Zichang Liu

To address the issues of sample ratio imbalance and scarcity of available failure data in unbalanced few-shot scenarios—where models tend to favor the class with more samples and are prone to overfitting, thereby affecting the accuracy of fault diagnosis—this manuscript proposes an intelligent fault diagnosis method for diesel engines under unbalanced few-shot conditions, based on Successive Multivariate Variational Mode Decomposition (SMVMD) and the optimized Deep Hybrid Kernel Extreme Learning Machine (DHKELM). By processing the data samples using the SMVMD method, the components of the Intrinsic Mode Functions (IMF) are obtained; based on the principle of minimizing envelope entropy, the most valuable component within the sample is selected, and its spectral amplitude is used as a fault feature to improve sample quality at the data level. To optimize the feature extraction process at the model level, the Improved Sparrow Search Algorithm (ISSA) is proposed to optimize the DHKELM fault diagnosis model. The proposed ISSA is used to optimize the hyperparameters of the DHKELM model; after normalizing the fault feature samples, they are used as model inputs to perform diesel engine fault diagnosis. Case validation and comparative analysis show that, under conditions of unbalanced few-shot datasets, the proposed method can accurately and intelligently distinguish between different types of diesel engine failures.

To address the issue where irrelevant information interferes with subtle fault features, making it difficult to clearly define the boundaries between different fault types and thereby affecting the accuracy of fault diagnosis, an intelligent fault diagnosis method for diesel engines is proposed based on the Synchro Extracting Enhanced Generalized S-Transform (SEEGST) and an improved Swin Transformer. By processing the raw signals using the SEEGST method, the acquired vibration signals are converted into time-frequency spectra, comprehensively revealing the feature information contained in the raw signals; An improved Swin Transformer model is proposed, which inserts a local enhancement perception module before each cascaded Swin Transformer block and replaces the Window-Based Multi-Head Self-Attention (W-MSA) and the Shifted W-MSA (SW-MSA) modules with a k-Nearest Neighbor (k-NN) attention mechanism; and replaces the Multi-Layer Perceptron (MLP) modules in the Swin Transformer blocks at the output stages of each Swin Transformer stage with a multi-scale channel attention mechanism module to address issues such as slow feature fusion inference and insufficient interaction of contextual feature information in existing models; By extracting and training time-frequency image features using the proposed model, we achieve diesel engine fault diagnosis. Case studies and comparative analyses show that the proposed method can accurately and intelligently locate diesel engine faults.

Performance-based warranties (PBWs) are widely used in industry and manufacturing. Given that PBW can impose financial burdens on manufacturers, rational maintenance decisions are essential for expanding profit margins. This paper proposes an optimization model for PBW decisions for systems affected by Gamma degradation processes, incorporating periodic inspection. A system performance degradation model is established. Preventive maintenance probability and corrective renewal probability models are developed to calculate expected warranty costs and system availability. A benefits function, which includes incentives, is constructed to optimize the initial and subsequent inspection intervals and preventive maintenance thresholds, thereby maximizing warranty profit. An improved sparrow search algorithm is developed to optimize the model, with a case study on large steam turbine rotor shafts. The results suggest the optimal PBW strategy involves an initial inspection interval of approximately 20 months, with subsequent intervals of about four months, and a preventive maintenance threshold of approximately 37.39 mm wear. When compared to common cost-minimization-based condition maintenance strategies and PBW strategies that do not differentiate between initial and subsequent inspection intervals, the proposed PBW strategy increases the manufacturer's profit by 1% and 18%, respectively. Sensitivity analyses provide managerial recommendations for PBW implementation. The PBW strategy proposed in this study significantly increases manufacturers' profits by optimizing inspection intervals and preventive maintenance thresholds, and manufacturers should focus on technological improvement in preventive maintenance and cost control to further enhance earnings.

Regarding the difficulty of extracting fault information in the faulty status of UAV (unmanned aerial vehicle) engines and the high time cost and large data requirement of the existing deep learning fault diagnosis algorithms with many training parameters, in this paper, a small-sample transfer learning fault diagnosis algorithm is proposed. First, vibration signals under the engine fault status are converted into a two-dimensional time-frequency map by multiple simultaneous squeezing S-transform (MSSST), which reduces the randomness of manually extracted features. Second, to address the problems of slow network model training and large data sample requirement, a transfer diagnosis strategy using the fine-tuned time-frequency map samples as the pre-training model of the ResNet-18 convolutional neural network is proposed. In addition, in order to improve the training effect of the network model, an agent model is introduced to optimize the hyperparameter network autonomously. Finally, experiments show that the algorithm proposed in this paper can obtain high classification accuracy in fault diagnosis of UAV engines compared to other commonly used methods, with a classification accuracy of faults as high as 97.1751%; in addition, we show that it maintains a very stable small-sample migratory learning capability under this condition.

Transformer is extensively employed in natural language processing, and computer vision (CV), with the self-attention structure. Due to its outstanding long-range dependency modeling and parallel computing capability, some leading researchers have recently attempted to apply Transformer to intelligent fault diagnosis tasks for mechanical equipment, and have achieved remarkable results. Physical phenomena such as changes in vibration, sound, and heat play a crucial role in the research of mechanical equipment fault diagnosis, which directly reflects the operational status and potential faults of mechanical equipment. Currently, intelligent fault diagnosis of mechanical equipment based on monitoring signals such as vibration, sound, and temperature using Transformer-based models remains a popular research topic. While some review literature has explored the related principles and application scenarios of Transformer, there is still a lack of research on its application in intelligent fault diagnosis tasks for mechanical equipment. Therefore, this work begins by examining the current research status of fault diagnosis methods for mechanical equipment. This study first provides a brief overview of the development history of Transformer, outlines its basic structure and principles, and analyzes the characteristics and advantages of its model structure. Next it focuses on three model variants of Transformer that have generated a significant impact in the field of CV. Following that, the research progress and current challenges of Transformer-based intelligent fault diagnosis methods for mechanical equipment are discussed. Finally, the future development direction of Transformer in the field of mechanical equipment fault diagnosis is proposed.

In order to solve the problem of the subtraction-average-based optimizer (SABO), which is difficult to effectively balance the local development and global search capability, golden sine SABO integrating multiple strategies (GSABO) is proposed. The improved Sine chaos mapping is introduced to refine the population initialization strategy of SABO in order to enrich the diversity of the population and improve the algorithm's search accuracy and speed. The position update method of SABO is improved by the golden sine algorithm to further enhance the local development and global search capability of SABO. The specific implementation steps of GSABO are described in detail, and the optimization ability of GSABO is tested using 23 benchmark functions. The test results show that the GSABO method is able to solve the optimization problem effectively compared with the current novel optimization algorithms, and has more excellent performance under most benchmark functions.

This paper tries to introduce a new intelligent method for the early fault diagnosis of diesel engines. Firstly, infrared thermography (IRT) is introduced into diesel engine condition monitoring, then infrared images of diesel engines in four health states, such as normal condition,...

The reliability and safety of diesel engines gradually decrease with the increase in running time, leading to frequent failures. To address the problem that it is difficult for the traditional fault status identification methods to identify diesel engine faults accurately, a diesel engine fault status identification method based on synchro squeezing S-transform (SSST) and vision transformer (ViT) is proposed. This method can effectively combine the advantages of the SSST method in processing non-linear and non-smooth signals with the powerful image classification capability of ViT. The vibration signals reflecting the diesel engine status are collected by sensors. To solve the problems of low time-frequency resolution and weak energy aggregation in traditional signal time-frequency analysis methods, the SSST method is used to convert the vibration signals into two-dimensional time-frequency maps; the ViT model is used to extract time-frequency image features for training to achieve diesel engine status assessment. Pre-set fault experiments are carried out using the diesel engine condition monitoring experimental bench, and the proposed method is compared with three traditional methods, namely, ST-ViT, SSST-2DCNN and FFT spectrum-1DCNN. The experimental results show that the overall fault status identification accuracy in the public dataset and the actual laboratory data reaches 98.31% and 95.67%, respectively, providing a new idea for diesel engine fault status identification.

The diesel engine, as the main power source of equipment, faces practical problems in the maintenance process, such as difficulty in fault location and a lack of preventive maintenance techniques. Currently, breakdown maintenance and cyclical preventive maintenance are the main means of maintenance support after a diesel engine failure, but these methods require professional maintenance personnel to carry out manual fault diagnosis, which is time-consuming. Prognostics and health management (PHM), as a new technology in the field of equipment maintenance support, has significant advantages in improving equipment reliability and safety, enhancing equipment maintenance support capability, and reducing maintenance support costs. In view of this, when introducing PHM into diesel engine maintenance support, the research progress and development trend of the key technologies of PHM for diesel engines are carried out with the objective of achieving precise maintenance and scientific management of diesel engines, and the key technologies demand traction. Firstly, the development history of PHM technology is reviewed, and its basic concept and main functions are introduced. Secondly, the system architecture of PHM for diesel engines is constructed, and its key technologies are summarized. Then, the research progress in the field of PHM for diesel engines is reviewed from four aspects: data acquisition, data processing, fault diagnosis, and health status assessment. Finally, the challenges faced by diesel engine PHM in engineering applications are analyzed, effective solutions to address these challenges are explored, and the future development trend is foreseen.