Condition Monitoring of Rotating Equipment Considering the Cause and Effects ...IJMERJOURNAL
ABSTRACT: This paper attempts to summarise and review the recent research and developments in diagnostics and prognostics of mechanical systems implementing Condition Monitoring with emphasis on models, algorithms and technologies for data processing and maintenance decision-making. Realising the increasing trend of using multiple sensors in condition monitoring, the authors also discuss different techniques for multiple sensor data fusion. The paper concludes with a brief discussion on current practices, possible future trends of Condition Monitoring with a brief outline on the novelty of the current research work.
Condition Monitoring of Rotating Equipment Considering the Cause and Effects ...IJMERJOURNAL
ABSTRACT: This paper attempts to summarise and review the recent research and developments in diagnostics and prognostics of mechanical systems implementing Condition Monitoring with emphasis on models, algorithms and technologies for data processing and maintenance decision-making. Realising the increasing trend of using multiple sensors in condition monitoring, the authors also discuss different techniques for multiple sensor data fusion. The paper concludes with a brief discussion on current practices, possible future trends of Condition Monitoring with a brief outline on the novelty of the current research work.
Motor Current Signature Analysis (MCSA) | Arrelic InsightsArrelic
Motor Current Signature Analysis (MCSA) are diagnostic technique that are being used to analyze motors, transformers, generators, alternators, distribution and other electric equipment.
This technology has the ability to test on line and off line electrical equipment and identify a variety of mechanical and electrical problems. These techniques can be used to analyze the driver, the driven load and the power supply. As a preventative maintenance tool, MCSA can be used to perform a one-time test or periodic testing to track and trend motor performance. MCSA allows for remote, non-intrusive testing of the equipment being monitored. The test analyzes the current waveform using complex mathematics. They are non-intrusive, invisible to the equipment being monitored and can be performed from remote connections such as Motor Control Centers (MCC).The information is captured and thereafter analyzed for the following possible defects:
Phase imbalance
Rotor bar faults
Winding lamination defects
Stator faults
Field studies have shown that up to 20% of all induction motors in use suffer from problems such as high resistance joints, cracked or broken rotor bars or air-gap eccentricities. Typically, starting a motor can induce five to six times the starting current in the rotor and stator windings. This can result in a number of problems upon start-up.
Effective techniques can be employed to detect and analyze critical electrical machinery to prevent failure and loss of production.
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
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.
Condition monitoring of rotating electrical machinesAnkit Basera
Condition monitoring of rotating electrical machines, Construction, Operation, Types, Specification Of Electrical Machines, Different Failure Modes Of Electrical Machines, Failure Modes And Root Causes In Rotating Electrical Machines
Development of a low cost test rig for standalone wecs subject to electrical ...ISA Interchange
In this paper, a contribution to the development of low-cost wind turbine (WT) test rig for stator fault diagnosis of wind turbine generator is proposed. The test rig is developed using a 2.5 kW, 1750 RPM DC motor coupled to a 1.5 kW, 1500 RPM self-excited induction generator interfaced with a WT mathematical model in LabVIEW. The performance of the test rig is benchmarked with already proven wind turbine test rigs. In order to detect the stator faults using non-stationary signals in self-excited induction generator, an online fault diagnostic technique of DWT-based multi-resolution analysis is proposed. It has been experimentally proven that for varying wind conditions wavelet decomposition allows good differentiation between faulty and healthy conditions leading to an effective diagnostic procedure for wind turbine condition monitoring.
Inter-turn short-circuit (ITSC) faults on the induction machine has received much attention in the recent years. Early detection of such faults in wind turbine systems would allow to avoid fluctuation on wind power output and maintain the reliability level. In this paper, Sliding Mode Observers (SMO)-based fault detection and isolation method is developed for induction generator (IG)-based variable-speed grid-connected wind turbines. Firstly, the dynamic model of the wind turbine and IG was given and then, the control was made based on Maximum Power Point Tracking (MPPT) method. The IG closed-loop via Indirect Rotor Flux Oriented Control (IRFOC) scheme was also described. Hence, the performance of the wind turbine system and the stability of injected power to the grid were analyzed under the ITSC fault conditions. The control schemes were proved to be inherently unstable under the faulty conditions. Then, robust SMO were investigated to design an ITSC fault detection and isolation scheme. Finally, simulation results of ITSC detection and isolation in the variable-speed grid-connected wind turbine with affected IG confirm the theoretical development.
Induction motor fault diagnosis by motor current signature analysis and neura...Editor Jacotech
This paper presents steps in designing dimensions of antenna and feed in axisymmetrical Cassegrain Antennas. Corrugated conical horns are used as feeds in applications with circular polarization. In the initial design steps, diameter of the main reflector is determined considering communication link budget, then feed dimensions are designed to create optimum aperture efficiency. In the last step of design, considering feed dimensions and the amount of its center phase displacements with respect to its aperture plane, dimensions of subreflector is determined to avoid additional blockage. To illustrate the procedure, an applicative design example is presented in X-band.
Induction Motors Faults Detection Based on Instantaneous Power Spectrum Analy...IDES Editor
A method of induction motor diagnostics based on
the analysis of three-phase instantaneous power spectra has
been offered. Its implementation requires recalculation of
induction motor voltages, aiming at exclusion from induction
motor instantaneous three-phase power signal the component
caused by supply mains dissymmetry and unsinusoidality. The
recalculation is made according to the motor known
electromagnetic parameters, taking into account the
electromotive force induced in stator winding by rotor currents.
The results of instantaneous power parameters computation
proved efficiency of this method in case of supply mains voltage
dissymmetry up to 20%. The offered method has been tested
by experiments. Its applicability for detection of several stator
and rotor winding defects appeared in motor simultaneously
has been proved. This method also makes it possible to
estimate the extent of defects development according to the
size of amplitudes of corresponding harmonics in the spectrum
of total three phase power signal.
In this paper, Reduced-Order Observer For Real-Time Implementation Speed Sensorless Control of Induction Using RT-LAB Softwareis presented. Speed estimation is performed through a reduced-order observer. The stability of the proposed observer is proved based on Lyapunov’s theorem. The model is initially built offline using Matlab/Simulink and implemented in real-time environment using RT-LAB package and an OP5600 digital simulator. RT-LAB configuration has two main subsystems master and console subsystems. These two subsystems were coordinated to achieve the real-time simulation. In order to verify the feasibility and effectiveness of proposed method, experimental results are presented over a wide speed range, including zero speed.
Reliability is concerned with decreasing faults and their impact. The earlier the faults are detected the better. That's why this presentation talks about automated techniques using machine learning to detect faults as early as possible.
DETECTION OF FAULT LOCATION IN TRANSMISSION LINE USING INTERNET OF THINGS (IOT)Journal For Research
Transmission lines are used to transmit electric power to distant large load centres. These lines are exposed to faults as a result of lightning, short circuits, faulty equipment’s, miss-operation, human errors, overload, and aging.To avoid this situation, and we need the exact location of fault occurrence. This problem ishandled by a set of resistors representing cable length in KMs and fault creation is made by a set of switches at every known KM to cross check the accuracy of the same. The fault occurring at what distance and which phase is displayed on a 16X2 LCD interfaced with the microcontroller. Calculated values are sends to the receiving section with help of Zigbee. Measured values are updated in PC and monitored with help of .NET. RTC is used here to time and date reference, that when the event occurs.
Motor Current Signature Analysis (MCSA) | Arrelic InsightsArrelic
Motor Current Signature Analysis (MCSA) are diagnostic technique that are being used to analyze motors, transformers, generators, alternators, distribution and other electric equipment.
This technology has the ability to test on line and off line electrical equipment and identify a variety of mechanical and electrical problems. These techniques can be used to analyze the driver, the driven load and the power supply. As a preventative maintenance tool, MCSA can be used to perform a one-time test or periodic testing to track and trend motor performance. MCSA allows for remote, non-intrusive testing of the equipment being monitored. The test analyzes the current waveform using complex mathematics. They are non-intrusive, invisible to the equipment being monitored and can be performed from remote connections such as Motor Control Centers (MCC).The information is captured and thereafter analyzed for the following possible defects:
Phase imbalance
Rotor bar faults
Winding lamination defects
Stator faults
Field studies have shown that up to 20% of all induction motors in use suffer from problems such as high resistance joints, cracked or broken rotor bars or air-gap eccentricities. Typically, starting a motor can induce five to six times the starting current in the rotor and stator windings. This can result in a number of problems upon start-up.
Effective techniques can be employed to detect and analyze critical electrical machinery to prevent failure and loss of production.
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
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.
Condition monitoring of rotating electrical machinesAnkit Basera
Condition monitoring of rotating electrical machines, Construction, Operation, Types, Specification Of Electrical Machines, Different Failure Modes Of Electrical Machines, Failure Modes And Root Causes In Rotating Electrical Machines
Development of a low cost test rig for standalone wecs subject to electrical ...ISA Interchange
In this paper, a contribution to the development of low-cost wind turbine (WT) test rig for stator fault diagnosis of wind turbine generator is proposed. The test rig is developed using a 2.5 kW, 1750 RPM DC motor coupled to a 1.5 kW, 1500 RPM self-excited induction generator interfaced with a WT mathematical model in LabVIEW. The performance of the test rig is benchmarked with already proven wind turbine test rigs. In order to detect the stator faults using non-stationary signals in self-excited induction generator, an online fault diagnostic technique of DWT-based multi-resolution analysis is proposed. It has been experimentally proven that for varying wind conditions wavelet decomposition allows good differentiation between faulty and healthy conditions leading to an effective diagnostic procedure for wind turbine condition monitoring.
Inter-turn short-circuit (ITSC) faults on the induction machine has received much attention in the recent years. Early detection of such faults in wind turbine systems would allow to avoid fluctuation on wind power output and maintain the reliability level. In this paper, Sliding Mode Observers (SMO)-based fault detection and isolation method is developed for induction generator (IG)-based variable-speed grid-connected wind turbines. Firstly, the dynamic model of the wind turbine and IG was given and then, the control was made based on Maximum Power Point Tracking (MPPT) method. The IG closed-loop via Indirect Rotor Flux Oriented Control (IRFOC) scheme was also described. Hence, the performance of the wind turbine system and the stability of injected power to the grid were analyzed under the ITSC fault conditions. The control schemes were proved to be inherently unstable under the faulty conditions. Then, robust SMO were investigated to design an ITSC fault detection and isolation scheme. Finally, simulation results of ITSC detection and isolation in the variable-speed grid-connected wind turbine with affected IG confirm the theoretical development.
Induction motor fault diagnosis by motor current signature analysis and neura...Editor Jacotech
This paper presents steps in designing dimensions of antenna and feed in axisymmetrical Cassegrain Antennas. Corrugated conical horns are used as feeds in applications with circular polarization. In the initial design steps, diameter of the main reflector is determined considering communication link budget, then feed dimensions are designed to create optimum aperture efficiency. In the last step of design, considering feed dimensions and the amount of its center phase displacements with respect to its aperture plane, dimensions of subreflector is determined to avoid additional blockage. To illustrate the procedure, an applicative design example is presented in X-band.
Induction Motors Faults Detection Based on Instantaneous Power Spectrum Analy...IDES Editor
A method of induction motor diagnostics based on
the analysis of three-phase instantaneous power spectra has
been offered. Its implementation requires recalculation of
induction motor voltages, aiming at exclusion from induction
motor instantaneous three-phase power signal the component
caused by supply mains dissymmetry and unsinusoidality. The
recalculation is made according to the motor known
electromagnetic parameters, taking into account the
electromotive force induced in stator winding by rotor currents.
The results of instantaneous power parameters computation
proved efficiency of this method in case of supply mains voltage
dissymmetry up to 20%. The offered method has been tested
by experiments. Its applicability for detection of several stator
and rotor winding defects appeared in motor simultaneously
has been proved. This method also makes it possible to
estimate the extent of defects development according to the
size of amplitudes of corresponding harmonics in the spectrum
of total three phase power signal.
In this paper, Reduced-Order Observer For Real-Time Implementation Speed Sensorless Control of Induction Using RT-LAB Softwareis presented. Speed estimation is performed through a reduced-order observer. The stability of the proposed observer is proved based on Lyapunov’s theorem. The model is initially built offline using Matlab/Simulink and implemented in real-time environment using RT-LAB package and an OP5600 digital simulator. RT-LAB configuration has two main subsystems master and console subsystems. These two subsystems were coordinated to achieve the real-time simulation. In order to verify the feasibility and effectiveness of proposed method, experimental results are presented over a wide speed range, including zero speed.
Reliability is concerned with decreasing faults and their impact. The earlier the faults are detected the better. That's why this presentation talks about automated techniques using machine learning to detect faults as early as possible.
DETECTION OF FAULT LOCATION IN TRANSMISSION LINE USING INTERNET OF THINGS (IOT)Journal For Research
Transmission lines are used to transmit electric power to distant large load centres. These lines are exposed to faults as a result of lightning, short circuits, faulty equipment’s, miss-operation, human errors, overload, and aging.To avoid this situation, and we need the exact location of fault occurrence. This problem ishandled by a set of resistors representing cable length in KMs and fault creation is made by a set of switches at every known KM to cross check the accuracy of the same. The fault occurring at what distance and which phase is displayed on a 16X2 LCD interfaced with the microcontroller. Calculated values are sends to the receiving section with help of Zigbee. Measured values are updated in PC and monitored with help of .NET. RTC is used here to time and date reference, that when the event occurs.
The adoption of machine learning techniques for software defect prediction: A...RAKESH RANA
The adoption of machine learning techniques for software defect prediction: An initial industrial validation
Presented at:
11th Joint Conference On Knowledge-Based Software Engineering, JCKBSE, Volgograd, Russia, 2014
Get full text of publication at:
http://rakeshrana.website/index.php/work/publications/
This paper analyzes the behaviour of a Voltage Source Converter Based HVDC system under DC pole to ground fault & AC faults for 2-level VSC-HVDC & 12-pulse VSC-HVDC system in order to better understand the system under such faults. DC line faults on HVDC systems utilising Voltage Source Converters (VSC) are a major issue for HVDC systems in which complete isolation of the faulted system is not a viable option. The occurrence of pole-to-ground faults on DC link is the most common fault in HVDC system. It was observed that with the occurrence of DC pole to ground faults leads to substantial over current in the AC grid system which may lead to damage of the converter valve. Simulation of 2-level VSC-HVDC under AC fault is carried out. The fault current magnitude is attempted with the mathematical analysis & which was found to be the same as the simulated result. This paper also compares the performance of the conventional 12-pulse (CSC) HVDC system with the PWM based 2-level VSC-HVDC & 12-pulse VSC-HVDC system.
Fault Diagnostics of Rolling Bearing based on Improve Time and Frequency Doma...ijsrd.com
The neural network based approaches a feed forward neural network trained with Back Propagation technique was used for automatic diagnosis of defects in bearings. Vibration time domain signals were collected from a normal bearing and defective bearings under various speed conditions. The signals were processed to obtain various statistical parameters, which are good indicators of bearing condition, then best features are selected from graphical method and these inputs were used to train the neural network and the output represented the bearing states. The trained neural networks were used for the recognition of bearing states. The results showed that the trained neural networks were able to distinguish a normal bearing from defective bearings with 83.33 % reliability. Moreover, the network was able to classify the bearings into different states with success rates better than those achieved with the best among the state-of-the-art techniques.
Various Types of Faults and Their Detection Techniquesin Three Phase Inductio...IJERA Editor
Artificial neural networks are extensively used for speed, torque estimation, and solid state drive control in both DC and AC machines. These Artificial intelligent techniques are increasingly used for condition monitoring and fault detection of machines. this paper present an overview of researches onThree phase Induction Motors Faults Detection Using Artificial Neural Network(ANN) , a general classification and brief description of the focus area for research and development in this direction are given under title of various types of faults and their detections techniques an improvement in three-phase squirrel-cage induction machine bearing, stator, eccentricity ,inter-turn, end-ring, broken-bar faults detection and diagnosis based on a neural network approach is presented .Future research directions are also highlighted.
Research Inventy : International Journal of Engineering and Scienceresearchinventy
Research Inventy : International Journal of Engineering and Science is published by the group of young academic and industrial researchers with 12 Issues per year. It is an online as well as print version open access journal that provides rapid publication (monthly) of articles in all areas of the subject such as: civil, mechanical, chemical, electronic and computer engineering as well as production and information technology. The Journal welcomes the submission of manuscripts that meet the general criteria of significance and scientific excellence. Papers will be published by rapid process within 20 days after acceptance and peer review process takes only 7 days. All articles published in Research Inventy will be peer-reviewed.
Applications of Artificial Neural Network and Wavelet Transform For Conditio...IJMER
The vibration analysis of rotating machinery indicates of the condition of potential faults such
as unbalance, bent shaft, shaft crack, bearing clearance, rotor rub, misalignment, looseness, oil whirl
and whip and other malfunctions. More than one fault can occur in a rotor. This paper describes the
application of Artificial Neural Network (ANN) and Wavelet Transform (WT) for the prediction of the
effect of the combined faults of unbalance and bearing clearance on the frequency components of
vibration signature of the rotating machinery. The experimental data of frequency components and
corresponding Root Mean Square (RMS) velocity (amplitude) data are used as inputs to train the ANN,
which consists of a three-layered network. The ANN is trained using an improved multilayer feed forward
back propagation Levenberg-Marquardt algorithm. In particular, an overall success rates achieved were
99.78% for unbalance, 99.81% bearing clearance, and 99.45% for the combined faults of unbalance and
bearing clearance. The wavelet transform approach enables instant to instant observation of different
frequency components over the full spectrum. A new technique combining the WT with ANN performs
three general tasks data acquisition, feature extraction and fault identification. This method is tested
successfully for individual and combined faults of unbalance and bearing clearance at a success rate of
99.99%.
Vibration Analysis of Industrial Drive for Broken Bearing Detection Using Pro...IAES-IJPEDS
A reliable monitoring of industrial drives plays a vital role to prevent from the performance degradation of machinery. Today’s fault detection system mechanism uses wavelet transform for proper detection of faults, however it required more attention on detecting higher fault rates with lower execution time. Existence of faults on industrial drives leads to higher current flow rate and the broken bearing detected system determined the number of unhealthy bearings but need to develop a faster system with constant frequency domain. Vibration data acquisition was used in our proposed work to detect broken bearing faults in induction machine. To generate an effective fault detection of industrial drives, Biorthogonal Posterior Vibration Signal-Data Probabilistic Wavelet Neural Network (BPPVS-WNN) system was proposed in this paper. This system was focused to reducing the current flow and to identify faults with lesser execution time with harmonic values obtained through fifth derivative. Initially, the construction of Biorthogonal vibration signal-data based wavelet transform in BPPVS-WNN system localizes the time and frequency domain. The Biorthogonal wavelet approximates the broken bearing using double scaling and factor, identifies the transient disturbance due to fault on induction motor through approximate coefficients and detailed coefficient. Posterior Probabilistic Neural Network detects the final level of faults using the detailed coefficient till fifth derivative and the results obtained through it at a faster rate at constant frequency signal on the industrial drive. Experiment through the Simulink tool detects the healthy and unhealthy motor on measuring parametric factors such as fault detection rate based on time, current flow rate and execution time.
Fault diagnosis of rolling element bearings using artificial neural network IJECEIAES
Bearings are essential components in the most electrical equipment. Procedures for monitoring the condition of bearings must be developed to prevent unexpected failure of these components during operation to avoid costly consequences. In this paper, the design of a monitoring system for the detection of rolling element-bearings failure is proposed. The method for detecting and locating this type of fault is carried out using advanced intelligent techniques based on a perceptron multilayer artificial neural network (MLP-ANN); its database uses statistical indicators characterizing vibration signals. The effectiveness of the proposed method is illustrated using experimentally obtained bearing vibration data, and the results have shown good accuracy in detecting and locating defects.
Conditioning Monitoring of Gearbox Using Different Methods: A ReviewIJMER
Gears are important element in a variety of industrial applications such as machine tool
and gearboxes. An unexpected failure of the gear may cause significant economic losses. For that
reason, fault diagnosis in gears has been the subject of intensive research. Vibration signal analysis
has been widely used in the fault detection of rotation machinery. Fault diagnosis plays an important
role in condition monitoring to enhance the machine time. In view of this, the present investigation
focused on the development of Fault diagnosis system of gearboxes based on the vibration signatures
and Artificial Neural Networks. In the present investigation to generate the vibration signatures an
experimental set-up has been fabricated with sensing and measuring equipment. The prominent faults,
wear, crack, broken tooth and insufficient lubrication of the gear were practically induced in the
present investigation. Vibration signatures of the gearbox were collected by transmitting the motion at
constant speed with gears having no fault, without applying any load. By inducing one fault at a time,
vibration signatures were collected with different degrees of wear on a gear tooth, a gear with a
broken tooth, tooth with crack and with insufficient lubrication. As the vibration data of maximum
amplitudes was found to be inseparable, fault diagnosis based on this data was not possible. Five
prominent statistical features were extracted based on data pertaining to maximum amplitudes of
vibration and used fault diagnosis. Due overlapping of this data, it was decided to use ANN based
fault diagnosis system for the present investigation. The set of statistical features were extracted based
on data pertaining to maximum amplitudes of vibration and used them as input parameters to the
ANN based fault diagnosis system designed.
Misalignment of shaft in rotating systems is one of the most common faults. Improper aligning of shafts through couplings often leads to severe vibration problems in many rotating machines. Vibration monitoring is a useful technique which provides valuable information regarding symptoms of machinery failures, and in turn may avoid costly breakdowns. In the present paper experiment investigation of parallel misalignment in rotating machinery is presented. by Mr. Bhawthankar A. A, Mr. Mane M. B, Mr. Phopale Y. A and Mr. Korshetti V. V 2018. EFFECT OF PARALLEL MISALIGNMENT IN ROTATING MACHINERY. International Journal on Integrated Education. 1, 1 (Dec. 2018), 82-84 https://journals.researchparks.org/index.php/IJIE/article/view/789/758 https://journals.researchparks.org/index.php/IJIE/article/view/789
Advance Current Monitoring Techniques to Detect and Diagnosis the Inter-Turn ...theijes
This paper presents the results of stator inter-turn fault detection and diagnosis in three phase induction motor using different types of current monitoring techniques such as: motor current signature analysis (MCSA), Current Concordia Vector, extend park vector approach (EPVA). Where the use of several signal processing techniques for extracting more information about the fault, will help us knowing the characteristics of each method in order to choose the best way to diagnose this type of machine fault. The tests show that the EPVA technique can diagnose the inter-turn fault with high accuracy compared with the other techniques. Because there is a similarity between the inter-turn frequency component and other machine faults frequency components (mixed air gap eccentricity, unbalance supply voltage) resulting a wrong discussion about the machine condition.
Fault Detection and Failure Prediction Using Vibration Analysis
1. Vibration Analysis: Fault Detection and Failure
Prediction
Tristan Plante, Ashkan Nejadpak, and Cai Xia Yang
University of North Dakota, Grand Forks, ND
Abstract — In industrial applications, the uptimeof machines can
be enhanced through equipment monitoring. This minimizes the
risks of unpredicted failures andconsequent plant outages. Since all
failure modes can cause an increase in machine vibrations,
monitoring this area is the predominant and most widely used
method to determine equipment condition, and to predict failures.
The objective of this study is to detect faults in rotating equipment
with the use of vibration analysis. A motor condition monitoring
experiment is set up, and the motor’s operational speedis controlled
by an AC motor drive. The vibration of the motor is measured and
monitored. The measuredvibration data is analyzedusing spectrum
analysis software and a MATLAB program. The overall vibration
level is calculated, the vibration severity is compared with the
standard severity table and is usedto determine the condition of the
motor. The specificnatural frequency corresponds with which kind
of fault or failure mode is identified. This information provide
insight on the condition of the machine.
I. INTRODUCTION
Scheduled and fail-then-fix maintenances are commonly used
by industries,but both tend to incur much higher costs.Predictive
or Condition-Based Maintenance based on known condition is
used to predict (and therefore assist in avoiding) unplanned
equipment failures. During observation of the vibration modes, a
relationship was found between the ranges of naturalfrequency of
vibration and the failure modes. By measuring and analyzing
measured vibration data, engineers are able to retrieve valuable
information on the status ofthe equipment, predict machine failure
patterns, and plan timely maintenance operations. To
progressively extend the time between failures for the monitored
equipment, the trend of vibration in frequency domain needs to be
observed frequently. The trend of the spectrum will provide
information on what type of faults are present within the system,
the severity of the fault, and will help determine the remaining
lifespan of the machine.
Understanding the concepts behind vibration data allow
engineers to detect faults and predict failures caused by equipment
defects, or deterioration such as unbalanced rotors, bearing
defects,a lack oflubrication, coupling issues, and misaligned axles
before they lead to catastrophic failure. To understand how
vibration analysis can be used to identify motor faults, one must
first understand thatallmechanical systems vibrate.This vibration
retains a unique signature which,given properanalysis,can tell an
operatorhow the systemis responding to its operating conditions.
Altering these conditions may revealdifferent signatures yet,at the
same time, patterns emerge suggesting a specific problem within
the system. Over time, certain patterns can become more evident
suggestinga machine may fail if left uncorrected.Recognizing and
categorizing these patters before equipment failure is the objective
of fault detection and predictive maintenance, and allows
corporations and industries to reduce spending in equipment repair
and replacement. This concept correlates to the method of
predictive based maintenance.
II. BACKGROUND INFORMATION
Fault detection
The goal of this experiment was to find evidence regarding
vibration patterns associated with specific electric motor faults.
Specifically, the objective of the experiment was to determine the
validity of using vibration analysis to conduct predictive based
maintenance. Based on previous research, there are several
common motor faults that can be identified using vibration
analysis such as imbalance, mechanical looseness, and bearing
faults [1]. Each fault condition’s severity and type can be assessed
based on the amplitudes ofthe correspondingpeaks as wellas their
respective locations on the frequency spectrum. Additionally,
certain types of faults can be determined based on the location
were data was recorded on the equipment. In other words, some
faults display a higher level of severity when the accelerometer is
placed on various locations of the motor. To demonstrate the
effects faults have on the motor’s corresponding vibration levels,
multiple tests were conducted on a three phase inverter duty
induction AC motor.
In addition to above mentioned methodologies for fault
analysis,some researchers have proposed fault diagnosis methods
based on terminal voltage and current measurements. In [2] – [4]
the effects of bearing and winding faults on the statorcurrent have
been studied. It is shown that using the frequency response
analysis one can perform health monitoring or life time prediction
on the motor.
Stages of Bearing Failure
Bearing faults are considered the most common case when
conducting rotating machinery maintenance; however, unlike
more basic faults, bearing faults appear in four stages. During
stage one, bearings operate at normal conditions, and can be
considered undamaged. At stage two, bearing defect frequencies
begin to appearas peaks on the frequency spectrum. According to
the article “Rolling Element Bearing Analysis” by Brian Graney
2. and Ken Starry, bearing defect frequencies can be calculated using
equations (1) – (4) according to [5]. The amplitudes of these
frequencies hint toward the conditions of the bearing, and often
increase over time. As the bearing deteriorates, it reaches stage
three where multiples of the bearing defect frequencies begin to
appear as peaks in the frequency spectrum. It is common practice
to replace these bearings afterreaching this stage. Finally, at stage
four, bearing defect frequencies disappearfrom the spectrumand
replaced by randomnoise in the low frequencies spectrum [6]. At
this stage, the bearing is at the risk of undergoing catastrophic
failure which can cost companies thousands in machine repair
and/or replacement. By replacing damaged bearings before they
fail, industries can drastically reduce the cost of replacing vital
machinery therefore outlining the importance of predictive based
maintenance on high value equipment.
III. EXPERIMENT SETUP
Three fault conditions were studied in a series of experiments.
Each of these experiments were run using a three-phase,inverter-
duty,ACelectric motor. This motor, was driven using a GS1-10P2
AC drive purchased from Automation Directed and operated at
1725 RPM. Using an accelerometer placed in the vertical axis of
the motor, vibration data was recorded using a NI PXI-4498 data
acquisition device purchased from National Instruments,and was
analyzed using the Sound and Vibration Assistant also purchased
from National Instruments.
Unbalance
To study the unbalanced rotor condition, a steel bolt was
mounted to one end of a three phase induction motor’s flywheel.
According to previous research,a motor with an unbalanced rotor
will display a large amplitude peak at one times the running speed
[1]. Operating at 1725 RPM (30Hz), the motor’s vibration data
was recorded using a data acquisition device and graphed using an
FFT in MATLAB. Figure 1 shows the setup for this experiment.
Fig. 1. Experiment setup (Unbalanced condition)
Mechanical looseness
Vibration patterns resulting from mechanical looseness were
also studied. By loosening mounting bolts on the three phase
electric motor, the body of the motor was allowed to move more
freely therefore altering the motor’s vibrational patterns. On the
frequency spectrum, peaks corresponding with mechanical
looseness are considered to appear as many multiples of the
motor’s running speed. Additionally, these peaks appear on a
raised noise floor and display random amplitudes [7]. Similarly to
the unbalance experiment, the three phase motor’s vibration data
was operated at 1725 RPM (30Hz), and recorded using a data
acquisition device.
Bearing Fault
In addition to mechanical looseness and unbalance, the
patterns relating to bearing failures were also studied. Using a
bearing from a three phase induction motor, a defect was created
on one of the bearing balls. Figure two shows the generated defect
of the rolling element within the motor’s bearing.
Fig. 2. Top view of defected bearing
The bearing defect frequencies were also calculated for the
motor’s 6203-2RS bearing. The values for which can calculated
using the following equations:
𝐵𝑃𝐹𝐼 =
𝑁
2
∗ 𝐹 ∗ (1 +
𝐵
𝑃
∗ 𝑐𝑜𝑠𝜃) (1)
𝐵𝑃𝐹𝑂 =
𝑁
2
∗ 𝐹 ∗ (1 −
𝐵
𝑃
∗ 𝑐𝑜𝑠𝜃) (2)
𝐹𝑇𝐹 =
𝐹
2
∗ (1 −
𝐵
𝑃
∗ 𝑐𝑜𝑠𝜃) (3)
𝐵𝑆𝐹 =
𝑃
2𝐵
∗ 𝐹 ∗ [1 − (
𝐵
𝑃
∗ 𝑐𝑜𝑠𝜃)
2
] (4)
Table 1 shows the calculated values for each bearing fault.
Much like the previous tests,the motorwas operated at 1725 RPM
(30Hz). By doing this, it was predicted the bearing’s
corresponding frequency spectrumwould exhibit traits correlating
to one of four stages ofbearing failure thus supporting the validity
of using vibration analysis to conduct predictive based
3. maintenance. The spectrum plots that have been used in this
analysis is based on the algorithmproposed in [8] and [9].
TABLE I. Bearing Fault Frequencies
Bearing Frequency Types Frequency (Hz)
Shaft Speed Frequency 28.750
Inner race defect frequency (BPFI) 142.223
Outer race defect frequency (BPFO) 87.777
Cage defect frequency (FTF) 10.972
Ball spin frequency (BSF) 57.323
Rolling element defect frequency 14.656
IV. VIBRATION DATA ANALYSIS
Ideal condition
Figure 3 displays the FFT graph for a three phase
induction motor operating at 1725 RPM (30 Hz). This data
was taken to act as the healthy/ideal condition. To clarify, no
fault conditions are placed on the motor. By monitoring the
condition of an ideal motor, comparisons can be made
between motors under fault conditions and that of an ideal
motor.
Fig. 3. Motor under normal operating conditions (0-1k Hz)
Unbalance Fault
Based on the data presented in figure 4, several peaks appear
to be present.The most notable of which is the peak at 30 Hz. The
30 Hz peakcorrelates to the running frequency ofthe motor/s drive
axle and has an amplitude of approximately 0.14 g. Compared to
the 30 Hz peak seen in figure 3, which displays the motoroperating
under normal conditions, the 30 Hz peak of figure 4 shows a
substantial increase in amplitude. Additionally, the overall noise
in figure 4 appears to have changed. These two observations
outline can be linked to the motors unbalance condition.
Fig. 4. Unbalance condition
Because the motor’s balanced state was the only condition
altered during this experiment, it can be stated that the differences
between figures 3 and 4 support the presence of unbalance within
the system. The unbalance fault condition can be associated with
a large increase in the operating speed frequencyas wellas a raised
noise floor. These two conditions agree with the findings of
previous research stated in the introduction as well as the
experiment setup section of this report.
Mechanical looseness
Figure 5 displays severalpeaks appearing in the low frequency
spectrum. What is most notable of these peaks is that their
frequency values are multiples of the running speed.Additionally,
these peaks possess a variety of amplitudes each large enough to
be considered hazardous to the motor’s overall health. If allowed
to operate over longer periods of time, it is likely the motor’s
lifespan will be reduced. Fortunately, mechanical looseness is
often easy to address. In this case, simply tightening the bolts on
the motor’s mounting feet resolves the issue. Figure 3 displays the
motor’s vibration data with a secure mount. Here, several of the
running frequency multiples are no longer present, and the
amplitudes of each peak are reduced.These differences show that
the presence mechanical looseness condition appears as several
multiples of the motor’s running frequency as well as a raised
noise floor in the spectrumand therefore agree with the conditions
stated in [1].
4. Fig. 5. Mechanical looseness condition
Bearing Fault
Based on the results from Figure 6, it appears the spectrum
lacks data relating to the specific bearing fault frequencies stated
in table 1; however, because of the severity of the damage placed
on the bearing, it is unlikely the motor can be considered to be
operating under normal conditions.
Fig. 6. Damaged bearing (0-1k Hz)
Several notes can be taken from figure 6. For instance, the
vibration spectrum displays a raised noise floor as well as a
number of low amplitude peaks appearing in the higher
frequencies; however, what is interesting to note is that none of
these frequencies appear to be whole number multiples of the
running speed,orthe bearing fault frequencies,nor do these peaks
appearto correspond with the unbalance,or mechanical looseness
fault conditions, yet it is obvious the motor’s vibration data has
been affected by the damaged bearing. Comparing these results to
the spectrumin figure 3, which shows the motor operating before
the bearing was damaged, shows how the motor’s vibration data
has undergone substantial change and is no longer operating in a
healthy state. This suggests two possibilities, either the data
collected was inaccurate, or the bearing could have reached stage
four of bearing failure.
While it may be easy to assume the data regarding the
damaged bearing was faulty, observing the motor’s vibration data
on a higherfrequency suggests stage fourofbearing failure.Figure
7 shows the motor’s vibration data from zero to ten thousand hertz
before the bearing was damaged, while figure 8 shows the same
data after the bearing was damaged.
Fig. 7. Undamaged bearing (0-10k Hz)
Fig. 8. Damaged Bearing (0-10k Hz)
When comparing figures 7 and 8, one may note the distinctive
difference in the overall noise vibration of the motor. Because of
this, the bearing could be in stage four of bearing failure. Stage
four of bearing failure displays large amounts of noise in higher
frequencies; however, at this stage,bearing defect frequencies no
longer appear [6]. This information provides an explanation as to
why the bearing defect frequencies are not present in the spectrum.
5. Should this be the case,depending on the importance of the motor,
it is important to replace the bearing immediately. It is common
practice to prevent more vital machine bearings from reaching
stage fourof bearing failure, otherwise the bearing is at the risk of
experiencing catastrophic failure resulting in damage to vital
machine components.
V. CONCLUSION
The results from each of the three tests support the use of
vibration analysis in predictive based maintenance. By comparing
the vibration data for each fault case to that of a healthy motor,
shown in figure 3, the patterns corresponding with each fault
condition are outlined.This, therefore,shows howcertain faults in
rotating mechanical systems can be determined using vibration
analysis. Future research will involve the analysis of vibration
trends. In other words, this research will involve predicting how,
and when, rotating equipment will fail. By developing a time
dependent procedure towards assessing motor faults, motors can
be operated for the maximum allowed time before being repaired
thus reducing overall maintenance costs.
ACKNOWLEDGEMENT
The work reported in this paper was funded by ND EPSCoR New
Faculty Start-up Award 43700-2725-UND0019805 and ND
EPSCoR Advanced Undergraduate Research Award.
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