## 1 Introduction
It approximates a signal sample at any moment as a linear combination of previous samples, and the weights for the linear combination are calculated by minimizing the mean squared error of the prediction for a signal frame. The resulting linear prediction coefficients are used to represent the frame. Although it has been traditionally used for speech, LPC is currently being improved and applied to non-speech signals as well [27; 28]. In this paper, we apply LPC to vibration signals from rolling bearings. We evaluate our technique using the Case Western Reserve University (CWRU) [29] and South Ural State University (SUSU) [30] datasets. LPC is used to extract features from signals and then an NN is used to classify signals according to the extracted features. The advantages of this approach are the simplicity during the feature extraction stage, the small dimension of the resultant feature vector, and the simple architecture of the NN. This is particularly note-worthy in comparison with the increasingly sophisticated models used elsewhere for diagnosing bearing failures. Despite its simplicity, the proposed method achieves higher accuracy than some other advanced techniques. The following are the main contributions of this article:  
1. A novel fault diagnosis method, which combines LPC and an NN, is proposed and used for bearing vibration signals. The LPC-NN method is less time-consuming and computationally less expensive compared to other advanced techniques. 3. Experimental results on the CWRU and the SUSU datasets demonstrate that LPC-NN outperforms modern competitive techniques, achieving 100% accuracy in most cases. The rest of the paper is organized as follows: The theoretical foundations of the LPC method, processing the signal for feature extraction, and the topology of the used NN are presented in Section 2. Section 3 describes the preparation of the dataset for the experiments. The experimental validation performed to evaluate the method is described in Section 4. In Sections 4.1 and 4.2, the performance of the LPC-NN method on one load and across different load domains was investigated. We used the CWRU dataset since the similar experiments were carried out on this dataset in [4,31]. The method LPC-NN turned out to be superior in accuracy to the powerful and computationally expensive method CNN-gcForest [4], and also the SVM, MLP, DNN, WDCNN, TICNN, and Ensemble TICNN [31] methods in some cases. In cases where LPC-NN did not give higher accuracy, it nevertheless demonstrated competitive performance. In Section 4.3, we compared the LPC-NN method in terms of time and accuracy with significantly different modern methods TSDIs-NN [32] and Hybrid CNN-MLP [33] for bearing fault diagnosis. For this, we used the SUSU dataset, on which the Hybrid CNN-MLP method was tested in [33], achieving superior accuracy and a significant improvement in time. Comparison with the TSDIs-NN method showed approximately the same computational costs, but a significant difference in accuracy in favor of the LPC-NN method. One of the traditional and widely used methods of bearing diagnostics is envelope analysis. However, in [33], we studied this method in detail in application to bearing fault diagnostics and showed that the traditional analysis of the envelope does not allow for accurate detection and localization of defects. In recent years, data-driven fault diagnosis technology based on deep learning has attracted enormous attention. The LPC-NN method is in particular contrast to the increasingly complex models offered today for rolling bearing diagnostics and is conducive to real-time industrial fault diagnosis.