scholarly journals Automatic analysis methods of vibration parameters for diagnosis of marine rotating machines

2020 ◽  
Author(s):  
Д.В. Грищенко
Keyword(s):  
Mutual Influence ◽  
Vibration Signal ◽  
Rotating Machines ◽  
Automatic Diagnosis ◽  
Operating Modes ◽  
Diagnosis Method ◽  
Unstable Vibration ◽  
Reasonable Prediction ◽  
Vibration Parameters ◽  
Machinery Diagnostics

Автоматическое диагностирование ответственных роторных машин по вибрации является одним из основных способов обеспечения их надежности и безопасности эксплуатации. Известные методы автоматической обработки вибрационных параметров в диагностических целях обладают ограниченной эффективностью в судовых условиях из-за нестабильной виброактивности машин в установившихся режимах их работы, вынуждающей завышать пороги опасности, и существенного взаимовлияния близко расположенных узлов и агрегатов, приводящего к ошибочным диагнозам. Для решения первой указанной проблемы предложен метод адаптации пороговых значений, позволяющий своевременно обнаружить и прогнозировать ухудшение технического состояния судовых роторных машин. Для решения второй проблемы предложен инвариантный к типу объекта контроля метод автоматического определения причин ухудшения технического состояния судовых роторных машин, который позволяет конфигурировать диагностические правила в табличном виде с возможностью учета влияния дефектов на вибрацию разнесенных в пространстве точек. Рассмотренные методы успешно используются в системах диагностирования роторного оборудования по вибрации. Automatic diagnosis of important rotating equipment using vibration signal is one of the main ways to ensure their reliability and operational safety. Known methods for automatically processing vibration parameters for machinery diagnostics have insufficient effectiveness on shipboard. The reason for this is unstable vibration activity in steady operating modes, which requires increasing thresholds, and the mutual influence of neighboring mechanical components and machines, which leads to erroneous diagnoses. The article provides methods to solve these problems. The first threshold adaptation method allows timely detection and reasonable prediction of marine machinery condition deterioration. The second automatic diagnosis method allows determining causes of this condition deterioration. The diagnosis method does not depend on the type of machine and uses the configuration of diagnostic rules in table form. In addition, this method allows to use defects influence on vibration at spaced control points. Declared methods are successfully applied in diagnostics systems of rotating machines.

Research on Roller Bearing with Fault Diagnosis Method Based on EMD and BP Neural Network

2014 ◽  
Vol 1014 ◽  
pp. 501-504 ◽  
Author(s):  
Shu Guo ◽  
You Cai Xu ◽  
Xin Shi Li ◽  
Ran Tao ◽  
Kun Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Roller Bearing ◽  
Vibration Signal ◽  
Classification Performance ◽  
Outer Race ◽  
Mode Decomposition ◽  
Diagnosis Method ◽  
Main Fault

In order to discover the fault with roller bearing in time, a new fault diagnosis method based on Empirical mode decomposition (EMD) and BP neural network is put forward in the paper. First, we get the fault signal through experiments. Then we use EMD to decompose the vibration signal into a series of single signals. We can extract main fault information from the single signals. The kurtosis coefficient of the single signals forms a feature vector which is used as the input data of the BP neural network. The trained BP neural network can be used for fault identification. Through analyzing, BP neural network can distinguish the fault into normal state, inner race fault, outer race fault. The results show that this method can gain very stable classification performance and good computational efficiency.

scholarly journals The method of dynamic operating modes investigation in electrohydraulic drive systems with improved accuracy of positioning executive elements

2018 ◽  
Vol 226 ◽  
pp. 04024
Author(s):  
Valeriy V. Grechikhin ◽  
Galina A. Galka ◽  
Anatoliy I. Ozerskiy ◽  
Mikhail E. Shoshiashvili
Keyword(s):  
Mutual Influence ◽  
Hydraulic Drive ◽  
Joint Work ◽  
Operating Modes ◽  
Hydraulic Machines ◽  
Dynamic Operating ◽  
Electrohydraulic Drive ◽  
Drive Systems ◽  
Improved Accuracy ◽  
Executive Elements

The article describes the method of dynamic operating modes investigation in electrohydraulic drive systems with improved accuracy of positioning output element. The method is the evolution of the fundamental positions of the mechanics of continuous media with moving boundaries as applied to the research of non-stationary processes accompanying the operation of hydraulic drive systems with piston hydraulic machines. The method is based on generalized modeling (technical, physical, mathematical and computer), takes into account the peculiarities of mutual influence of electric and hydraulic machines during their joint work as part of the electrohydraulic drive, which raises the level and adequacy of actuators simulation, as well as the reliability of the assessment of their technical condition. The method extends the field of research, improves the accuracy of the calculation of the positioning of the executive elements, taking into account the different dynamic modes of the drives under study.

Research of the Vertical Vibrations of Tandem Mill Based on Wavelet Analysis

2015 ◽  
Vol 724 ◽  
pp. 279-282
Author(s):  
Chun Hua Ren ◽  
Xu Ma ◽  
Ze Ming Li ◽  
Yan Hong Ding
Keyword(s):  
Wavelet Analysis ◽  
Wavelet Decomposition ◽  
Vibration Signal ◽  
Vertical Vibration ◽  
Rolling Speed ◽  
Vibration Data ◽  
Signal Features ◽  
Operating Side ◽  
Vertical Vibrations ◽  
Vibration Parameters

In this paper, the defect sheet was captured coincidentally. According to the defective product’s characteristics, we suspected to be caused by the vertical vibration of the roll. When the rolling speed reached a certain value, the vibration of the fourth stand can be feel. The experiment of the vibration data collection was taken to compare the vibration parameters of rolling operating side with those of drive side by wavelet analysis. The result states that the abnormal vibration signal features can be extracted in a special frequency segment of wavelet decomposition, and the vibration frequency to the roll is confirmed which appeared product defects.

Gear Fault Diagnosis Methods Based on EMD and HMM

2013 ◽  
Vol 333-335 ◽  
pp. 1684-1687
Author(s):  
Bin Wu ◽  
Song He Zhang ◽  
Yue Gang Luo ◽  
Shan Ping Yu
Keyword(s):  
Fault Diagnosis ◽  
Vibration Signal ◽  
Modulation Amplitude ◽  
Double Frequency ◽  
Gear Mesh ◽  
Gear Fault ◽  
Diagnosis Method ◽  
Modulation Signal ◽  
Gear Fault Diagnosis ◽  
Modulation Sideband

Due to the feature and the forms of motion of the gears, the vibration signal of the gear is mainly the frequency modulation, amplitude modulation, or hybrid modulation signal corresponding to the gear-mesh frequency and its double frequency signal. When faults arise on the gears, the number and shape of the modulation sideband will be changed. The structures and forms of the FM composition differ according to the type of faults. According to the above mentioned characteristic, this essay raises a method to disassemble the gear vibrate signal, points out the formulas to build up characteristic vector, on that basis, the essay raised a gear fault diagnosis method based on EMD and Hidden Markov Model (HMM), this method can identify the working condition of the normal gears, snaggletooth gears, and pitting gears.

scholarly journals Research on Mechanical Defect Detection and Diagnosis Method for GIS Equipment Based on Vibration Signal

Energies ◽  
2021 ◽  
Vol 14 (17) ◽  
pp. 5507
Author(s):  
Liang He ◽  
Jie Yang ◽  
Ziwei Zhang ◽  
Zongwu Li ◽  
Dengwei Ding ◽  
...  
Keyword(s):  
Experimental Research ◽  
Detection System ◽  
Engineering Application ◽  
Vibration Signal ◽  
Signal Amplitude ◽  
Field Application ◽  
Large Current ◽  
Three Phase ◽  
Diagnosis Method ◽  
Detection And Diagnosis

Gas insulated switchgear equipment (GIS) is widely used in power system, and more attention has been paid to discharge defects than mechanical defects. However, since mechanical defects are a major cause of the failure in GIS, it is of great significance to carry out relevant research on mechanical defects. Detection and diagnosis methods of mechanical defects based on vibration signal are studied in this paper. Firstly, vibration mechanisms of GIS are analyzed. Due to structural differences between single phase insulated type GIS and three phase insulated type GIS, there are big differences in vibration mechanisms between the two types of GISs. Secondly, experimental research on mechanical defects is carried out based on a 110 kV GIS equipment and a self-developed vibration detection system; results show that mechanical defects can be diagnosed by analyzing signal amplitude, frequency spectrum and waveform distortion rate, and a large current is more beneficial for diagnosing mechanical defects. Lastly, field application has been carried out on 220 kV GIS equipment, and a poor contact defect is found, demonstrating that abnormal diagnosis can be realized by method proposed in this paper. Experimental research and field application demonstrate the feasibility and effectiveness of detection and diagnosis method for mechanical defects based on vibration signal and provide experience for subsequent engineering application.

scholarly journals A Reliable Fault Diagnosis Method for a Gearbox System with Varying Rotational Speeds

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3105 ◽  
Author(s):  
Cong Dai Nguyen ◽  
Alexander Prosvirin ◽  
Jong-Myon Kim
Keyword(s):  
Fault Diagnosis ◽  
Reference Signal ◽  
Vibration Signal ◽  
Support Vector ◽  
Health States ◽  
Informative Signal ◽  
Vibration Signals ◽  
Optimal Output ◽  
Gear Fault ◽  
Diagnosis Method

The vibration signals of gearbox gear fault signatures are informative components that can be used for gearbox fault diagnosis and early fault detection. However, the vibration signals are normally non-linear and non-stationary, and they contain background noise caused by data acquisition systems and the interference of other machine elements. Especially in conditions with varying rotational speeds, the informative components are blended with complex, unwanted components inside the vibration signal. Thus, to use the informative components from a vibration signal for gearbox fault diagnosis, the noise needs to be properly distilled from the informational signal as much as possible before analysis. This paper proposes a novel gearbox fault diagnosis method based on an adaptive noise reducer–based Gaussian reference signal (ANR-GRS) technique that can significantly reduce noise and improve classification from a one-against-one, multiclass support vector machine (OAOMCSVM) for the fault types of a gearbox. The ANR-GRS processes the shaft rotation speed to access and remove noise components in the narrowbands between two consecutive sideband frequencies along the frequency spectrum of a vibration signal, enabling the removal of enormous noise components with minimal distortion to the informative signal. The optimal output signal from the ANR-GRS is then extracted into many signal feature vectors to generate a qualified classification dataset. Finally, the OAOMCSVM classifies the health states of an experimental gearbox using the dataset of extracted features. The signal processing and classification paths are generated using the experimental testbed. The results indicate that the proposed method is reliable for fault diagnosis in a varying rotational speed gearbox system.

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