COMBINED SIGNAL PROCESSING METHOD FOR DIAGNOSIS AND MONITORING OF THE INDUCTION MOTORS OPTIMIZED FOR EMBEDDED SYSTEMS

2021 ◽  
pp. 18-27
Author(s):  
A. L. Morozov
Keyword(s):  
Signal Processing ◽  
Embedded Systems ◽  
Induction Motors ◽  
Vibration Signal ◽  
Constant Load ◽  
Load Level ◽  
Monitoring Systems ◽  
Vibration Signals ◽  
Advantages And Disadvantages ◽  
Fractional Moments

Induction Motors (IM) play a key role in modern industry, so the condition monitoring systems are becoming increasingly relevant. Commercial monitoring systems are usually based on the measurement of IM’s vibrations and the further processing of the measured vibration signals. For those purposes the embedded systems (such as microcontrollers and inexpensive processors) are used. Embedded systems have limited resources, so data processing algorithms should have low computational complexity and require little memory. In this paper, the wellknown methods of processing vibration signals for fault diagnosis of the IM are considered and their main advantages and disadvantages for the implementation in embedded systems are highlighted. The previously proposed method based on a combination of the fast Fourier transform and the statistics of the fractional moments is optimized for vibration signal processing and implementation in embedded systems. The efficiency of diagnosis of such faults as eccentricity and a broke rotor bar, using the proposed method, is verified on the radial vertical vibrations measurements of the real motors under different constant load levels: no load, 50 % of the rated load, 75% of the rated load. The results show that this approach allows accurately diagnose the considered faults independently from the load level.

Fault Detection of Broken Rotor Bar Using an Improved form of Hilbert–Huang Transform

2018 ◽  
Vol 17 (02) ◽  
pp. 1850012 ◽  
Author(s):  
F. Sabbaghian-Bidgoli ◽  
J. Poshtan
Keyword(s):  
Signal Processing ◽  
Fault Detection ◽  
Hilbert Transform ◽  
Wavelet Packet ◽  
Amplitude Spectrum ◽  
Vibration Signal ◽  
External Noise ◽  
Vibration Signals ◽  
Broken Rotor Bar ◽  
Hilbert Huang Transform

Signal processing is an integral part in signal-based fault diagnosis of rotary machinery. Signal processing converts the raw data into useful features to make the diagnostic operations. These features should be independent from the normal working conditions of the machine and the external noise. The extracted features should be sensitive only to faults in the machine. Therefore, applying more efficient processing techniques in order to achieve more useful features that bring faster and more accurate fault detection procedure has attracted the attention of researchers. This paper attempts to improve Hilbert–Huang transform (HHT) using wavelet packet transform (WPT) as a preprocessor instead of ensemble empirical mode decomposition (EEMD) to decompose the signal into narrow frequency bands and extract instantaneous frequency and compares the efficiency of the proposed method named “wavelet packet-based Hilbert transform (WPHT)” with the HHT in the extraction of broken rotor bar frequency components from vibration signals. These methods are tested on vibration signals of an electro-pump experimental setup. Moreover, this project applies wavelet packet de-noising to remove the noise of vibration signal before applying both methods mentioned and thereby achieves more useful features from vibration signals for the next stages of diagnosis procedure. The comparison of Hilbert transform amplitude spectrum and the values and numbers of detected instantaneous frequencies using HHT and WPHT techniques indicates the superiority of the WPHT technique to detect fault-related frequencies as an improved form of HHT.

scholarly journals Vibration Sideband Modulations and Harmonics Separation of a Planetary Helicopter Gearbox with Two Different Configurations

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Nader Sawalhi
Keyword(s):  
Signal Processing ◽  
Vibration Signal ◽  
Complete Removal ◽  
Processing Algorithm ◽  
Integer Number ◽  
Separation Algorithm ◽  
Signal Processing Algorithm ◽  
Vibration Signals ◽  
Gear Mesh ◽  
Harmonic Extraction

This paper examines the spectrum and cepstrum content of vibration signals taken from a helicopter gearbox with two different configurations (3 and 4 planets). It presents a signal processing algorithm to separate synchronous and nonsynchronous components for complete shafts’ harmonic extraction and removal. The spectrum and cepstrum of the vibration signal for two configurations are firstly analyzed and discussed. The effect of changing the number of planets on the fundamental gear mesh frequency (epicyclic mesh frequency) and its sidebands is discussed. The paper explains the differences between the two configurations and discusses, in particular, the asymmetry of the modulation sidebands about the epicyclic mesh frequency in the 4 planets arrangement. Finally a separation algorithm, which is based on resampling the order-tracked signal to have an integer number of samples per revolution for a specific shaft, is proposed for a complete removal of the shafts harmonics. The results obtained from the presented separation algorithms are compared to other separation schemes such as discrete random separation (DRS) and time synchronous averaging (TSA) with clear improvements and better results.

The Method Based on Pattern Filter for Sonnd & Vibration Signal

2013 ◽  
Vol 333-335 ◽  
pp. 526-530
Author(s):  
Miao Rong Lv ◽  
Bao Jian Wei ◽  
Jian Lu ◽  
Jian Bo Diao
Keyword(s):  
Signal Processing ◽  
Model Building ◽  
Fourier Method ◽  
Vibration Signal ◽  
Complete Separation ◽  
Processing Method ◽  
Signal Extraction ◽  
Filter Method ◽  
Vibration Signals ◽  
Signal Processing Method

The difficulty of the signal processing is not the acquisition of the signals, but how to get the reasonble interpretations from the signals. Since the 1960s, Wavelet Transform, Fast Fourier method and other theoryies have done some works by some innovative processing methods to achieve a breakthrough. But for their limitions, these methods can not achieve a complete separation if the there are two or more signals in one time domain or frequency domain. In this article, a new engineering signal processing method-pattern filter method has is introduced, by which the signal extraction, sepration and noise reduction can be achieved successfully. Experiments show that this method can not only make a reasonable separation of the various vibration signals, but also give the typical signal extractions and model building ways.

scholarly journals An Improved Empirical Mode Decomposition Method for Vibration Signal

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Xiaohan Liu ◽  
Guangfeng Shi ◽  
Weina Liu
Keyword(s):  
Signal Processing ◽  
Power Spectrum ◽  
Empirical Mode Decomposition ◽  
Decomposition Method ◽  
Vibration Signal ◽  
Vibration Monitoring ◽  
Blasting Vibration ◽  
Vibration Signals ◽  
Mode Decomposition ◽  
Empirical Mode Decomposition Method

With the development of electronic measurement and signal processing technology, nonstationary and nonlinear signal characteristics are widely used in the fields of error diagnosis, system recognition, and biomedical instruments. Whether these features can be extracted effectively usually affects the performance of the entire system. Based on the above background, the research purpose of this paper is an improved vibration empirical mode decomposition method. This article introduces a method of blasting vibration signal processing—Differential Empirical Mode Decomposition (DEMD), combined with phosphate rock engineering blasting vibration monitoring test, and Empirical Mode Decomposition (EMD) to compare and analyze the frequency screening of blasting vibration signals, the aliasing distortion, and the power spectrum characteristics of the decomposed signal. The results show that compared with EMD, DEMD effectively suppresses signal aliasing and distortion, and from the characteristics of signal power spectrum changes, DEMD extracts different dominant frequency components, and the frequency screening effect of blasting vibration signals is superior to EMD. It can bring about an obvious improvement in accuracy, and the calculation time is about 4 times that of the EMD method. Based on the ground analysis of ground motion signals, this paper uses the EMD algorithm to analyze measured ground blast motion signals and study its velocity characteristics and differential time, which provides a new way of studying motion signals.

scholarly journals Vibration Analysis of Partially Damaged Rotor Bar in Induction Motor under Different Load Condition Using DWT

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Jose Rangel-Magdaleno ◽  
Hayde Peregrina-Barreto ◽  
Juan Ramirez-Cortes ◽  
Roberto Morales-Caporal ◽  
Israel Cruz-Vega
Keyword(s):  
Induction Motor ◽  
Induction Motors ◽  
Mechanical Load ◽  
Load Condition ◽  
Monitoring Systems ◽  
Discrete Wavelet ◽  
Progressive Damage ◽  
Vibration Signals ◽  
Wavelet Transform Analysis ◽  
Load Conditions

The relevance of the development of monitoring systems for rotating machines is not only the ability to detect failures but also how early these failures can be detected. The purpose of this paper is to present an experimental study of partially damaged rotor bar in induction motor under different load conditions based on discrete wavelet transform analysis. The approach is based on the extraction of features from vibration signals at different level of damage and three mechanical load conditions. The proposed analysis is reliable for tracking the damage in rotor bar. The paper presents an analysis and extraction of vibration features for partially damaged rotor bar in induction motors. The experimental analysis shows the change in behavior of vibration due to load condition and progressive damage.

Means for providing information about danger to an employee equipped with an individual vibrating warning system

2019 ◽  
Vol 572 (5) ◽  
pp. 21-23
Author(s):  
Rafał Młyński ◽  
Emil Kozłowski ◽  
Leszek Morzyński
Keyword(s):  
Work Environment ◽  
Warning System ◽  
Vibration Signal ◽  
Original Research ◽  
Vibration Signals ◽  
Moving Vehicle ◽  
Research Results ◽  
Advantages And Disadvantages ◽  
Hearing Protectors ◽  
The Impact

The use of hearing protectors is a frequent way to avoid the impact of noise present in the work environment. However, it should be kept in mind that the use of hearing protectors, while reduces the threat created by noise, also diminishes the perception of sounds that are important for the safety of the employee. In such cases, employee’s safety can be improved/increased by using a system to detect the near presence of a moving vehicle. Such a system should be able to transmit information on detected danger to an employee using hearing protectors. The article discusses the possible ways of providing such information to employees using hearing protectors. The advantages and disadvantages of using acoustic, light and vibration signals for this purpose were considered. The authors also present original research results to confront the possibility of perceiving the vibration signal produced by two types of wearables.

BOU-Based Cycle Determination for Different Kinds of Mechanical Vibration Signals

2012 ◽  
Vol 220-223 ◽  
pp. 2217-2223 ◽  
Author(s):  
Miao Rong Lv ◽  
Jian Lu ◽  
Zhi Qiang Chen
Keyword(s):  
Signal Processing ◽  
Mechanical Vibration ◽  
Vibration Signal ◽  
Simulation Method ◽  
Fault Detection And Diagnosis ◽  
Basic Operation ◽  
Vibration Signals ◽  
Operation Unit ◽  
Simulation Results ◽  
Detection And Diagnosis

The cycle of each vibration component of a mixed signal is a very important parameter for reciprocating mechanical vibration signal processing. The cycle determination also has an important influence to obtain the typical characteristics of every sub-signal, to achieve the fault detection and diagnosis of the equipment. But signals acquired at the scene tend to a mixture of a variety of vibration components that have their own periodic characteristics. This paper mainly proposes a method based on the conception of the basic operation unit(BOU) for mechanical vibration signal processing, and the principles and processes of this method are described in detail. Simulation method is introduced in order to explain this principles. Furthermore, A Delphi programming is developed to realize this simulation implementation, and simulation results are demonstrated to fully verify its correctness. Finally, a method to quickly determine the various vibration component cycles is put forward.

Recent Advances in Vibration Signal Processing Techniques for Gear Fault Detection-A Review

2013 ◽  
Vol 430 ◽  
pp. 78-83 ◽  
Author(s):  
Rusmir Bajrić ◽  
Ninoslav Zuber ◽  
Safet Isić
Keyword(s):  
Signal Processing ◽  
Fault Detection ◽  
Vibration Signal ◽  
Analysis Time ◽  
Time Frequency ◽  
Detection Techniques ◽  
Advantages And Disadvantages ◽  
Gear Fault ◽  
Processing Techniques ◽  
Signal Processing Techniques

This paper provides a review of the literature, progress and changes over the years on fault detection of gears using vibration signal processing techniques. Analysis of vibration signals generated by gear in mesh has shown its usefulness in industrial gearbox condition monitoring. Vibration measurement provides a very efficient way of monitoring the dynamic conditions of a machine such as gearbox. Various vibration analysis methods have been proposed and applied to gear fault detection. Most of the traditional signal analysis techniques are based on the stationary assumption. Such techniques can only provide analyses in terms of the statistical average in the time or frequency domain, but can not reveal the local features in both time and frequency domains simultaneously. Frequency/quefrency analysis, time/statistical analysis, time-frequency analysis and cyclostationarity analysis are reviewed in regard for stationary and nonstationary operation. The use of vibration signal processing detection techniques is classified and discussed. The capability of each technique, fundamental principles, advantages and disadvantages and practical application for gear faults detection have been examined by literature review.

Fault Diagnosis of Elevator Based AR Bi-Spectrum

2012 ◽  
Vol 468-471 ◽  
pp. 1743-1748
Author(s):  
Jing Yu Yi ◽  
Yi Jian Huang
Keyword(s):  
Time Series ◽  
Signal Processing ◽  
Fault Diagnosis ◽  
Gaussian White Noise ◽  
Vibration Signal ◽  
Ar Model ◽  
Vibration Signals ◽  
Working Status ◽  
Non Gaussian ◽  
Analysis Of Time Series

According to the characteristics of the elevator fault vibration signals, proposing a Based on analysis of time series AR bi-spectrum elevator fault diagnosis. When the zero-mean, non-Gaussian white noise elevator device, the vibration signal using sampling to establish time series autoregressive model (AR model), resulting in AR bi-spectrum. Bi-spectrum signal processing is a new, powerful signal processing technology, which can be described the nonlinear coupling, suppression Gaussian noise and retention of phase information, you can get the elevator working status of the different dynamic characteristics. The results show that bi-spectral analysis with AR elevator failure is feasible and effective.

Ultrasonic and Vibration Signals Processing for Computing Dynamic Modulus of Elasticity of Intact and Damaged Wood

2010 ◽  
Vol 33 ◽  
pp. 295-298
Author(s):  
Song Yuan Ni ◽  
Hua Dong Xu ◽  
Li Hai Wang
Keyword(s):  
Signal Processing ◽  
Time Domain ◽  
Modulus Of Elasticity ◽  
Dynamic Modulus ◽  
Vibration Signal ◽  
Head Wave ◽  
Vibration Test ◽  
Response Functions ◽  
Vibration Signals ◽  
Dynamic Modulus Of Elasticity

To effectively and quickly estimate the dynamic modulus of elasticity (MOE) of wood, ultrasonic and vibration test techniques are employed to measure the intact and damaged wood specimens. The ultrasonic and vibration signals in time domain are obtained respectively. Then, the time of flight is received by picking up the position of ultrasonic head wave and it is used to compute the dynamic MOE of wood. Simultaneously, the frequency response functions of wood are received by analyzing the frequency domain signals transformed from time domain using Fourier formulas, and they are used to pick up the intrinsic frequencies to calculate the dynamic MOE measured by vibration test. Finally, the correlations of dynamic MOE tested by these two methods are discussed. The research results show that it is feasible to estimate the dynamic MOE of wood via ultrasonic and vibration signal processing.

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