An accurate and efficient machine fault diagnosis approach using a recurring broad learning model

Abstract

In most of previous machine fault diagnosis, the performance of traditional methods was over-dependent on high-quality feature extraction from original signals. Recently, deep learning–based fault recognition methods can successful automatically learn high-level hidden features from measured signals, but a deep neural network has too much hyperparameter tuning and a complicated architecture, so the training process is time-consuming. To address these issues, a novel machine fault diagnosis approach using a recurring broad learning model is reported in this article. This method uses a recurring broad learning model first proposed in this article, which learns high-level hidden features from sensor signals with different working status of a machine effectively and efficiently. Experimental results conducted on two public machine fault datasets verify the accuracy and efficiency of the proposed approach for machine fault diagnosis separately. Compared with many existing mainstream methods, the state-of-the-art framework presented reveals its superiority on both datasets. The accuracy achieves nearly 100% on both bearing dataset (Dataset 1) and gearbox-bearing dataset (Dataset 2). In addition, it can obviously realize faster training than most of the methods reported in this article.

Transformer winding mechanical fault diagnosis method based on closing transient acoustic vibration signal

“…When the inherent vibration frequency and electrodynamic excitation vibration frequency are close to the resonance phenomenon, the transformer vibration increases sharply, internal winding insulation falls off, and the pad is displaced. Mechanical failure, such as transformers continuing to run, will occur more accidents [2]. The fourth type of failure is the cumulative effect of the winding resulting in mechanical failure.…”

Fault Diagnosis of Rotating Machinery: A Highly Efficient and Lightweight Framework Based on a Temporal Convolutional Network and Broad Learning System

“…Zhao et al [ 19 ] proposed a fault diagnosis framework for rotors based on principal component analysis (PCA) and BLS, reducing the linear correlation between the data and eliminating redundant fault features. Guo et al [ 20 ] designed a novel recurring BLS fault diagnosis model based on the original BLS. The model inherited the advantages of the BLS and achieved nearly 100% accuracy on two public datasets.…”