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

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

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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 blue ford 8n 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 archstone pets 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.