This document discusses vibration monitoring and analysis techniques for machine maintenance. It covers three types of maintenance schemes: breakdown, preventive, and condition-based maintenance. Vibration monitoring is described as the most common condition monitoring method, where vibration levels are measured to predict failures. Two types of vibration monitoring systems - periodic and permanent - are outlined. Vibration analysis techniques including time-domain and frequency-domain analysis are explained. Data acquisition and interpretation methods are also summarized. The role of computers in vibration-based condition monitoring programs is briefly described.
This document provides an overview of vibration analysis and predictive maintenance. It discusses maintenance philosophies like breakdown, preventive, predictive, and proactive maintenance. Predictive maintenance uses condition monitoring techniques like vibration analysis to determine the condition of machines and identify faults. Vibration analysis measures characteristics like displacement, velocity, acceleration, frequency, and phase to determine how much vibration is present, what defects are causing it, and which machine parts are affected. Understanding vibration signatures can reveal problems like unbalance, misalignment, looseness, and bearing defects.
This document provides an introduction to vibration analysis and maintenance. It discusses the different types of maintenance including reactive, preventive, predictive, and proactive maintenance. It then covers vibration fundamentals including waveform, spectrum, frequency, amplitude, and motion. Specific vibration faults like unbalance, misalignment, and bearing defects are examined. The use of spectrum and time waveform analysis to diagnose machine faults is also explained.
This document discusses machine vibration diagnosis through FFT analysis. It provides examples of using FFT analysis to diagnose issues like rotor unbalance, shaft misalignment, field asymmetry, and a loose belt drive wheel. FFT analysis allows identifying fault frequencies in the machine's vibration spectrum to pinpoint the root cause of issues. The document also discusses ISO standards for vibration severity, components vulnerable to damage, and practical diagnosis techniques.
The document discusses vibration analysis techniques used for predictive maintenance. It begins with an introduction to SKF Reliability Systems and their network in Asia Pacific. It then covers maintenance philosophies focused on prevention rather than failure. Key concepts of vibration analysis are explained, including how measurements are performed, common measurement types and units, and analyzing vibration spectra. The document provides examples of vibration data and outlines how spectra are used to identify common machine faults.
This document discusses advances in fault detection and diagnosis in industry. It covers condition monitoring techniques like vibration analysis, lubricant analysis, and thermography. It discusses the differences between fault, failure, and malfunction and describes fault detection as detecting small faults early through techniques like limit checking and trend analysis. Fault diagnosis involves diagnosing faults in processes, parts, and devices using analytical and heuristic methods. Condition monitoring systems are discussed along with fault detection models using process variables. Data analysis techniques and online enterprise asset management are also covered.
This document discusses vibration monitoring and analysis. It defines vibration as the motion of mechanical parts back and forth from their neutral position, which is caused by induced forces and freedom of movement. Excessive vibration can have harmful effects like increased load on bearings, higher stresses on components, and reduced equipment efficiency. Common problems that cause vibration include unbalance, misalignment, looseness, and defects. Vibration monitoring involves measuring parameters like displacement, velocity, acceleration, and using tools like FFT analysis to identify frequencies associated with faults. Understanding phase and trends in vibration spectra over time helps with condition monitoring and predictive maintenance of machinery.
it is related to the subject dynamics of machinery in that measurement of vibration, instrument used for vibration measurement, control of vibration and related part is covered
Condition monitoring of rotating machines pptRohit Kaushik
This document discusses condition monitoring of rotating machines. It covers various techniques for monitoring parameters like temperature, vibration, electrical signals and fluxes to detect faults in machines like motors and generators. Local temperature can be monitored using devices embedded in the insulation near hot parts like the winding or core. Vibration is commonly monitored at various frequencies to analyze faults in components. Electrical signals like current and flux are also monitored to detect issues in windings or rotors. Overall, condition monitoring aims to continuously evaluate equipment health and detect early-stage faults in machines.
This presentation is equipped with the basic concepts of Condition Monitoring. The methods and analysis, circumscribed by Condition Monitoring, are summarized with an addition of application in this presentation.
The document discusses vibration measurement. It describes how vibrations are measured to analyze mechanical systems. The key steps are exciting a structure, sensing its response with a transducer, conditioning the signal, and analyzing it. Common exciters include impact hammers and shakers. Transducers like accelerometers convert motion to electrical signals. Conditioning prepares signals for analysis using digital filtering or fast Fourier transforms. Proper equipment selection depends on the application and desired frequency range and force.
Vibration Monitoring-Vibration Transducers-Vibration TroubleshootingDhanesh S
Vibration monitoring involves measuring machine vibrations using transducers like accelerometers and analyzing the vibration signals. This helps identify potential issues like imbalance, misalignment, bearing problems, and gear damage. Vibration is measured through devices that collect time signal data and analyze it using techniques like spectral analysis and envelope analysis to produce a vibration signature. This signature provides information on individual frequency amplitudes that can indicate machine faults and their locations. Maintaining machines involves ongoing vibration monitoring to detect issues early and ensure equipment reliability.
This document discusses condition monitoring and vibration monitoring of machines. It begins by defining condition monitoring as assessing the state of machinery by measuring parameters over time to detect deterioration and potential failures. Vibration monitoring is then introduced as a common method that involves measuring frequency and amplitude of vibrations to identify issues. The history and types of vibration monitoring systems are reviewed, including periodic offline and continuous online systems. It concludes by outlining steps for establishing a condition monitoring program, such as determining the appropriate system, creating a machinery list, and documenting key machine characteristics.
1) Vibration is the motion of mechanical parts back and forth from its position of rest. It is caused by an induced force and freedom for movement.
2) Vibration amplitude can be measured as displacement, velocity, or acceleration, with different units providing information about strain, fatigue, and forces.
3) Vibration analysis can detect faults like unbalance, misalignment, bearing defects, and more by examining the ratios of horizontal, vertical, and axial amplitudes and frequency spectrum characteristics.
this slide deals with the basic concepts related to mechanical vibrations for more information you can go through any mechanical vibration book available for engineering students
Condition monitoring & vibration analysisJai Kishan
Condition monitoring and vibration analysis are used to monitor the health and integrity of machines in a chemical plant. Non-destructive testing techniques like vibration analysis are used to detect issues like unbalance, misalignment, looseness and resonance before they cause breakdowns. The document outlines the various non-destructive testing and condition monitoring activities performed at NFL Bathinda, including scheduled vibration monitoring and analysis of rotating equipment, alignment checks, ultrasonic testing, and more. Specific fault detection methods and vibration signatures that could indicate issues like unbalance, misalignment, looseness, and resonance are also described.
The document discusses various aspects of condition monitoring through vibration analysis. It defines condition monitoring and different types of maintenance. It explains why condition monitoring is important and some key physical parameters that are measured. It then focuses on condition monitoring through vibration analysis, discussing concepts like amplitude, frequency, causes of vibration, and analyzing case studies of different machines. Key points covered include vibration measurement and analysis, identifying issues like unbalance, misalignment, looseness and bearing defects.
The document discusses vibration theory, including definitions of acceleration, velocity, displacement and simple harmonic motion. It describes quantifying vibration amplitude using peak-to-peak, peak, average and RMS levels. It also covers the differences between time and frequency domain analysis and concepts of phase angle measurement in condition monitoring. Condition monitoring strategies aim to focus on critical machinery by defining detectable faults and relevant measurement parameters.
Condition Monitoring of Rotating Equipment Considering the Cause and Effects ...IJMERJOURNAL
This document summarizes research on condition monitoring of rotating equipment using vibration analysis. It discusses various signal processing techniques used for fault detection and diagnosis, including time-domain analysis, frequency domain analysis, time-frequency analysis using wavelet transforms, and support vector machines. It also reviews literature on prognostics approaches that use condition data to predict failures through artificial intelligence techniques. The document aims to provide an overview of recent developments in diagnostic and prognostic models, algorithms, and technologies for processing sensor data from condition monitoring systems.
This document discusses motor current signature analysis (MCSA) for detecting faults in induction motors. MCSA analyzes current signals to identify faults by comparing signatures from healthy and faulty motors. It has advantages over other monitoring methods as it does not require additional sensors. Signal processing techniques like fast Fourier transforms (FFT), short-time Fourier transforms, and wavelet transforms are used to analyze current signals in the frequency domain and detect fault frequencies. An algorithm is presented that uses the standard deviation of wavelet coefficients to detect faults like loose connections or stator resistance unbalancing. MCSA can detect faults at an early stage to prevent further damage.
This document discusses condition monitoring of machinery. It defines condition monitoring as monitoring parameters that can indicate developing failures. It discusses methods of condition monitoring including vibration monitoring, thermography analysis, and oil analysis. It also discusses establishing a condition monitoring program which involves determining the appropriate monitoring system, creating a list of machines to monitor, and selecting measurement locations and time intervals.
This document discusses modal analysis and condition monitoring of machines. It describes various machine condition monitoring techniques such as aural and visual monitoring using microphones and stroboscopes, vibration monitoring to detect issues like defective bearings, thermal monitoring using devices like pyrometers and thermocouples, and wear debris monitoring of lubricating oil. It also outlines different machine maintenance techniques including breakdown maintenance which is performed after failure, preventive maintenance on a set time schedule, and condition-based maintenance based on periodic health monitoring and scheduling only when needed.
Assessment of Gearbox Fault DetectionUsing Vibration Signal Analysis and Acou...IOSR Journals
Maintenance is a set of organised activities that are carried out in order to keep an item in its best
operational condition with minimum cost acquired. Predictive maintenance (PdM) is one of the maintenance
program that recommends maintenance decisions based on the information collected through condition
monitoring techniques, statistical process control or equipment performance for the purpose of early detection
and elimination of equipment defects that could lead to unplanned downtime of machinery or unnecessary
expenditures. Particularly Gears and rolling element bearings are critical elements in rotating machinery, so
predictive maintenance is often applied to them. Fault signals of gearboxes or rolling-element bearings are nonstationary.
This paper concludes with a brief discussion on current practices of PDM methodologies such as
vibration analysis and Acoustic Emission analysis, which are widely used as they offers a complimentary tool
for health monitoring or assessment of gears in rotating machineries
Condition monitoring of induction motor with a case studyIAEME Publication
This document summarizes a study on condition monitoring of an induction motor. It discusses various monitoring methods like temperature monitoring, vibration analysis, motor current signature analysis, and shaft voltage measurement. Temperature monitoring identified hotspots indicating potential insulation or cooling issues. Vibration analysis found peaks corresponding to unbalance, misalignment, and bearing or looseness issues. Motor current signature analysis identified rotor bar and joint issues by analyzing current waveforms. Together these methods provided a comprehensive assessment of the motor's health to guide maintenance.
Condition monitoring of induction motor with a case studyIAEME Publication
This document summarizes a study on condition monitoring of an induction motor. The study utilized multiple monitoring techniques including temperature monitoring, vibration analysis, motor current signature analysis, and shaft voltage measurement. Temperature, vibration, and shaft voltage readings were found to be within normal limits, indicating the motor was in good health. Motor current signature analysis detected no issues, further confirming the healthy state of the motor. The study demonstrated how a combination of condition monitoring techniques can evaluate the overall condition and help plan preventive maintenance for motors.
Vibration analysis uses sensors to measure vibrations in machines and identify potential failures or faults. Analyzing vibration data can help predict maintenance needs and improve machine reliability. Sensors are mounted on machines using various methods to measure vibrations generated by rotating components like motors, pumps and gears. The vibration measurements are analyzed to diagnose issues and plan repairs for machines before failures occur.
This document discusses condition monitoring techniques used to assess the health of equipment. It defines condition monitoring as assessing equipment using measurements and monitoring of parameters. The key steps in condition monitoring are identifying critical systems, selecting monitoring techniques, setting baseline readings, collecting and assessing data, diagnosing faults, and reviewing the system. Common monitoring techniques discussed include vibration analysis, temperature monitoring, lubricant analysis, and visual inspection using tools like borescopes. Specific methods like ferrography, spectroscopy, and infrared thermography are also summarized.
mechine vibration diagnotics for beginerAngga896790
The document discusses machine vibration diagnosis through frequency analysis. It begins with an example of diagnosing excessive vibration in a belt-driven exhaust fan. Vibration measurements and FFT analysis revealed that the source of high vibration was unbalance in the fan's drive belt wheel, not a problem with the motor as initially suspected. It then discusses using trend analysis of characteristic vibration parameters over time to monitor machine condition, and the benefits of a two-level monitoring strategy using overall measurements and more in-depth spectral analysis when alarm thresholds are exceeded.
IRJET- Trouble Shooting of Rotary Equipments using Vibration AnaysisIRJET Journal
This document discusses vibration analysis as a technique for troubleshooting and predicting failures in rotary equipment. It begins with an introduction to predictive maintenance using condition monitoring tools like vibration analysis. Next, it covers causes of vibration in rotating machines, vibration measurement and analysis methods, and the instruments used. Key points made include that vibration analysis can identify specific machine faults from vibration signatures, and that periodic vibration monitoring allows problems to be detected early before failures occur. The goals of a vibration analysis program are to minimize downtime and maintenance costs in industrial operations.
This document summarizes an experiment investigating machinery vibration characteristics using a comprehensive experimental setup. The experimental setup consisted of components like pulleys, shafts, ball bearings, and an overhung impeller powered by an electric motor. Vibrations from these components were measured using a CSI 2140 machinery health analyzer in terms of frequency, amplitude, and phase angle. Mass imbalance was detected as the source of high vibrations. Measures like polar plot analysis were taken to reduce vibration severity and meet ISO standards. The experiment aimed to interpret machinery condition by analyzing experimental vibration data obtained from the multi-component setup.
Fault Detection and Failure Prediction Using Vibration AnalysisTristan Plante
This document discusses using vibration analysis to detect faults and predict failures in rotating equipment like electric motors. It describes an experiment where vibration data was collected from a motor under normal operation and different fault conditions (unbalance, mechanical looseness, bearing defect). The data was analyzed using spectrum analysis software and MATLAB. Specific fault frequencies were identified that corresponded to the type of fault. The results support using vibration analysis to monitor equipment condition and enable predictive maintenance by detecting issues before catastrophic failures occur.
Development of a Condition Monitoring Algorithm for Industrial Robots based o...IJECEIAES
Signal processing plays a significant role in building any condition monitoring system. Many types of signals can be used for condition monitoring of machines, such as vibration signals, as in this research; and processing these signals in an appropriate way is crucial in extracting the most salient features related to different fault types. A number of signal processing techniques can fulfil this purpose, and the nature of the captured signal is a significant factor in the selection of the appropriate technique. This chapter starts with a discussion of the proposed robot condition monitoring algorithm. Then, a consideration of the signal processing techniques which can be applied in condition monitoring is carried out to identify their advantages and disadvantages, from which the time-domain and discrete wavelet transform signal analysis are selected.
Mems Based Motor Fault Detection in Windmill Using Neural NetworksIJRES Journal
Today wind turbine technology is one of the fastest growing power generation technologies operating in large numbers at harsh and difficult environment sites and it is difficult to monitor each and every windmill separately. There are times when faults occur in motors of windmills are not detected in earlier stage and we come to know about damage when motor gets fully damaged. Here we using wireless monitoring based on MEMS accelerometer sensor which senses the vibrations occurring in the motor and based on the severity of vibrations, sensor sends the data to the controlling unit to take further action. Neural network based work is included to get the accurate and precise vibratory signals to detect fault at a very early stage to avoid full damage to the motor.
This document summarizes a research paper on using MEMS sensors and neural networks to detect faults in the motors of wind turbines. It begins with an abstract that overviews using an accelerometer sensor to detect vibrations in the motor and send the data to a control unit. It then provides background on existing vibration-based fault detection methods and proposes a new method using MEMS sensors, wavelet packet transform analysis of the sensor data, and a neural network classifier to detect faults at an early stage. The document concludes that this method allows accurate and reliable condition monitoring of wind turbines to prevent motor damage.
This document presents a study on using acoustic signal analysis to detect faults in bearings. The study develops an experimental setup to acquire acoustic signals from bearings under different conditions, including with and without defects. The acoustic signals are processed using techniques like fast Fourier transforms and wavelet transforms to extract information about faults. Signals are analyzed from bearings with no defects, misalignment, looseness, missing balls, and combinations of defects. Results show the acoustic signal energy at different frequencies for healthy and faulty bearings. This acoustic signal analysis technique can be used to detect bearing faults and failures.
1) The document discusses using discrete wavelet transforms to analyze vibration signals from roller bearings to detect faults. It proposes a new feature - summing the squared wavelet decomposition coefficients at each level - and compares it to the traditional energy-based feature.
2) An experiment is described where vibration signals are collected from a test rig under normal conditions and with introduced inner race, outer race, and combined faults. The signals are decomposed using discrete wavelet transforms.
3) Features are then extracted from the wavelet decompositions using both the proposed summed squared coefficient feature and the traditional energy-based feature. A decision tree is used to classify the features and determine which feature performs better at detecting the faults.
This document describes a proposed vehicle density sensor system to manage traffic congestion. The system uses sensors like infrared sensors placed on roads to detect vehicle density in real-time. A microcontroller collects this sensor data and sends it to a computer. Based on the detected vehicle density, the computer then dynamically adjusts the timing of traffic lights at intersections to reduce congestion. By taking into account current traffic conditions, this proposed system aims to more efficiently control traffic flows compared to traditional fixed-time traffic light systems.
Agricultural Profitability through Resilience: Smallholder Farmers' Strategie...IJAEMSJORNAL
This study investigated the knowledge strategies and coping utilized by smallholder farmers in Guimba, Nueva Ecija to reduce and adjust to the effects of climate change. Smallholder farmers, who are frequently susceptible to climate change, utilize various traditional and innovative methods to strengthen their ability to withstand and recover from these consequences. Based on the results of this study, farmers in Guimba, Nueva Ecija demonstrate a profound comprehension of the adverse weather conditions, such as typhoons, droughts, and excessive rainfall, which they ascribe to climate change. While they have a fundamental understanding of climate change and its effects, their knowledge of scientific intricacies is restricted, indicating a need for information that is particular to the context. Although farmers possess knowledge about climate change, they are not actively engaging in proactive actions to adapt to it. Instead, they rely on reactive coping mechanisms. This highlights the necessity for targeted educational and communicative endeavors to promote the acceptance and implementation of approaches. Furthermore, the absence of available resources poses a significant barrier to achieving successful adaptation, highlighting the importance of pushing for inexpensive and feasible measures for adaptation. Farmers recognize the benefits of agroforestry and have started integrating the growth of fruit trees, particularly mangoes, into their coping techniques.
Modified O-RAN 5G Edge Reference Architecture using RNNijwmn
Paper Title
Modified O-RAN 5G Edge Reference Architecture using RNN
Authors
M.V.S Phani Narasimham1 and Y.V.S Sai Pragathi2, 1Wipro Technologies, India, 2Stanley College of Engineering & Technology for Women (Autonomous), India
Abstract
This paper explores the implementation of 6G/5G standards by network providers using cloud-native technologies such as Kubernetes. The primary focus is on proposing algorithms to improve the quality of user parameters for advanced networks like car as cloud and automated guided vehicle. The study involves a survey of AI algorithm modifications suggested by researchers to enhance the 5G and 6G core. Additionally, the paper introduces a modified edge architecture that seamlessly integrates the RNN technologies into O-RAN, aiming to provide end users with optimal performance experiences. The authors propose a selection of cutting-edge technologies to facilitate easy implementation of these modifications by developers.
Keywords
5G O-RAN, 5G-Core, AI Modelling, RNN, Tensor Flow, MEC Host, Edge Applications.
Volume URL: https://airccse.org/journal/jwmn_current24.html
Abstract URL: https://aircconline.com/abstract/ijwmn/v16n3/16324ijwmn01.html
Youtube URL: https://youtu.be/rIYGvf478Oc
Pdf URL: https://aircconline.com/ijwmn/V16N3/16324ijwmn01.pdf
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Vijay Engineering and Machinery Company (VEMC) is a leading company in the field of electromechanical engineering products and services, with over 70 years of experience.
1. LOHITKUMAR VASTRAD
1
BMS College of Engineering
Department of Mechanical
Engineering
VIBRATION MONITORING AND ANALYSIS
presentation
by
Mechanical department,
2. MACHINE MAINTENANCE TECHNIQUES
Three types of maintenance schemes are be used in practice
1.Breakdown maintenance: In this technique the machine is allowed to fail
and after failure it will be replaced by another machine.
2. Preventive maintenance: Maintenance is performed at fixed intervals such
as every 3000 operating hours or once a year.
3. Condition-based maintenance: Measurements are carried out between
fixed intervals such that the changes are observed in running conditions
regularly.
2
3. LITERATURE REVIEW
1. Joëlle Courrech and Ronald L. Eshleman in their paper titled
“Condition monitoring of Machinery” have reviewed the basic concept
of Vibration based condition monitoring and discussed some of its
application.
2. J. Mitchell in his work “An Introduction to Machinery Analysis and
Monitoring” has explained the concept of vibration analysis technique
to achieve condition monitoring.
3
4. Several methods can be used to monitor the condition of a machine, as
indicated below.
METHODS OF CONDITION MONITORING TECHNIQUE
4
5. VIBRATION MONITORING
Vibration monitoring is the most common method employed for achieving
condition monitoring of a system.
Because any mechanical reciprocating or rotating machines generate their
own vibration patterns (signatures) during operation.
So, in this method the vibration levels of the machinery are initially
measured with the help of suitable transducers.
These vibration levels measured can be used to predict the catastrophic
failure of the machinery and planning for subsequent scheduling of
repairing works.
5
6. Warning signs of machine failure
Figure 2 : The warning signs of machine failure
( Courtesy: Mechanical Vibrations- SS Rao)6
7. VIBRATION MONITORING (Contd..)
Raw signals obtained contain a lot of background noise which makes it
difficult or even impossible to extract useful information by simply
measuring the overall signal.
It becomes necessary to develop an appropriate filter to remove the
unwanted signals.
Vibration should be measured at carefully chosen points and directions in
order to capture useful monitoring data,.
7
8. Types of Vibration Monitoring systems
Vibration monitoring systems are broadly classified into two types
1. Periodic monitoring system or off-line monitoring:
In this type of monitoring system machine vibration is measured or
recorded initially and later it will be analyzed in the field.
It is usually used when
Very early warning of faults is required.
Measurements are made at many locations on a machine.
Machines are complex.
8
9. 2. Permanent monitoring system or online monitoring:
In this type of monitoring system machine vibrations are measured
continuously at selected points of the machine.
These measurements obtained will be constantly compared with
acceptable levels of vibrations as per the vibration severity charts, given
by standards such as ISO 2372.
The main function of this system is to protect the equipment by providing
a warning and subsequently shut the machine down when a preset safety
limit is exceeded.
Here transducers are mounted permanently at the selected measurement
points on the machine.
9
10. VIBRATION ANALYSIS
Vibration Analysis is a two step process involving the ACQUISITION and
INTERPRETATION of machinery vibration data.
Its purpose is to determine the mechanical condition of a machine and
specific mechanical or operational defects.
The Data Acquisition procedure is a means of systematic measuring and
recording of the vibration characteristics needed to analyze a problem.
The Data Interpretation involves comparing the recorded data with
characteristic vibrations of various standard causes.
10
11. Vibration Analysis Techniques
a) Time-Domain Analysis:
Time-domain analysis uses the time history of the signal.
The signal is stored in an oscilloscope or a real time analyzer and
transient impulses are noted.
For e.g. discrete damages such as broken teeth in gears and cracks in
races of bearings can be identified easily from the waveform of the casing
of a gearbox.
Figure 4: Time-domain waveform of a faulty gearbox
(Courtesy: Mechanical Vibrations- Singiresu S. Rao,
University of Miami)
11
12. b) Frequency-Domain Analysis:
The frequency-domain signal or frequency spectrum is a plot of the amplitude
of vibrations versus the frequency.
As the machine starts developing faults, its vibration level and the shape of the
frequency spectrum changes.
By comparing the frequency spectrum of the machine in damaged condition
with the reference frequency spectrum of the machine in good condition, the
nature and location of the fault can be detected.
12
13. Another important characteristic of this spectrum is that each rotating element in a
machine generates frequency which can be easily identified, as illustrated in Fig.6
Figure 6: Relationship between machine
components and the vibration spectrum.
(Courtesy: Mechanical Vibrations-
Singiresu S. Rao, University of Miami)
13
14. DATAACQUISITION
Data acquisition is the essential first step in vibration analysis.
The right data must be acquired under the right conditions to completely
interpret a machine’s condition.
Data acquisition can be done in several ways depending on the available
instruments.
Displacement, Velocity and Acceleration are the measurement parameters
used in Data acquisition.
14
15. Common types of measurements used in Data
acquisition
i. Overall vibration amplitude measurements: These measurements provide a
quick check of general machinery condition.
ii. Amplitude Vs Frequency measurements: Amplitude Vs Frequency
measurements provide frequency spectrum which is used to pinpoint the
problem to a specific frequency or range of frequencies.
iii. Amplitude Vs Time measurements: These measurements can be made
during machine operation to detect vibrations that would not be apparent from
Amplitude Vs Frequency analysis.
iv. Phase measurements: Phase measurements are important when analyzing
mechanical problems in machinery.
Phase measurements offer a convenient way to determine how one part is
vibrating relative to another part.
15
16. DATA INTERPRETATION
Once the necessary information have been collected by any means
(manual, or semi-automatic or automatic) the next step is to review
and compare the readings with the standard vibration pattern of
various defects.
If a machine part has some defect, the frequency of vibration
resulting from this defect will be a multiple of the RPM.
This multiplying factor will be different for different defects.
16
17. Some causes Of Vibration and its identification through data
interpretation
i) Unbalance: It is found that the vibration caused due to unbalance will be identified by
observing the Vibration amplitude vs frequency data curves generated by the recorder.
ii) Mechanical looseness: The vibration due to looseness can be detected from Amplitude
vs Frequency when taking the reading in vertical direction.
iii) Misalignment : A comparative axial vibration is the best indication of misalignment
or a bent shaft.
17
18. USE OF COMPUTERS IN CONDITION MONITORING
PROGRAMS
Computers can be of great help in handling, filing, storing data and in performing tedious
computations such as spectrum comparison and trend analysis.
A condition monitoring system which incorporates computer must include.
i. A recording device for storing the analog or digital time signals or frequency spectra.
ii. An analyzer with both fast Fourier transform (FFT) narrowband analysis and advanced
diagnostic techniques.
iii. A computer and appropriate software which provides
a. Management of the measurement program, storage of reference spectra.
b. A comparison of spectra and a printout of significant changes.
c. Trend analysis of any chosen parameter (individual component or overall level in a
given frequency range).
18
19. CONCLUSION
The technique of vibration based condition monitoring helps in
finding the defects in system and to predict the catastrophic failure
of the system.
The applications of vibration monitoring have been highlighted and
recent developments in the use of computers in condition monitoring
programs have also been presented.
19
20. REFERENCES
[1] Eshleman, R. L., “Machinery Vibration Analysis II Notes,” Vibration Institute,
Willowbrook, Ill., 2000.
[2] Eshleman, R. L.: “Basic Machinery Vibrations,” VI Press, Clarendon Hills, Ill., 1999.
[3] Mitchell, J. S.: “An Introduction to Machinery Analysis and Monitoring,” Penwell
Publishing Company, Tulsa, Okla., 1981
[4] Joëlle Courrech and Ronald L. Eshleman,: "Condition monitoring of machinery", A paper
on Condition monitoring and analysis.
[5] A book on "Mechanical Vibrations" by Singiresu S. Rao, University of Miami.(ISBN 978-
0-13-212819-3) Page no: 870-928.
20