enhanced, and misdiagnosis is easily caused. This article proposes cyclostationarity blind deconvolution via eigenvector screening, abbreviated as ESCYCBD, for fault feature enhancement and extraction of rotating machinery in order to solve these problems. Different from existing deconvolution methods which use the maximum value of the index as a criterion to select the filter coefficients, this method proposes the concept of eigenvector subspace, which contains the optimal eigenvector as filter coefficients that are ignored by CYCBD. Subsequently, the Fourier coefficients related to a series of cyclic frequencies are extended for compound fault signals. Besides, by using the harmonic characteristics of fault features in the envelope spectrum, fault feature recognition and optimal selection are carried out in eigenvector subspaces. At the same time, the concept of signals set is created, by which means different feature modes of different fault can be extracted at one time. The analysis results of simulation signals and experimental data show that the proposed ESCYCBD has robustness and accuracy in extracting fault feature modes, whether it is single fault diagnosis or compound fault diagnosis.

Generalized Shannon entropy sparse wavelet packet transform for fault detection of traction motor bearings in high-speed trains

An effective structural health monitoring method of traction motor bearings is a powerful guarantee for the safety operation of high-speed trains. However, it is exceptionally difficult to detect bearing fault characteristics from the vibration signals of traction motor bearings operating at high rotational speeds. In this scenario, a generalized Shannon entropy sparse wavelet packet transform (GSWPT) for fault detection of motor bearings is proposed in this paper. Firstly, a generalized Shannon entropy sparse regularization method is proposed to obtain sparse wavelet reconstruction coefficients by extending the definition of the Shannon information entropy, and the non-convex sparse regularization function is minimized by synergistic swarm optimization algorithm. Then, the wavelet node coefficients are weighted according to the second-order cyclostationarity index of the wavelet packet node to further enhance the sparsity of the reconstructed signal. Moreover, the optimal decomposition level of GSWPT is adaptively selected by the maximum sparsity and cyclostationarity criterion. Particularly, in order to verify the bearing fault detection performance of GSWPT in practical engineering, a bearing fault dynamic model of traction motor in high-speed train was established based on Hertz contact theory and the fourth-order Runge-Kutta method to obtain simulated data under strong Gaussian white noise, and a corresponding test platform was constructed to collect experimental data under different operating conditions. Finally, the applications on the simulated and experimental signals of traction motor bearings in high-speed trains demonstrate that GSWPT significantly outperforms the conventional wavelet packet transform, dual-tree complex wavelet packet transform, blind deconvolution, modal decomposition, and Infogram methods to some extent for fault detection.

The Prediction of the Remaining Useful Life of Rotating Machinery Based on an Adaptive Maximum Second-Order Cyclostationarity Blind Deconvolution and a Convolutional LSTM Autoencoder.

The prediction of the remaining useful life (RUL) is important for the conditions of rotating machinery to maintain reliability and decrease losses. This study proposes an efficient approach based on an adaptive maximum second-order cyclostationarity blind deconvolution (ACYCBD) and a convolutional LSTM autoencoder to achieve the feature extraction, health index analysis, and RUL prediction for rotating machinery. First, the ACYCBD is used to filter noise from the vibration signals. Second, based on the peak value properties, a novel health index (HI) is designed to analyze the health conditions for the denoising signal, showing a high sensitivity for the degradation of bearings. Finally, for better prognostics and health management of the rotating machinery, based on convolutional layers and LSTM, an autoencoder can achieve a transform convolutional LSTM network to develop a convolutional LSTM autoencoder (ALSTM) model that can be applied to forecast the health trend for rotating machinery. Compared with the SVM, CNN, LSTM, GRU, and DTGRU methods, our experiments demonstrate that the proposed approach has the greatest performance for the prediction of the remaining useful life of rotating machinery.

Fault Diagnosis of Rotating Machinery Using an Optimal Blind Deconvolution Method and Hybrid Invertible Neural Network.

This paper proposes a novel approach to predicting the useful life of rotating machinery and making fault diagnoses using an optimal blind deconvolution and hybrid invertible neural network. First, a new optimal adaptive maximum second-order cyclostationarity blind deconvolution (OACYCBD) is developed for denoising vibration signals obtained from rotating machinery. This technique is obtained from the optimization of traditional adaptive maximum second-order cyclostationarity blind deconvolution (ACYCBD). To optimize the weights of conventional ACYCBD, the proposed method utilizes a probability density function (PDF) of Monte Carlo to assess fault-related incipient changes in the vibration signal. Cross-entropy is used as a convergence criterion for denoising. Because the denoised signal carries information related to the health of the rotating machinery, a novel health index is calculated in the second step using the peak value and square of the arithmetic mean of the signal. The novel health index can change according to the degradation of the health state of the rotating bearing. To predict the remaining useful life of the bearing in the final step, the health index is used as input for a newly developed hybrid invertible neural network (HINN), which combines an invertible neural network and long short-term memory (LSTM) to forecast trends in bearing degradation. The proposed approach outperforms SVM, CNN, and LSTM methods in predicting the remaining useful life of bearings, showcasing RMSE values of 0.799, 0.593, 0.53, and 0.485, respectively, when applied to a real-world industrial bearing dataset.

Compound Defects Feature Extraction Method of Rotate Vector Reducers Based on Optimized Maximum Second-Order Cyclostationarity Blind Deconvolution

Compound Defects Feature Extraction Method of Rotate Vector Reducers Based on Optimized Maximum Second-Order Cyclostationarity Blind Deconvolution

A maximum cyclic impulses energy ratio deconvolution fused with enhanced envelope derivative operator frequency spectrum for early fault diagnosis of rolling bearing

Blind deconvolution is a powerful tool for rolling bearing fault diagnosis. As one of deconvolution methods, maximum second-order cyclostationarity blind deconvolution (CYCBD) is proved to be effective in extracting bearing fault characteristics. However, the performance of CYCBD method is greatly compromised by setting of fault characteristic frequency (FCF) in advance. Moreover, its performance decreases dramatically under the interference of random shocks and strong noise. To address these issues, a new deconvolution method, named as maximum cyclic impulses energy ratio deconvolution (MCIERD) fused with enhanced envelope derivative operator frequency spectrum (EEDOFS) is proposed in this research. In this method, the EEDOFS is proposed to estimate the FCF. Furthermore, the cyclic impulses energy ratio (CIER) is employed as the deconvolution indicator. Moreover, the hybrid firefly and particle swarm optimization algorithm is used to solve the optimal filter coefficients by maximizing the CIER. Simulation results show that EEDOFS exhibits a greater robustness in estimating FCF accurately under strong interferences and MCIERD performs well in extracting fault cyclic impulses under the interference of heavy noise and random shocks. Finally, three run-to-failure bearing datasets are employed for experimental validation, whose results demonstrate the effectiveness of EEDOFS in accurate estimating FCF and identifying the early bearing fault. Meanwhile, MCIERD fused with EEDOFS is proved to have greater advantages in extracting early bearing fault feature.

A ship-radiated noise classification method based on domain knowledge embedding and attention mechanism

Ship classification based on machine learning (ML) has proven to be a significant underwater acoustic research direction. One of the critical challenges rests with how to embed domain signal knowledge into ML models to obtain suitable features that highly correlate with the classification and create better predictors. In this paper, a novel ML-based ship classification model, Hierarchical Underwater Acoustic Transformer (HUAT), is proposed to improve the classification performance. Firstly, the Detection of Envelope Modulation on Noise (DEMON) spectra of ship-radiated noise signals are estimated by cyclostationary analysis. The motivation for using a DEMON-based preprocessing scheme is that valuable propeller information can be revealed by exploiting the second-order cyclostationarity of ship-radiated noise signals. Secondly, the useful features of DEMON spectra are enhanced using a multi-head self-attention module, and the potential features of the Mel spectrograms are extracted employing a Convolutional Neural Network (CNN) module. The two kinds of features are fused to provide ship classification patterns. The challenge of feature learning in the deep classification model is reduced by leveraging domain-related classification knowledge. Finally, the Swin Transformer, based on shifted window self-attention mechanism, is used to learn high-level feature representations and conduct ship classification. Experimental results show that the HUAT model achieves excellent classification performance on ship-radiated noise datasets, ShipsEar and DeepShip. And its classification efficiency is better than the model based on traditional Transformer architecture. In addition, the proposed method provides technical support for the underwater intelligent system capable of automatically sensing sailing vessels and recognizing vessel types.

Identification of the error excitation in gear systems: A mediator algorithm between simulation and experiment

There are three primary sources of internal excitation in gear systems: time-varying mesh stiffness, meshing damping and tooth error. Compared with the first two excitations, it is more difficult to evaluate or measure the error excitation. The error excitation is usually assumed empirically or even neglected in the majority of dynamic models. To address this issue, we desired to propose a mediator algorithm between the simulation and the experiment to identify the error excitation of gear systems. Based on the second-order cyclostationarity of gear signals, a signal processing procedure is proposed to acquire the mediator signal with a high signal-to-noise ratio. The mediator signal is served as the “media” between the simulation and experiment. The error parameter is updated repeatedly in the iterative optimization until the optimal matching is found between the simulation and the experiment. Simultaneously, the pre-decomposition technique is proposed to accelerate the solution of the simulation dynamic model. The performance of the proposed identification algorithm is verified both numerically and experimentally. Moreover, noise immunity analysis shows that the proposed identification algorithm is robust to noise, damping and phase differences between the experiment and simulation. The proposed identification algorithm provides an indirect approach to estimating the error excitation when the direct measurement is unavailable.

Period-refined CYCBD using time synchronous averaging for the feature extraction of bearing fault under heavy noise

Deconvolution methods have been widely used in machinery fault diagnosis. However, their application would be confined due to the heavy noise and complex interference since the fault feature in the measured signal becomes rather weak. Time synchronous averaging (TSA) can enhance the periodic components and suppress the others by the comb filter function. And in the iteration process of the deconvolution methods, the filtered signal after each iteration can be further processed using TSA, and the time delay with maximum Gini index value is refined as the iterative period for the next iteration. Benefitting from these advantages, a period-refined maximum second-order cyclostationarity blind deconvolution (PRCYCBD) using TSA is proposed for the weak fault detection of rolling element bearings (REBs) in this paper. Firstly, without any prior knowledge, the proposed method which can estimate the period more accurately is more suitable for the weak fault detection of REBs, especially incipient fault. Secondly, TSA is firstly applied to estimate the iterative period rather than just depending on the Signal Noise Ratio (SNR) of the filtered signal in the iterative process . Furthermore, the new improvement frame can be expanded to other deconvolution methods using iterative algorithms, especially under heavy noise. Finally, a simulation with a slight bearing fault as well as two real experimental data including the vibration signal with the wind turbine bearing fault and the acoustical signal with the locomotive wheel bearing fault is used to verify the superiority of the proposed PRCYCBD compared with the traditional minimum entropy deconvolution and the traditional autocorrelation-improved cyclostationarity blind deconvolution.

Use of generalized Gaussian cyclostationarity for blind deconvolution and its application to bearing fault diagnosis under non-Gaussian conditions

Blind deconvolution (BD) methods can extract fault signatures from noisy observations. Among all the BD methods, maximum second-order cyclostationarity blind deconvolution (CYCBD) is an effective method for extracting weak periodic impulses related to bearing faults. CYCBD is done by maximizing the second-order cyclostationarity of a signal through the indicator of second-order cyclostationarity (ICS2, which can be calculated using the squared envelope spectrum, SES). The effectiveness of the SES is under the assumption that signals are Gaussian distributed. However, SES, ICS2 and CYCBD can be ineffective when signals are corrupted by highly leptokurtic noise.A recent study separates the detection of non-stationarity from non-Gaussianity by releasing the hypothesis of Gaussianity to generalized Gaussianity. The indicator IGGCS/GGS can detect the cyclostationarity of a signal corrupted by highly impulsive noise. In addition, it can be approximated by the β-power envelope spectrum (PES). Inspired by the relationship between IGGCS/GGS and ICS2, a blind deconvolution method by maximizing the generalized Gaussian cyclostationarity (CYCBDβ) is proposed in this study. Compared with CYCBD, the robustness of CYCBDβ against strong non-Gaussian noise increases. However, the original shape parameter estimation method has two limitations, which introduces difficulties in using IGGCS/GGS as the criterion for BD. (1) The fault period must be known in advance. (2) The fault period should be an integer for the synchronous average. In addition, a serious problem of CYCBD is that the used cyclic frequencies must match fault frequencies. Otherwise, the performance of CYCBD can be severely compromised. To better incorporate the shape parameter estimation into the BD method, the GGCS model using fault frequencies for synchronous average is proposed. In addition, a fault frequencies estimation approach has been added to the generation of the weighting matrix of CYCBDβ. Benefiting from these improvements, the proposed CYCBDβ has two advantages compared with CYCBD: (1) the robustness of CYCBDβ against strong non-Gaussian noise is higher, and (2) CYCBDβ can extract fault features when fault frequencies are unknown. The effectiveness and robustness of the method are validated using simulations and real vibration datasets.

Squared envelope sparsification via blind deconvolution and its application to railway axle bearing diagnostics

A sparse squared envelope is crucial for efficient and accurate diagnosis of bearing faults. Blind deconvolution (BD) is a well-established sparse feature enhancement method for the diagnostics of rolling bearings. Traditional BD methods, such as minimum entropy deconvolution (MED), are susceptible to random transients, making it difficult to enhance fault features of rolling bearings subject to strong random shocks. Deconvolution methods that take the fault characteristic frequency (or fault impulse period) of interest as an algorithm input parameter, such as maximum second-order cyclostationarity blind deconvolution (CYCBD), can alleviate this deficiency. However, bearing fault features are difficult to be enhanced by these methods when the specified characteristic frequency deviates from the actual value greatly. To overcome these problems, the modified smoothness index (MSI) of the squared envelope is proposed as the objective function of the deconvolution method, and a new BD method is developed to achieve a sparse squared envelope for fault diagnosis of rolling bearings. Furthermore, the methodology is extended to the frequency domain, and another new BD method that utilizes the MSI of the squared envelope spectrum as the objective function is established to achieve a sparse squared envelope spectrum for bearing diagnostics. These two proposed BD methods are robust to random transients and do not require characteristic frequency or impulse period as an input parameter for feature enhancement. The performance of the two proposed BD methods is verified on experimental datasets from two different railway axle bearing test rigs and compared with the state-of-the-art deconvolution methods. The results show that the two proposed methods can effectively enhance repetitive transient features in noisy vibration signals and accurately diagnose different faults of railway axle bearings.

CFFsBD: A Candidate Fault Frequencies-Based Blind Deconvolution for Rolling Element Bearings Fault Feature Enhancement

The repetitive transient impulses are typical symptoms of rolling bearing faults. The indicator of second-order cyclostationarity (ICS2)-based blind deconvolution (CYCBD) maximizing the cyclostationary behavior of the excitation source proves to be an effective method for enhancing the periodic cyclostationarity component caused by bearing defect. However, the deviation of the fault characteristic frequency—a common phenomenon easily caused by roller creep or measurement systems, etc., can lead to the collapse of this method in practical applications. Additionally, this method is prone to induce the generation of pseudo-cyclostationary components under the guidance of an inappropriate cyclic frequency. Thus, this work proposes a candidate fault frequencies (CFFs)-based blind deconvolution, abbreviated as CFFsBD, for fault feature enhancement of bearings. This idea comes from the concept of CFFs—a collection of frequencies most likely to be associated with bearing fault that can be identified by mining the local time–frequency features of the signal to be analyzed. A new indicator constructed by using CFFs to replace the cycle frequencies in ICS2 is utilized as the criterion to guide the solution of the deconvolution filter. This new indicator is a generalized version of ICS2, and thus, CYCBD can be considered a special case of the proposed CFFsBD. The performance of the CFFsBD is demonstrated by the analysis of simulated and experimental signals of faulty bearings. The results highlight the robustness and stability of the proposed CFFsBD in extracting fault-induced cyclostationary symptoms.

Research on Rolling-Element Bearing Composite Fault Diagnosis Methods Based on RLMD and SSA-CYCBD

Aiming at the problem that it is difficult to separate and extract the composite fault features of rolling-element bearings, a composite fault diagnosis method combining robust local mean decomposition (RLMD), sparrow search algorithm (SSA), maximum second-order cyclostationarity blind deconvolution (CYCBD), is proposed. First, the RLMD is used to decompose the product function of the signal, and the two indicators, the excess and the correlation coefficient are then used as evaluation criteria to select the appropriate components for reconstruction. The reconstructed signal is then inputted into the SSA-optimized CYCBD algorithm, by specifying the objective function parameter which separates the faults and obtains multiple single fault signals with optimal noise reduction. Finally, envelope demodulation analysis is used for the multiple single fault signals, to obtain the characteristic frequencies of the corresponding faults, so as to complete the fault separation and feature extraction of composite faults. In order to verify the effectiveness of the method, the initial signals and the actual signals generated by the computer shall be used. The algorithm is verified using the XJTU-SY rolling-element bearing dataset, which shows the good performance of the method.

Aerodynamic noise separation of helicopter main and tail rotors using a cascade filter with Vold-Kalman filter and cyclic Wiener filter

The noise issue has become an important factor limiting the massive application of helicopters as the requirements for helicopter reliability and comfort continue to increase. As the primary noise source of helicopters, the main and tail rotor aerodynamic noise should be separated for individual noise reduction investigations. According to the frequency domain characteristics, the rotor aerodynamic noise can be divided into tonal noise and broadband noise. A cascade filtering method with Vold-Kalman filter and cyclic Wiener filter is proposed in this paper to separate the tonal and modulated broadband noise of the main and tail rotors, respectively. The classical method only considers the periodic characteristics of the main and tail rotor aerodynamic noise for harmonic separation. The broadband noise is not considered within the separation of interest due to its randomness, which leads to the inaccuracy of the conventional method for the aerodynamic noise separation of the main and tail rotors. The broadband noise exhibits a second-order cyclostationarity due to the periodic modulation of the rotational motion of the rotor. According to its cyclostationarity, while considering the periodicity of tonal noise for harmonic separation, the proposed cascade filtering method also performs the separation of broadband noise. More precise separations of the main and tail rotor can be obtained by this method. The efficacy of the proposed cascade filtering method is demonstrated in both the simulation and wind tunnel noise measurement.

Weak fault detection of rolling element bearing combining robust EMD with adaptive maximum second-order cyclostationarity blind deconvolution

To solve the difficulty in weak fault detection of rolling element bearing (REB), a fusion method by combining robust empirical mode decomposition (REMD) with adaptive maximum second-order cyclostationarity blind deconvolution (AMCBD) is proposed in the paper. The advantage of REMD in determining the optimal iteration number of a sifting process and the advantage of AMCBD in setting the key parameter (targeted cyclic frequency or fault period) appropriately are utilized comprehensively by the proposed method. Firstly, in view of the multi-component and modulation characteristic of the vibration signal of REB, REMD is used to extract the useful component from the multi-component and modulated signal. Then, AMCBD is used to process the selected useful component to further highlight the cyclostationary and impulse characteristics of the vibration signal of faulty REB. Compared with traditional maximum second-order cyclostationarity blind deconvolution (MCBD) method, AMCBD has the advantage of no needing prior knowledge of the faulty REB such as the targeted cyclic frequency or fault period. At last, envelope spectral (ES) is applied on the signal handled by AMCBD and satisfactory fault extraction feature result is obtained. Effectiveness of the proposed method is verified through simulated, experimental, and engineering signals, and its superiority is also presented through comparison study.

Enhanced spectral coherence and its application to bearing fault diagnosis

Spectral coherence (SCoh) is one of the most dedicated methods for characterizing the second-order cyclostationarity of bearing faults. Under the assumption of second-order cyclostationarity, the SCoh of the bearing vibration signal is expected to have non-zero values along lines–determined by fault-related cyclic frequencies. However, the bearing vibration signal produced by a complex mechanical system always exhibits mixed cyclostationarity–a combination of several orders of cyclostationarity, rather than pure second-order cyclostationarity. The fault-unrelated cyclic frequencies exhibiting a series of non-zero elements on the bivariate plane are served as a tricky problem to interfere with the fault identification. To address this issue, an enhanced SCoh is proposed by combining two basic operators designed by using median filtering and autocorrelation, respectively. Analysis of simulated signals, vibration datasets obtained from artificial fault bearing experiments, and accelerated bearing degradation tests are conducted to verify and compare the performance of the improved SCoh.

Comparative study on the use of acoustic emission and vibration analyses for the bearing fault diagnosis of high-speed trains

A wheel set bearing is an important supporting component of a high-speed train. Its quality and performance directly determine the overall safety of the train. Therefore, monitoring a wheel set bearing’s conditions for an early fault diagnosis is vital to ensure the safe operation of high-speed trains. However, the collected signals are often contaminated by environmental noise, transmission path, and signal attenuation because of the complexity of high-speed train systems and poor operation conditions, making it difficult to extract the early fault features of the wheel set bearing accurately. Vibration monitoring is most widely used for bearing fault diagnosis, with the acoustic emission (AE) technology emerging as a powerful tool. This article reports a comparison between vibration and AE technology in terms of their applicability for diagnosing naturally degraded wheel set bearings. In addition, a novel fault diagnosis method based on the optimized maximum second-order cyclostationarity blind deconvolution (CYCBD) and chirp Z-transform (CZT) is proposed to diagnose early composite fault defects in a wheel set bearing. The optimization CYCBD is adopted to enhance the fault-induced impact response and eliminate the interference of environmental noise, transmission path, and signal attenuation. CZT is used to improve the frequency resolution and match the fault features accurately under a limited data length condition. Moreover, the efficiency of the proposed method is verified by the simulated bearing signal and the real datasets. The results show that the proposed method is effective in the detection of wheel set bearing faults compared with the minimum entropy deconvolution (MED) and maximum correlated kurtosis deconvolution (MCKD) methods. This research is also the first to compare the effectiveness of applying AE and vibration technologies to diagnose a naturally degraded high-speed train bearing, particularly close to actual line operation conditions.

Adaptive maximum second-order cyclostationarity blind deconvolution and its application for locomotive bearing fault diagnosis

Maximum second-order cyclostationarity blind deconvolution (CYCBD) outperforms other deconvolution methods in retrieving the weak periodic impulses related to bearing incipient faults. However, the main challenge in the practical application of CYCBD is how to set several key parameters appropriately, the uppermost of which is the targeted cyclic frequency or fault period. It may attribute to the fact that the advantage of CYCBD is greatly compromised by the provided period. To overcome the above limitations, an adaptive CYCBD (ACYCBD) is presented in this article. In the proposed method, a powerful tool, envelope harmonic product spectrum (EHPS), is tailored to estimate the true cyclic frequency or period precisely. Then, the estimated result instead of a coarsely provided value is regarded as the targeted cyclic frequency. Furthermore, in the presence of heavy energy harmonics or strong external noise, EHPS still has strong robustness in the fault period identification. Compared with the original CYCBD, ACYCBD can extract the weak impulses submerged in the raw vibration signal without any prior information about the period. Finally, the effectiveness and advantages of ACYCBD are revealed by employing it on the synthesized signals and experimental data collected from a locomotive bearing test rig.