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Latest articles for Measurement Science and Technology

IOPscience

  • Special issue on the 20th International Symposium on Flow Visualization (ISFV20)
  • Fault diagnosis and prognosis of railway vehicle system
  • An advanced fault diagnosis approach for wind turbine planetary gearbox based on optimized multi-layer attention denoising autoencoders
    Fault diagnosis of wind turbine planetary gearboxes is essential for maintaining their operational reliability; this paper introduces a novel method focused on fault diagnosis. Initially, raw vibration signals from the gearbox are transmitted to a data processing system where blind source separation and ensemble local mean decomposition are employed to extract sparse components. These sparse samples are used to optimize a deep fault diagnosis architecture based on multi-layer denoising autoencoders, which effectively extract fault features. By integrating the attention mechanism and chaotic quantum particle swarm optimization, the model enhances feature extraction, leading to improved fault classification accuracy. In two experiments based on different datasets, the diagnosis accuracy of fault types reaches 99.20% and 96.73%, respectively, while the diagnosis accuracy of corresponding fault severity is 99.12% and 93.60%. Experimental results validate the effectiveness of our method in the diagnosis of gearbox faults, demonstrating robust performance in complex operating environments.

  • Hybrid contrastive representations and SBO-based samples generation for rotating machinery anomaly detection based on driven-end current signal
    While deep learning methods based on cross-entropy function have made great advancements in maintaining equipment reliability, their learning paradigm appears less suitable for anomaly detection tasks, which focus on extracting specific target features and ignore the discriminability between classes. Besides, incomplete dataset still hinders the robustness of intelligent detection models, especially when the training dataset only contains normal samples. Therefore, a hybrid contrastive representation and soft Brownian offset (SBO)-based samples generation (HCRS) method based on driven-end current signals is proposed to distinguish normal and abnormal samples in incomplete data scenarios where only normal signals are available. In the proposed HCRS detection framework, an autoencoder is initially trained using only normal current samples. It is then combined with the SBO method to generate abnormal samples, relieving the issues of incomplete dataset. Subsequently, a supervised contrastive learning-based deep feature extractor is trained using both the generated abnormal samples and the collected normal samples. This process aims to learn high-level fine-grained representations with discriminability. Finally, these learned representations are utilized to train a data-driven classifier, enabling effective anomaly detection in rotating machinery. In addition, experiments on two datasets suggest that the proposed HCRS can effectively achieve higher accuracy anomaly detection with only normal current signals and outperform existing detection approaches.

  • Multi-sensor adaptive fusion and convolutional neural network-based acoustic emission diagnosis for initial damage of the engine
    Aiming at the problems of traditional fault diagnosis means that are difficult to identify initial damage, as well as the poor reliability and fault tolerance with a single sensor, an acoustic emission (AE) diagnosis method for initial damage of the engine based on multi-sensor adaptive fusion and convolutional neural network (CNN) is proposed. Firstly, under the premise of utilizing parametric analysis to characterize the multi-sensor AE signals, the feature parameter entropy is used to determine the primary and secondary relationships between multi-sensor signals, and then the AE feature parameter matrix is formed by adaptive fusion. Secondly, CNN is employed to mine and learn the fault feature combinations from the AE feature parameter matrix by multi-layer fusion to realize the identification and diagnosis for initial damage of the engine. Finally, the proposed method is validated on the engine test bench designed for initial damage identification and is compared with conventional methods in terms of diagnostic performance. The results demonstrate that the proposed method can achieve an identification accuracy of 98.83% for initial damage, and has advantages in various aspects such as TAMSE, K, F1mic and F1mac, which explicitly provides a theoretical and methodological basis for identifying initial faults comprehensively and accurately. This research not only enriches the theory and methods in the field of structural health monitoring, but also provides strong technical support for engine health management, which is expected to play a key role in the maintenance and guarantee of aviation engines in the future.