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In accordance with the vibration characteristics of ball screw feed systems, a hybrid modeling method is proposed to study its dynamic behavior. Partially, the ball screw is modeled as a continuous body, and the remaining components are considered lumped masses, allowing for a realistic description of the dynamics of the feed system. The axial, torsional, transverse, and bending vibration models of a ball screw carriage system are established via the Rayleigh-Ritz series method based on the Timoshenko beam assumption. The established model that added the Timoshenko beam assumption obtains the coupling vibration displacement between the transverse and bending vibrations of the lead screw, which is close to real situations. Numerical simulations are conducted to investigate the changes of the natural frequency and modal shapes of ball screw systems with carriage positions. Results show that the carriage position has significant influence on the amplitude and direction of axial and transverse vibrations, substantial influence on the direction of the bending vibration, and minimal influence on the amplitude and direction of torsional vibration. These results indicate that the proposed hybrid model performs well to predict the vibration characteristics of the feed system. Moreover, the carriage position and carriage load also have a remarkable effect on the frequency response of the feed system. These results, along with the modeling approach, provide an important basis for the further study of in-machining monitoring and vibration controller design. [ABSTRACT FROM AUTHOR]

Blade cracks pose deadly threats to aviation safety and have caused serious aviation accidents in recent years. In order to diagnose blade cracks at the early stage, the harmonic vibration of cracked blade was analyzed theoretically and experimentally. Firstly, a recursive form solution was deduced for the complex the nonlinear dynamic equation of blade vibration, revealing the close relationship between harmonic component power and crack parameters. Furthermore, the upper bound of the adjacent harmonic component power ratio was obtained by theoretical derivations. The results show that the harmonic power decreases as the harmonic component order increases, and the degree of attenuation is decided by the crack parameters. Therefore, a crack detection approach was proposed according to the power ratio of harmonic components. The advantage of this method is that the blades do not need to be in a resonant state and can process vibration data at all rotational frequencies. This improves data utilization and diagnostic robustness. The recommended method was validated by the simulation analysis of stainless-steel and titanium blades. Finally, a test bench for blade vibration was set up whose highlight was the use of optical sensors for non-contact measurements of blade vibration. The results of both simulation and testbed experiment were much consistent with the theoretical inference. • Although a closed-form solution to the nonlinear kinetic equation of blade vibration is theoretically not available, we obtain a solution in a recursive form. • Further, the energy attenuation function of adjacent harmonic components is defined, and it is theoretically derived that there is an upper bound of the energy attenuation function. Moreover, the upper bound of the energy attenuation function is determined by the harmonic order, crack location, and crack depth. • The results of simulation and test bench experiments are consistent with theoretical inferences. [ABSTRACT FROM AUTHOR]

It seems a universally accepted doctrine that chatter resistance of a machining system can be improved with increasing stiffness of its components. However, the viewpoint that the tool stiffness reduction would be beneficial to cutting stability of flexible components has been demonstrated by the compliant tool-workpiece chatter model. This paper presents a comprehensive investigation of the dynamic interaction between the tool and the workpiece during turning operations and its effect on the cutting stability. Following the formulation of the chatter model, the effect of the coupling dynamics of the cutting system on the limiting depth of cut (doc), a conservative but reliable indicator for evaluating the stability level of the machining process, is highlighted. It is shown that stability can be enhanced by judicious increase of the tool flexibility and/or the workpiece damping. Also, an inflexion point generally exists on the cutting position-dependent limiting doc stability curve, revealing that the stability appears to be unchanged once the stiffness of the tool or workpiece is increased to a certain degree. As a result, the analysis results lay the foundation of a tool dynamics adaptation (TDA) approach to machining chatter control. It suggests an adaptive adjustment of the tool dynamics to the machined workpiece to enhance stability. The proposed approach was validated through turning experiments, in which the tool shank was installed parallel to the length of the flexible workpiece. The experimental results have shown an effective extension of the chatter-free cutting margin when tuning dynamics of the compliant tool-workpiece system. Given the reality that there is no absolutely rigid workpiece or tool in industrial applications, the proposed chatter control strategy can be adopted as a generic approach for the turning or boring processes. [ABSTRACT FROM AUTHOR]

Acoustic emission (AE) signals are useful for the condition monitoring of mechanical seals as tribological regimes affect the AE signatures. In this paper the investigation develops a mathematical model that can predict the energy of an AE signal under different tribological regimes. The developed model has been validated with experimental studies and satisfactory results have been perceived. Therefore, the model has strong potential to be used to obtain tribological behaviour of mechanical seals and hence develop a reliable and accurate condition monitoring system under varying operating conditions. [ABSTRACT FROM AUTHOR]

Modulation signal bispectrum (MSB) analysis is an effective method to obtain the fault frequency for rolling bearing, but harmonics make fault frequency dense and even frequency aliasing. Carrier frequency of bearing is generally determined by its structure and inherent characteristics and changes with the increase of the damage degree, so it is hard to be accurately found. To solve these problems, this paper proposes a sparse modulation signal bispectrum analysis method. Firstly the vibration signal is demodulated by MSB analysis and its bispectrum is obtained. After the frequency domain filtering, the carrier frequency is computed based on the characteristics of energy concentration at the carrier frequency on MSB. By shift-frequency MSB (SF-MSB), the carrier frequency is moved to the coordinate origin, the entire MSB is shifted for the same distance, and SF-MSB is obtained. At last, the bispectrum is shifted to the frequency zero point and diagonal slices are performed to obtain a sparse representation of MSB. Experimental results show that sparse MSB (S-MSB) method can not only eliminate the interference of harmonic frequency, but also make the extracted characteristic frequency of fault more obvious. [ABSTRACT FROM AUTHOR]

Gears are important mechanical components for power transmissions. Tooth wear is one of the most common failure modes, which can present throughout a gear’s lifetime. It is significant to accurately monitor gear wear progression in order to take timely predictive maintenances. Motor current signature analysis (MCSA) is an effective and non-intrusive approach which is able to monitor faults from both electrical and mechanical systems. However, little research has been reported in monitoring the gear wear and estimating its severity based on MCSA. This paper presents a novel gear wear monitoring method through a modulation signal bispectrum based motor current signal analysis (MSB-MCSA). For a steady gear transmission, it is inevitable to exist load and speed oscillations due to various errors including wears. These oscillations can induce small modulations in the current signals of the driving motor. MSB is particularly effective in characterising such small modulation signals. Based on these understandings, the monitoring process was implemented based on the current signals from a run-to-failure test of an industrial two stages helical gearbox under a moderate accelerated fatigue process. At the initial operation of the test, MSB analysis results showed that the peak values at the bifrequencies of gear rotations and the power supply can be effective monitoring features for identifying faulty gears and wear severity as they exhibit agreeable changes with gear loads. A monotonically increasing trend established by these features allows a clear indication of the gear wear progression. The dismantle inspection at 477 h of operation, made when one of the monitored features is about 123% higher than its baseline, has found that there are severe scuffing wear marks on a number of tooth surfaces on the driving gear, showing that the gear endures a gradual wear process during its long test operation. Therefore, it is affirmed that the MSB-MSCA approach proposed is reliable and accurate for monitoring gear wear deterioration. [ABSTRACT FROM AUTHOR]

Failures of bearing would cause sudden downtime and severe accidents that may require significant maintenance and economic consequences. Therefore, condition monitoring of bearing is extremely important for ensuring reliable and efficient operation. The analysis of electric power supply signals offers a non-intrusive and cost-effective method for detecting various incipient faults in rotary machines. However, using inverter or variable speed drive would influence the electric power supply signals, causing difficulties in detection and diagnosis results. To reduce these influences and develop effective detection techniques, this paper focuses on using electric power supply signals as an approach to detect incipient faults in the bearing of driving induction motor based on high-order bispectral analysis. It takes the advantage of providing abundant information from electric signatures of downstream driving motors to improve the performance of bearing's health monitoring. Modulation signal bispectrum (MSB) and cyclic bispectrum (CCB) exhibit significance to detect normal and abnormal bearing conditions, whereas remarkable effort is dedicated from MSB result due to the inclusion of phase information with amplitude modulation in both lower and higher sidebands. It can conclude that effective measurements of electrical power supply signals utilize reliable fault diagnosis information in the mechanical components of the driving motor. [ABSTRACT FROM AUTHOR]

Abstract: Friction stir welding is a relatively new solid-state joining technique which is widely adopted in different industry fields to join different metallic alloys that are hard to weld by conventional fusion welding. Friction stir welding is a highly complex process comprising several highly coupled physical phenomena. The complex geometry of some kinds of joints and their three dimensional nature make it difficult to develop an overall system of governing equations for theoretical analyzing the behavior of the friction stir welded joints. The experiments are often time consuming and costly. To overcome these problems, numerical analysis has frequently been used since the 2000s. This paper reviews the latest developments in the numerical analysis of friction stir welding processes, microstructures of friction stir welded joints and the properties of friction stir welded structures. Some important numerical issues such as materials flow modeling, meshing procedure and failure criteria are discussed. Numerical analysis of friction stir welding will allow many different welding processes to be simulated in order to understand the effects of changes in different system parameters before physical testing, which would be time-consuming or prohibitively expensive in practice. The main methods used in numerical analysis of friction stir welding are discussed and illustrated with brief case studies. In addition, several important key problems and issues remain to be addressed about the numerical analysis of friction stir welding and opportunities for further research are identified. [Copyright &y& Elsevier]

34441193 8 Xiao Y., Xue J., Li M., Yang W. Low-pass filtering empirical wavelet transform machine learning based fault diagnosis for combined fault of wind turbines. However, the majority of the wind turbine fault diagnosis research is focused on single faults rather than combined faults. They took into account the discrepancy of the fault features shown by different fault types, and formed the feature center for fault diagnosis by extracting the features of the samples of each fault type. [Extracted from the article]

Self-piercing riveting (SPR) is a high-speed mechanical fastening technique which is suitable for point-joining advanced lightweight sheet materials that are dissimilar, coated, and hard to weld. Major advances have been made in recent years in SPR technique. Latest literature relating to finite element analysis (FEA) of SPR joints is reviewed in this paper. The recent development in FEA of SPR joints are described with particular reference to three major factors that influence the success of SPR technique: SPR process, failure mechanism, and mechanical behavior of SPR joints. The main FE methods used in FEA of SPR joints are discussed and illustrated with brief case studies from the literature. Areas where further useful progress can be made are also identified. [ABSTRACT FROM AUTHOR]

Abstract: Acoustic emission analysis is an effective diagnostic method in modern tribo-monitoring. Many experimental studies have been conducted to investigate the relationship between AE parameters and wear and friction. This paper develops a theoretical model to correlate acoustic emissions to sliding friction based on elastic asperity contact of materials. It is found that sliding speed, load supported by asperities, the number of asperity contact and surface topographic characteristics influence the energy of AE signal. With the help of this model it is possible to estimate the contact load supported by asperities from AE measurement, which is a critical parameter to evaluate lubrication conditions in engineering applications. The establishment of this model will provide a theoretical basis for future studies on the motioning and prognostics of tribological process using AE technology. [Copyright &y& Elsevier]

Abstract: Instantaneous angular speed (IAS)-based condition monitoring is an area in which significant progress has been achieved over the recent years. This condition monitoring technique is less known compared to the existing conventional methods. This paper presents model-predicted simulation and experimental results of broken rotor bar faults in a three-phase induction motor using IAS variations. The simulation was performed under normal, and a broken rotor bar fault. The present paper evaluates through simulating and measuring the IAS of an induction motor at broken rotor bar faults in both time and frequency domains. Experimental results show a good agreement with the model-predicted simulation results. Three vital key features were extracted from the angular speed variations. One feature is the modulating contour of pole pass frequency periods in time domain. The other two features are in frequency domain. The primary feature is the presence of the pole pass frequency component at the low-frequency region of the IAS spectrum. The secondary feature which are the multiple of pole pass frequency sideband components around the rotor speed frequency component. Experimental results confirm the validity of the simulation results for the proposed method. The IAS has demonstrated more sensitivity than current signature analysis in detecting the fault. This research also shows the power of angular speed features as a useful tool to detect broken rotor bar deteriorations using any economical transducer such as low-resolution rotary shaft encoders; which may well be already installed for process control purposes. [Copyright &y& Elsevier]

Abstract: In order to discriminate small changes for early fault diagnosis of rotating machines, condition monitoring demands that the measurement of instantaneous angular speed (IAS) of the machines be as accurate as possible. This paper develops the theoretical basis and practical implementation of IAS data acquisition and IAS estimation when noise influence is included. IAS data is modelled as a frequency modulated signal of which the signal-to-noise ratio can be improved by using a high-resolution encoder. From this signal model and analysis, optimal configurations for IAS data collection are addressed for high accuracy IAS measurement. Simultaneously, a method based on analytic signal concept and fast Fourier transform is also developed for efficient and accurate estimation of IAS. Finally, a fault diagnosis is carried out on an electric induction motor driving system using IAS measurement. The diagnosis results show that using a high-resolution encoder and a long data stream can achieve noise reduction by more than 10dB in the frequency range of interest, validating the model and algorithm developed. Moreover, the results demonstrate that IAS measurement outperforms conventional vibration in diagnosis of incipient faults of motor rotor bar defects and shaft misalignment. [Copyright &y& Elsevier]

Abstract: Many different methods have been developed for the measurement of angular speed. Each successive method has attempted to improve measurement performance using a different strategy to process encoder signals based on two basic principles: counting the number of pulses in a given time duration and measuring the elapsed time for a single cycle of encoder signal. A review of existing methods reveals that little effort has been made in the measurement of instantaneous, multi-channel, wide-range angular speed. As a result, this paper aims to develop a method that provides instantaneous speed information in the form of angular displacement. It addresses the general process and considerations that ensure effective measurement of instantaneous angular speed (IAS). The paper then presents two different techniques: one is based on a general-purpose data acquisition system, another uses pure software implementation. Both methods aim to maximise the use of hardware resources without incurring additional costs in the form of upgrades to the measurement system. Finally, an optimisation of measurement system parameters for a reliable IAS measurement has been carried out based on measurement error analysis. [Copyright &y& Elsevier]

Due to excessive service load, inappropriate operating conditions or simply end of life fatigue, damage can occur in gears. When a fault, either distributed or localised, is incurred by gears, the stiffness and consequently vibration characteristics of the damaged tooth will change. A possible non-destructive technique for damage detection and severity assessment can be derived from vibration analysis. This paper presents the use of vibration analysis in the detection, quantification, and advancement monitoring of damage incurred by spur gear teeth. The stiffness of a single spur gear tooth is analysed theoretically, and due to the difficulties in measuring the gear tooth stiffness, an experimental procedure based on the modal analysis is developed to assess the severity of the gear tooth damage. A pair of spur gears was tested under accelerated wear conditions, and conventional time and frequency domain techniques are applied to the gear vibrations to indicate the presence and progression of the wear. The developed modal stiffness assessment technique is then used to quantify the resulting wear damage to the spur gear teeth. [Copyright &y& Elsevier]

IAN873, Dongfang93114 and Reyan73397, created through vegetative propagation for their high yield and excellent cold resistance, are major clones planted in China. In this work, latexes with rubber particles of the same size from these clones are separated from fresh natural rubber latex, and corresponding rubber films are prepared from each latex. The structure and components of each film are measured. This indicates that the characteristics of the rubbers obtained from latexes with similar particle sizes show some resembling trends among different clones, while for specific samples, those characteristics vary depending on the clone. The molecular weight is generally highest in IAN873 and lowest in Reyan73397. Rubber chains in small rubber particles are longer, and large rubber particles show a wider molecular weight distribution. The gel content of every sample from Reyan73397 is lower than the other two clones. The nitrogen content increases with the size of rubber particles in all clones. The ester content of small rubber particles in IAN873 and Reyan73397 is almost zero. Large rubber particles have more branching points formed via esters. This study provides a new perspective on the influence of clones on the relationship between characteristics of natural rubber and the size of rubber particles in natural rubber latex. [ABSTRACT FROM AUTHOR]

Maintaining exceptional product quality and boosting processing efficiency requires precise evaluation of various aspects of the turning process, including the cutting depth, feed rate, and size of the workpiece. This article presents a novel approach for observing the turning process state using modulation signal bispectrum (MSB) and motor current signals. A nonlinear model was established that clarifies the load torque oscillations during turning, which in turn affects the amplitude and phase modulation of the motor stator current. Random noise can be efficiently minimized using the MSB algorithm, allowing the extraction of the current-modulation characteristic sideband phase and amplitude from the collected current signal. This technique enables clear representation and enhanced monitoring of load torque changes throughout the turning process. The proposed method was validated via mathematical simulations and universal lathe tests, with the results indicating that the MSB phase and amplitude values effectively capture both dynamic and static torque alterations during the turning operation, making this approach a valuable tool for overseeing the turning process. [ABSTRACT FROM AUTHOR]

Global orbits connect the saddle points in an infinite period through the homoclinic and heteroclinic types of manifolds. Different from the periodic movement analysis, it requires special strategies to obtain expression of the orbit and detect the associated profound dynamic behaviors, such as chaos. In this paper, a global dynamic frequency method is applied to detect the homoclinic and heteroclinic bifurcation of the complicated nonlinear systems. The so-called dynamic frequency refers to the newly introduced frequency that varies with time t, unlike the usual static variable. This new method obtains the critical bifurcation value as well as the analytic expression of the orbit by using a standard five-step hyperbolic function-balancing procedure, which represents the influence of the higher harmonic terms on the global orbit and leads to a significant reduction of calculation workload. Moreover, a new homoclinic manifold analysis maps the periodic excitation onto the target global manifold that transfers the chaos discussion of non-autonomous systems into the orbit computation of the general autonomous system. That strategy unifies the global bifurcation analysis into a standard orbit approximation procedure. The numerical simulation results are shown to compare with the predictions. [ABSTRACT FROM AUTHOR]

A mechanical molecular spring isolator (MMSI) is proposed for the purpose of isolating the low-frequency vibration of a heavy payload. The MMSI is a passive vibration isolation technique mimicking molecular spring isolator characteristics of high-static-low-dynamic stiffness (HSLDS). An MMSI consists of a piston-cylinder container filled with the liquid and some hydraulic spring accumulators. The piston would support a lump of mass and be subjected to a specific external vibration excitation force. Those accumulators can get intercommunication by the liquid to produce the transformation from high static stiffness to low dynamic stiffness. The stiffness model of the MMSI with several identical accumulators is established based on the hydrostatic law. After that, some parameters that significantly influence the stiffness characteristics are studied. Results show that the stiffness property of this kind of MMSI demonstrates a piecewise linearity of three segments. It applies the averaging method to acquire amplitude-frequency and phase-frequency relationships of the piecewise linear vibration isolation system. An inevitable jump phenomenon may occur when the exciting force reaches the critical value. The vibration isolation performance is evaluated by energy transmissibility. Finally, an experimental prototype was designed to carry out quasi-static and dynamic experiments to verify the stiffness model and the dynamic properties as an HSLDS vibration isolator. [ABSTRACT FROM AUTHOR]

Chatter happens frequently in turning of flexible parts, jeopardizing the dynamic stability of the operation. The chatter stability prediction is dependent on the accurate estimation of the structural parameters of the cutting system. This paper aims to quantify the influence of the support conditions on dynamics of the flexible parts in straight turning. Given the time-varying cutting tool position during machining, first, the chatter stability of the cutting system along the cutting path is analyzed, and the critical stiffness expression for chatter onset prediction is deduced. Then, two models of the spindle-workpiece-tailstock system using analytical and numerical methods are constructed and compared. The simulation results showed that increasing the spindle bearing or tailstock support stiffness cannot effectively improve the stiffness distribution and the eigenfrequency of the machined rod, whereas the frequency of the system tends to be increased evidently with the chuck size decreased. In comparison with the experimental results, it has been shown that the model involving the effect of spindle bearing and center support enables to produce more accurate modal parameters estimation and chatter onset location prediction than the simplified model. Besides, the measured displacement signals demonstrate a good mapping relation with the theoretical analysis results. The present study could provide an in-depth understanding of the dynamic behavior of flexible workpieces in turning operations. [ABSTRACT FROM AUTHOR]

Graphical abstract Abstract The self-piercing riveting (SPR) of 1420 aluminium–lithium alloy sheets is investigated. Static tests with lap-shear loads and fatigue tests using the tension–tension loading mode were applied to obtain the joint performances and failure modes. The mechanism analysis regarding failure and fretting was employed using a scanning electron microscope (SEM) and an energy dispersive X-ray spectroscopy (EDX). The 1420 Al–Li alloy sheets can be joined effectively using SPR. The fatigue load–fatigue life curve with a 95% confidence level is obtained using a variation coefficient method. The joints failed in the rivet pulled out from the locked sheet in static trials, and the locked sheet fractured during the fatigue tests. The fretting wear can be observed at the contact interface between the rivet tail and the locked sheet or between both sheets at different load levels. The fretting region changes at a high or low load level, and it is highly relevant to the fatigue rupture paths. [ABSTRACT FROM AUTHOR]

Abstract This article introduces a revised common trend framework to monitor nonstationary and dynamic trends in industrial processes and shows needs for each improvement on the basis of three application studies. These improvements relate to (i) the extension of the common trend framework to include sets that contain stationary and nonstationary variables, (ii) handling cases where residuals are not drawn from multivariate normal distributions and (iii) the application of the framework to larger variable sets. Existing work does not adequately address these practically important issues. Industrial application studies highlight the needs for (i) the extended framework to model data sets containing stationary and nonstationary variables, (ii) handling statistics that are not based on normally distributed residuals and (iii) the use of Chigira procedure to robustly extract common trends. The extended framework is compared to traditional approaches. [ABSTRACT FROM AUTHOR]

This paper presents a method based on the extraction of the largest amplitude impact transients (ELAIT) for diagnosing the rolling element defect in bearings. As a defected rolling element causes two largest amplitude impact transients (LAITs) during a spin period when the element passes the load zone centre, LAITs are separated for each rolling element according to the kinematics of the bearing operation. By applying band-pass filtering, demodulation, low-pass filtering, and ensemble averaging to these LAITs, an enhanced signature named envelope ensemble average (EEA) is obtained for each rolling element, which allows a reliable indication of the defected elements. The robustness of the method is evaluated by investigating the localised fault model of rolling bearings with the inclusion of phase errors caused by rotational speed oscillation and rolling element slippage along with additive white noises. Evaluation results show that EEA signatures are very sensitive to element defects and give an accurate indication of the most probably defected element, and the ELAIT method is robust to rotational speed oscillation and slippage. The same performance is also achieved when the method was validated with experimental signals from a test rig of machinery fault simulation, showing effectiveness and robustness in detecting rolling element defects in an operated bearing. Besides, the proposed method can be easily implemented online as it does not need a tachometer and is implemented at low computation cost. [ABSTRACT FROM AUTHOR]

Operational modal analysis (OMA) is a powerful vibration analysis tool and widely used for structural health monitoring (SHM) of various system systems such as vehicles and civil structures. Most of the current OMA methods such as pick-picking, frequency domain decomposition, natural excitation technique, stochastic subspace identification (SSI), and so on are under the assumption of white noise excitation and system linearity. However, this assumption can be desecrated by inherent system nonlinearities and variable operating conditions, which often degrades the performance of these OMA methods in that the modal identification results show high fluctuations. To overcome this deficiency, an improved OMA method based on SSI has been proposed in this paper to make it suitable for systems with strong nonstationary vibration responses and nonlinearity. This novel method is denoted as correlation signal subset-based SSI (CoS-SSI) as it divides correlation signals from the system responses into several subsets based on their magnitudes; then, the average correlation signals with respective to each subset are taken into as the inputs of the SSI method. The performance of CoS-SSI was evaluated by a simulation case and was validated through an experimental study in a further step. The results indicate that CoS-SSI method is effective in handling nonstationary signals with low signal to noise ratio (SNR) to accurately identify modal parameters from a fairly complex system, which demonstrates the potential of this method to be employed for SHM. [ABSTRACT FROM AUTHOR]

The novelty of this paper is to numerically investigate the effect of manufacturing error and misalignment on the transient lubrication behavior of dynamically loaded journal bearings with micro-groove. Based on the average Reynolds equation considering the mass conservation cavitation algorithm, the asperity contact model, and the force balance equation, the mixed lubrication model under time-varying dynamic loads is developed. Meanwhile, mathematical functions are given for the bearings with different surface profiles, including the circumferential and axial manufacturing errors of the bearing, the horizontal and vertical deflection angles of the journal, and the micro-grooves with different distribution forms. According to this model, the lubrication characteristics of the bearing are systematically investigated at different horizontal and vertical deflection angles, amplitudes, spatial numbers, and phase angles of the circumferential waviness, as well as shape profiles of the axial error. In addition, the performance parameters of dynamically loaded journal bearings with ideal and actual surfaces under different micro-groove distributions are comparatively evaluated. Numerical results show that manufacturing errors and misalignment have a remarkable effect on the transient behavior of dynamically loaded journal bearings, and the coupling effect will be more consistent with practical engineering. It can be found that the distribution form of the micro-groove surface directly affects the friction reduction effect of bearing systems. The numerical model can serve as a meaningful guideline for the optimum design of dynamically loaded journal bearing with micro-groove. [ABSTRACT FROM AUTHOR]

Machining long slender workpieces still presents a technical challenge on the shop floor due to their low stiffness and damping. Regenerative chatter is a major hindrance in machining processes, reducing the geometric accuracies and dynamic stability of the cutting system. This study has been motivated by the fact that chatter occurrence is generally in relation to the cutting position in straight turning of slender workpieces, which has seldom been investigated comprehensively in literature. In the present paper, a predictive chatter model of turning a tailstock supported slender workpiece considering the cutting position change during machining is explored. Based on linear stability analysis and stiffness distribution at different cutting positions along the workpiece, the effect of the cutting tool movement along the length of the workpiece on chatter stability is studied. As a result, an entire stability chart for a single cutting pass is constructed. Through this stability chart the critical cutting condition and the chatter onset location along the workpiece in a turning operation can be estimated. The difference between the predicted tool locations and the experimental results was within 9% at high speed cutting. Also, on the basis of the predictive model the dynamic behavior during chatter that when chatter arises at some cutting location it will continue for a period of time until another specified location is arrived at, can be inferred. The experimental observation is in good agreement with the theoretical inference. In chatter detection respect, besides the delay strategy and overlap processing technique, a relative threshold algorithm is proposed to detect chatter by comparing the spectrum and variance of the acquired acceleration signals with the reference saved during stable cutting. The chatter monitoring method has shown reliability for various machining conditions. [ABSTRACT FROM AUTHOR]

Gear tooth surface wear is a common failure mode. It occurs over relatively long periods of service nonetheless, it degrades operating efficiency and leads to other major failures such as excessive tooth removal and catastrophic breakage. To develop accurate wear detection and diagnosis approaches at the early phase of the wear, this paper examines the gear dynamic responses from both experimental and numerical studies with increasing extents of wear on tooth contact surfaces. An experimental test facility comprising of a back-to-back two-stage helical gearbox arrangement was used in a run-to-failure test, in which variable sinusoidal and step increment loads along with variable speeds were applied and gear wear was allowed to progress naturally. A comprehensive dynamic model was also developed to study the influence of surface wear on gear dynamic response, with the inclusion of time-varying stiffness and tooth friction based on elasto-hydrodynamic lubrication (EHL) principles. The model consists of an 18 degree of freedom (DOF) vibration system, which includes the effects of the supporting bearings, driving motor and loading system. It also couples the transverse and torsional motions resulting from time-varying friction forces, time varying mesh stiffness and the excitation of different wear severities. Vibration signatures due to tooth wear severity and frictional excitations were acquired for the parameter determination and the validation of the model with the experimental results. The experimental test and numerical model results show clearly correlated behaviour, over different gear sizes and geometries. The spectral peaks at the meshing frequency components along with their sidebands were used to examine the response patterns due to wear. The paper concludes that the mesh vibration amplitudes of the second and third harmonics as well as the sideband components increase considerably with the extent of wear and hence these can be used as effective features for fault detection and diagnosis. [ABSTRACT FROM AUTHOR]

• A full generalization of Gini index is proposed for sparse quantification. • Fully generalized Gini indices are theoretically proven to be sparsity measures. • Two key performances of fully generalized Gini indices are investigated. • Fully generalized Gini indices exhibit excellent performance in characterizing repetitive transients. • Fully generalized Gini indices can effectively achieve bearing condition monitoring and fault diagnosis. The classic Gini index (GI) is generalized recently by using nonlinear weight sequences as sparsity measures for sparse quantification and machine condition monitoring. The generalized GIs with different weight parameters are more robust to random transients. However, they show insufficient performance in discriminating repetitive transients under noise contamination. To overcome this shortage, this paper proposes a two-parameter generalization method to tune not only the weight parameter but also the norm order, allowing for a full generalization of the classic GI to quantify transient features and leading to new statistical indicators which are named fully generalized GIs (FGGIs). Mathematical derivations show that FGGIs satisfy at least four of the six typical attributes of sparsity measures and that those with weight parameter equal to one satisfy at least five sparse attributes, proving that they are a new family of sparsity measures. Numerical simulations demonstrate that FGGIs can monotonically evaluate the sparseness of the signals and that the FGGIs with appropriate parameters exhibit improved performance in resisting random transient interferences and discriminating noise-contaminated repetitive transients compared to traditional sparsity measures. The performance of FGGIs in the condition monitoring of rolling element bearings is validated using two different run-to-failure experiment datasets, including a gradual failure and a sudden failure. The results show that increasing the norm order can improve the capability of FGGIs to characterize transient fault features, allowing more accurate trending of bearing health conditions, and therefore achieving better condition monitoring performance than the traditional sparsity measures. [ABSTRACT FROM AUTHOR]

This paper numerically investigates the effect of turbulence and journal misalignment on the lubrication characteristics of micro-grooved bearings with low-viscosity lubricant. The generalized average Reynolds equation satisfying the mass conservation cavitation algorithm is developed by integrating the average flow model proposed by Patir and Cheng, the Ng–Pan turbulent model, and the P- Θ model proposed by Elrod and Adams. With this model, the finite difference method is used in the numerical procedure. Moreover, the mathematical models of micro-grooves with different bottom shapes, that is, rectangle, isosceles triangle, left triangle, and right triangle, are given. The validity of the proposed model is verified by the comparisons with the published literature. Based on numerical simulation, the minimum film thickness, eccentricity ratio, attitude angle, maximum film pressure, friction torque, misalignment moment, film thickness, and pressure distributions under different external loads, rotational speeds, radial clearances, misalignment angles, and micro-groove parameters between models with and without turbulence and misalignment are comparatively analyzed. The numerical results reveal that turbulence may occur under heavy external load, high rotational speed, and large radius clearance. Concurrently, turbulence increases the minimum fluid film thickness and attitude angle, decreases the eccentricity ratio and friction torque, and enhances the bearing capacity. Furthermore, the larger misalignment angle results in the smaller minimum film thickness, eccentricity ratio and attitude angle, and the larger maximum film pressure, misalignment moment, and axial tilt of film pressure. Numerical simulations can provide theoretical guidance for the optimization of the geometrical parameters of micro-grooved bearings. [ABSTRACT FROM AUTHOR]

Grinding residual stress (GRS) has been understood to have a great impact on the strength of gear fatigue life. However, there are lack of detailed studies on the influences in terms of mechanism and magnitude. To fill this gap, this study proposed a new method based on finite element analysis (FEA) to calculate the overall meshing stress on a gear tooth surface by including both the GRS and elastohydrodynamic lubrication (EHL) film pressure. Applying this method to a spiral bevel gear has resulted in a number of interesting results that GRS produced to reveal its mechanism of action through a full meshing cycle. The acquired understanding would be useful for development of manufacturing processes. [ABSTRACT FROM AUTHOR]

This paper presents an online method for the assessment of the dynamic performance of the chassis frame in a heavy-duty dump truck based on a novel stochastic subspace identification (SSI) method. It introduces the use of an average correlation signal as the input data to conventional SSI methods in order to reduce the noisy and nonstationary contents in the vibration signals from the frame, allowing accurate modal properties to be attained for realistically assessing the dynamic behaviour of the frame when the vehicle travels on both bumped and unpaved roads under different operating conditions. The modal results show that the modal properties obtained online are significantly different from the offline ones in that the identifiable modes are less because of the integration of different vehicle systems onto the frame. Moreover, the modal shapes between 7 Hz and 40 Hz clearly indicate the weak section of the structure where earlier fatigues and unsafe operations may occur due to the high relative changes in the modal shapes. In addition, the loaded operations show more modes which cause high deformation on the weak section. These results have verified the performance of the proposed SSI method and provide reliable references for optimizing the construction of the frame. [ABSTRACT FROM AUTHOR]

Skidding is an inevitable phenomenon in rolling element bearing but accelerates the bearing wear and shortens the bearing life. Recognizing skidding behavior under different clearances will benefit to bearing health monitoring. In this study, an improved bearing dynamic model is developed to investigate its vibration characteristics and skidding behavior taking into all bearing structure components, time-varying oil film characteristics and bearing clearances. A novel method is proposed to include time-varying oil film stiffness and thickness under different clearances into the bearing dynamic model. A fast algorithm is adopted through interpolation during the model solving process. Furthermore, vibration responses and skidding behavior under different clearances are analyzed. Finally, a designed synchronous differential encoder (SDE) system tests the skidding behavior of bearings and verifies the proposed model through measuring the angular velocity of cage transformed by distance test. Numerical result shows that both RMS and ball pass frequency of outer race (BPFO) increase with bearing clearances. The slip ratio represents growth with the increase of bearing clearances, which is verified by the designed experiment with good consistency with the simulation. It can be foreseen that this novel dynamic model with SDE system can be well performed for bearing skidding detection and fault detection. [ABSTRACT FROM AUTHOR]

Bearing waviness is a kind of geometric unevenness on the surface of bearing components, which has vital influence on lifetime, vibration and noise. In order to accurately evaluate the impact of waviness on bearing operating performance and diagnostic features, it is necessary to reveal the mapping relationship between waviness excitation and vibration characteristics. However, current simulated waviness when modeling bearing vibrations is usually simplified by a uniform or non-uniform sinusoidal function, which cannot characterize the real topography of bearing waviness and lead to inaccuracy of diagnostics and prognostics. To address this issue, a novel waviness characterizing method based on Gaussian filter is developed in this study. Based on the proposed waviness characterizing method, a bearing waviness dynamic model is developed and vibration responses under various amplitude and order of waviness are investigated by simulation and experiment. Results show that the established waviness characterizing method can generate waviness curves closer to the actual shape. The bearing waviness dynamic model and the based vibration responses reveals unusual random phenomenon due to different waviness effects. These findings provide theoretical support for accurate identification of waviness on vibration characteristics, which has great significance on condition monitoring and fault diagnosis of bearing. [ABSTRACT FROM AUTHOR]

• Residual unbalance and bearing dynamic coefficients are estimated simultaneously. • Dual augmented Kalman filter is applied successfully in the estimation method. • The method performs a strong robustness in different measurement noise levels. • The reliability of the method is verified in different measurement configurations. Simultaneous estimation of the residual unbalance and bearing dynamic coefficients is of great significance to the dynamic analysis and rotor design of rotating machinery. However, traditional estimation methods are susceptible to routine scattering caused by the ill-posed problem and require external excitation forces, making them inconvenient and costly for engineering applications. Therefore, a novel simultaneous estimation method based on the augmented state-space model of the system combined with the dual augmented Kalman filter (DAKF) method is proposed for the double-disc rotor-bearing system without the external excitation force. The principle of this method is designed through an iterative strategy in the time domain, which only requires the steady-state unbalance responses of the two selected locations. To prevent the coupling relationship between the unbalance and the bearing dynamic coefficients from affecting the results, the estimation of the bearing dynamic coefficients is indirectly converted to the determination of the equivalent oil-film force according to the dynamic theory. Besides, features of the coupling relationship are investigated by the sensitivity analyses, proving that the coupling relationship has a significant influence on the estimation performance, especially for the bearing dynamic coefficients. Furthermore, simulations and experimental studies show that the proposed method can effectively estimate the bearing dynamic coefficients and unbalance parameters under different measurement noise levels, rotation speeds and measurement configurations. This study provides a comprehensive investigation into the estimation of dynamic characteristics and thereby establishes a reliable foundation for further assessment of the rotor-bearing system's overall performance. [ABSTRACT FROM AUTHOR]

• Proposed a semantic construction method using adaptive resonance demodulation, which improves the interpretability of semantic features. • Proposed a semantic-induced zero-shot diagnosis framework to identify unknown compound faults using single fault samples of railway bearings. For the challenges of diverse compound faults and low identification accuracy of railway bearings, a new zero-shot diagnosis model based on adaptive resonance demodulation semantic is proposed for the compound fault diagnosis of railway bearings. The model adopts adaptive resonance demodulation to identify the optimal resonance frequency band rich in fault information in bearing signals, and constructs the single and compound fault semantics of samples without separating the compound fault signals, thus improving the interpretability of semantic features. Moreover, spatial Euclidean distance is used to measure the similarity of features and semantics in the mapping space, which enables the identification of unknown compound faults by single faults. Verification through railway bearing data shows that this model effectively improves the compound fault identification accuracy under zero samples and is better than the comparison models. The research results can provide theoretical reference for the research and application of railway bearing fault diagnosis. [ABSTRACT FROM AUTHOR]

• A novel method for pressure prediction and cavitation evaluation of water jacket in engines is proposed. • The pressure amplification factor for a thin liquid layer under modal vibration is derived. • The nonlinear relationship between liner vibration and coolant pressure is investigated to reveal the mechanism of vibration-induced cavitation. • Effects of acoustic parameters and liner modal parameters on cavitation are discussed. • The correctness and rationality of the method is verified by experiment. Vibrations of the cylinder liner in internal combustion engines cause coolant cavitation, inducing cavitation erosion. Owing to the lack of a comprehensive understanding of vibration-induced cavitation, the cavitation erosion has been long an unresolved problem influencing engine lifetime and reliability and concerned more in recent years due to lightweight designs. This study proposes a novel pressure prediction method that considers the dynamic properties of a cylinder liner and the pressure amplification effect of a thin water jacket. A pressure amplification factor for a thin water jacket, defined as the ratio of the acoustic pressure to the plane progressive wave pressure generated by the same liner vibration, was derived for the first time. The pressure amplification mechanism in the thin water jackets was revealed. The cavitation risk area on the cylinder liner was predicted, and the effects of acoustic parameters and liner modal properties on cavitation were analysed. The theoretical analyses agreed with the experimental results, proving the accuracy of the proposed method. Moreover, it has found that the thin-layer configuration of the water jacket significantly reduced the vibration threshold for triggering cavitation. Vibration-induced cavitation in cylinder liners is closely related to the liner constraint modes and acoustic properties of the water jackets. The research results enrich the theoretical understanding of vibration-induced cavitation and provide theoretical support for the prediction and mitigation of cavitation erosion in internal combustion engines. [Display omitted] [ABSTRACT FROM AUTHOR]

• A metastructure-aided bearing fault signal enhancing diagnosis method is proposed. • Drum-shaped metastructure creates selective frequency band enhancement. • Artificially adjustable resonance for full band demodulation. • Cost-efficient rolling bearing fault diagnosis with low sampling rate. Rolling bearings, extensively utilized in rotating machinery, have critical significance for online fault diagnosis in the domains of industrial equipment maintenance and accident prevention. Presently, fault diagnosis methods heavily rely on identifying the optimal resonance band in the high-frequency range (several kHz) to achieve high signal-to-noise ratio (SNR) fault information. However, these approaches, which necessitate high sampling rate sensing systems and complex algorithm deployments, contradict the practical requirements for cost-effective sensors and edge computing in online diagnosis. To address this contradiction, this paper introduces a novel Drum-shaped Metastructure (DMS) to enhance weak bearing fault signals, thus promoting the detection performance of conventional sensors. The DMS is constructed with a drumhead metastructure consisting of a central block mass attached with four spiral beams, of which its length can be adjusted by a tunable frequency mechanism (TFM) for different frequency responses of interest. The detail of its selective frequency enhancement characteristics is first studied through numerical simulations within a frequency range of 200 Hz to 1000 Hz. Subsequently, the effectiveness of the weak signal enhancement is verified on various bearing test systems, which utilize a prototype DMS fabricated by 3D printing. The results present a significant enhancement in the SNRs of the bearing fault signal, which is achieved by demodulating the full frequency band without the need for complex algorithms. Therefore, the proposed DMS provides a new cost-efficient approach to weak bearing fault diagnosis and online monitoring. [ABSTRACT FROM AUTHOR]

Intelligent transportation necessitates advanced perception and cognitive systems that can provide continuous feedback from the vehicle. However, sensors relying on batteries face challenges such as high maintenance costs and environmental issues due to the limited lifespan of the power source. To overcome these challenges, this paper reports an efficient battery-free solution for transportation monitoring. The solution utilizes a speed-amplified rotary energy harvester (SAREH) to power various wireless Bluetooth sensors, enabling continuous monitoring of the vehicle's motion state. The SAREH combines a contra-rotating mechanism with a friction pendulum, resulting in excellent power output in a compact design. Experimental results demonstrate the ability of SAREH to extract power from vehicles operating at speeds ranging from 180 to 1260 rpm. The maximum power output and corresponding power density are measured as 712 mW and 34 mW cm−3, respectively. The prototype successfully powers portable electronics and supports battery-free navigation, triaxial acceleration, and temperature multi-sensors during real road and railway simulation tests. Additionally, the SAREH operates as a highly sensitive speed sensor and an early-warning system for detecting the vehicle's motion state. These results represent a significant advancement in intelligent transportation systems by showcasing the practicality of self-powered wireless monitoring capabilities on vehicles. [Display omitted] • A novel speed-amplified energy harvesting technique is proposed. • The proposed system incorporates contra-rotating and friction pendulum mechanisms. • The prototype enables installation in confined spaces while achieving a high power density of up to 34 mW cm−3. • The prototype simultaneously powers daily electronics and supports battery-free wireless sensors. • The prototype operates as a speed sensor and an early-warning system for detecting the motion state of the vehicle. [ABSTRACT FROM AUTHOR]

Abstract: In this study, a novel fault diagnosis system for rotating machinery using thermal imaging is proposed. This system consists of bi-dimensional empirical mode decomposition (BEMD) for image enhancement, a generalized discriminant analysis (GDA) for feature reduction, and a relevance vector machine (RVM) for fault classification. Firstly, the thermal image obtained from machine conditions is decomposed into intrinsic mode functions (IMFs) by using BEMD. At each decomposed level, the IMF is expanded and fused with the residue by gray-scale transformation and principal component analysis fusion technique, respectively. The enhanced image is then formed by the improved IMFs in reconstruction process. Subsequently, feature extraction is applied for the enhanced images to obtain histogram features which characterize the thermal image and contain useful information for diagnosis. The high dimensionality of the achieved feature set can be reduced by GDA implementation. Moreover, GDA also assists in the increase of the feature cluster separation. Finally, the diagnostic results are performed by RVM. The proposed system is applied and validated with the thermal images of a fault simulator. A comparative study of the classification results obtained from RVM, support vector machines, and adaptive neuro-fuzzy inference system is also performed to appraise the accuracy of these models. The results show that the proposed diagnosis system is capable of improving the classification accuracy and efficiently assisting in rotating machinery fault diagnosis. [Copyright &y& Elsevier]

[Display omitted] • The counterweight acts as the friction pendulum to form the all-in-one prototype. • The power density can reach 1982 W m−3. • The prototype can drive the daily used electric appliance and wireless sensor. • The prototype can serve as a speed sensor to detect the motion state of vehicles. • The prototype supports self-powered and real-time bogie frames monitoring. To maintain desirable service quality and operational safety, a wireless monitoring unit integrated with the vibration energy harvesting technology becomes an available choice to achieve self-powered, maintenance-free, and real-time monitoring of the train. However, owing to the bulky size and split design, to collect the mechanical energy from the bogie frame movement is still a considerable challenge for conventional harvesters. Here, we proposed a compact all-in-one on-rotor electromagnetic energy harvester. The key novelty is that a counterweight acts as the friction pendulum to produce the desired relative motion between the coils and magnet and make the device more easily install on the wheelset. Besides, the layout of the magnetic materials and coils is optimized to improve the conversion efficiency. The output performance under broad train speeds of 420–820 rpm is systematically studied to verify the improvements of the power density (up to 1982 W m−3), and the converted electricity successfully powers the daily electric appliance and the commercial wireless Bluetooth sensors. Additionally, the harvester serves as a speed sensor to detect the motion state of the vehicle. This work makes significant progress towards potential applications in the embedded self-powered wireless condition monitoring units. [ABSTRACT FROM AUTHOR]

This paper presents an investigation into sideband vibro-acoustics suppression of the permanent magnet synchronous motor (PMSM), associated with several periodic signal-based dithering modulations. Mathematical derivations of four typical periodic signal-based schemes are deduced with the power spectral density (PSD) method. The individual modulation parameters, such as bandwidth and modulation ratio, are determined the values to minimize the sideband vibro-acoustics. The frequency distributions of the sideband components are discussed by the analytical derivations. The relationship between the electromagnetic components and mechanical responses is reconstructed by coupling with temporal features. For verification, the experimental tests with a prototype PMSM are introduced under different operational conditions. According to the results and figures, both magnitude suppression and frequency distribution are accurately correlated with sideband harmonic components as well as vibro-acoustic responses. This investigation thus clearly holds promise for the evaluation and optimization in powertrain used by electric vehicles. [ABSTRACT FROM AUTHOR]

Rolling bearings are important parts of mechanical equipment. However, the early failures of the bearing are usually masked by heavy noise. This brings about difficulties to the extraction of its fault features. Therefore, there is a need to develop a reliable method for early fault detection of the bearing. Considering this issue, a novel fault diagnosis method using the improved wavelet threshold denoising and fast spectral correlation (Fast-SC) is proposed. First, to solve the discontinuity of the hard threshold function and avoid the constant deviation triggered by the soft threshold function, a piecewise continuous threshold function is proposed by using a new threshold selection rule to denoise the original signal. In the new threshold function, the adjuster α is introduced to improve the traditional wavelet denoising algorithm, so as to enhance the signal-to-noise ratio (SNR) of the original signal more effectively. Then, the denoised signal is analysed by Fast-SC to identify the rolling bearing fault features. Finally, simulation analysis and experimental data demonstrate that the proposed approach is effective for rolling bearing fault detection compared with Fast-SC and the combined method based on traditional wavelet threshold and Fast-SC. [ABSTRACT FROM AUTHOR]

• A multi-sensor information fusion covariance matrix scheme is presented for multi-sensor feature fusion. • A novel MCFD is proposed to demodulate fault modulation features from original vibration signals. • A MCFD-based framework is developed for multi-sensor driven gearbox intelligent fault diagnosis. • Experiment results demonstrated the effectiveness of the proposed MCFD-based framework. Accurate fault detection is extremely important to ensure stable gearbox operation. Data-driven schemes using cyclic spectral have received significant attention due to their robust demodulation performance. However, these schemes are mainly applied to process single sensor signals, and they are unable to accurately obtain precise fault features. This paper proposed a novel multiscale cyclic frequency demodulation (MCFD)-based feature fusion framework for multi-sensor driven gearbox intelligent fault diagnosis. Firstly, the MCFD is proposed to analyze the vibration signals from multi-sensor driven gearbox, which acquires the multi-sensor mode information without setting parameters in advance. Thereafter, the grey relational degree between the multi-sensor mode information and original signal is calculated, and its results are normalized to obtain the relationship coefficients. Finally, the acquired coefficients are performed for multi-sensor information fusion to form the covariance matrix for gearbox fault diagnosis. The effectiveness of the proposed feature fusion framework is validated using the gearbox case. The comparative experiments indicate that this framework outperforms comparative algorithms for multi-sensor driven gearbox fault diagnosis. [ABSTRACT FROM AUTHOR]

• ORS is developed to be installed on the rotating shaft for vibration measurements. • The developed ORS largely improves the SNR of vibration collections than OHS. • The FFT-based fault diagnosis theory for the rolling bearing is presented. • ORS can achieve easier and more robust Hilbert envelope analysis for diagnosis. • Results validate the performance of ORS for early rolling bearing fault diagnosis. The traditional on-house sensing (OHS) accelerometer for vibration measurements causes poor signal-to-noise ratio (SNR) and complicated fault modulations, which increases the difficulty and complexity for early bearing fault diagnosis. To overcome these challenges, this paper develops a wireless triaxial on-rotor sensing (ORS) system to largely improve the SNR and deduces fast Fourier transform (FFT) and Hilbert envelope analysis for accurate early rolling bearing fault diagnosis, which largely improves accuracy and efficiency for early fault diagnosis. First, the development of the ORS system for wireless vibration measurements is given. Second, the theoretical diagnostic relationships between dynamic ORS signals and rolling bearing faults are derived for FFT and Hilbert envelope analysis for the first time. Finally, the induction motor tests with outer and inner race faults successfully validate that both simple FFT and Hilbert envelope analysis can achieve more robust early rolling bearing fault diagnosis compared to OHS measurements. [ABSTRACT FROM AUTHOR]

• The proposed method extends the cyclic frequency range to Fs /2. • The designed scale factor in CWT can help locate important frequency bands. • TKEO is improved to process the single-carrier signal in the frequency domain. • The developed method can enhance the fault features effectively. • Induction motor tests validate the superiority of the method for early fault detection. The cyclostationary analysis techniques have been extensively explored for the purpose of fault detection in rotating machinery. However, there are still huge challenges because of both limited detection frequency range and low fault identification accuracy. This paper proposes an improved cyclostationary method to enhance incipient fault features. Firstly, the continuous wavelet transform is used to accurately locate important frequency bands, and the fault modulation mechanism or fast kurtogram can be adopted to design the optimal wavelet transform scale factor. Secondly, the Teager-Kaiser energy operator is improved to be used in the frequency domain for the weak fault feature enhancement. Finally, fault features are presented in the cyclic frequency domain through spectral coherence and enhanced envelope spectrum. The proposed method is verified through both numerical simulation and experiments, including incipient half-broken rotor bar, and rolling bearing outer race faults in induction motors. [ABSTRACT FROM AUTHOR]

In the motor current signal, the characteristic frequency of broken rotor bar (BRB) fault is modulated by the supply frequency and it decreases with the decrease of the load, resulting it to be easily buried under light load conditions. Teager–Kaiser energy operator (TKEO) has shown better performance to detect the BRB faults than classical methods, such as envelope analysis and spectral analysis. However, the original definition of TKEO leads to its result lack of physical meanings and the causal processing in TKEO can lead to phase distortion and nonideal filter characteristics. Therefore, this article proposes a normalized frequency-domain energy operator (FDEO) for the BRB fault diagnosis, which does not require causal processing and calculates multiple differentiations in the frequency domain with equal accuracy in one operation. Furthermore, the normalized FDEO removes the influence of the supply frequency followed by the spectral analysis to extract fault features. The mathematical model of induction motor (IM) under healthy and faulty condition is studied in this article. Then, the proposed approach is experimentally validated with seeded one and two BRB faults operating under various load conditions. To verify the effectiveness, the results are compared with the TKEO, envelope analysis, and spectral analysis. It was found that the proposed method provides slightly obvious fault features with respect to the TKEO, especially when the IMs run under light load conditions with two BRB faults. [ABSTRACT FROM AUTHOR]

To understand the complicated dynamic behavior of a Nonlinear Piezoelectric Energy Harvester (NPEH), this paper develops an improved Complex Dynamic Frequency (CDF) method based on complex normal form. CDF introduces a dynamic frequency factor and establishes a set of algebraic equations in handling the effect of higher-order nonlinear terms in a wide frequency band to obtain periodic responses of NPEH. Numerical and experimental studies verify that the proposed CDF gives consistent and accurate predictions of the systems with both weak and strong nonlinearity. Furthermore, through an implicit relationship between magnet arrangement and output performance, one may effectively control the sweep frequency with softening and hardening characteristics. That is a major breakthrough toward the further nonlinear design for broad bandwidth harvesters. As the application, the experimental results reveal the high response profiles can be in a wide frequency range from 10.8 Hz to 24.5 Hz for the NPEH developed that allows an output power of 9 times higher than the conventional linear structure. [ABSTRACT FROM AUTHOR]

Planetary gearboxes are widely used in mechanical transmission systems due to their large transmission ratio and high transmission efficiency. In a planetary gearbox, the sun gear is usually set to float to balance the sharing of loads among planet gears. However, this floating set will result in the variation of pressure angle, overlap ratio, and meshing phase in the meshing progress and when gear faults occur, the variation will be enlarged. In the previous studies, these parameters were reduced to constant. To study the influence of the dynamic parameters on the vibration response of planetary gearboxes under different operating conditions, a new lumped-parameter model containing the time-varying pressure angle (TVPA), time-varying overlap ratio (TVOR), and time-varying meshing phase (TVMP) is established. Based on this model, the vibration response mechanism of the sun gear is analyzed. Moreover, the comparison with the previous model is made and the rule of phase modulation caused by these dynamic parameters is revealed. By comparing the dynamic responses under different loads and rotation speeds, the phase modulation is studied in detail. Finally, the sun gear fault is introduced, and the phase modulation is analyzed in different fault degrees. This study can provide theoretical reference for the condition monitoring and fault diagnosis of planetary gearbox based on vibration analysis. [ABSTRACT FROM AUTHOR]

Rolling element bearings are one of the critical elements in rotating machinery of energy engineering systems. A defective roller of bearing moves in and out of the load zone during each revolution of the cage. Larger amplitude impact transients (LAITs) are produced when the defective roller passes the load zone centre and the defective area strikes the inner or outer races. A series of LAIT segments with higher signal to noise ratio are separated from a continuous vibration signal according to the bearing geometry and kinematics. In order to eliminate the phase errors between different LAIT segments that can arise from rotational speed fluctuations and roller slippages, unbiased autocorrelation is introduced to align the phases of LAIT segments. The unbiased autocorrelation signals make the ensemble averaging more accurate, and hence, archive enhanced diagnostic signatures, which are denoted as LAIT-AEAs for brevity. The diagnostic method based on LAIT separation and autocorrelation ensemble average (AEA) is evaluated with the datasets captured from real bearings of two different experiment benches. The validation results of the LAIT-AEAs are compared with the squared envelope spectrums (SESs) yielded based on two state-of-the-art techniques of Fast Kurtogram and Autogram. [ABSTRACT FROM AUTHOR]