A Computationally Efficient Method for the Diagnosis of Defects in Rolling Bearings Based on Linear Predictive Coding

作     者：Mohammad, Mohammad Ibryaeva, Olga Sinitsin, Vladimir Eremeeva, Victoria 

作者机构：South Ural State Univ Sch Elect Engn & Comp Sci Chelyabinsk 454080 Russia 

出 版 物：《ALGORITHMS》 (Algorithms)

年 卷 期：2025年第18卷第2期

页      面：58-58页

核心收录：

基　　金：Ministry of Science and Higher Education of the Russian Federation FENU-2023-0010 

主　　题：signal processing condition monitoring rolling bearing linear predictive coding neural networks 

摘      要：Monitoring the condition of rolling bearings is a crucial task in many industries. An efficient tool for diagnosing bearing defects is necessary since they can lead to complete machine failure and significant economic losses. Traditional diagnosis solutions often rely on a complex artificial feature extraction process that is time-consuming, computationally expensive, and too complex to deploy in practice. In actual working conditions, however, the amount of labeled fault data available is relatively small, so a deep learning model with good generalization and high accuracy is difficult to train. This paper proposes a solution that uses a simple feedforward artificial neural network (NN) for classification and adopts the linear predictive coding (LPC) algorithm for feature extraction. The LPC algorithm finds several coefficients for a given signal segment containing information about the signal spectrum, which is sufficient for further classification. The LPC-NN solution was tested on the Case Western Reserve University (CWRU) and South Ural State University (SUSU) datasets. The results demonstrated that, in most cases, LPC-NN yielded an accuracy of 100%. The proposed method achieves higher diagnostic accuracy and stability to load changes than other advanced techniques, has a significantly improved time performance, and is conducive to real-time industrial fault diagnosis.