Bearing fault diagnosis using normalized diagnostic feature-gram and convolutional neural network

In engineering practice, the collection of equipment vibration signals is prone to interference from the external environment, resulting in abnormal data and imbalanced data in different states. Traditional support vector machine (SVM), support matrix machine (SMM) and other methods have advantages in balancing sample classification, but have limitations in obtaining low rank information, making it difficult to perform classification tasks under data imbalance. Therefore, a novel classification method that targets matrices as the input, called flexible dynamic matrix machine (FDMM), is proposed in this paper. First, FDMM establishes a regularization term using a flexible low-rank operator and sparse constrain, which can better take into account matrix structure information. Then, the upper bound of the loss function is truncated, reducing the impact of the loss on the construction of the decision hyperplane. Finally, the recognition performance of imbalanced data is improved by adjusting the game values of different categories of samples through dynamic adjustment function. Experimental results demonstrate that superior classification accuracy and generalization performance can be achieved with the FDMM method when applied to two roller bearing datasets.

Section: Fdmmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Research on roller bearing fault diagnosis method based on flexible dynamic adjustable strategy under data imbalance

Pan,

Li,

Yan

et al. 2024

“…The intelligent diagnosis method for bearing faults based on deep learning is currently a research hotspot in bearing fault diagnosis, but the weak interpretability of deep learning limits its application in industry [6,7]. Due to the clear diagnostic mechanism of signal processing-based fault diagnosis methods, researching advanced signal processing methods is still of great significance [8,9]. Signal processingbased fault diagnosis methods involves two key steps: feature extraction and fault classification.…”

Section: Introductionmentioning

confidence: 99%

Fault diagnosis of rolling bearing based on SEMD and ISSA-KELMC

Hu,

Zhao,

et al. 2024

Enhancing the operational reliability of rotary machinery relies significantly on the effective diagnosis of faults in rolling bearings. This study introduces an innovative method to improve the accuracy of fault diagnosis of rolling bearings during operation. First, we propose a sine empirical mode decomposition (SEMD) designed to effectively mitigate mode mixing and decompose the vibration signals of rolling bearings into a series of intrinsic mode functions. Subsequently, we constructed and optimized a kernel extreme learning machine classifier (KELMC) using the improved sparrow search algorithm (ISSA). Within ISSA, the Opposition-based Learning method is refined and applied to enhance the optimization performance of the sparrow search algorithm. Finally, the paper presents a novel method for the fault diagnosis of rolling bearings based on SEMD and ISSA-KELMC, which can effectively extract the fault features and accurately recognize the fault types of rolling bearings by taking advantage of the SEMD and ISSA-KELMC. The effectiveness of the proposed method was verified through two simulation and fault diagnosis experiments. The results demonstrated the efficiency of the method in diagnosing faults in rolling bearings under both consistent and variable working conditions. This approach is valuable for fault diagnosis and condition monitoring of rotating machinery. 

“…With the development of modern industrial information technology, rotary machinery and equipment play an indispensable role in energy, manufacturing, and aerospace. The accurate and timely identification of the faults in the operation of equipment is critical in terms of ensuring safety and preventing economic losses and catastrophic accidents [1,2]. In recent years, a huge amount of data [3,4] has been accumulated from information in industrial systems, which has driven the unprecedented development of intelligent methods.…”

Section: Introductionmentioning

confidence: 99%

Prior knowledge-based residuals shrinkage prototype networks for cross-domain fault diagnosis

Zheng

et al. 2023

In engineering practice, device failure samples are limited in the case of unexpected catastrophic faults, thereby limiting the application of deep learning in fault diagnosis. In this study, we propose a prior knowledge-based residual shrinkage prototype network (PK-RSPN) to resolve the fault diagnosis challenges under limited labeled samples. First, our method combines general supervised learning and metric meta-learning to extract prior knowledge from the labeled source data by utilizing a denoised residual shrinkage network (RSN). Further, the knowledge extracted from the supervised learning is used for prototype metric training to achieve a better feature representation for the fault diagnosis. Finally, our approach outperforms a series of baseline methods in the few-shot cross-domain diagnostic task on the gearbox and bearing datasets. A diagnosis accuracy of more than 95% has been achieved in a variety of working conditions for diagnostic tasks, which is far higher than the existing basic method.