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A Computationally Efficient Method for the Diagnosis of Defects in Rolling Bearings Based on linear predictive coding

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ALGORITHMS 2025年第2期18卷 58-58页

作者： Mohammad, Mohammad Ibryaeva, Olga Sinitsin, Vladimir Eremeeva, Victoria South Ural State Univ Sch Elect Engn & Comp Sci Chelyabinsk 454080 Russia

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.

关键词： signal processing condition monitoring rolling bearing linear predictive coding neural networks

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Detection of Oscillatory Failures in Hydraulic Actuators of Aircraft using linear predictive coding and Signal Spectrum Analysis 22

Detection of Oscillatory Failures in Hydraulic Actuators of ...

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22nd World Congress of the International Federation of Automatic Control (IFAC)

作者： Ashraf, Fawad Farooq Mobeen, Surrayya Khan, Hafiz Zeeshan Iqbal Haydar, Muhammad Farooq Riaz, Jamshed Ctr Excellence Sci & Appl Technol Islamabad Pakistan Inst Space Technol Dept Aeronaut & Astronaut Islamabad Pakistan

ISBN: (纸本)9781713872344

This paper focuses on fault detection for oscillatory failures in hydraulic actuators of aircraft. These oscillatory failures, if not rectified in time, can cause severe loads on the airframe and eventually lead to structural damage. In this paper, a novel oscillatory failure case (OFC) detection algorithm is proposed which uses a nonlinear observer based on the mathematical model of the actuator in order to generate residual following a linear predictive coding analysis of the residual to detect oscillatory behavior. Finally, OFC decisions are made after the quantification of the residual in frequency domain. To illustrate the effectiveness of the proposed algorithm, results are presented using a high-fidelity industrial benchmark simulation. Furthermore, a comparative study is provided against an existing technique. Copyright (c) 2023 The Authors.

关键词： Oscillatory Failure Hydraulic Actuator Fault Detection linear predictive coding Signal Spectrum

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Dominant frequency component tracking of noisy time-varying signals using the linear predictive coding pole processing method

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ELECTRONICS LETTERS 2022年第2期58卷 79-81页

作者： Xu, Jin Davis, Mark de Frein, Ruairi Technol Univ Dublin Sch Elect & Elect Engn Dublin Ireland

The linear predictive coding pole processing (LPCPP) method proposed in our previous work overcomes the shortcomings of the LPC method, especially its sensitivity to noise and the filter order. The LPCPP method is a parameterised method that involves processing the LPC poles to produce a series of reduced-order filter transfer functions to estimate the dominant frequency components of a signal. This paper analyses the ability of the LPCPP method to track the frequency changes of noisy, time-varying signals in real-time. linear chirped frequency modulation signals are used in a series of experiments to simulate signals with different rates of frequency change. The results show that the LPCPP method can achieve real-time tracking of the dominant frequency in the signal and outperforms the LPC method under different frequency change rates and different noise levels. Specifically, the valid estimate percentage of LPCPP is up to 41.3% higher than that of LPC which indicates that the LPCPP method significantly improves the validity of frequency estimates.

关键词： signal processing noisy time-varying signals linear predictive coding chirp modulation real-time tracking LPC method transfer functions LPC poles parameterised method frequency change rates frequency estimates filtering theory frequency estimation reduced-order filter transfer functions dominant frequency component tracking frequency modulation linear chirped frequency modulation signals filter order linear predictive coding pole processing method LPCPP method

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LPCSE: Neural Speech Enhancement through linear predictive coding

LPCSE: Neural Speech Enhancement through Linear Predictive C...

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IEEE Global Communications Conference (GLOBECOM)

作者： Liu, Yang Tang, Na Chu, Xiaoli Yang, Yang Wang, Jun Univ Sheffield Sheffield S Yorkshire England Univ Chinese Acad Sci Beijing Peoples R China Terminus Grp Beijing Peoples R China UCL London England

ISBN: (纸本)9781665435406

The increasingly stringent requirement on quality-of-experience in 5G/B5G communication systems has led to the emerging neural speech enhancement techniques, which however have been developed in isolation from the existing expert-rule based models of speech pronunciation and distortion, such as the classic linear predictive coding (LPC) speech model because it is difficult to integrate the models with auto-differentiable machine learning frameworks. In this paper, to improve the efficiency of neural speech enhancement, we introduce an LPC-based speech enhancement (LPCSE) architecture, which leverages the strong inductive biases in the LPC speech model in conjunction with the expressive power of neural networks. Differentiable end-to-end learning is achieved in LPCSE via two novel blocks: a block that utilizes the expert rules to reduce the computational overhead when integrating the LPC speech model into neural networks, and a block that ensures the stability of the model and avoids exploding gradients in end-to-end training by mapping the linear prediction coefficients to the filter poles. The experimental results show that LPCSE successfully restores the formants of the speeches distorted by transmission loss, and outperforms two existing neural speech enhancement methods of comparable neural network sizes in terms of the Perceptual evaluation of speech quality (PESQ) and Short-Time Objective Intelligibility (STOI) on the LJ Speech corpus.

关键词： speech enhancement linear predictive coding neural networks formant distortion

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Identification and classification of coronavirus genomic signals based on linear predictive coding and machine learning methods

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2023年第Part1期80卷 000-000页

作者： Khodaei, Amin Shams, Parvaneh Sharifi, Hadi Mozaffari-Tazehkand, Behzad Univ Tabriz Fac Elect & Comp Engn 29 Bahman Blvd Tabriz Iran Istanbul Aydin Univ Comp Engn Dept Istanbul Turkiye

Corona disease has become one of the problems and challenges of humankind over the past two years. One of the problems that existed from the first days of this epidemic was clinical symptoms similar to other infectious vi-ruses such as colds and influenza. Therefore, diagnosis of this disease and its coping and treatment approaches is also been difficult. In this study, Attempts has been made to investigate the origin of this disease and the genetic structure of the virus leading to it. For this purpose, signal processing and linear predictive coding approaches were used which are widely used in data compression. A pattern recognition model was presented for the detection and separation of covid samples from the influenza virus case studies. This model, which was based on support vector machine classifier, was tested successfully on several datasets collected from different countries. The obtained results performed on all datasets by more than 98% accuracy. The proposed model, in addition to its good performance accuracy, can be a step forward in quantifying and digitizing medical big data information.

关键词： linear predictive coding Corona DNA Sequence Support vector machine Signal processing Machine learning

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Detection of Oscillatory Failures in Hydraulic Actuators of Aircraft using linear predictive coding and Signal Spectrum Analysis

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IFAC-PapersOnLine 2023年第2期56卷 360-365页

作者： Fawad Farooq Ashraf Surrayya Mobeen Hafiz Zeeshan Iqbal Khan Muhammad Farooq Haydar Jamshed Riaz Centers of Excellence in Science and Applied Technologies Islamabad Pakistan Department of Aeronautics & Astronautics Institute of Space Technology Islamabad Pakistan

关键词： Oscillatory Failure Hydraulic Actuator Fault Detection linear predictive coding Signal Spectrum

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Automatic oscillations detection and quantification in process control loops using linear predictive coding

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ENGINEERING SCIENCE AND TECHNOLOGY-AN INTERNATIONAL JOURNAL-JESTECH 2020年第1期23卷 123-143页

作者： Sharma, Sachin Kumar, Vineet Rana, K. P. S. Netaji Subhas Univ Technol Instrumentat & Control Engn Dept Azad Hind Fauj MargSect 3 New Delhi 110078 India

Oscillations in control loops are very common and primarily responsible for product quality variations and therefore may reduce profitability of the plant. Further, there can also be multiple oscillations at the same time due to multiple sources such as improper tuned controllers, disturbances and nonlinearity etc. Detection and estimation of oscillation is desired for their effective compensation which becomes a challenging task particularly in presence of noise. This paper presents a novel oscillation detection method based on linear predictive coding (LPC). In the proposed technique roots of the linear predictive polynomial are used to detect the oscillations. Further, for the quantification spectrum of cross-correlation of linear predictive residual and original data is used. The effectiveness of the proposed oscillation detection technique has been demonstrated with several simulation examples and actual plant data. The comparative investigation with the empirical mode decomposition method, generally employed for this task, revealed that proposed technique is simple, fast, robust and capable of handling slow varying trends. (C) 2019 Karabuk University. Publishing services by Elsevier B.V.

关键词： Oscillation detection linear predictive coding Stiction linear predictive polynomial Cross-correlation

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Normal and Murmur Heart Sound Classification Using linear predictive coding and k-Nearest Neighbor Methods 3

Normal and Murmur Heart Sound Classification Using Linear Pr...

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3rd International Conference on Informatics and Computational Sciences (ICICoS)

作者： Sofwan, Aghus Santoso, Imam Pradipta, Himawan Arfan, M. Zahra, Ajub Ajulian M. Diponegoro Univ Dept Elect Engn Semarang Indonesia

ISBN: (纸本)9781728146102

Heart rate sounds have a special pattern that is in accordance with a person's heart condition. An abnormal heart will cause a distinctive sound called a murmur. Murmurs caused by various things that indicate a person's condition. Through a Phonocardiogram (PCG), it can be seen a person's heart rate signal wave. Normal heartbeat and murmurs have a distinctive pattern, so that through this pattern it can be detected a person's heart defects. This study will make a classification program that will sense normal heart sounds and murmurs. This program uses feature extraction methods using LPC (linear predictive coding) and classification using k-NN (k-Nearest Neighbor) to identify these 2 heart conditions. The data that will be used as a database consists of samples of normal heart rate sounds and murmurs, and also data obtained from the heart rate detection device in the .wav, mono format. The system for detecting heart abnormalities consists of three main parts, namely: recording heart rate sounds, feature extraction using LPC with order 10, and feature lines using k-NN with 3 types of distances and variations of k. From the results of testing with these types of distance, the obtained average accuracy value of Chebyshev, City Block, and Euclidean are 96.67, 91.67, and 93.33 percent, respectively. In addition, the value of k equal 3 is the most optimal value of k with an average level of 96.67 percent.

关键词： heart abnormalities linear predictive coding k-nearest neighbour

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The application of linear predictive coding for RoIP data transmission in the HFC network 6

The application of Linear Predictive Coding for RoIP data tr...

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6th IEEE International Conference on Network Infrastructure and Digital Content (IEEE IC-NIDC)

作者： Cui, Chengyu Park, Sungkwon Jung, Joonyoung Lee, Jewon Hanyang Univ Dept Elect & Comp Engn Seoul South Korea Elect & Telecommun Res Inst Daejeon South Korea

ISBN: (纸本)9781538660676

Recently, HFC (hybrid fiber cable) has been widely used due to the demand for economical and efficient digital services. HFC can be used for an integrated digital service broadband network access technology, through the photoelectric conversion process to achieve data transmission between different devices and equipment. Therefore, RoIP (Radio Over IP) technology for two-way communication is revealed without changing the original system hardware structure. RoIP is a technology that converts radio frequency signal generated at a transmitting end into digital signal and transmits it using an optical IP network. However, during the signal conversion process, a large amount of data generated needs to be compressed to achieve efficient data transmission. In this paper, we reduce the amount of data by combining linear predictive coding techniques, which is widely used in speech signal processing, with nonlinear quantization coding. By using this method, RF signals can be transmitted efficiently. We measured the compression ratio and the magnitude of the error vector representing the degree of signal damage during the compression process and compared it with previous experimental results to verify the availability of these schemes.

关键词： HFC RoIP IP network linear predictive coding Nonlinear quantization coding

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Steganography integrated into linear predictive coding for low bit-rate speech codec

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MULTIMEDIA TOOLS AND APPLICATIONS 2017年第2期76卷 2837-2859页

作者： Liu, Peng Li, Songbin wang, Haiqiang Chinese Acad Sci Inst Acoust Haikou Lab Haikou 570105 Peoples R China Natl Network New Media Engn Res Ctr Beijing 100190 Peoples R China Univ Southern Calif Los Angeles CA USA

The extensive use of Voice over IP (VoIP) applications makes low bit-rate speech stream a very suitable steganographic cover media. To incorporate steganography into low bit-rate speech codec, we propose a novel approach to embed information during linear predictive coding (LPC) process based on Matrix Embedding (ME). In the proposed method, a mapping table is constructed based on the criterion of minimum distance of linear-predictive-Coefficient-Vectors, and embedding position and template are selected according to a private key so as to choose the cover frames. The original speech data of the chosen frames are partially encoded to get the codewords for embedding and then the codewords that need to be modified for embedding are selected according to the secret bits and ME algorithm. The selected codeword will be changed into its best replacement codeword according to the mapping table. When embedding k (k > 1) bits into 2 (k) -1 codewords, the embedding efficiency of our method is k times as that of LPC-based Quantization Index Modulation method. The performance of the proposed approach is evaluated in two aspects: distortion in speech quality introduced by embedding and security under steganalysis. The experimental results demonstrate that the proposed approach leads to a better performance with less speech distortion and better security.

关键词： Information hiding linear predictive coding Matrix embedding Quantization index modulation

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