An efficient lightweight neural network using BiLSTM-SCN-CBAM with PCA-ICEEMDAN for diagnosing rolling bearing faults

Abstract

This study proposes an efficient rolling bearing fault diagnosis model of a hybrid neural network with a lightweight attention mechanism. Firstly, to achieve the low complexity of deep learning computation, data reduction and denoising are performed by sparse convolutional network (Principal Component Analysis and Improved Complete Ensemble Empirical Modal Decomposition of Adaptive Noise), then processed data is imported to the hybrid neural network model with CBAM (Convolutional Block Attention Module ). The BiLSTM-SCN (Bi-directional Long Short-Term Memory and Sparse Convolutional Networks ) are used in the backbone of the model. A lightweight, generalized attention mechanism is introduced to the last layer of the model for enhancing feature learning, which can further improve the diagnostic accuracy and efficiency. Compared with existing deep learning fault diagnosis models, In simulating the most realistic cross-conditions and cross-platform conditions, which leads to the random nature of fault generation and makes model diagnosis more complex, the proposed method still maintains less running time and excellent diagnostic accuracy. Finally, the experimental results fully prove that the model has reliable robust and efficient, and it achieves the best balance of diagnostic accuracy and diagnostic efficiency of the hybrid deep learning model.

Remaining useful life prediction of lithium-ion batteries using EM-PF-SSA-SVR with gamma stochastic process

“…With a lot of research based on data-driven methods in recent years [11], ML models have achieved very good results in degradation diagnosis [12]. Its greatest advantage is that it enables efficient utilization of large amounts of observable data, eliminates the need for manual selection of input data features, and is simple and practical [13]. The essence of this approach is to learn historical degradation data and use its excellent nonlinear fitting ability to achieve a nonlinear mapping relationship between health indicators and RUL [14].…”

A hierarchical transformer-based adaptive metric and joint-learning network for few-shot rolling bearing fault diagnosis

“…In addition to the design of the classifier, the structure of the encoder also affects the information extraction ability of the model. Currently, the mainstream feature extraction networks for fault diagnosis mostly consist of variants of convolutional neural networks (CNN) [25,26] or recurrent neural networks (RNN) [27,28]. These architectures adopt a sequential processing approach for modeling, often requiring the stacking of multiple layers or the accumulation of information over several time steps to capture long-range interdependencies.…”

Design of a progressive fault diagnosis system for hydropower units considering unknown faults

“…the efficient and stable operation of hydropower stations [1][2][3]. Vibration is the basic manifestation of the operating state of a hydropower unit, containing the characteristics of the unit state and most fault information [4,5]. Analyzing the vibration signal is an important link in the monitoring and fault diagnosis research of hydropower units.…”

“…However, although LSTM has powerful memory functions, its unidirectional transmission structure makes it unable to fully utilize sequence information [21]. The bidirectional LSTM (BiLSTM) neural network can use the advantages of bidirectional structure to realize the learning of forward and backward information of input sequence nodes, improve the longterm dependence of learning, and improve the accuracy of the model to a certain extent [5].…”