During the operation of wind turbine bearings, the rotational speed continuously fluctuates with changes in wind intensity. At different rotational speeds, bearing faults exhibit distinct characteristic patterns and the accuracy of intelligent diagnostic methods significantly decreases. Regarding this challenge, a multi-scale fusion transfer model based on HigherHRNet (MSFTM-HigherHRNet) is proposed in this paper for bearing fault diagnosis of wind turbine generators. The proposed MSFTM-HigherHRNet model can effectively reduce the distribution differences of fault data at different speeds and achieve high-precision fault diagnosis. Firstly, to enhance the interpretability of the model, an S-transformation is performed on the vibration signals from both the source and target domains to extract features in the time-frequency domain. Secondly, HigherHRNet is utilised to extract multi-scale features. Feature extraction and reconstruction are performed separately for each scale, followed by multi-scale feature fusion to obtain a deep fault feature. Furthermore, a subdomain alignment module (SAM) is constructed. The unsupervised classification results of samples using K-means yields subdomains under different categories and transfer learning is employed to align the subdomains, thereby reducing the distribution differences at different rotational speeds. Finally, the effectiveness of this method is verified using actual wind field test data. The results show that its diagnostic accuracy exceeds 95%, which is superior to existing fault diagnosis methods at present. This research provides an effective solution path for the fault diagnosis of wind turbine bearings under variable working conditions.
Accurate crankshaft angular displacement measurement is critical for the precision of indicator diagrams in marine diesel engines, yet it is significantly compromised by torsional vibrations, especially under dynamic operating conditions. Traditional methods relying on static models or end-mounted sensors struggle to provide real-time correction for this dynamic error. To address this challenge, a high-precision real-time correction strategy is developed, based on a multi-degree-of-freedom torsional vibration model. The forced torsional angles of each cylinder are calculated in real time via a modal superposition method employing QR decomposition for eigenvalue solution. A closed-loop feedback mechanism is established, dynamically compensating for non-uniform angular signals through Newton interpolation combined with a weighted least-squares (NI-WLS) global optimisation. Experimental validation on a MAN 6S70MC-C diesel engine at three characteristic speeds (30 r/min, 50 r/min and 70 r/min) demonstrates the effectiveness of the proposed method. Significant corrections to top dead centre (TDC) position errors are achieved, with a maximum correction of 0.0803° observed at 70 r/min. The absolute indicated power error is consistently reduced across the speed range, with corrections of up to 10.6 kW per cylinder, thereby substantially enhancing the fidelity of indicator diagram analysis. This work provides a novel, model-based tool for real-time performance monitoring and efficiency optimisation in marine diesel engines.
Thermal barrier coatings (TBCs) play a crucial role in providing thermal protection for high-temperature alloys, particularly in aero engine and gas turbine applications. The thickness of the top ceramic coating (TC), which is the primary factor governing the thermal insulation performance and remaining lifespan of high-temperature components, diminishes during service. Although eddy current testing (ECT) is widely used to measure the thickness of conductive materials and their non-conductive coatings, conventional implementations exhibit insufficient resolution for low-conductivity materials such as TBCs. To address this problem, a high-frequency chirp pulse was used to excite the eddy current system. The autocorrelation characteristics of chirp signals were leveraged to implement pulse compression, thereby improving the signal-to-noise ratio (SNR) of the output peak. Building on this, local carrier peak fitting was employed to further enhance peak stability and detection sensitivity. A thickness measurement prototype was developed and implemented on a field-programmable gate array (FPGA) and the proposed method was validated using a custom-built experimental set-up. The experimental results demonstrate that the theoretical resolution limit approaches 0.3 μm, with a relative standard deviation (RSD) of less than 0.8%. These results indicate that the developed system offers high measurement sensitivity and is suitable for measuring the thickness of low-conductivity TBCs.
Deep cone thickeners (DCTs) are widely used in mineral processing to achieve high underflow concentrations; however, excessive loading of the rake spindle can lead to pressurised raking, material overflow and even structural failure. Reliable monitoring of spindle mechanical states is therefore essential for safe and stable operation. In this study, a digital twin (DT)-based condition monitoring framework is developed for the rake spindle of a deep cone thickener. A computational fluid dynamics (CFD)-based model is used to capture the relationship between the internal flow field and spindle mechanical response. Simulation results and experimental data are employed to develop machine learning models, including multi-layer perceptron (MLP) and support vector machine (SVM) models, for predicting the maximum equivalent stress of the spindle. Based on these models, a DT-enabled condition monitoring system is constructed by integrating physical sensing data, data-model fusion and a real-time evolutionary virtual model. The system enables continuous synchronisation between the physical spindle and its virtual counterpart, providing real-time visualisation and mechanical state assessment. The experimental results obtained from a small-scale deep cone thickener demonstrate that the proposed DT framework accurately captures the evolution of spindle mechanical states. This provides an effective solution for real-time condition monitoring and early overload detection in deep cone thickener operations.
Transformer oil temperature is a key quantity characterising the thermal load and insulation state of equipment; its evolution is jointly driven by load, environment and cooling conditions, exhibiting non-linearity and non-stationarity. Traditional mechanistic and time-series models struggle to capture multi-scale structures, while direct deep learning is constrained by mode mixing and insufficient characterisation of long-term dependencies. Therefore, this paper proposes an entropy-guided multi-scale decomposition-parallel temporal network integrated framework (VSC-PTT). By using entropy-guided decomposition composed of variational mode decomposition (VMD), sample entropy (SampEn) and complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), it obtains multi-scale sub-sequences with clear frequency bands and aligns and fuses VMD and CEEMDAN modes along the time axis to construct 11-dimensional multi-scale temperature features. These oil temperature (OT)-derived features are further combined with six load-related auxiliary variables, namely HUFL, HULL, MUFL, MULL, LUFL and LULL, to form a 17-dimensional hybrid input feature set for oil temperature prediction. On this basis, a parallel temporal convolutional network (TCN)-Transformer network is built to jointly model local and long-term dependencies, enabling fine-grained oil temperature prediction. Experimental results show that VSC-PTT achieves a coefficient of determination (R 2 ) of 0.9977, a mean squared error (MSE, dimensionless, calculated on normalised oil temperature values) of 0.0219 and a mean absolute error (MAE, dimensionless, calculated on normalised oil temperature values) of 0.0160 on a published dataset (Electricity Transformer Temperature-hourly 2 (ETTh2)); it achieves the best overall performance among the compared models, with prediction curves closely matching measured values and residual distributions that are concentrated and symmetric, demonstrating generalisation capability and robustness.
