Simulated Vibration Signals Research Articles

Research on Fault Feature Extraction of Rolling Bearing Based on Adaptive DFIF and CYCBD

Due to the fact that the fault features of rolling bearing is easily drowned out by strong noise, it is difficult to exploit the impact components which contain fault information, a combination method based on snake optimizer (SO), direct fast iterative filtering (DFIF) and maximum second order cyclostationary blind deconvolution (CYCBD) is proposed, namely, SO-DFIF-CYCBD, for rolling bearing fault features extraction. Firstly, the fault vibration signal is adaptively decomposed employing parameter optimized DFIF to acquire several intrinsic mode function (IMF) components. Secondly, the sensitive components are screened for reconstruction based on the kurtosis-correlation coefficient criterion. Finally, the reconstructed vibration signal is deconvoluted utilizing the CYCBD method to reinforce the fault shock components, and obvious frequency components are extracted using envelope demodulation to distinguish the fault type. The reliability of the proposed approach are verified through simulation vibration signals and actual rolling bearing signals. In addition, compared to other combination model techniques in this article, the proposed approach can extract richer fault feature information and has stronger fault feature extraction capabilities.

Vibration properties of full ceramic bearing under elastohydrodynamic fluid lubrication based on the energy approach

As an important part of rotating machinery, heat generation due to friction is inevitable during the rotating process of bearings. A large amount of heat causes the temperature of lubricating oil to increase, viscosity of lubricating oil to decrease, and lubricating performance decreases. Addressing the issue of inadequate lubrication between the rolling element and raceway due to increased lubricating oil temperature, this study focuses on full-ceramic ball bearings. It examines the impact of temperature fluctuations on the bearing's lubrication condition and investigates the vibration characteristics of the full-ceramic bearing. The variation in the viscosity of lubricating oil with respect to temperature is measured via a rotary viscometer. A dynamic model of the silicon nitride full-ceramic ball bearing, which considers the effects of elastohydrodynamic lubrication, is established. The impacts of changes in lubricating oil viscosity due to different temperatures on oil film pressure, oil film thickness, and bearing vibration are analyzed and then compared with experimental data. It is observed that under conditions of constant load and speed, the film thickness of the lubricating oil decreases as the viscosity decreases. Additionally, the second peak value of oil film pressure is reduced with the declining viscosity of the lubricating oil. As the viscosity decreases, the vibration of the bearing increases. The amplitude discrepancy between the simulated vibration signal and experimental vibration signal of the full-ceramic bearing is found to be less than 10 %, verifying the validity of the dynamic model.

Differential evolution-based time domain decomposition method for multi-impact vibration signals of reciprocating machinery

Abstract To solve the problem of extracting the impact component from the complex time-domain vibration signal of reciprocating machinery vibration signals, a differential evolution (DE)-based time domain decomposition method is proposed to achieve adaptive extraction of impact components. The method establishes new decomposition window containing three adjustment parameters to adapt to multiple forms of impact components. Furthermore, with the optimization objectives of minimizing reconstruction loss, amplitude moment loss, and similarity loss, a decomposition parameter optimization algorithm based on DE is established to achieve the optimization process of decomposition parameters. The results of processing simulated and actual vibration signals of diesel engines show that the new method can adaptively and accurately identify the impact component and impact time center in the vibration component, with a signal reconstruction loss of less than 2.5% and a decomposition time of only 54.1 s.

Fault signature extraction of rolling bearings under variable speed via time–frequency overlap group sparse representation

The high-fidelity extraction of the nonperiodic fault transients of rolling bearings under variable speed is of significant importance, while still a challenge for early-stage fault diagnosis of major equipment such as aero engines and wind turbines. A method termed continuous symmetric Laplace wavelet transform (CSLWT) enhanced time–frequency overlap group sparse (OGS) representation is proposed in this paper. A symmetric Laplace mother wavelet function is proposed which could satisfy the admissibility condition of wavelet decomposition and reconstruction, upon which the CSLWT is fabricated and adopted as the sparse representation dictionary to match the nonperiodic fault transients. Since the decomposition coefficients of nonperiodic fault impulses via the CSLWT exhibit group sparsity on the time–frequency plane with unknown intervals, the time–frequency OGS model is adopted and then solved for extraction of the nonperiodic fault transients. Moreover, a data driven, multi-objective optimization strategy is presented for determining the hyper-parameters of the sparse model. The correlated kurtosis of angular envelope signal (CK-AE) and harmonic-to-noise energy ratio of the order spectrum (HNR-OS) are introduced as the optimization objectives, and the Pareto front is solved based on the NSGA-II algorithm, upon which the acceptable solutions set is chosen from the Pareto front, and the proper hyper-parameters are further determined via proposed normalized solutions distance density sorting of the acceptable solutions set. The performance of the proposed method is validated via processing both simulated and experimental vibration signals, as well as compared with some tradition fault diagnosis methods.

Chatter and Surface Waviness Analysis in Oerlikon Face Hobbing of Spiral Bevel Gears

A vectorized analytical model for the cutting dynamics in the spiral bevel gear face hobbing process has been developed, which is based on machine tool kinematics and vibration vectorization. The structural modal parameters of the cutter head spindle system are obtained through experimental modal analysis with hammer impact testing. The analytical model is utilized to simulate the generation of simulated vibration acceleration signals during spiral bevel gear hobbing. A wavelet threshold denoising method is applied to process the simulated vibration signals of the spiral bevel gear face hobbing with added white noise. Signal processing methods, including short-time Fourier transform are employed for time-domain analysis, frequency-domain analysis, and time–frequency-domain analysis of measured signals and simulated signals, thereby extracting the corresponding statistical features. In addition to the results of the experimental modal analysis, the causes of chatter in spiral bevel gear hobbing are discussed in detail, revealing that the main factor is cutter head vibration in the Y direction of the Hunan ZDCY CNC EQUIPMENT YKA2260 machine tool used in this research. The error in the time-domain characteristic parameters between simulated signals and measured vibration acceleration signals is within 15%, with a difference of 3.5% in spectral peak values. The predicted tooth surface morphology from simulation matches the actual morphology on the workpiece, comprehensively validating the reliability of the cutting dynamics model for the spiral bevel gear face hobbing process. Another conclusion drawn from numerical simulation experiments is that the amount of tooth surface waviness of the spiral bevel gears is the ratio of tool chatter frequency to cutting fundamental frequency.

A new fault component selection strategy based on statistical detection for slewing bearing weak signal de-noising

Slewing bearing is a critical transmission component in large-size construction machinery due to its low-speed and heavy-load conditions. Fault prognostics and health management of slewing bearing are crucial for ensuring their high availability and profitable operation. However, the presence of background noise in construction machinery signals restricts the applicability of existing signal processing approaches in prognostics and health management. To address this challenge, a novel signal de-noising method is proposed based on adaptive decomposition, along with a new strategy for recognizing fault components using statistic detection through kernel principal component analysis (KPCA). First, robust local mean decomposition is utilized to adaptively decompose the fault and normal vibration signal over the entire service life. Then, product functions (PFs) decomposed by fault and normal vibration signal are used for KPCA anomaly detection. Finally, the fault PFs are reconstructed to obtain the de-noised signal. The effectiveness of the proposed method is validated through the use of both simulated and experimental vibration signals obtained from a slewing-bearing life-cycle test. The results illustrate that the proposed method has superior de-noising capability and decomposition efficiency, making it an effective signal preprocessing technique for prognostics and health management.

SEACKgram: a targeted method of optimal demodulation-band selection for compound faults diagnosis of rolling bearing

Rolling bearing plays an important role in carrying and transmitting power in rotating machinery, and the bearing fault is easy to lead to mechanical accidents, resulting in huge losses and casualties. Therefore, the condition monitoring and diagnosis of rolling bearings are very important to improve the safety of equipment. Compound fault is a common fault evolved from the initial defect, which is characterized by randomness, coupling, concealment, and secondary. The existence of these characteristics brings great challenges to the accurate diagnosis of compound faults. In the diagnosis of compound faults, the traditional methods that select the single optimal demodulation frequency band for analysis and identification sometimes cannot completely extract multiple fault components, which are prone to miss diagnosis and misdiagnosis. In order to solve this problem, the SEACKgram method is proposed by constructing a Square Envelope Unbiased Autocorrelation Correlation Kurtosis (SEACK) index. The frequency band of the original signal is divided by the Maximal Overlap Discrete Wavelet Packet Transform, and the SEACK index is used to quantitatively describe the fault signals of different frequency bands. According to the different fault periods, the resonant frequency bands of the maximum SEACK value are selected, then the resonance band signal is analyzed by square envelope spectrum, and the fault type is identified according to the fault characteristic frequency. The simulated and experimental vibration signals of rolling bearings with compound faults are used to verify the feasibility of the proposed method. The results show that the proposed SEACKgram can improve the accuracy of compound faults identification and would be applied in engineering practice to a certain extent.

Optimization of rolling bearing dynamic model based on improved golden jackal optimization algorithm and sensitive feature fusion

Some simplified modeling parameters in the traditional rolling bearing dynamics model led to poor consistency between the simulated and measured vibration signals. In addition, the simulated signal has low accuracy, which in turn reduces the accuracy of bearing fault diagnosis based on the simulated signal. Thus, to resolve the above-mentioned issues, this study presents a method for optimizing rolling bearing dynamics models based on an improved golden jackal optimization (IGJO) algorithm and sensitive feature fusion. Firstly, this study proposes an IGJO algorithm using a dimension-by-dimension reverse learning strategy and adaptive weights to address the interplay of dimensions in the multidimensional optimization process and the imbalance between the global and local search abilities of the GJO. Secondly, a fusion strategy for bearing fault-sensitive features is proposed based on the binary's improved golden jackal optimization (B-IGJO) algorithm and principal component analysis (PCA). In this strategy, the Sigmoid function discretization method is used to obtain the B-IGJO algorithm, which is then applied to the measured signal to select the bearing fault sensitivity features. These features are analyzed using PCA to obtain the fused sensitivity feature expression. Finally, the fusion-feature expressions are used to calculate the fusion-sensitive features of the measured and simulated vibration signals, and then the residuals of the two are used as the objective function for model optimization. The parameters of the rolling bearing dynamics model are updated using the IGJO. The proposed method is experimentally verified through a single pitting fault dynamics model of the outer ring of the rolling bearing. In conclusion, our results confirm the effectiveness and feasibility of the proposed method.

A Double Interpolation and Mutation Interval Reconstruction LMD and Its Application in Fault Diagnosis of Reciprocating Compressor

The accuracy and stability of the envelope estimation function are enduring issues throughout the research process of LMD. This paper presents double interpolation and mutation interval reconstruction local mean decomposition (DIMIRLMD) to improve the stability of the demodulation process and the accuracy of PF components. DIMIRLMD first proposes a mutation interval reconstruction envelope algorithm using extreme symmetry points to suppress the demodulation mutation phenomenon, which disturbs the stability of the demodulation process, and then selects the optimal PF component from a double interpolation PF component library based on the index of orthogonality (IO) for a better hierarchical property. DIMIRLMD was employed to analyze the simulation signal and vibration signal of a reciprocating compressor in an oversized bearing clearance state, and the results illustrate its performances are more excellent than those of three other LMD methods. Furthermore, the envelope frequency spectrum obtained from the proposed LMD presents a clear double rotation fault frequency and lower noise disturbance.

A transfer learning strategy based on numerical simulation driving 1D Cycle-GAN for bearing fault diagnosis

Most transfer learning (TL) models generally need the fault data from similar scenarios to achieve cross-domain bearing fault diagnosis. However, due to the bearings are mostly in normal state and the fault experiment cost is high, it is hard to collect the complete fault data in any real scenario. Thus, the problem of no fault data still limits the application of various intelligent diagnosis methods, including TL methods. To solve this problem, this paper proposed a novel transfer learning (TL) strategy by driving the one-dimensional (1D) cycle-consistent generative adversarial network (Cycle-GAN) with numerical simulation. In this strategy, the bearing dynamic model is constructed to generate the simulated vibration signals under normal and fault states. Furthermore, a 1D Cycle-GAN is first devised as the TL model for signal-to-signal translation. By using all simulated signals (normal and fault) and real normal signals, this 1D Cycle-GAN is trained to translate each simulated signal into a nearly real signal. Two experiments indicate that this strategy can generate satisfactory fault signals to solve the problem of no fault data in bearing fault diagnosis.

Gas turbine blade fracturing fault diagnosis based on broadband casing vibration

Blade fault is a catastrophic failure of gas turbines. In order to improve the reliability of blade during operation, condition monitoring is one of the effective methods. However, there always exist two problems with blade monitoring: 1) challenges in warning of the occurrence of blade failure in advance and 2) difficulties finding the location after blade failure. In this article, we solve these problems by excavating the characteristics of blade-related signals in broadband casing vibration with advanced signal processing methods and machine learning technology. First, a novel method--Sparse Harmonic Product Spectrum (SHPS)--is proposed to accurately calculate blade passing frequency from gas turbine broadband casing vibration. The SPHS relies on Fourier transform, and its calculation utilizes the physical relationship between fundamental frequency and blade passing frequencies. Combining Vold-Kalman filter with adaptive parameter optimization process (AVKF), the blade-related vibration can be separated from casing vibration even in strong noise. Analysis of simulated casing vibration signal is used to verify the effectiveness and superiority of proposed method. Based on blade-related vibration, we build a gas turbine blade condition model in an unsupervised learning manner. The model can excavate potential blade failures earlier and more accurately than conventional threshold methods. Then, three coefficients are constructed according to the blade-related vibration characteristics to identify the blade fault location in multi-stage system. Moreover, the effectiveness of the proposed blade diagnosis framework is verified using actual industrial gas turbine blade fracturing failure data.

SNR enhancement with a non-local means image-denoising method for a Φ-OTDR system.

Orthogonal pulse pairs generated by the polarization beam splitter (PBS) and the polarization maintaining-optical switch (PM-PSW) can effectively suppress the polarization fading in phase-sensitive optical time-domain reflectometry (Φ-OTDR) systems, but the PM-PSW also brings a lot of noise when switching the optical path periodically. Therefore, a non-local means (NLM) image-processing method is proposed to enhance the signal-to-noise ratio (SNR) of a Φ-OTDR system. Compared with the existing traditional noise reduction methods based on the one-dimensional signal, the method makes full use of redundant texture and self-similarity of multidimensional data. The NLM algorithm can obtain the estimated denoising result value of current pixels by the weighted average of pixels with similar neighborhood structures in the Rayleigh temporal-spatial image. To validate the effectiveness of the proposed approach, we have carried out experiments on the actual signals obtained from the Φ-OTDR system. In the experiment, a sinusoidal waveform of 100Hz is applied at 20.04km of the optical fiber as a simulated vibration signal. The switching frequency of PM-PSW is set to 30Hz. The experimental result shows that the SNR of vibration positioning curve is 17.72dB before denoising. After using the NLM method based on image-processing technology, the SNR reaches 23.39dB. Experimental results demonstrate that this method is feasible and effective in improving SNR. This will help to realize accurate vibration location and recovery in practical applications.

Vibration-Based Measurement of Activated Cylinders in Reciprocating Machines

Vibration signals from rotating and reciprocating machines provide insights into machine health and operating parameters. Reciprocating machines often feature cylinder deactivation systems for increased efficiency. This article presents a vibration-based method for the detection of activated cylinders in reciprocating machines. Simulated compressor vibration signals demonstrate the effect of cylinder conditions on spectral-coherence and mean-envelope-spectrum (MES) estimates. Physical parameters of the mechanical system and the results of the simulations advise the proposed method. With an appropriately chosen region of interest (ROI), the MES-based indicator (MESbI) correctly identified the number of activated cylinders of a four-cylinder reciprocating refrigeration compressor in both field and laboratory test setups. A discussion section addresses measurement uncertainty and calibration, and provides an example application.

The effect of time-varying operating condition on the crack induced impulses and its application to gearbox tooth crack diagnosis

Gearbox tooth crack diagnosis under Time-Varying Operating Conditions (TVOC) is a challenging issue. TVOC induce both Amplitude Modulation (AM) and Frequency Modulation (FM) effects into gearbox vibration signals, which results in difficulties in tooth crack diagnosis. To overcome this problem, the TVOC-induced AM and FM effects on vibration signals of gearboxes with tooth cracks need to be well studied. Many studies have been reported to investigate the influence of TVOC on gearbox vibration signals using dynamic modelling, some of which only focused on healthy gearboxes while others only considered piecewise constant operating conditions, failing to make comprehensive analyses involving both tooth cracks and TVOC. This paper aims to conduct a comprehensive study on how TVOC affect vibration signals of gearboxes with tooth cracks using dynamic simulation. Firstly, gear tooth mesh stiffness is evaluated considering both tooth crack severity progression and operating condition variations, through which gearbox vibration responses are generated under TVOC. A signal analysis procedure is proposed with its focus placed on the Crack Induced Impulses (CII), which are extracted from gearbox vibration signals using the adaptive harmonic decomposition method or its modified version. Envelope analysis is conducted on the CII to study how the CII are affected by TVOC, and a linear dependence of the AM effect of the CII on TVOC is identified. Based on the identified linear dependence, a novel Condition Indicator (CI), which is sensitive to crack growth while insensitive to operating condition variations, is proposed to track tooth crack severity progression under TVOC. The linear dependence of the AM effect of the CII on TVOC and the effectiveness of the proposed CI for tooth crack diagnosis are demonstrated using both simulated gearbox vibration signals and experimental gearbox vibration datasets.

Balancing of Motor Armature Based on LSTM-ZPF Signal Processing

Signal processing is important in the balancing of the motor armature, where the balancing accuracy depends on the extraction of the signal amplitude and phase from the raw vibration signal. In this study, a motor armature dynamic balancing method based on the long short-term memory network (LSTM) and zero-phase filter (ZPF) is proposed. This method mainly focuses on the extraction accuracy of amplitude and phase from unbalanced signals of the motor armature. The ZPF is used to accurately extract the phase, while the LSTM network is trained to extract the amplitude. The proposed method combines the advantages of both methods, whereby the problems of phase shift and amplitude loss when used alone are solved, and the motor armature unbalance signal is accurately obtained. The unbalanced mass and phase are calculated using the influence coefficient method. The effectiveness of the proposed method is proven using the simulated motor armature vibration signal, and an experimental investigation is undertaken to verify the dynamic balancing method. Two amplitude evaluation metrics and three phase evaluation metrics are proposed to judge the extraction accuracy of the amplitude and phase, whereas amplitude and frequency spectrum analysis are used to judge the dynamic balancing results. The results illustrate that the proposed method has higher dynamic balancing accuracy. Moreover, it has better extraction accuracy for the amplitude and phase of unbalanced signals compared with other methods, and it has good anti-noise performance. The determination coefficient of the amplitude is 0.9999, and the average absolute error of the phase is 2.4°. The proposed method considers both fidelity and denoising, which ensuring the accuracy of armature dynamic balancing.

Principal component analysis (PCA) based sparrow search algorithm (SSA) for optimal learning vector quantized (LVQ) neural network for mechanical fault diagnosis of high voltage circuit breakers

An essential piece of equipment for the protection and management of the electricity grid is a high voltage(HV) circuit breaker. The reliability and stability of the electricity grid are directly affected by the condition of operation of a high voltage circuit breaker. This study proposes a PCA-SSA-LVQ-based high-voltage circuit breaker diagnosis method to identify mechanical faults of high-voltage circuit breakers more quickly and effectively. Laboratory simulation of vibration signals for different fault states of high voltage circuit breakers. Finite intrinsic mode functions(IMF) are obtained by empirical modal decomposition(EMD) of the simulated vibration signal. The envelope spectrum of the IMF is extracted using the Hilbert envelope transform, and the concept of information entropy is used to determine the entropy value of the envelope as the characteristic quantity of the signal. Using principal component analysis, one can decrease the eigenvectors’ dimension. The high voltage circuit breaker LVQ neural network fault diagnosis model is made, and the initial weight of the LVQ neural network is optimized using the SSA optimization algorithm. The accuracy was improved in the end. The optimized and non-optimized LVQ neural networks were compared. The results show that the PCA-SSA-LVQ neural network has a higher diagnostic rate and faster training convergence. The main contribution of this paper is to reduce the redundancy of feature vectors using the PCA algorithm, optimize LVQ neural network by the SSA algorithm, and creatively use these three algorithms together. The training speed and the accuracy of state recognition are improved.

Element analysis and its application in rotating machinery fault diagnosis

Conventional signal processing methods make it difficult to extract the fault impulse features from the target signal, and the time-frequency representation has energy ambiguity. Thus, it is critical to develop new approaches for mechanical fault diagnostics. In this paper, the element analysis method, which was originally utilized in the marine field, is applied to the field of mechanical fault diagnosis for the first time. A de-noising technique of rotating machinery signals based on the element analysis method is proposed. The proposed method first determines the corresponding wavelet parameters according to the mechanical fault signals and constructs the element model. Then the method performs the Morse wavelet transform on the element model, and calculates the signal impulse point from the wavelet transform to obtain the signal’s fault characteristic frequency. Furthermore, the method can also reconstruct the signal by utilizing a small number of solitary points in the time or scale plane. The performance of the method is verified by analyzing simulated signals and mechanical vibration signals collected from different experimental platforms. The results demonstrate that the method has excellent signal characteristic extraction capability and successfully diagnoses different kinds of rotating machinery faults.

Analysis of vibration signals and detection for multiple tooth cracks in spur gearboxes

Tooth crack diagnosis is important to ensure the reliability of gearboxes. However, most reported studies on tooth crack diagnosis only involved the scenario of one tooth crack in a gearbox, which is not always the case since gearboxes may also suffer from multiple tooth cracks in the industry. To overcome this problem, this study first brings some insights into the vibration characteristics of a spur gearbox with multiple tooth cracks using dynamic modelling. Three scenarios of multiple tooth cracks are considered, including two nonadjacent tooth cracks on the pinion and a healthy gear, two adjacent tooth cracks on the pinion and a healthy gear, and one tooth crack on the pinion and one tooth crack on the gear. Tooth mesh stiffness is analytically evaluated using the potential energy method and is further inserted into the spur gearbox dynamic model to generate vibration responses. Analyses of mesh stiffness and vibration responses are conducted in both time and frequency domains. Besides, a novel method focusing on the crack induced impulses (CII) is proposed to detect the number and locations of multiple tooth cracks. Firstly, the CII are extracted from gearbox vibration signals using a new strategy based on the singular value decomposition. Time synchronous average (TSA) is conducted on the CII to get the TSA signals for both the pinion and the gear. Afterwards, detection of the number and locations of multiple tooth cracks is achieved via analyzing the squared envelopes of the TSA signals for both the pinion and the gear. The obtained insights into vibration characteristics for multiple tooth cracks and the effectiveness of the proposed method for detecting the number and locations of multiple tooth cracks are demonstrated using both simulated gearbox vibration signals and experimental gearbox vibration datasets.

Simulation-Driven Domain Adaptation for Rolling Element Bearing Fault Diagnosis

State-of-the-art deep learning models remain data-intensive, requiring large training datasets to ensure their generalization ability. However, in industry, it is quite expensive or impractical to obtain massive training samples for condition monitoring practitioners. This article proposes a simulation-driven domain adaptation method to circumvent the data deficiency issue using physical-based simulations. A bearing phenomenological model is developed to generate simulated vibration signals. In the frame of domain adaptation transfer learning, a domain adversarial neural network (DANN) is proposed utilizing the simulated data as the source domain. The DANN can align the coarse supervised source domain data and the fine supervised target domain data to conduct adversarial training. Experimental results indicate that the proposed method can reach high classification accuracy using a small amount of real data. Compared to nonadapted and other transfer learning models, the proposed method demonstrates superior performance for bearing fault diagnosis, which is very promising for real industrial applications.

Maximum reweighted-kurtosis deconvolution: a fully blind and adaptive method for restoration of gear fault impulse trains

Deconvolution based on vibration signals has been proven to be an effective tool in gear fault diagnosis. However, for many common methods, precisely restoring the fault impulse train is still a challenging task due to the great dependence on prior knowledge and the empirical determination of filter parameters. In this paper, a fully blind and adaptive method termed maximum reweighted-kurtosis deconvolution (MRKD) is proposed. A new deconvolution criterion, i.e., reweighted-kurtosis, is defined. This criterion possesses great robustness to impulse interferencesand thus has great potential to solve the problem of previous kurtosis-based methods in which a single dominant impulse is deconvolved instead of the impulse train induced by a localized fault. Furthermore, a parameter-adaptive strategy is developed to adaptively determine the appropriate filter parameters. As such, the proposed method does not require any prior knowledge of the target fault impulse train and addresses the critical issue of many common methods specifying filter parameters empirically. The proposed method is validated through simulated and real vibration signals. Comparison with the most popular deconvolution methods indicates that MRKD outperforms other methods for the restoration of a gear fault impulse train.