We theoretically propose and experimentally demonstrate a simple and compact approach for the first generation of multi-longitudinal mode quadrature laser self-mixing (MQLSM) vibration sensor. This is achieved by constructing two polarized self-mixing signals with adjustable phase shifts in multi-longitudinal mode lasers, leveraging the inherent characteristics of multi-longitudinal mode spacing. It is found that, in the prescribed MQLSM vibration sensor, a pair of self-mixing signals with quadrature phase difference can be retrieved on demand by resorting to the quadrature-phase construction algorithm together with a circle fitting process, which makes the target vibration accessible based on the related quadrature demodulation method. More impressively, our proposed MQLSM sensor’s utility is exemplified in fault diagnosis of bearings as a proof of concept. Experimental results indicate that the vibration measurement error is less than 40 nm at a vibration amplitude of 5.00 μm and the measurement relative error of the characteristic frequency of fault bearing is less than 0.65%, thus enabling high-accuracy, compact, and robust MQLSM sensor. The findings presented here may hold extensive potential applications in online measurement, dynamic performance analysis, and beyond.

Through the analysis of data from the SCADA system of a wind turbine unit in a specific offshore wind farm located in Zhanjiang, it was observed that the most prevalent type of fault is bearing alarms on the gearbox’s generator side. Considering the growing need for intelligent offshore wind turbine maintenance, this study employed GRA on SCADA data collected over a significant duration from a representative wind turbine unit. Relevant features were extracted, with temperature serving as the target parameter. To address the challenge of long-term dependencies in long-term time series forecasting tasks, this study uniquely combined the GRU with the advanced Transformer neural network which incorporates attention mechanisms, to predict the temperature trend of the gearbox’s generator-side bearing. Based on the prediction residuals obtained during normal operation and their subsequent analysis, the study devised an effective anomaly detection process to identify early abnormal states of the gearbox’s generator-side bearing. Comparative performance evaluations were conducted, comparing the combined model with its individual component models, as well as the traditional LightGBM, in terms of temperature time series prediction and their application in anomaly detection. The results unequivocally demonstrate that the combined model outperforms both the individual models and LightGBM in terms of time series prediction accuracy and anomaly detection effectiveness, indicating an enhanced ability to handle long-term memory challenges. Furthermore, the combined model exhibits great potential of practical application for the early warning of gearbox bearing anomalies during actual wind turbine daily operation and maintenance, providing a valuable solution for the offshore wind turbine industry.

Rolling bearings are critical in industrial mining machinery. Due to strong Gaussian noise, frequent random shocks, and disordered loads in industrial settings, it is usually difficult to detect weak fault symptoms in vibration signals from a bearing. To detect incipient bearing faults, this paper proposes a new multi-domain kernel extreme learning machine (MKELM) based on variational modal decomposition (VMD) and a cyclic correntropy function. A normalized approximation algorithm for a cyclic correntropy function (NACCF) was first built to suppress the impulsive background noise. This approach is suitable for machine learning. To eliminate the Gaussian noise effectively, genetic mutation particle swarm optimization (GMPSO) with cyclic information entropy (CIE) was used to optimize the VMD parameters. The CIE was created as a fitness function in GMPSO to search for the best hyperparameters. It can be used to select effective intrinsic mode functions (IMFs) to reconstruct denoised signals. Then, statistical functions based on NACCF were used to extract the cyclic frequency-domain characteristics of the denoised signal, and the singular values of the IMFs were obtained as time-domain features of the signal. Finally, the multi-dimensional features from the two domains were input into MKELM to classify the health of the bearing. Experimental studies were carried out to investigate the proposed method in bearing fault detection and identification. The results demonstrated the effectiveness of the proposed method in motor-bearing failure detection and its robustness to noise when analyzing bearing vibration signals under different working loads.

The supervisory control and data acquisition (SCADA) system of wind turbines continuously collects a large amount of monitoring data during their operation. These data contain abundant information about the operating status of the turbine components. Utilizing this information makes it feasible to provide early warnings and predict the health status of the wind turbine. However, due to the strong coupling between the various components of the wind turbine, the data exhibits complex spatiotemporal relationships, multiple state parameters, strong non-linearity, and noise interference, which brings great difficulty to anomaly detection of the wind turbine. This paper proposes a new method for detecting abnormal operating conditions of wind turbines, based on a cleverly designed multi-layer linear residual module and the improved temporal convolutional network (TCN) with a new norm-linear-ConvNeXt architecture (NLC-TCN). Initially, the NLC-TCN deep learning reconstruction model is trained with historical data of normal behavior to extract the spatiotemporal features of state parameters under normal operational conditions. Subsequently, the condition score of the unit is determined by calculating the average normalized root mean square error between the reconstructed data and actual data. The streaming peaks-over-threshold real-time calculation of the anomaly warning threshold, based on extreme value theory, is then used for preliminary fault monitoring. Moreover, by shielding the fault alarm for low wind speeds and implementing a continuous delay perception mechanism, issues related to wind speed fluctuations and internal and external interference are addressed, enabling early warning for faulty units. Finally, the effectiveness and reliability of the proposed method are validated through comparative experiments using actual offshore wind farm SCADA data. The performance of the proposed method surpasses that of other compared methods. Additionally, the results of the proposed method were evaluated using the uniform manifold approximation and projection dimensionality reduction technique and kernel density estimation.

Safety is a crucial prerequisite for the operation and development of rail transit systems. With the rapid growth of the rail industry, train speeds are increasing, and operational densities are becoming higher, thereby placing greater demands on the safety and reliability of train operations. As a core component of the train’s power system, the health of the bogie transmission system bearing is directly linked to the safety of train operations and transportation efficiency. To address these challenges, this paper proposes a fault diagnosis method for rolling bearings using Generative AI techniques, specifically the MpResCNN-BiLSTM model enhanced with an attention mechanism. First, the method integrates multi-scale cascaded midpoint residual blocks with a bidirectional long short-term memory network (BiLSTM) to extract multi-resolution features from the raw data. Second, the model leverages the exponential linear unit (ELU) activation function and attention mechanisms to improve expressiveness, generalization, noise resistance, and feature selection capabilities. Experimental results demonstrate that the proposed model achieves 100% accuracy in noiseless and low-noise environments, and 99.57% accuracy under high-noise conditions (signal-to-noise ratio = 1). Compared with other traditional models, the accuracy of this model is significantly improved. These results effectively validate the superiority and feasibility of the method for bearing fault diagnosis. This paper presents a novel and effective deep learning method for bearing fault diagnosis, with considerable potential for widespread application and further innovation in intelligent transportation technologies.

Modulation signal bispectrum (MSB) analysis is an effective method to obtain the fault frequency for rolling bearing, but harmonics make fault frequency dense and even frequency aliasing. Carrier frequency of bearing is generally determined by its structure and inherent characteristics and changes with the increase of the damage degree, so it is hard to be accurately found. To solve these problems, this paper proposes a sparse modulation signal bispectrum analysis method. Firstly the vibration signal is demodulated by MSB analysis and its bispectrum is obtained. After the frequency domain filtering, the carrier frequency is computed based on the characteristics of energy concentration at the carrier frequency on MSB. By shift-frequency MSB (SF-MSB), the carrier frequency is moved to the coordinate origin, the entire MSB is shifted for the same distance, and SF-MSB is obtained. At last, the bispectrum is shifted to the frequency zero point and diagonal slices are performed to obtain a sparse representation of MSB. Experimental results show that sparse MSB (S-MSB) method can not only eliminate the interference of harmonic frequency, but also make the extracted characteristic frequency of fault more obvious.