•An improved dynamic model of ACBB considering various friction torques and ball motion state is proposed.•The previous acoustic emission model is extended to describe more degrees of freedom of ...ACBB.•Effects of speed, load, and defect sizes on the acoustic characteristics for ACBB are investigated.•Comparison results between the proposed and reported models depict the superiority of established approach.
Due to their special characteristics, angular contact ball bearings (ACBBs) are broadly applied in various mechancial systems. The working performance of rotating machinery can be determined by the internal ACBBs. Vibration and acoustic characteristics of ACCBs in various rotating machinery are becoming increasingly significant for the high-performance mechanical systems. An in-depth recognition of acoustic characteristics of ACBB can be helpful for condition monitoring of rotating machinery. This study proposes an improved dynamic model of ACBB to consider the influences of elastic hysteresis, differential sliding friction torques, and elastohydrodynamic lubrication (EHL) rolling on the ball motion state. A modified time-dependent excitation (TDE) model is developed for describing different kinds of defects in the outer ring of ACBB. The acoustic emission models given by Sharma 8 and Patil 9 are extended to describe more degrees of freedom of ACBB. Effects of speed, load, and defect sizes on the acoustic characteristics for ACBB are investigated. Comparison results between the proposed and reported models depict the superiority of established approach here. Note that the load, speed, and defect can greatly affect the acoustic characteristics of ACBB. The results improved that this model has ability to obtain the accurate acoustic characteristics of a defective ACBB.
The large-scale machinery generally requires multiple accelerometers for condition monitoring. The multichannel vibration signals carry a wealth of fault information. Synchronous fault feature ...extraction using multichannel signals will significantly improve the diagnostic performance. The multivariate entropy method is able to synchronously extract the fault features from multiple sensors, but how to recognize the single fault occurring on different channels remains unexplored. In this article, we propose a novel feature extraction method called variational embedding multiscale diversity entropy, which constructs the phase space with different structures. The proposed variational phase space construction strategy will generate a different probability distribution for each channel, which results in a better separability for multichannel feature extraction. Combined with the random forest classifier, a novel fault diagnosis scheme is developed for condition monitoring of the large-scale machinery. One simulated signal and two experimental data are designed to validate the effectiveness of the proposed strategy. The results demonstrate that the proposed method has the best multichannel feature extraction ability compared with three existing methods: multivariate multiscale sample entropy, multivariate multiscale fuzzy entropy, and multivariate multiscale permutation entropy.
•EMD manifold is proposed for true mode extraction in machinery fault diagnosis.•Sensitive modes with different noise are fused nonlinearly by manifold learning.•The true mode is adaptively learned ...with the assistance of random noise.•The new method has the merits of mode mixing alleviation and noise removal.•The improved performance is verified in enhanced machinery fault diagnosis.
One challenge of the existing noise-assisted methods for solution of mode mixing problem of empirical mode decomposition (EMD) is that, the decomposed modes contain much residual noise derived from both added and self-contained noise. This paper proposes a new noise-assisted method, called EMD manifold (EMDM), for enhanced fault diagnosis of rotating machines. The major contribution is that the new method nonlinearly and adaptively fuses the fault-related modes containing different noise via a manifold learning algorithm, by which true fault-related transients are preserved, while fault-unrelated components including mode-mixing-induced components and the residual noise derived from both the added and self-contained noise are greatly suppressed. First, the sensitive mode is located among the modes obtained by the EMD method according to a newly proposed criterion. Then, a high-dimensional matrix is constructed of the sensitive modes obtained through a small number of EMD trials on the signals plus noise of different amplitudes. Finally, the manifold learning algorithm is performed on the high-dimensional matrix to extract intrinsic manifold of the fault-related transients. The high-dimensional matrix is repeatedly constructed with random noise added to adjust local data distribution of the matrix for adaptive EMDM feature learning. Experimental studies on gearbox and bearing faults are conducted to validate the proposed method and its enhanced performance over traditional noise-assisted EMD methods.
The diagnosis of rotor-casing rub, one of the most common malfunctions in rotating machinery, is usually straightforward in machines equipped with proximitors measuring relative rotor-casing ...displacement. However, constructive reasons, aeroderivative gas turbines are frequently equipped with accelerometers instead of proximitors. Since these turbines are increasingly used in many industrial applications, this poses substantial maintenance and safety problems. In this work, we apply a properly designed method based on Continuous Wavelet Transform (CWT) to the detection of early single–point rub in gas turbines by means of accelerometers on the machine casing. This procedure is validated on an experimental rotor rig with a flexible cylindrical casing. With this method we expect to prevent machine damage or failure by supplying a very clear picture of rotor-casing rub from the very onset. This is monitored under diverse conditions with accelerometers on casing points and with proximitors assembled between rotor shaft and casing. The acceleration signals collected are then post-processed and analyzed using the Discrete Fourier Transform (DFT) and the proposed CWT-based method. The signal post-processing parameters of the signal post-processing were adjusted with the help of a computational model of the experimental rig. We prove that an adequately designed CWT-based algorithm outlines the signal features that better characterize single-point rub, and unlike the DFT it is sensitive to an outbreak of rub within a time series.
•Task assignment model.•Dynamic and static simulation.•Crop harvesting simulation.
Task assignment is a key problem in multi-machine cooperative navigation. In the context of regional farmland ...operation, multiple agricultural machines often need to complete multiple tasks together. In order to realize the management of multiple agricultural machinery cooperation, studies on task assignment based on the improved ant colony algorithm have been conducted under the farmland operation environment. First, a task assignment model of multiple agricultural machinery cooperation was established by combining dynamic and static task assignments. Then, according to the task assignment model, the task assignment process based on the improved ant colony algorithm was established while considering the match between supply and demand, the operation capacity of the agricultural machinery, and the operation cycle and path cost. Finally, the dynamic and static task assignments of multiple agricultural machinery cooperation based on the improved ant colony algorithm were simulated on MATLAB. Taking the crop harvesting experiment as an example, according to the actual farmland location information of the Zhuozhou Experimental Farm, the different (agricultural machinery, task) combinations were set, and the task assignment results were compared and analyzed. Results showed that the path costs of harvester and grain transporters were reduced by 51.27% and 22.00% respectively, When the quantities of tasks were set to 11, indicating that the improved ant colony algorithm can effectively reduce the path cost. When the quantities of tasks were set to 5, 11, 16 and 22, the average operation cycles were shortened by 67.32%, 37.50%, 55.95%, and 56.37% respectively. The problem of “nearby” in the task assignment was solved to a certain extent, the overload of some agricultural machinery and the idle of other agricultural machinery were avoided, and the operation cycle was shortened. At the same time, based on the static task assignment, the dynamic task assignment was realized in the two scenarios of new tasks and malfunctioning harvesters, thus laying a foundation for further solving the scheduling management problem of multiple agricultural machinery cooperation under a complex farmland operation environment.
In many plants, vibration and noise problems occur due to fluid flow, which can greatly disrupt smooth plant operations. These flow-related phenomena are called Flow-Induced Vibration.This book ...explains how and why such vibrations happen and provides hints and tips on how to avoid them in future plant design. The world-leading author team doesn't assume prior knowledge of mathematical methods and provide the reader with information on the basics of modeling. The book includes several practical examples and thorough explanations of the structure, the evaluation method and the mechanisms to aid understanding of flow induced vibration.* Helps ensure smooth plant operations * Explains the structure, evaluation method and mechanisms * Shows how to avoid vibrations in future plant design
Over the last decade, structural aspects involving iron‑sulfur (Fe/S) protein biogenesis have played an increasingly important role in understanding the high mechanistic complexity of mitochondrial ...and cytosolic machineries maturing Fe/S proteins. In this respect, solution NMR has had a significant impact because of its ability to monitor transient protein-protein interactions, which are abundant in the networks of pathways leading to Fe/S cluster biosynthesis and transfer, as well as thanks to the developments of paramagnetic NMR in both terms of new methodologies and accurate data interpretation. Here, we review the use of solution NMR in characterizing the structural aspects of human Fe/S proteins and their interactions in the framework of Fe/S protein biogenesis. We will first present a summary of the recent advances that have been achieved by paramagnetic NMR and then we will focus our attention on the role of solution NMR in the field of human Fe/S protein biogenesis.
•Impact of solution NMR in mitochondrial and cytosolic Fe/S protein biogenesis•Dedicated paramagnetic NMR methodologies to characterize challenging Fe/S proteins•NMR unravels the mechanisms of 2Fe-2S and 4Fe-4S cluster assembly.•NMR elucidates electron transfer pathways required to assemble Fe/S clusters.•NMR describes mechanisms of Fe/S cluster trafficking and insertion in apo proteins.
To assess health conditions of rotating machinery efficiently, multiple accelerometers are mounted on different locations to acquire a variety of possible faults signals. The statistical features are ...extracted from these signals to identify the running status of a machine. However, the acquired vibration signals are different due to sensor's arrangement and environmental interference, which may lead to different diagnostic results. In order to improve the fault diagnosis reliability, a new multisensor data fusion technique is proposed. First, time-domain and frequency-domain features are extracted from the different sensor signals, and then these features are input into multiple two-layer sparse autoencoder (SAE) neural networks for feature fusion. Finally, fused feature vectors can be regarded as the machine health indicators, and be used to train deep belief network (DBN) for further classification. To verify the effectiveness of the proposed SAE-DBN scheme, the bearing fault experiments were conducted on a bearing test platform, and the vibration data sets under different running speeds were collected for algorithm validation. For comparison, different feature fusion methods were also applied to multisensor fusion in the experiments. Experimental results demonstrated that the proposed approach can effectively identify the machine running conditions and significantly outperform other fusion methods.
The uptake of ammonium, nitrate, phosphorus, and potassium ions by roots is mediated by specific ion transporter or channel proteins, and protein phosphorylation regulation events occurring on these ...proteins and their regulators determine their ultimate activity. Elucidating the mechanism by which protein phosphorylation modification regulates nutrient uptake will advance plant breeding for high nutrient-use efficiency. In this review, it is concluded that the root nutrient absorption system is composed of several, but not all, members of a specific ion transporter or channel family. Under nutrient-starvation conditions, protein phosphorylation-based regulation of these proteins and associated transcription factors increases ion transporter- or channel-mediated nutrient uptake capacity via direct function activity enhancement, allowing more protein trafficking to the plasma membrane, by strengthening the interaction of transporters and channels with partner proteins, by increasing their protein stability, and by transcriptional activation. Under excessive nutrient conditions, protein phosphorylation-based regulation suppresses nutrient uptake by reversing these processes. Strengthening phosphorylation regulation items that increase nutrient absorption and weakening phosphorylation modification items that are not conducive to nutrient absorption show potential as strategies for increasing nutrient use efficiency.
