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This paper presents a hydrodynamic lubrication model to investigate the static and dynamic characteristics of micro-grooved bearings considering journal misalignment. An averaged Reynolds equation with the mass conservation is derived and numerical procedure is solved by the finite difference method. The influences of eccentricity ratio, misalignment and micro-groove parameters on lubrication performances of aligned/misaligned journal bearings are compared systematically. Mathematical expressions of five micro-grooves with different geometric shapes i.e., straight-groove, left spiral-groove, right spiral-groove, left herringbone-groove and right herringbone-groove are given. The results show that with the increase of degree of misalignment, the load carrying capacity, main stiffness and damping coefficients increase, and the friction coefficient decreases, while the effect of misalignment angle on bearing performance is opposite. The results also indicated that the left herringbone-groove has better performance improvement for aligned journal bearing, while the right spiral-groove has better performance improvement for misaligned journal bearing under the optimal micro-groove parameters, especially at low eccentricity ratio. Moreover, the amount of the performance improvement of micro-grooved bearing depends on the design of the micro-groove parameters. This study can provide theoretical guidance for the design optimization of micro-grooved journal bearings with misalignment.

Identifying a spectral frequency band with abundant fault information from spectral coherence is essential for improved envelope spectrum-based bearing diagnosis. Both blind features and targeted features have been employed to distinguish informative spectral frequency band of spectral coherence. However, how to select appropriate feature to correctly discriminate the optimal frequency band of spectral coherence in different scenarios is problematic. In this study, a new targeted feature is presented to quantify the signal-to-noise ratio in narrow frequency bands of spectral coherence, and further a method based on the proposed feature is developed to distinguish an optimal spectral frequency band of spectral coherence for bearing diagnostics. The efficiency of the developed method, typical blind feature-based methods and typical targeted feature-based methods in identifying the defect-sensitive frequency band of spectral coherence and bearing fault diagnosis is validated and compared using simulated signals with different interference noises and bearing experimental signals. The advantages and limitations of typical blind and targeted feature-based methods in different scenarios are summarized to guide the application. The results demonstrate that the developed targeted feature can efficiently evaluate bearing failure information in the cyclic frequency domain, and the presented approach can accurately discriminate the failure-related spectral frequency band of spectral coherence and detect different bearing faults compared with the methods based on the state-of-the-art features.

Epicyclic gearboxes are prevalent in a variety of important engineering systems such as automotive, aerospace, wind turbines, civil equipment and industrial robots owing to the merits of compact structure and high power density. To ensure the productivities of such important systems, condition monitoring techniques are being actively studied to resolve the challenge of varying transmission paths and multiple modulations in vibration signals acquired from ring gear housing. This paper proposes a new Modulation Signal Bispectrum (MSB) Enhanced Squared Envelope method to analyse the vibration signals acquired by a special On-Rotor Sensing (ORS) transducer that is mounted on the shaft of an epicyclic gearbox. A vibration signal model of ORS measurements from epicyclic gearboxes is presented to show the multiple modulation influences. Moreover, inevitable noise influences are also investigated on the conventional squared envelope and the state-of-the-art spectral correlation analysis. On this base, MSB is introduced to suppress these influences for accurately extracting equally spaced harmonics in the squared envelope and is then integrated to isolate fault signatures, allowing effective fault detection and diagnosis of epicyclic gearboxes, which lead to novel contributions of an MSB Enhanced Squared Envelope (MSB-ESE) approach. An experimental study of compound epicyclic gear faults was conducted to demonstrate the superior performance of MSB-ESE along with the special ORS technique in detecting and diagnosing the compound faults on sun and planet gears.

Weak fault feature extraction is of great significance to the fault diagnosis of rolling bearing. At the early stage of defects, fault features are usually weak and easily submerged in strong background noise, which makes feature information extremely difficult to be excavated. This paper proposes an iterative morphological difference product wavelet (MDPW) to address this issue. In this scheme, firstly, the morphological difference product filter (MDPF) is developed using the combination morphological filter-hat transform operator and difference operator. The MDPF is then incorporated into a morphological undecimated wavelet to construct the MDPW, which can achieve noise suppression and fault feature enhancement. Subsequently, the optimal iteration numbers that influence the performance of MDPW is determined using the fault severity indicator, which effectively extracts periodic impulse related to the failure of rolling bearing. Finally, the fault identification is inferred by the occurrence of fault defect frequencies in the MDPW spectrum with the optimal iteration numbers. The validity of the iterative MDPW is evaluated through numerical simulations and experiment cases. The analysis results demonstrate that the iterative MDPW has higher diagnosis accuracy than existing algorithms (e.g., adaptive single-scale morphological wavelet and weighted multi-scale morphological wavelet). This research provides a new perspective for improving the weak fault feature extraction of rolling bearing.

Machine learning, especially deep learning, has been highly successful in data-intensive applications, however, the performance of these models will drop significantly when the amount of the training data amount does not meet the requirement. This leads to the so-called Few-Shot Learning (FSL) problem, which requires the model rapidly generalize to new tasks that containing only a few labeled samples. In this paper, we proposed a new deep model, called deep convolutional meta-learning networks (DCMLN), to address the low performance of generalization under limited data for bearing fault diagnosis. The essential of our approach is to learn a base model from the multiple learning tasks using a support dataset and finetune the learnt parameters using few-shot tasks before it can adapt to the new learning task based on limited training data. The proposed method was compared to several few-shot learning methods, including methods with and without pre-training the embedding mapping, and methods with finetuning the classifier or the whole model by utilizing the few-shot data from the target domain. The comparisons are carried out on one-shot and ten-shot tasks using the CWRU bearing dataset and a cylindrical roller bearing dataset. The experimental result illustrates that our method has good performance on the bearing fault diagnosis across various few-shot conditions. In addition, we found that the pre-training process does not always improve the prediction accuracy.

In machining processes, chatter vibrations are always regarded as one of the major limitations for production quality and efficiency. Accurate and timely monitoring of chatter is helpful to maintain stable machining operations. At present, most chatter monitoring methods are based on the energy level at specified chatter frequencies or frequency bands. However, the spectral features of chatter could change during machining operations due to complexity and time-varying dynamics of the physical machining process. The purpose of this paper is to investigate the time-varying chatter features in turning of thin-walled tubular workpieces from the perspective of entropy. The airborne acoustics was selected as the source of information for machining condition monitoring. First, corresponding to the distinguishing surface topographies relevant to machining conditions, the features of the sound signal emitted during turning of the thin-walled cylindrical workpieces were extracted using the spectral analysis and wavelet packet transform, respectively. It was shown that the dominant vibration frequency as well as the energy distribution could shift with the transition of the machining status. After that, two relative entropy indicators based on the spectrum and the wavelet packet energy were constructed to identify chattering events in turning of the thin-walled tubes. The experimental results demonstrate that the proposed indicators could accurately reflect the transition of machining conditions with high sensitivity and robustness in comparison with the traditional FFT-based methods. The achievement of this study lays the foundations of the online chatter monitoring and control technique for turning of the thin-walled tubular workpieces.

Thanks to the fast development of micro-electro-mechanical systems (MEMS) technologies, MEMS accelerometers show great potentialities for machine condition monitoring. To overcome the problems of a poor signal to noise ratio (SNR), complicated modulation, and high costs of vibration measurement and computation using conventional integrated electronics piezoelectric accelerometers, a triaxial MEMS accelerometer-based on-rotor sensing (ORS) technology was developed in this study. With wireless data transmission capability, the ORS unit can be mounted on a rotating rotor to obtain both rotational and transverse dynamics of the rotor with a high SNR. The orthogonal outputs lead to a construction method of analytic signals in the time domain, which is versatile in fault detection and diagnosis of rotating machines. Two case studies based on an induction motor were carried out, which demonstrated that incipient bearing defect and half-broken rotor bar can be effectively diagnosed by the proposed measurement and analysis methods. Comparatively, vibration signals from translational on-casing accelerometers are less capable of detecting such faults. This demonstrates the superiority of the ORS vibrations in fault detection of rotating machines.

With electric vehicles (EVs) being widely accepted as a clean technology to solve carbon emissions in modern transportation, lithium-ion batteries (LIBs) have emerged as the dominant energy storage medium in EVs due to their superior properties, like high energy density, long lifespan, and low self-discharge. Performing real-time condition monitoring of LIBs, especially accurately estimating the state of charge (SOC) and state of health (SOH), is crucial to keep the LIBs work under safe state and maximize their performance. However, due to the non-linear dynamics caused by the electrochemical characteristics in LIBs, the accurate estimations of SOC and SOH are still challenging and many technologies have been developed to solve this challenge. This paper reviews and discusses the state-of-the-art online SOC and SOH evaluation technologies published within the recent five years in view of their advantages and limitations. As SOC and SOH are strongly correlated, the joint estimation methods are specifically reviewed and discussed. Based on the investigation, this study eventually summarizes the key issues and suggests future work in the real-time battery management technology. It is believed that this review will provide valuable support for future academic research and commercial applications.

With the development of Industry 4.0 and requirement of smart factory, cellular manufacturing system (CMS) has been widely concerned in recent years, which may leads to reducing production cost and wip inventory due to its flexibility production with groups. Intercellular transportation consumption, sequence-dependent setup times, and batch issue in CMS are taken into consideration simultaneously in this paper. Afterwards, a multi-objective flexible job-shop cell scheduling problem (FJSCP) optimization model is established to minimize makespan, total energy consumption, and total costs. Additionally, an improved non-dominated sorting genetic algorithm is adopted to solve the problem. Meanwhile, for improving local search ability, hybrid variable neighborhood (HVNS) is adopted in selection, crossover, and mutation operations to further improve algorithm performance. Finally, the validity of proposed algorithm is demonstrated by datasets of benchmark scheduling instances from literature. The statistical result illustrates that improved method has a better or an equivalent performance when compared with some heuristic algorithms with similar types of instances. Besides, it is also compared with one type scalarization method, the proposed algorithm exhibits better performance based on hypervolume analysis under different instances.

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.

Cost-effective technology for condition monitoring and fault diagnosis is of practical importance for equipment maintenance and accident prevention. Among many fault diagnosis methods, sound-based sensing technology has been highly regarded due to its rich information, non-contact and flexible installation advantages. However, noise from the environment and other machines can interfere with sound signals, decreasing the effectiveness of acoustic sensors. In this paper, a novel trumpet-shaped acoustic metamaterial (TSAM) with a high enhancement of sound wave selection is proposed to detect rotating machinery faults. Firstly, a numerical calculation was carried out to test the characteristics of the proposed metamaterials model. Secondly, a finite element simulation was implemented on the model to verify the properties of the designed metamaterials. Finally, an experiment was conducted based on an electrical fan to prove the effectiveness of the designed metamaterials. The results of the signal-to-noise ratio show more than 25% improvement, consistently demonstrating the potentiality of the designed acoustic metamaterials for enhancing the weak fault signal in acoustic sensing and the capabilities of contributing to a more cost-effective fault diagnosis technology.