1.Literature Survey Advances in Diagnostic Techniques for Induction Machines
This paper investigates diagnostic techniques for electrical machines with special reference to induction machines and to papers published in the last ten years. A comprehensive list of references is reported and examined, and research activities classified into four main topics: 1) electrical faults; 2) mechanical faults; 3) signal processing for analysis and monitoring; and 4) artificial intelligence and decision-making techniques.
A review of induction motors signature analysis as a medium for faults detection
This paper is intended as a tutorial overview of induction motors signature analysis as a medium for fault detection. The purpose is to introduce in a concise manner the fundamental theory, main results, and practical applications of motor signature analysis for the detection and the localization of abnormal electrical and mechanical conditions that indicate, or may lead to, a failure of induction motors. The paper is focused on the so-called motor current signature analysis which utilizes the results of spectral analysis of the stator current. The paper is purposefully is purposefully written without “state “state-of-the-of-the-art” art” terminology for the benefit of practicing engineers in facilities today who may not be familiar with signal processing.
Condition monitoring and fault diagnosis of electrical motors — A review
Recently, research has picked up a fervent pace in the area of fault diagnosis of electrical machines. The manufacturers and users of these drives are now keen to include diagnostic features in the software s oftware to improve salability and reliability. Apart from locating specific harmonic components in the line
current (popularly known as motor current signature analysis), other signals, such as speed, torque, noise, vibration etc., are also explored for their frequency contents. Sometimes, altogether different techniques, such as thermal measurements, chemical analysis, etc., are also employed to find out the nature and the degree of the fault. In addition, human involvement in the actual fault detection decision making is slowly being replaced by automated tools, such as expert systems, neural networks, fuzzy-logic-based systems; to name a few. It is indeed evident that this area is vast in scope. Hence, keeping in mind the need for future research, a review paper describing different types of faults and the signatures they generate and their diagnostics’ schemes will not be entirely out of place. In particular, such a review helps to avoid repetition of past work and gives a bird’s eye view to a new researcher in this area.
The use of the wavelet approximation signal as a tool for the diagnosis of rotor bar failures
The aim of this paper is to present a new approach for rotor bar failure diagnosis in induction machines. The method focuses on the study of an approximation signal resulting from the wavelet decomposition of the start up stator current. The presence of the left sideband harmonic is used as evidence of the rotor failure in most diagnosis methods based on the analysis of the stator current. Thus, a detailed description of the evolution of the left sideband harmonic during the start up transient is given in this paper; for this purpose, a method for calculating the evolution of the left sideband during the start up is developed, and its results are physically explained. This paper also shows that the approximation signal of a particular level, which is obtained from the discrete wavelet transform of the start up stator current, practically reproduces the time evolution of the left sideband harmonic during the start up. The diagnosis method proposed here consists of checking if the selected
approximation signal fits well the characteristic shape of the left sideband harmonic evolution described in this paper. The method is validated through laboratory tests. The results prove that it can constitute a useful tool for the diagnosis of rotor bar breakages.
Diagnosis of broken-bar fault in induction machines using discrete wavelet transform without slip estimation
The aim of this paper is to present a wavelet-based method for broken bar detection in squirrel-cage induction machines. The The frequency-domain methods, which are commonly used, need speed information or accurate slip estimation for frequency-component localization in any spectrum. Nevertheless, the fault frequency bandwidth can be well defined for any quirrelcage induction machine due to numerous previous investigations. The proposed approach consists in the energy evaluation of a known bandwidth with timescale analysis using the discrete wavelet transform. This new technique has been applied to the stator-current space-vector magnitude and the instantaneous magnitude of the stator-current signal for different broken-bar fault severities and load levels
Monitoring of rotor-bar defects in inverter-fed induction machines at zero load and speed
Rotor-cage fault detection in inverter-fed induction machinesis still difficult nowadays as the dynamics introduced by the control or load influence the fault-indicator signals commonly applied. In addition, detection is usually possible only when the machine is operated above a specific load level to generate a significant rotor-current magnitude. This paper proposes a new method of detecting rotorbar defects atzero load and almost at standstill. The
method uses the standard current sensors already present in modern industrial inverters and, hence, is non invasive. It is thus well suited as a start-up test for drives. By applying an excitation with voltage pulses using the switching of the inverter and then measuring the resulting current slope, a new fault indicator is obtained. As a result, it is possible to clearly identify the faultinduced asymmetry in the machine's transient reactances. Although the transient-flux linkage cannot penetrate the rotor because of the cage, the faulty bar locally influences the zigzag flux, leading to a significant change in the transient reactances. Measurement results show the applicability and sensitivity of the proposed method.
Monitoring of defects in induction motors through air-gap torque observation
This paper suggests a method to monitor defects such as cracked rotor bars and the shorted stator coils in induction motors. motors. Air-gap torque can be calculated while the motor is running. No special down time for measurement is required. Data of the air-gap torque for a motor should be periodically kept for comparison purposes. Since more data than just a line current are taken, this method offers other potential possibilities that cannot be handled by examining only a line current. The theoretical foundation for this proposed method is presented. Experiments conducted on a 5-hp motor show the validity validity and potential of this approach. Further studies are planned planned to extend the proposed method in detail and to monitor defects developed in other types of rotating machines
Estimation of frequency components in stator current for the detection of broken rotor bars in induction machines
This paper presents a novel high-resolution signal processing technique for non-intrusive detection of broken bar fault condition in induction machine rotor. The technique is based on parametric spectral estimation of stator current waveform recorded while a machine is running. The frequency components that are related to broken rotor bar condition are very close to the fundamental frequency, and this combined with low signal to noise ratio makes the task of detecting a broken rotor bar condition difficult. The method proposed is based on the least squares fit of the predefined parametric signal model. The problem is nonlinear with a number of local minimum values in the feasible region. Classical nonlinear least squares methods, like Levenberg-Marquardt or Nelder-Mead algorithms, can converge to a local minimum giving inaccurate spectral estimation parameters. To overcome this problem we employed the th e global optimization algorithm based on grid search. The grid on which the search for optimum is performed is constructed using the Hyperbolic Cross Points (HCP). The global search on the HCP grid is complemented with the Nelder-Mead local search algorithm, to refine the result. We are able to estimate broken rotor bar frequencies and the associated amplitudes with high accuracy for wide range of motor operating conditions and severities of broken rotor bar faults. The results presented in the paper show that HCP algorithm can be used to diagnose broken bar fault in induction motor using very short current signal segments and during light motor loadings.
The detection of inter-turn short circuits in the stator windings of operating motors
This paper develops a winding-function-based method for modeling polyphase cage induction motors with inter-turn inter-turn short circuits in the machine sta -tor winding. Analytical consideration which sheds light on some components of the stator current spectra of both healthy and faulty machines is developed. It is shown that, as a result of the nature of the cage rotor, no new frequency components of the line current spectra can appear as a consequence of the fault. Only a rise in some of the frequency components which already exist in the line current spectra of a healthy machine can be observed. An experimental setup comprising a 3 kW deltaconnected motor loaded by a generator was used to validate this approach. The experimental results obtained clearly validate the analytical and simulation results
Induction motor stator faults diagnosis by a current Concordia patternbased fuzzy decision system
This paper deals with the problem of detection and diagnosis of induction motor faults. Using the fuzzy logic strategy, a better understanding of heuristics underlying the motor faults detection and diagnosis process can be achieved. The proposed fuzzy approach is based on the stator current Concordia patterns. Induction motor stator currents are measured, recorded, and used for Concordia patterns computation under different operating conditions, particularly for different load levels. Experimental results are presented in terms of accuracy in the detection of motor faults and knowledge extraction feasibility. The preliminary results show that the proposed fuzzy approach can be used for
accurate stator fault diagnosis if the input data are processed in an advantageous way, which is the case of the Concordia patterns.
A model of asynchronous machines for stator fault detection and isolation
This paper presents a new model of asynchronous machines. This model allows one to take into account unbalanced stator situations which can be produced by stator faults like short circuits circuits in windings. A mathematical mathematical transformation is defined and applied to t o the classical abc model equations. All parameters which affect this new model model can be known online. This makes makes the model very useful for control algorithms and fault detection and isolation algorithms. The model is checked by comparing simulation data with actual data obtained from laboratory experiments.
An impedance identification approach to sensitive detection and location of stator turn-to-turn faults in a closed-loop multiple-motor drive
A single closed-loop inverter drive with multiple motors connected to it is a type of drive topology commonly used in steel processing industry, electric railway systems, and electric vehicles. However, condition monitoring for this type of drive configuration remains largely unexplored. This paper proposes an impedance identification approach to detectand locate locate the stator turn-to-turn fault in a multiple-motor drive system. Sensitive and fa ---st fault detection is achieved by utilizing the characteristics of current regulators in the motor controller. Experimental results show that the proposed method can reliably detect and locate the stator turn fault on two shaft-coupled 5-hp induction machines under different operating conditions and fault levels with no need of any machine parameters. Although originally developed for
multiple-motor drives, the detection scheme can also be directly applied to most of the conventional co nventional closed-loop induction motor drives.
Effects of time-varying loads on rotor fault detection in induction machines
This paper addresses the problem of motor current spectral analysis for the detection of non idealities in the air gap flux density when in the presence of an oscillating or position-varying load torque. Several Several schemes have been proposed for the detection of air gap eccentricities and broken broken rotor bars. The analysis of these effects, however, generally assumes that the load torque is constant. If the load torque varies with the rotational speed, then the motor current spectral harmonics produced by the load will overlap the harmonics caused by the fault conditions. The motor current spectral components in the presence of various fault and load conditions are reviewed. The interaction of the effects on the actual stator current spectrum caused by the fault condition and the torque oscillations are shown to be separable only if the angular position of the fault with respect to the load torque characteristic is known. This is an important result in the formulation of an online fault detection scheme that measures only a single phase of the stator current. Since the spatial location of the fault is not known, its influence on a specific current harmonic component cannot be separated from the load effects. Therefore, online detection schemes must rely on monitoring a multiple frequency signature and identifying those components not obscured by the load effect. Experimental results which show the current spectra of an induction machine under eccentric air gap and broken rotor bar conditions are given for both fixed and oscillating loads
Dynamic simulation of dynamic eccentricity in induction machines-winding function approach
The paper describes a method for the dynamic simulation of dynamic rotor eccentricity in squirrel cage rotor induction machines. The method is based on a winding function approach, which allows for all harmonics of magnetomotive force to be taken into account. It is demonstrated how this complex dynamic regime can be modeled using the mutual inductance curves of symmetrical machine using proper scaling techniques. Experimental results demonstrate the effectiveness of the proposed technique and validate the theoretical analysis
Performance analysis of a three-phase induction motor under mixed eccentricity condition
A substantial proportion of induction motor faults are eccentricity related. In practice, static and dynamic eccentricities happen to exist together. With this point in mind, an analytical analytical approach to evaluate the the performance ofa three phase induction motor under mixed eccentricconditions eccentricconditions is presented in this paper. A clear and step-by-step theoretical analysis, explaining explaining completely the presence of certain harmonics in the line-current spectrum in the presence of eccentricity, is discussed. More importantly, importantly, it is shown for the first time that a link exists between the low- and high-frequency elements of these harmonics. It is also shown that these high-frequency components are not very strong in all types of machine. These results will be useful in generating rules and laws to formulate online tools for machine condition monitoring. Finiteelement results to substantiate the inductance values used in the simulation are also included. The analysis is validated by the line-current spectrum of the eccentric machine obtained through simulation using the modified windingfunction approach (MWFA) and experimentation.
Dynamic simulation of cage induction machine with air gap eccentricity
This paper describes a method for the dynamic simulation of a cage induction machine with static and dynamic air gap eccentricity. The method is based on the winding function theory and extension of this theory to the nonuniform airgap case. A method of calculating all inductances in a machine with both static and dynamic air gap eccentricity is presented and a numerical analysis of a machine with specified parameters is presented. Stator line current spectra collected experimentally for both the static and dynamic eccentricity conditions confirm the results obtained by b y the proposed numerical model.
Detection of eccentricity faults in induction machines based on nameplate parameters
Eccentricity-related faults in induction motors have been studied extensively over the last few decades. They can exist in the form of static or dynamic eccentricity or both, in which case it is called a mixed eccentricity fault. These faults cause bearing damage, excessive vibration and noise, unbalanced magnetic pull, and under extreme conditions, stator-rotor rub which may seriously damage the motors. Since eccentricity faults are often associated with large induction machines, the repair or replacement costs arising out of such a scenario may easily run into tens and thousands of dollars. Previous research works have shown that it is extremely difficult to detect such faults if they appear individually, rather than in mixed form, unless the number of rotor bars and the pole-pair number conform to certain relationships. In this paper, it is shown that the terminal voltages of induction machines at switch-off reveal certain features that can lead to the detection of these faults in individual form, even in machines that do not
show these signatures in line-current spectrum in steady state, or to the detection of the main contributory factor in case of mixed eccentricity.
Bearing damage detection via wavelet packet decomposition of the stator current
Bearing faults are one of the major causes of motor failure. The bearing defects induce vibration, resulting in the modulation of the stator current. In this paper, the stator current is analyzed via wavelet packet decomposition to detect bearing defects. The proposed method enables the analysis of frequency bands that can accommodate the rotational speed dependence of the bearing defect frequencies. The wavelet packet decomposition also provides a better treatment of nonstationary stator current than currently used Fourier techniques.
Detecting motor bearing faults
Three-phase induction motors are the workhorses of industry because of their widespread use. They are used extensively for heating, cooling, refrigeration, pumping, conveyors, and similar applications. They offer users simple, rugged construction, easy maintenance, and cost-effective pricing. These factors have promoted standardization and development of a manufacturing infrastructure that has led to a vast installed base of motors; more than 90% of all motors used in industry worldwide are ac induction motors. Causes of motor failures are bearing faults, insulation faults, and rotor faults. Early detection of bearing faults allows replacement of the bearings, rather than replacement of the motor. The same types of bearing defects that plague such larger machines as 100 hp are mirrored in lower hp machines which have the same type of bearings. Even though the replacement of defective bearings is the
cheapest fix among the three causes of failure, it is the most difficult one to detect. Motors that are in continuous use cannot be stopped for analysis. We have developed a circuit monitor for these motors. Incipient bearing failures are detectable by the presence of characteristic machine vibration frequencies associated with the various modes of bearing failure. We will show that circuit monitors that we developed can detect these frequencies using wavelet packet decomposition and a radial basis neural network. This device monitors an induction motor's current and defines a bearing failure.
Fault detection of linear bearings in brushless ac linear motors by vibration Analysis
Electric linear motors are spreading s preading in industrial automation because they allow for direct drive applications with very high dynamic performances, high reliability, and high flexibility in trajectory generation. The moving part of the motor is linked to the fixed part by means of linear bearings. As in many other electric machines, bearings represent one of the most vulnerable parts because they are prone to wear and contamination. In the case of linear roller bearings, this issue is even more critical as the rail cannot be easily fully enclosed and protected from environmental contamination, contamination, unlike the radial rotating rotating bearing counterpart. This paper presents a diagnostic method based on vibration analysis to identify which signature is related to a specific fault.
2.Introduction
Condition monitoring and fault diagnosis of induction motors are of great importance in production lines. It can significantly reduce the cost of maintenance and the risk of unexpected failures by allowing the early detection of potentially catastrophic faults. In condition based maintenance, one does not schedule maintenance or machine replacement based on previous records or statistical estimates of machine failure. Rather, one relies on the information provided by condition monitoring systems systems assessing the machine's condition. Thus the key for the success of condition based maintenance is having an accurate means of condition assessment and fault diagnosis. On-line condition monitoring uses measurements taken while a machine is in operating condition. There are around 1.2 billion of electric motors used in the United States, which consume about 57% of the generated electric power. Over 70% of the electrical energy used by manufacturing and 90% in process industries are consumed by motor driven systems. Among these motor systems, squirrel-cage induction motors (SCIM) have a dominant percentage because they are robust, easily installed, controlled, and adaptable for many industrial applications. SCIM find applications in pumps, fans, air compressors, and machine Tools, mixers, and conveyor belts, as well as many other industrial applications. Moreover, induction motors may be supplied directly from a constant frequency sinusoidal power supply or by an a.c. variable frequency drive. Thus condition based maintenance is essential for an induction motor. It is estimated that about 38% of the induction motor failures are caused by stator winding faults, 40% by bearing failures, 10% 10 % by rotor faults, and 12% by miscellaneous faults. Bearing faults and stator winding faults contribute a major portion to the induction motor failures. Though rotor faults appear less significant than bearing faults, most most of the bearing failures are caused by shaft misalignment, rotor eccentricity, and other rotor related faults.
Besides, rotor faults can also result in excess heat, decreased efficiency, reduced insulation life, and iron core damage. So detection of mechanical and electrical faults are equally important in any electrical elec trical motor. In the proposed method , vibration signals are obtained using piezoelectric sensor and motor current signature analysis is performed using Hall Effect sensor. The features of the signal are analyzed using wavelet packet transform. Besides other signal processing techniques, wavelet packet transform is preferred because it has certain advantages. Traditional signal processing techniques like Fourier transform can perform only on stationary signals. Since it is not well well suited for non-stationary signals Short time Fourier transform (STFT) is used. STFT uses a constant window function as a base to obtain the frequency spectrum coefficients. The size of the window function cannot be changed which led to the need for wavelet transform. Wavelet transform uses a varying size window function as its base. In wavelet transform low frequency signals are decomposed repeatedly to obtain low frequency information. In wavelet transform the information about high frequency signals are limited. In the proposed method, wavelet packet transform decomposes both low frequency and high frequency information. It can analyze both stationary and non-stationary signals. There are many classifier models to effectively classify the faulty data from the healthy one. They are: Analytical model-based methods, Artificial Intelligence-based methods. Analytical model based methods are efficient monitoring systems for providing warning and predicting certain faults in their early stages. Artificial Intelligence based methods are of two categories: categories: Knowledge Knowledge based models and Data based models. When When considering fault diagnostics of induction induction motor it is difficult to to develop an analytical model model that describes the performance of a motor under all its operation points. It is difficult for a human expert to distinguish faults from the healthy operation. Though analytical based methods
and knowledge based methods are effective classification methods, their performance in induction motors is not good. Moreover conventional methods cannot be applied effectively for vibration signal diagnosis due to their lack of adaptability and the random nature of vibration signal. In such a situation, data based models are used to classify faults in induction motors. Data based models are applied when: the process model is not known in the analytical form — the — expert knowledge of the process performance under faults is not available Some of the popular data based models are neural networks, fuzzy systems and Support vector machine. Neural networks and fuzzy logic are widely used in the field of fault diagnostics. Fuzzy logic provides a systematic framework to process vague and qualitative knowledge. Using fuzzy logic it is possible to classify a fault in terms of its degree of severity.Artificial neural network are modeled with artificial neurons. Each artificial neuron accepts several inputs, applies preset weights to each input and generates a non-linear output based on the result. The neurons are connected in layers between the inputs and outputs. Support Vector Machine, a novel machine learning technique is used in this paper. It is based on statistical learning theory, and is introduced during the early 90’s. SVM is opted in this paper since it is shown to have better generalization properties than traditional classifiers. Efficiency of SVM does not depend on the number of features of classified entities. property is very useful in fault diagnostics, because the number of features to be chosen to be the base of fault classification is thus not limited.
Industrial Motor’s Motor’s condition monitoring systems collect data from the main components such as the generator, the gearbox, the main bearing, and the shaft. The purpose of this data-gathering is to minimize downtime and
maintenance costs while increasing energy availability and the lifetime service of wind turbine components. An ideal condition monitoring system would monitor all the components using a minimum number of sensors. There have been a few literature reviews on Industrial Motor’s condition Motor’s condition monitoring. This chapter aims to review the most recent advances in condition monitoring and fault diagnostic techniques with a focus on wind turbines and their subsystems related to mechanical fault. This section summarizes the monitoring and diagnostic methods for the major subsystems in Industrial Motor’s such Motor’s such as gearbox, bearing, and generator which are the primary focus of this study.
2.1.1 Gearbox and Bearing
Gearbox fault is widely acknowledged as the leading issue for wind turbine drive train condition monitoring among all subsystems [11-19]. Gear tooth damage and bearing faults are both common in the Industrial Motor’s. Motor’s. According to McNiff [27], bearing failure is the leading factor in turbine gearbox problems. In particular, it was pointed out that the gearbox bearings tend to fail in different rates. Among all bearings in a planetary gearbox, the planet bearings, the intermediate shaft-locating bearings, and the high-speed locating bearings tend to fail at the fastest rate, while the planet carrier bearings, hollow shaft bearings, and non-locating bearings are least likely to fail. This study indicates that more detailed stress analysis of the gearbox is needed in order to achieve a better understanding of the failure mechanism and load distribution which would lead to improvement of drive train design and sensor allocation. Vibration measurement and spectrum analysis are typical choices for gearbox monitoring and diagnostics. For instance, Yang et al. developed a neural network based diagnostic framework for gearboxes in [28]. The relatively slow speed of the wind turbine sets a limitation in early fault diagnosis using the vibration monitoring method. Therefore, acoustic emission
(AE) sensing, which detects the surface stress waves generated by the rubbing action of failed components, has recently been considered a suitable enhancement to the classic vibration based methods for multisensory monitoring scheme for gearbox diagnosis, especially for early detection of pitting, cracking, or other potential potential faults. Lekou et al. presented their study using AE in parallel with vibration, temperature, and rotating speed data for health monitoring [29]. It was shown that monitored periodic statistics of AE data can be used as an indicator of damage presence and damage severity in Industrial Industrial Motor’s. Motor’s. Chen et al. set up a finite-element (FE) simulation simulation study of stress wave based diagnosis for the rolling element bearing of the wind turbine gearbox. It is noteworthy that FE analysis is a good complementary tool to the experimental based study, with which the physical insight of various levels of faults can be investigated. Notice that AE measurement features very high frequencies compared to other methods, so the cost of data acquisition systems with high sampling rates needs to be considered. Besides, it is noise-rich information from AE measurement. Advanced algorithms are needed to extract useful information. For mechanical faults of the drive train, the electrical analysis was investigated. Diagnosis of gear eccentricity was studied using current and power signals. It is noteworthy that the data were obtained from a wind turbine emulator, incorporating the properties of both natural wind and the turbine rotor aerodynamic behavior. Although the level of turbulence simulated was not described, the demonstrated performance was still promising for practical applications. Torque measurement has also been utilized for drive train fault detection. The rotor faults may cause either a torsional oscillation or a shift in the torque-speed ratio. Also, shaft torque has a potential to be used as an indicator for decoupling the fault-like perturbations due to higher load. However, inline torque sensors are usually expensive and difficult to install.
Therefore, using torque measurement for drive train fault diagnosis and condition monitoring is still not practically feasible.
2.1.2 Generators
The Industrial generators are also subject to failures in bearing, stator, and rotor
among others components. For induction machines, about 40% of
failures are related to bearings, 38% to the stator, and 10% to the rotor. The major faults in induction machine stators and rotors include inter-turn faults in the opening or shorting of one or more circuits of a stator or rotor winding, abnormal connection of the stator winding, dynamic eccentricity, broken rotor bars of cracked end-rings for cage rotor, static and/or dynamic air-gap eccentricities, among others. Faults in induction machines may produce some of the following phenomena: unbalances and harmonics in the air-gap flux and phase currents, increased torque oscillation, decreased average torque, increased losses and reduction in efficiency, and excessive heating in the winding.
2.1.3 Machine Vibration Analysis
Vibration analysis is a proven and effective technology being used in condition monitoring. For the measurement of vibration, different vibration vibration transducers are applied, according to the frequency range. Vibration measurement is commonly done in the gearbox, turbines, bearings, and shaft. For wind turbine application, the measurement is usually done at critical locations where the load condition is at maximum, for example, wheels and bearings of the gearbox, the main shaft of turbine, and bearings of the generator. Different types of sensors are employed for the measurement of vibration: acceleration sensors, velocity sensors, and displacement sensors. Different vibration frequencies in a rotation machine are directly correlated to the structure, geometry, and speed of the machine. By determining the relation
between types of defects and their characteristic frequencies, the causality of problems can be determined, and the remaining useful life of components can be estimated. The history of the equipment, its failure statistic, vibration trend, and degradation pattern are of vital importance in determining the health of the system and its future operating condition. Using vibration analysis, the presence of a failure, or even an upcoming failure, can be detected because of the increase or modification in vibrations of industrial equipment. Since an analysis of vibrations is a powerful tool for the diagnosis of equipment, a number of different techniques have been developed. There are methods that only distinguish failures at a final state of evolution and there are others, more complex, that identify defects at an early ea rly phase of development.
2.2 Review Conclusions
To achieve an accurate and reliable condition monitoring system for wind turbines, it is necessary to select measurable parameters as well as to choose suitable signal processing methods. In some examples, electrical sensors installed around the generator are highly recommended as they are non-invasive and easy to implement compared to the mechanical ones. In wind turbines, because of the noisy environment due to the presence of power electronics converters, signal to noise ratio of measured signals is low and the usage of electrical parameters are often more problematic than in a lab environment. Inaccurate signal analysis leads to various false alarms which makes fault detection unreliable. To overcome this drawback, several approaches have been proposed by introducing the vibration measurement and using vibrations as an index for detecting mechanical fault in the system. However, those methods have been applied mostly for drive train failure, bearing faults, and gear tooth damage by using acoustic emission (AE) techniques for detection. Therefore, to enhance the effectiveness and thorough of condition-based predictive
maintenance, dissertation proposes a vibration based monitoring system for rotor imbalance conditions.
3.1 HARDWARE IMPLEMETATION 3.1.1 ADXL330 MEMS Accelerometer
Figure 3.1 MEMS Accelerometer The ADXL330 is a complete three-axis acceleration measurement system on a single monolithic IC. The ADXL330 has a measurement range of ±6g.The block diagram is illustrated in Figure 3.2. It contains a micro-machined sensor and signal conditioning circuit to implement the open loop acceleration measurement architecture. The output signals are analog voltages that are proportional to acceleration. The accelerometer can measure the static acceleration of gravity in tilt sensing applications as well as dynamic acceleration resulting from motion, shock, or vibration. Deflection of the structure is measured using a differential capacitor that consists of independent fixed plates and plates attached to the moving mass. The fixed plates are driven by 180° out-of-phase square waves. Acceleration deflects the moving mass and unbalances the differential capacitor resulting in a sensor output whose amplitude is proportional to acceleration. Phase-sensitive demodulation techniques are then used to determine the magnitude and direction of the acceleration.
Figure 3.2 Block Diagram of ADXL330 MEMS Accelerometer The demodulator output is amplified and brought off-chip through a 32kΩ resistor. The user then sets the signal bandwidth of the device by adding a capacitor. This filtering improves measurement resolution and helps prevent aliasing. The user selects the bandwidth of the accelerometer using the CX, CY, and CZ capacitors at the XOUT, YOUT, and ZOUT pins. Bandwidths can be selected to suit the application, with a range of 0.5 Hz to 1600 Hz for X and Y axes, and a range of 0.5 Hz to 550 Hz for the Z axis.
3.1.1.1 Features
3-axis sensing
Small, low-profile package : 4 mm × 4 mm × 1.45 mm
LFCSP
Low power: 180μA at VS = 1.8 V (typical)
Single-supply operation: 1.8 V to 3.6 V
10,000 g shock survival
Excellent temperature stability
BW adjustment with a single capacitor per axis ax is
RoHS/WEEE lead-free complian
3.1.1.2 Pin configuration
Figure 3.3 Pin Configuration Table 3.1 Pin function Description of ADXL330 Tri-axial Accelerometer Pin no.
Mnemonic
Description
Pin no.
Mnemonic
Description
1.
NC
No connect
9.
NC
No Connect
2.
ST
Self test
10.
YOUT
Y Channel Output
3.
COM
Common
11.
NC
No Connect
4.
NC
No connect
12.
X OUT
X Channel Output
5.
COM
Common
13.
NC
No Connect
6.
COM
Common
14.
VS
Supply Voltage (1.8V to 3.6V)
7.
COM
Common
15.
VS
Supply Voltage (1.8V to 3.6V)
8.
ZOUT
Z Channel Out
16.
NC
No Connect
Figure 3.4 ADXL330 Tri-axial Accelerometer Mounted on the Motor Housing
The wireless sensor network is implemented and an accelerometer is also integrated in this monitoring system for detecting the vibration signals. Vibration signals were collected using ADXL330 tri-axial accelerometer mounted on the motor housing in Figure 3.3 All three-axis vibration signals are acquired at a sampling rate of 2kHz by a 12-bit ADC conversion.
3.1.1.3 Applications
Cost-sensitive, low power, motion- and tilt-sensing
applications
Mobile devices
Gaming systems
Disk drive protection
Image stabilization
Sports and health devices
3.1.1.4 Advantages
Apart from the significant cost saving over traditional force balance accelerometers, due to
the nature of their design micro-
electromechanical systems sensors have a much much better high frequency response. Where most earthquake accelerometers are specified as having a frequency response of DC to 50Hz, 100Hz or in some cases 200Hz, the seismic-oriented MEMS sensors have a much much higher frequency range. For example, the Silicon Designs units units used in the ESS-1221 sensor have a frequency response of DC to 400Hz, 400Hz, and the Colibrys SF3000L MEMS sensors extend to 1000Hz.
Frequency response is important when recording strong motion, particularly for events at close range where high frequencies have not been attenuated with distance. In blast monitoring, where the source can be only dozens of metres away from the sensor, frequencies of 5000Hz or more can be recorded, so it it is possible that large, nearby earthquakes could achieve their peak accelerations in frequencies above 200Hz. Earthquake recorders typically record data at 100sps and 20 0sps, meaning that frequencies above 50Hz or 100Hz are not recorded. More can be learnt about earthquakes by using MEMS accelerometers and recorders capable of sampling at up to 2000sps. Condition monitoring and fault diagnosis of induction motors are of great importance in in production lines. It can significantly reduce the cost of maintenance and and the risk of unexpected failures by allowing the early detection of potentially catastrophic faults. In condition based maintenance, one does not schedule maintenance or machine replacement based on previous records or statistical estimates estimates of machine failure. Rather, one relies on the information provided by condition monitoring systems assessing the machine's condition. Thus the key for the success of condition based maintenance is having an accurate means of condition assessment and fault fault diagnosis. On-line condition monitoring uses measurements taken while a machine is in operating condition. There are around 1.2 billion of electric motors used in the United States, which consume about 57% of the generated electric power. Over 70% of the electrical electrical energy used by manufacturing and 90% in in process industries are consumed by motor driven systems.
Among these motor systems, squirrel-cage induction motors (SCIM) have a dominant percentage because they are robust, easily installed, controlled, and adaptable for many industrial industr ial applications. SCIM find applications applications in pumps, fans, air air compressors, machine machine tools, mixers, and conveyor belts, as well as many other industrial applications. Moreover, induction motors may be supplied directly from a constant frequency sinusoidal power supply or by an a.c. variable frequency drive. Thus condition based maintenance is essential for an induction motor. It is estimated that about 38% of the induction motor failures are caused by stator winding winding faults, 40% by bearing failures, 10% by rotor faults, and 12% by miscellaneous faults. Bearing faults and and stator winding faults contribute a major portion to the induction motor failures. Though rotor faults appear less significant than bearing faults, most of the bearing failures are caused by shaft misalignment, rotor rotor eccentricity, and other rotor related faults. Besides, rotor faults can also result in excess heat, decreased efficiency, reduced insulation life, and iron core damage. So detection of mechanical and electrical faults are equally important in any electrical motor. In the proposed method, vibration signals are obtained using piezoelectric sensor and motor current signature analysis is performed using Hall Effect sensor. The features of the signal are analyzed using wavelet packet transform. Besides other signal processing techniques, techniques, wavelet packet transform is preferred because it has certain advantages. Traditional signal processing techniques like Fourier transform can perform only on stationary signals. Since it it is not well suited for nonstationary signals short time Fourier transform (STFT) is used. STFT uses a constant window function as a base to obtain the frequency spectrum
coefficients. The size of the window function cannot be changed which led to the need for wavelet transform. Wavelet transform uses a varying size window function as its base. In wavelet transform low frequency signals are decomposed repeatedly to obtain low frequency information. In wavelet transform the information about high frequency signals are limited. In the proposed method, wavelet packet transform decomposes both low frequency and high frequency information. It can analyze both stationary and non-stationary signals. There are many classifier models to effectively classify the faulty data from the healthy one. They are: Analytical model-based methods, Artificial Intelligence-based methods. analytical model based methods are efficient monitoring systems for providing warning and predicting certain faults in their their early stages. Artificial Intelligence based methods are of two categories: Knowledge based models and Data based models. When When considering fault diagnostics of induction motor it is difficult to develop an analytical model that describes the performance of a motor under all its operation points. It is difficult for a human expert to distinguish faults from the healthy operation. Though analytical based methods and knowledge based methods are effective classification methods, their performance in induction motors is not good. Moreover conventional methods cannot be applied effectively for vibration signal diagnosis due to their lack of adaptability and the random nature of vibration signal. In such a situation, data based models are used to classify faults in induction motors. Data based models are applied when: — the the process model is not known in the analytical form — expert expert knowledge of the process performance under faults is not available
Some of the popular data based models are neural networks, fuzzy systems and Support vector machine. Neural networks and fuzzy logic are widely used in the field of fault diagnostics. Fuzzy logic provides a systematic framework to process vague and qualitative knowledge. Using fuzzy logic it is possible to classify a fault in terms of its degree of severity. Artificial neural network are modeled with artificial neurons. Each artificial neuron accepts several inputs, applies preset weights to each input and generates a non-linear output based on the result. The neurons are connected in layers between the inputs and outputs. Support Vector Machine, a novel machine learning technique is used in this paper. It is based on statistical learning theory, and is introduced during the early 90’s. SVM is opted in this paper since it is shown to have better generalization generaliza tion properties than traditional classifiers. Efficiency of SVM does not depend on the number of features of classified entities. property is very useful in fault diagnostics, because the number of features to be chosen to be the base of fault classification is thus not limited.
CONCLUSION
One of the most serious problems in Industrial Motor’s Motor’s is the possibility of mechanical failure, f ailure, specially sp ecially for rotating parts of o f gears and generators. Therefore, a machine health monitoring system is a very important tool in Industrial Motor’s. Motor’s. Moreover, wireless sensor technologies make it possible to measure and control the vibrations of the machine during operation. The methods of mechanical fault detection through vibration analysis have been analyzed and assessed based on their ability to detect machine abnormalities. By using an MEMS accelerometer which is low cost, light in weight, compact in size and low
in power consumption, a vibration detection method is proposed in this dissertation. Machine vibration analysis in time and frequency domain has been analyzed and a severity detection technique is also established. These are the essential components for an advance health monitoring system. The implementation of mechanical fault monitoring system can be used to estimate the range of severity levels, which makes it possible to detect the abnormalities before failure. It is very useful part of the condition based predictive maintenance. This control technique works well both under the normal and disturbance operation. This enhancement of the vibration suppression capabilities opens up the possibility of improving the performance of the windmill. This will greatly improve the power quality and reduce the downtime when there is wear and tear on the mechanical components, such as shaft, gear box, and rotating parts.
Dissertation Contributions
This study presents a excellent health monitoring for Industrial Motor’s systems Motor’s systems to detect the severity level of mechanical fault conditions. Moreover, a new 3 axis sensor is proposed to monitor the wind turbine output during imbalance conditions. The major contributions of this dissertation are:
Develop and implement a MEMS based wireless sensor network for a health monitoring which will be able to detect detect mechanical fault conditions based on the vibration signature.
Propose and validate vibration based detection techniques to predict the level of fault severity and be able to estimate the usable life of the equipment.
To reduce the downtime and manual procedures by continues monitoring of wind turbine operations.
Developing a new predictive monitoring system instead of protective system to ensure the Good condition of wind turbines.
To develop a wireless monitoring system to overcome the drawbacks of wired networks.
The overall cost of the monitoring system gets reduced due to no usage of wired conductors for signal transmission.
The low cost microcontrollers and low power consumption equipments are used.
