A Stochastic Learning Algorithm for Machine Fault Diagnosis

Zhipeng Dong,Jianshe Kang,Shaohui Zhang,Yucheng Liu,Changqing Shen

doi:10.1155/2022/5790185

Abstract

Industrial big data bring a large number of high-dimensional sample datasets. Although a deep learning network can well mine the internal nonlinear structure of the dataset, the construction of the deep learning model requires a lot of computing time and hardware facilities. At the same time, there are some nonlinear problems such as noise and fluctuation in industrial data, which make the deep architecture extremely complex and the recognition accuracy of the diagnosis model difficult to guarantee. To solve this problem, a new method, named stochastic learning algorithm (SL), is proposed in this paper for dimension reduction. The proposed method consists of three steps: firstly, to increase the computational efficiency of the model, the dimension of the high-dimensional data is reduced by establishing a random matrix; secondly, for enhancing the clustering influence of the sample, the input data are enhanced by feature processing; thirdly, to make the clustering effects more pronounced, the noise and interference of the data need to be processed, and the singularity value denoising method is used to denoise training data and test data. To further prove the superiority of the SL method, we conducted two sets of experiments on the wind turbine gearbox and the benchmark dataset. It can be seen from the experimental results that the SL method not only improves the classification accuracy but also reduces the computational burden.

Highlights

In addition to deep learning methods, shallow learning algorithms (PCA, KNN, LPP, etc.) are still largely applied in the artificial intelligence area [32,33,34,35,36,37]. This kind of shallow learning algorithm has the advantages of simple structure, low hardware environment configuration requirements, and relatively high computational efficiency, it has limitations that are difficult to overcome, such as classifying data containing a large number of variables and a simplified sample set, and the problem of nonlinear nature [38]
The number of decision trees is an important parameter of random forest (RF), which will affect the RF’s classification accuracy and computational efficiency
The feature is denoising based on SVD to improve the classification rate. erefore, this method has outstanding advantages: (1) the sample dimension is greatly reduced after processing; (2) the calculation efficiency of the random forest is improved; and (3) the recognition effect of random learning is ensured by the reinforcement process

Summary

A Stochastic Learning Algorithm for Machine Fault Diagnosis

Industrial big data bring a large number of high-dimensional sample datasets. a deep learning network can well mine the internal nonlinear structure of the dataset, the construction of the deep learning model requires a lot of computing time and hardware facilities. There are some nonlinear problems such as noise and fluctuation in industrial data, which make the deep architecture extremely complex and the recognition accuracy of the diagnosis model difficult to guarantee. To solve this problem, a new method, named stochastic learning algorithm (SL), is proposed in this paper for dimension reduction. To further prove the superiority of the SL method, we conducted two sets of experiments on the wind turbine gearbox and the benchmark dataset It can be seen from the experimental results that the SL method improves the classification accuracy and reduces the computational burden

Introduction

Stochastic Learning Algorithm for Condition Recognition

Fault Diagnosis for Wind Turbine Gearbox

Findings

Fault Diagnosis for the Benchmark Dataset