Search

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]

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]

• 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]

• 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]

• A new calculation model of the tooth torsional deformation caused by the NDL along TWD is presented. • A double-layer iterative model for TVMS and load distribution of gears is proposed considering the effects of NDL along TWD. • The proposed model can achieve the decoupling calculation of TVMS and the load along TWD caused by the early-stage crack. • FE method proves the accuracy and high efficiency of the proposed model. • The effects of crack parameters and torque on the tooth torsional deformation, TVMS and load distribution are revealed. To reveal the coupling relationship between the time-varying mesh stiffness (TVMS) and the load distribution along the tooth width direction (TWD) of gears with early-stage crack (ESC), a double-layer iterative analytical model for the TVMS and load distributions of gears is proposed considering the effects of the non-uniformly distributed load (NDL) along TWD caused by the ESC. In the proposed model, an analytical model of tooth torsional deformation and a parallel slice stiffness model of the tooth pair with ESC are separately developed based on the slicing method. On this basis, a double-layer iterative calculation method for the TVMS and load distributions is proposed, in which the coupling relationships between the slice stiffness and load distribution along TWD as well as the TVMS and load distribution between the meshing tooth pairs are respectively presented with the inner- and outer- layer iterations. Finite element (FE) models are established to verify the proposed double-layer iterative model. The effects of crack parameters and applied torque on the tooth torsional deformation, TVMS, and load distributions of the gear with ESC are finally investigated based on the proposed model. The results show that the proposed model can realize the accurate and fast decoupling calculation of the TVMS and load distributions of gears with ESC. This study can provide a basis for the establishment of the refined ESC fault diagnosis method and the rapid evaluation of the load distributions of gears with asymmetric errors or faults along TWD. [ABSTRACT FROM AUTHOR]

• Generalized envelopes and generalized envelope spectra are proposed for signal demodulation. • The performance of generalized envelope spectra in rolling bearing fault diagnosis is revealed using simulations and experiments. • The product envelope spectrum is proposed as an enhanced spectral analysis tool. • Product Envelope Spectrum Optimization-gram (PESOgram) is developed for rolling bearing fault diagnosis. • PESOgram shows excellent capability in identifying bearing fault characteristic frequencies. The vibration signal of a faulty rolling bearing exhibits typical non-stationarity – often in the form of cyclostationarity. The spectrum tools often used to characterize cyclostationarity mainly include envelope spectrum, squared envelope spectrum and log-envelope spectrum. In this paper, new detection methods of cyclostationarity are developed for obtaining a larger family of envelope analysis and their effectiveness in rolling bearing fault diagnosis is evaluated rigorously. Firstly, based on the simplified Box-Cox transformation, the generalized envelope signals are constructed from the analytic signal for demodulation purposes, and then a spectrum family named generalized envelope spectra (GESs) is proposed to reveal cyclostationarity. Especially, GESs with different transformation parameters exhibit different performance advantages against the random impulse noise and Gaussian background noise which are commonly present in rolling bearing vibration signals. Subsequently, a novel spectrum tool that combines the performance advantages of different GESs, called product envelope spectrum (PES), is developed to strengthen the capability to detect cyclostationarity. Finally, an enhanced envelope analysis named Product Envelope Spectral Optimization-gram (PESOgram) is proposed to improve the accuracy and robustness of PES for rolling bearing fault diagnosis in the presence of different fault-unrelated interference noises. The performance of the PESOgram method is validated on numerically generated signal and experimental signals collected from two railway axle bearing test rigs and compared with several state-of-the-art envelope analysis methods. The results demonstrate the effectiveness of the proposed method for fault diagnosis of rolling bearings and its advantages over other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

• An improved form of SWNSE is developed by generalizing the order of the norm. • A design framework for improved Gini indices is established and four specific forms of improved Gini index are proposed. • Improved Gini indices are proven to be sparsity measures. • A series of improved Gini indices are proposed for characterizing repetitive transients. • The improved Gini indices with norm order 3 exhibit better comprehensive performance. Sparsity measures are important and effective tools for accurately characterizing fault features and degradation trends of rotating machinery. In the past few decades, numerous sparsity measures, such as kurtosis, negentropy, Lp/Lq norm, pq-mean, smoothness index and Gini index, have been proposed and introduced for condition monitoring, fault diagnosis and remaining life prediction of rotating machinery. However, it is difficult for traditional sparsity measures to possess strong random impulse resistance and fault impulse discernibility simultaneously. To design robust sparsity measures for repetitive transient characterization and machine condition monitoring, an improved form of the sum of weighted normalized square envelope (SWNSE) is firstly developed by generalizing the order of the norm as a framework for adaptively quantifying the repetitive transients. Under this framework, a rank-dependent generalized Gini index (RDGGI) is proposed, allowing four specific forms of RDGGI to be reasonably constructed by introducing four weight sequence design methods. Theoretical derivation shows that the quantitative indices produced by the four specific forms of RDGGI satisfy at least five of the six attributes of sparsity measures, proving that the newly designed quantitative indices are sparsity measures, and are also scale-invariant and have a limited magnitude range between 0 and 1. Furthermore, a series of improved Gini indices are derived by setting typical norm orders and designing appropriate monotonically decreasing weight sequences, and simulation analysis is conducted to verify their three key properties in characterizing transient features. Finally, the performance of the improved Gini indices in characterizing bearing fault features for condition monitoring is verified using vibration datasets from two bearing run-to-failure experiments. Simulation and experimental results show that the improved Gini indices with norm order 3 can simultaneously possess strong resistance to random impulses and discernibility of fault impulses, demonstrating the superior performance of improved Gini indices in bearing condition monitoring. [ABSTRACT FROM AUTHOR]

• A gear-shaft-bearing-housing dynamic model is proposed to reveal the modulation between bearing and gear. • Vibration Characteristics of bearing clearances under the effect of gear meshing is investigated. • An indicator is proposed based on MSB-SE for bearing clearances monitoring. • A run-to-failure gearbox test rig is designed to verify the effectiveness of the proposed indicator. Internal radial clearance is a key factor influencing bearing fatigue life. Moreover, bearings inevitably suffer from various wears and tears, which result in gradual increase of clearance and shorten bearing life. Monitoring bearing clearance changes using vibration can effectively indicate the bearing wear and provide good leading time to perform maintenances. Previous studies show that vibration at ball pass frequency on outer race (BPFO) can be based for clearance monitoring. However, such clearance induced vibration has not been well understood, especially under complicated dynamic interactions such as in a gearbox system. To fill this gap, this paper presents a nonlinear gear-shaft-bearing-housing vibration model with fourteen degree of freedom (DOF) to investigate the vibration responses under the dynamic gear meshing force and progressively changed radial clearances at first. Then, the model was verified through a two-stage spur gearbox. Furthermore, bearing characteristics with different radial clearances under the influence of gear are revealed and indicator based on modulation signal bispectrum-sideband estimator (MSB-SE) was proposed. Finally, vibration data from a run-to-failure gearbox test rig was utilized to verify the effectiveness of the MSB-SE indicator for bearing clearances monitoring. Simulation results show that BPFO is modulated on gear meshing frequency (GMF) and BPFO amplitude from envelope spectrum increases with bearing clearances under the influence of gear meshing. Indicator based on MSB-SE, possessing the capability of purifying the interferences of gear meshing and strong noises, is effective to capture the variance of bearing clearances. The experiment based on a run-to-failure gearbox test rig provided evidence for the effectiveness of the proposed indicator, which is more accurate than BPFO amplitude from conventional envelope analysis and time-domain indicators, such as RMS and kurtosis. These findings are of significance for bearing fault diagnosis and maintenance. [ABSTRACT FROM AUTHOR]