scholarly journals Signal Processing Methods for Identification of Induction Motor Bearing Fault

2019 ◽  
Vol 8 (3) ◽  
pp. 143-151
Keyword(s):  
Signal Processing ◽  
Feature Extraction ◽  
Induction Motor ◽  
Dominant Mode ◽  
Processing Methods ◽  
Vibration Signals ◽  
Bearing Fault ◽  
Vibration Signal Analysis ◽  
Signal Processing Methods ◽  
Motor Bearing

To diagnose early faults as soon as possible, the feature extraction of vibration signals is very important in real engineering applications. Recently, the advanced signal processing-based weak feature extraction method has been becoming a hot research topic. The dominant mode of failure in rolling element bearings is spalling of the races or the rolling elements. Localized defects generate a series of impact vibrations every time whenever running roller passes over the surface of a defect. Therefore, vibration analysis is a conventional method for bearing fault detection. However, the measured vibration signals of rotating machinery often present nonlinear and non-stationary characteristics. This paper deals with the diagnosis of induction motor bearing based on vibration signal analysis. It provides a comparative study between traditional signal processing methods, such as Power Spectrum, Short Time Fourier Transform, Wavelet Transform, and Hilbert Transform. Performances of these techniques are assessed on real vibration data and compared for healthy and faulty bearing.

Medical Signal Processing

2021 ◽  
pp. 81-103
Author(s):  
Vidhya S. ◽  
Sharmila Nageswaran
Keyword(s):  
Signal Processing ◽  
Feature Extraction ◽  
Data Acquisition ◽  
Acoustic Signals ◽  
Processing Methods ◽  
Automated Processing ◽  
Signal Processing Methods ◽  
The Brain ◽  
Medical Signal ◽  
Neurological System

This chapter introduces sleep, the pattern of sleep, wakefulness, disorders associated with sleep, diseases of heart and lungs that can be identified by analysing one's sleep. Sleep is generally equated to the neurological system and the brain. It is believed that sleep can be identified only with EEG. This chapter also explores the usage of EEG in detecting the disorders associated with sleep, and more emphasis is given to the bio signals other than EEG, which includes ECG, PPG, acoustic signals that can be used in understanding the sleep and its related disorders. It explains the biomedical devices that are used for sleep-related studies. This chapter explores the stages of sleep signal processing where the authors have suggested how to reduce noises at the stage of data acquisition. Further topics explore various signal processing methods that need to be adapted in various stages, namely preprocessing, filtering, feature extraction, validation, and automated processing.

Cyclostationary Analysis of Measured Pump Acoustic and Vibration Signals

2018 ◽  
Author(s):  
Paul Kalbfleisch ◽  
Svenja Horn ◽  
Monika Ivantysynova
Keyword(s):  
Signal Processing ◽  
Natural Phenomenon ◽  
Processing Methods ◽  
Vibration Signals ◽  
Electrical Systems ◽  
Positive Displacement ◽  
Noise Vibration ◽  
Cyclostationary Signals ◽  
Processing Techniques ◽  
Signal Processing Methods

The stationary signal assumption is convenient as its signal processing methods are the minimum effort required to characterize periodic signals and therefore the most common. However, signals from rotating machines have been found to naturally be characterized as cyclostationary. The existent of natural phenomenon such as, shaft imbalances, turbulent fluid flows, friction, combustion forces, and torsional vibrations create modulation effects, that can be seen in the measured signals. These observed modulations in pump noise and vibration signals are synonymous to amplitude modulations (AM), frequency modulations (FM), and potentially phase modulations in electrical systems. Having this knowledge, the fluid power noise, vibration, and harshness (NVH) researchers can draw from an enormous amount of progress made in the modern telecommunication signal processing methods of cyclostationary signals. This article introduces the basic concepts of cyclostationary signals, some of their signal processing techniques, and a simple example of analysis for a positive displacement machine through the cyclostationary paradigm.

scholarly journals Compound Fault Diagnosis for Gearbox Based Using of Euclidean Matrix Sample Entropy and One-Dimensional Convolutional Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-26
Author(s):  
Decai Zhang ◽  
Xueping Ren ◽  
Hanyue Zuo
Keyword(s):  
Neural Network ◽  
Signal Processing ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Sample Entropy ◽  
Processing Methods ◽  
Vibration Signals ◽  
One Dimensional ◽  
Signal Processing Methods ◽  
Matrix Sample

Vibration signals of gearbox under different loads are sensitive to the existence of the fault and composite fault vibration signals are complex. Traditional fault diagnosis methods mostly rely on signal processing methods. It is difficult for signal processing methods to separate effective information from those fault signals. Therefore, traditional fault diagnosis methods are difficult to accurately identify those faults. In this paper, a one-dimensional convolutional neural network (1-D CNN) intelligent diagnosis method with improved SoftMax function is proposed. Local mean decomposition (LMD) decomposes the signals into different physical fictions (PF). PFs are input into the matrix sample entropy based on Euclidean distance (MESE), and the PFs which best reflect fault characteristics are selected. Finally, the PFs by MESE are used to train the CNN to identify the faults of parallel-shaft gearbox. Experiment shows that MESE can quickly and accurately select the PFs with the most significant fault features. 1-D CNN can get nearly 100% recognition rate with less time and the CNN of SoftMax improved can effectively eliminate LMD endpoint effect. This method can successfully identify single faults, combination faults, and faults under different loads of the gearbox. Compared with other methods, this method has the characteristics of high efficiency, accuracy, and strong anti-interference. Therefore, it can effectively solve the problem of complex fault signal decomposition of gearbox and can diagnose the gearbox fault under different load operation. It has great significance for gearbox fault diagnosis in actual production.

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