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The misalignment and lead crown relief make the tooth partial contact along the tooth width direction, which greatly affects the mesh characteristics. In this paper, a mesh stiffness model of spur gear pairs with misalignment and lead crown relief is proposed based on the slice theory, in which the slice coupling effect is presented. The slice coupling is simulated as a spring and the corresponding model is developed. Then, the solving procedure of the mesh stiffness model is studied. The deformation transfer model is derived between the springs in contact state and non-contact state. An iterative computation method is designed to calculate the mesh deformation of tooth pairs. Finally, the mesh stiffness model is validated by the finite element method for a spur gear pair with ideal tooth profile, misalignment and lead crown relief. Effects of the misalignment and lead crown relief on mesh stiffness of gear pairs are also studied. The results reveal that the proposed model is accurate, effective and efficient for spur gear pairs with ideal tooth profile, misalignment and lead crown relief. It is suggested that the slice coupling should be considered in mesh stiffness calculation of gear pairs with misalignment and lead crown relief.

Ice accretion on wind turbine blades is one of the major faults affecting the operational safety and power generation efficiency of wind turbines. Current icing detection methods are based on either meteorological observing system or extra condition monitoring system. Compared with current methods, icing detection using the intrinsic supervisory control and data acquisition (SCADA) data of wind turbines has plenty of potential advantages, such as low cost, high stability, and early icing detection ability. However, there have not been deep investigations in this field at present. In this paper, a novel intelligent wind turbine blade icing detection method based on the wind turbine SCADA data is proposed. This method consists of three processes: SCADA data preprocessing, automatic feature extraction, and ensemble icing detection model construction. Specifically, deep autoencoders network is employed to learn multilevel fault features from the complex SCADA data adaptively. And the ensemble technique is utilized to make full use of all the extracted features from different hidden layers of the deep autoencoders network to build the ensemble icing detection model. The effectiveness of the proposed method is validated using the data collected from actual wind farms. The experimental results reveal that the proposed method is able to not only adaptively extract valuable fault features from the complex SCADA data, but also obtains higher detection accuracy and generalization capability compared with conventional machine learning models and individual deep learning model.

Feature extraction is a major challenge in operational reliability assessment, which requires techniques and prior knowledge. Deep auto-encoder (DAE) is a popular deep learning method and is widely used in feature extraction. However, low generalization ability and structure parameters design are still the major problems of DAE for operational reliability assessment. To overcome the two problems, an ensemble DAE is proposed for operational reliability assessment. Firstly, different structure parameters are employed to design a series of DAEs for feature learning from the measured data. Secondly, a feature ensemble strategy is designed to enhance the generalization ability of the DAE model, in which the features learned by different DAEs are clustered to remove the irrelevant DAEs and select the more general feature subset. Finally, the operational reliability indicator is defined by the Euclidean distance of the selected features and the operational reliability model is developed. The proposed method is utilized to analyze the experimental bearings and the results indicate that the proposed method is effective for operational reliability assessment.

Real-time monitoring and rapid evaluation of bearing operating conditions, especially for the reliability evaluation and remaining useful life (RUL) prediction, are major challenges in the rotating machinery field. A two-dimensional (2-D) deep convolution neural network (CNN) is proposed for rapidly evaluating reliability and predicting RUL, in which a signal conversion method is proposed for converting the one-dimensional signal into the 2-D image to satisfy the input requirements of the 2-D CNN. Different activation functions are employed to implement the conversion of the input data to the output data for each layer of the network, and dropout is only adopted in the hidden layer to change the network structure to prevent overfitting. The maximum correlation entropy with regular terms is employed as the loss function of the model to obtain better training performance compared with the mean square error (MSE). Then, the rolling bearing degradation vibration data is applied to the proposed model to verify the accuracy and rapidity. The results show that the proposed method has good accuracy and fast calculation ability in bearing reliability evaluation and RUL prediction, especially, its time consumption is shorter than that by other deep learning networks.

Diabetes mellitus represents a chronic metabolic disorder characterized by impaired lipid homeostasis and carbohydrate metabolism, gradually leading to persistent hyperglycemia. The extracts of Rhodiola species are widely used as herbal medicine or dietary supplement in Asia, Europe and the United States. Salidroside, a p-hydroxyphenethyl-β-glucoside compound, is the main active ingredient of the Rhodiola root. Recently, various studies have suggested that Rhodiola and salidroside may have pharmacological properties that could be used in the treatment of diabetes, as studies have confirmed that AMP-activated protein kinase (AMPK) and AMPK-related signaling are connected with its beneficial effects. This review aims to summarize the research progress of Rhodiola and salidroside in the treatment of diabetes. A detailed summary of AMPK and AMPK-related signaling induced by Rhodiola and salidroside are discussed.

High glucose (HG)-induced nucleotide-binding and oligomerization (NACHT) domain, leucine-rich repeat (LRR), and pyrin domain (PYD)-containing protein 3 (NLRP3) inflammasome activation leads to diabetic neuropathic pain. We recently showed that salidroside could suppress NLRP3 inflammasome activation in hepatocytes exposed to HG. The aim of this study was to evaluate the analgesic effect of salidroside on diabetic rats and to explore its underlying mechanisms. Rat models with diabetic neuropathic pain were induced by high-fat diet feeding combined with low dose streptozotocin injections. Doses of salidroside at 50 and 100 mg.kg−1.day−1 were administered by gavage to diabetic rats for 6 weeks. Mechanical allodynia test, thermal hyperalgesia test and biochemical analysis were performed to evaluate therapeutic effects. Primary dorsal root ganglion (DRG) cells exposed to HG at 45 mM were used to further study the effects of salidroside on the AMP-activated protein kinase (AMPK)-NLRP3 inflammasome axis and insulin sensitivity in vitro. Salidroside administration improved hyperglycemia, ameliorated insulin resistance, and alleviated neuropathic pain in diabetic rats. Moreover, salidroside induced AMPK activation and suppressed NLRP3 inflammasome activation in the DRGs of diabetic rats. In addition, salidroside treatment relieved oxidative stress, improved insulin sensitivity and regulated the AMPK-NLRP3 inflammasome axis in HG-treated DRGs in vitro. Furthermore, AMPK inhibition in vivo or AMPK silencing in vitro abolished the beneficial effects of salidroside on diabetic neuropathic pain. Together, these results indicate that salidroside alleviates diabetic neuropathic pain through its regulation of the AMPK-NLRP3 inflammasome axis in DRGs.

Mathematical models based on instant environmental inputs are increasingly applied to optimize the operation of wastewater treatment plants (WWTPs) for improving treatment efficiency. This study established a numerical model consisting of the activated sludge module ASM3 and EAWAG bio-P module, and calibrated the model using data from a full-scale experiment conducted in a WWTP in Nanjing, China. The calibrated model was combined with online sensors for water temperature, chemical oxygen demand, -N and -P to optimize and dynamically adjust the operation of the WWTP. The results showed that, compared to the original default operation mode, the effluent water quality was significantly improved after optimization even without supplementation of external carbon or alkalinity, and the required aeration rate in spring, summer, autumn, and winter was reduced by 15, 41, 33 and 11%, respectively. The study indicated that there was the potential for application of closed-loop automatic control to regulate operating parameters to improve wastewater treatment processes through the integration of data on influent characteristics and environmental conditions from sensors, and results from simulation models.

An improved time-varying mesh stiffness (TVMS) model of a helical gear pair is proposed, in which the total mesh stiffness contains not only the common transverse tooth bending stiffness, transverse tooth shear stiffness, transverse tooth radial compressive stiffness, transverse gear foundation stiffness and Hertzian contact stiffness, but also the axial tooth bending stiffness, axial tooth torsional stiffness and axial gear foundation stiffness proposed in this paper. In addition, a rapid TVMS calculation method is proposed. Considering each stiffness component, the TVMS can be calculated by the integration along the tooth width direction. Then, three cases are applied to validate the developed model. The results demonstrate that the proposed analytical method is accurate, effective and efficient for helical gear pairs and the axial mesh stiffness should be taken into consideration in the TVMS of a helical gear pair. Finally, influences of the helix angle on TVMS are studied. The results show that the improved TVMS model is effective for any helix angle and the traditional TVMS model is only effective under a small helix angle.

As a new type of transmission, the single-roller enveloping hourglass worm gear (SEHWG) is widely used in precision mechanical transmission systems. Time-varying mesh stiffness (TVMS) is one of the most important excitations, which has great influences on the dynamic characteristics of SEHWG systems. In this paper, a model for calculating the TVMS of the SEHWG is proposed, in which bending stiffness, shear stiffness, radial compression stiffness, and foundation stiffness are considered in the worm stiffness calculation. And bending stiffness, shear stiffness, and foundation stiffness are considered in the worm wheel stiffness calculation. The process of calculating the periodical TVMS of the SEHWG is also presented. Then, the TVMS of the SEHWG by the proposed method is compared with that of the finite element method for the model validation. Finally, the influences of the roller radii on TVMS are analyzed. The results show that the mesh stiffness of the TVMS has a tendency to rise first and then fall with the increasing roller radii.

Time-varying mesh stiffness (TVMS) and the dynamic coupling between the helical gears have a great influence on the vibration characteristics of a helical gear rotor system. Considering the effects of TVMS and adopting two coupling models (lateral-torsional coupling model and lateral-torsional-axial-swing coupling model), the dynamic behavior of a helical gear system was studied. First, an analytical model was used to analyze TVMS of a helical gear pair where the helical tooth is simulated by many spur tooth slices along the direction of the tooth width and the mesh stiffness of each slice is calculated using the energy method. Then, considering the effects of the TVMS excitation, the finite element model of a helical gear rotor system was established. Gear mesh was simulated by the above-mentioned two coupling models to investigate the effects of coupling forms on the system vibration characteristics. The strain energy was used to distinguish the dominant mode and dominant shaft of a gear system in natural characteristics analysis. The results show that the full coupling model can analyze accurately the vibration characteristics of the system and the axial and swing motions cannot be ignored in vibration analysis. Finally, the effects of helix angle on TVMS and vibration responses of a helical gear system were also studied.

RUL prediction of bearings plays an essential role in avoiding unwanted downtime and improving machines' reliability. A linear reliability indicator approach for RUL prediction is proposed. Multiple deep auto-encoder models are established to extract deep features, and a clustering method is employed to select the original feature set. Monotonicity is set as a criterion for evaluating and selecting original optimal features from the original feature set and a linear reliability indicator is established through it. Based on linear reliability indicator and feature interpolation, the original feature set is transformed into a mapping feature set. Finally, the mapping feature set is trained by a reliability evaluation model and the particle filter is employed to predict RUL. To demonstrate the excellent capability of the proposed approach, the vibration signal from the public PRONOSTIA bearing datasets is used. Experiment results show that the proposed approach can achieve high accuracy in RUL prediction.

A model is introduced for analyzing the influence of tooth shape deviations and assembly errors on the helical gear mesh stiffness, loaded transmission error, tooth contact stress and tooth root stress. The helical gear is approximated as a series of independent spur gear slices along axial direction whose face-width is relatively small. The relative position relationships among those sliced teeth in mesh are developed with tooth profile errors and the stiffness of the sliced tooth is calculated by the potential energy method. From the equilibriums of the forces, gear mesh stiffness, loaded transmission error, tooth contact stress and tooth root stress are calculated. Then two cases are presented for validation of the model. It is demonstrated that the model is effective for calculating the stiffness of helical gear pairs. Finally, the effects of the tooth tip reliefs, lead crown reliefs and misalignments on the gear mesh stiffness, transmission error, tooth contact stress and tooth root stress are analyzed. The results show that mesh stiffness decreases, loaded transmission error, the maximum tooth contact stress and the maximum tooth root stress grow with the increasing tooth tip relief, lead crown relief and misalignment. And tooth edge has concentrated tooth contact stresses with a gear misalignment.

Remaining useful life (RUL) prediction has been a hotspot topic, which is useful to avoid unexpected breakdowns and improve reliability. Different bearing failure behaviors caused by multiple failure modes may lead to inconsistent feature distribution, which affects the prediction model performance. To accurately predict the RUL of bearing under different failure behaviors, a transferable convolutional neural network (TCNN) is proposed to learn domain invariant features. In the proposed method, a convolutional neural network is employed to extract the degradation features. Then multiple-kernel maximum mean discrepancies are integrated into optimization objective to reduce distribution discrepancy. The trained TCNN can be used to predict RUL by feeding data. Its effectiveness is verified by a run-to-failure bearing dataset. The comparison results reveal that the proposed method avoids the influence of kernel selection, improves the performance of domain adaptation effectively, and achieves a better RUL prediction performance.

A multi-ensemble method is proposed based on deep auto-encoder (DAE) for fault diagnosis of rolling bearings. At first, several DAEs with different activation functions are trained to obtain different types of features, which are merged into a feature pool. Then, the features in the feature pool are evaluated and selected. The classifiers are constructed for each feature. The classification accuracy is used as evaluation index and good features are selected. Finally, the train data is cross-divided and multi sample-sets with selected features are constructed. Each sample-set is used to train a classifier, the final diagnosis result is obtained by majority voting among these classification results. The proposed method is validated by two experimental bearing vibrations signals and also compared with other methods. The results revealed that different types of features can be obtained by DAEs with different activation functions. The proposed method has a high accuracy and good generalization ability.

This paper investigates the dynamic behaviors of a perforated gear system considering effects of the gear crack propagation paths and this study focuses on the effects of a crack propagating through the rim on the time-varying mesh stiffness (TVMS) and vibration responses. Considering the effects of the extended tooth contact, a finite element (FE) model of a gear pair is established based on ANSYS software. TVMS of the perforated gear with crack propagating through tooth and rim are calculated by using the FE model. Furthermore, a lumped mass model is adopted to investigate the vibration responses of the perforated gear system. The results show that there exist three periods related to slots of the gear body in a rotating period of the perforated gear. Gear cracks propagating through tooth and rim both reduce the gear body stiffness and lead to reduction of TVMS besides the crack tooth contact moment, and the TVMS weakening for the former is less than that for the latter. Moreover, the results also show that the gear crack propagating through the rim (CPR) has a greater effect on vibration responses than the gear crack propagating through the tooth (CPT) under the same crack level. Vibration level increases with the increasing crack depth, especially for the gear with CPR.

Dynamic characteristics of cracked gear systems, also known as cracked-gear rotor systems, have received increasing interests among industry and academy in the past two decades. This paper reviews published papers on the dynamics of cracked gear systems. These studies mainly focused on three topics: crack propagation prediction, time-varying mesh stiffness (TVMS) calculation and vibration response calculation; Study objects involve the spur gear, helical gear and planetary gear; Different modeling methods including analytical method, finite element (FE) method, combined analytical-FE approach were adopted. More specifically, this review is composed of three related parts according to the above three topics. The first part involves the prediction of the crack propagation path based on two-dimensional (2D) or three-dimensional (3D) gear models, which provides a basis for the hypothesis of crack path in the process of TVMS calculation of cracked gear pairs. The second part summarizes the TVMS calculation methods including analytical methods, FE methods, combined analytical-FE approaches and experimental methods. The final part reviews the dynamic models for vibration analysis of cracked gear systems including lumped mass models and FE models, where the crack effects are characterized by introducing TVMS of cracked gear pairs into the system dynamic models. The well known open problems about cracked gear dynamics are finally stated, and some new research interests are also pointed out. The review will provide valuable references for future studies on dynamics of cracked gears.

A dynamic model of a multi-shaft helical geared rotor system is presented. Rotating shafts of the system are modeled as Timoshenko beams. A general three-dimensional dynamic model of helical gear pairs with geometric eccentricity is developed for the gear mesh and bearing flexibility is included in the model as well. The transmission error and gear geometric eccentricity are simulated as excitations. Eigenvalue solution and the modal summation technique are used to predict the natural frequencies and forced responses of the system. Then two geared rotor system models are presented for validation of the gear dynamic model. It is demonstrated that the gear mesh model is effective for general geared rotor systems, spur and helical gears, one-stage and multi-stage systems. Finally, forced responses of an example system are analyzed to demonstrate the influences of the helical gear geometric eccentricity and the coupling between gear geometric eccentricity and rotor mass unbalance.

Due to the imbalanced distribution of real near-infrared spectroscopy data, it is difficult for traditional machine learning methods to correctly classify samples during the modeling process. In general, near-infrared spectral data sets are high-dimensional and have few samples. In order to enhance the classification accuracy of machine learning, here we propose an ensemble-based learning approach. Specifically, the proposed method first generates a number of samples using a variational autoencoder (VAE) network, and merges these with the original data to form a new balanced data set. Then a classification model is built using the multi-feature fusion cascade forest (FCForest) method. We verified and evaluated our approach using an imbalanced near-infrared spectroscopy data set from citrus greening. The experimental results showed that using VAE to generate the samples improved the classification accuracy for the imbalanced data. Furthermore, by using the FCForest method on the new, balanced data set, the performance of the classifier was further improved.