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Considering the intricate structure, challenging maintenance requirements, and the interdependent nature of the detection parameters within a wind turbine gearbox, this study employs a stacked auto-encoder model for the offline analysis and modeling of standard operational data from the gearbox. The deviation in health factors post-model reconstruction serves as a metric for monitoring the gearbox’s operational status, with the unit’s health score being derived from an enhanced encoder architecture.

The anti-foaming protection is designed to prevent the oil from overheating due to intense oil churning and to reduce torque losses. The numerical simulation will be performed on gearbox housing in the SolidWorks Flow module in two variants: without anti-foaming protection and with anti-foaming protection, both for 3 values of the speed: 1000, 1200, 1400 rpm and 3 values of the oil height: 70, 100, 126 mm. The aim of the paper is to quantify by simulation the torque losses in the gearbox housing for different values of speed and oil level.

A fault diagnosis approach based on multi-sensor data fusion is a promising tool to deal with complicated damage detection problems of mechanical systems. Nevertheless, this approach suffers from two challenges, which are (1) the feature extraction from various types of sensory data and (2) the selection of a suitable fusion level. It is usually difficult to choose an optimal feature or fusion level for a specific fault diagnosis task, and extensive domain expertise and human labor are also highly required during these selections. To address these two challenges, we propose an adaptive multi-sensor data fusion method based on deep convolutional neural networks (DCNN) for fault diagnosis. The proposed method can learn features from raw data and optimize a combination of different fusion levels adaptively to satisfy the requirements of any fault diagnosis task. The proposed method is tested through a planetary gearbox test rig. Handcraft features, manual-selected fusion levels, single sensory data, and two traditional intelligent models, back-propagation neural networks (BPNN) and a support vector machine (SVM), are used as comparisons in the experiment. The results demonstrate that the proposed method is able to detect the conditions of the planetary gearbox effectively with the best diagnosis accuracy among all comparative methods in the experiment.

Aiming at the problem of low prediction accuracy of wind turbine gearbox temperature, the improved whale algorithm is used to optimize the long and short term memory network to predict the oil temperature of gearbox. The input data is based on the grey correlation degree to mine the parameters that are highly correlated with the oil temperature of the gear box, and the prediction model under healthy state is established. Finally, the experimental analysis shows that the method used in this paper has good accuracy in the oil temperature prediction of gearbox.

Virtual models boost smart manufacturing by simulating decisions and optimization, from design to operations, explain Fei Tao and Qinglin Qi. Virtual models boost smart manufacturing by simulating decisions and optimization, from design to operations, explain Fei Tao and Qinglin Qi. An illustration of a digital twin city

Gearboxes frequently operate under variable speed conditions, leading to the non-stationarity of collected monitoring signals. This paper proposes a sparse linear parameter-varying autoregressive moving average (Spa LPV-ARMA) model for fault detection of gearboxes under variable speed conditions. The Spa LPV-ARMA model integrates the advantages of LPV concepts, sparse models, and ARMA models. The proposed model has a more compact structure compared to the reported Spa LPV-AR model, which results in a lower ratio of parameter number to data amount, and the compact model structure also allows the Spa LPV-ARMA model to have higher computational efficiency during the testing phase. A simulation study was conducted to validate the performance of the proposed method. The results demonstrate that the Spa LPV-ARMA model exhibits superior modeling accuracy compared to the reported Spa LPV-AR model. Additionally, the fault detection method based on the Spa LPV-ARMA model achieves a higher fault detection rate when contrasted with a model that solely considers the autoregressive component.

With the rapid development of smart manufacturing, some challenges are emerging in the production management, including the utilization of information technology and the elimination of dynamic disturbance. A digital twin-driven production management system (DTPMS) can dynamically simulate and optimize production processes in manufacturing and achieve real-time synchronization, high fidelity, and real-virtual fusion in cyber-physical production. This paper focuses on establishing DTPMS for production life-cycle management. First, we illustrate how to integrate digital twin technology and simulation platforms. Second, a framework of DTPMS is proposed to support a cyber-physical system of production workshop, including product design, product manufacturing, and intelligent service management. Finally, the proposed DTPMS is applied to the production process of a heavy-duty vehicle gearbox. The experimental results indicate that the defective rate of products and the in-process inventory are reduced by 34% and 89%, respectively, while the one-time pass rate of product inspection is increased by 14.2%, which demonstrates the feasibility and effectiveness of the DTPMS.

The recent advancements in wearable exoskeletons have highlighted their effectiveness in assisting humans for both rehabilitation and augmentation purposes. These devices interact with the user; therefore, their actuators and power transmission mechanisms are crucial for enhancing physical human–robot interaction (pHRI). The advanced progression of 3D printing technology as a valuable method for creating lightweight and efficient gearboxes enables the exploration of multiple reducer designs. However, to the authors’ knowledge, only sporadic implementations with relatively low reduction ratios have been reported, and the respective experimental validations usually vary, preventing a comprehensive evaluation of different design and implementation choices. In this paper, we design, develop, and examine experimentally multiple 3D-printed gearboxes conceived for wearable assistive devices. Two relevant transmission ratios (1:30 and 1:80) and multiple designs, which include single- and double-stage compact cam cycloidal drives, compound planetary gearboxes, and cycloidal and planetary architectures, are compared to assess the worth of 3D-printed reducers in human–robot interaction applications. The resulting prototypes were examined by evaluating their weight, cost, backdrivability, friction, regularity of the reduction ratio, gear play, and stiffness. The results show that the developed gearboxes represent valuable alternatives for actuating wearable exoskeletons in multiple applications.

As the core component of industrial transmission systems, the fault diagnosis technology of gearboxes is crucial. However, due to the scarcity of fault samples, effective training of fault diagnosis network models has become a major challenge. In response to this issue, this paper proposes a transmission fault diagnosis method based on spectral kurtosis feature enhancement. This method processes vibration signals through Fourier transform and fast spectral kurtosis transform, calculates the weight of each frequency band through frequency domain signal and spectral kurtosis map, enhances the fault characteristics of the frequency spectrum signal, and generates multiple feature enhanced signals. Then, using a spectral kurtosis channel attention convolutional network and combining it with spectral kurtosis channel weights to calculate the channel weights of each sample, fault diagnosis is achieved. The experimental results show that our network surpasses SA-CNN (Self attention mechanism convolutional network) and CA-MCNN (Channel attention mechanism convolutional network) in diagnosing gearboxes with small sample sizes, with accuracy rates 18.77% and 4.86% higher, respectively. This approach significantly reduces the risks of misdiagnosis and missed diagnosis, thereby enhancing the reliability and safety of equipment operation.

Fault diagnosis is essential for gearbox safety. However, traditional intelligent models are challenging to develop due to limited fault data availability. Incorporating prior knowledge into the learning process can mitigate this data scarcity. This paper introduces a deep neural network integrating prior knowledge for few-shot gearbox fault diagnosis. Initially, prior fault features are constructed using domain knowledge, allowing initial gearbox health assessment without parameter training. Subsequently, a deep convolutional neural network extracts adaptive features from vibration data. An attention module then combines these adaptive features with prior features, enabling accurate fault identification. The proposed method is validated through gearbox fault data analysis. Experimental results demonstrate its superiority in fault diagnosis accuracy compared to existing methods in small sample scenarios.