Motor Current Signature Analysis, Vibration Analysis, PeakVue for Rotating Machines - Assignment Example

Motorcurrent signature analysis,Vibration Analysis,PeakVue for Rotating Machine Journal bearing can be defined as a type of plain bearing which is designed to low the friction by supporting radial loads. When the load is light and the motion is relatively continuous, journal bearings are normally used. They are commonly used in crankshafts. These bearings are also called as radial or sleeve bearings (What is the definition of journal bearing?). Journal bearing is a simple bearing which comprises of a shaft. The shaft is also known as journal. The crankshaft rotates in the bearing with a layer of oil that separates the two parts through fluid dynamic effect. In the journal bearing, the shaft and bearings are simple polished cylinders where lubricants are used to fill the gap. The lubricant that is used reduces the friction between the two surfaces. It allows the moment of one side against the other in an easy way. The lubricant used is so thick that the surface does not come into contact each other while rotating. When oil is used as a lubricant, it is applied through a hole in the bearing through pressure. This is done mainly on heavily loaded bearings. For small cylinder engines, simple oil slinger in the sump and an ideal hole for feeding the oil in the shell of the bearing is enough. A journal box is a casing that houses the journal bearing. Generally there are two types of journal bearings. They are hydrodynamically lubricated bearings and hydrostatically lubricated bearings. Both are liquid journal bearings. The difference between these two types of bearings is in the way of pressure that is used to support the bearing is primarily and consequently maintained. In hydrostatic bearing the pressure is always present that is always desirable. This is achieved by the help of an external pump that forces lubricants into the system. The main purpose of the pump is to provide a degree of pressure that aims to complement the pressure created by the rotation of the bearing. Whereas in hydrodynamic bearing, the pressure in the oil film is preserved through the rotation of the shaft. Normally hydrodynamic bearings require more care in the field of design and operation than hydrostatic bearings because they are more prone to wear and tear. The lubrication in such bearings does not work unless there is shaft rotation. More over in low rotational speed, the lubricant will not be able to attain complete separation of the shaft and the bush. To rectify this problem hydrodynamic bearings are assisted by secondary bearings which supports the shaft during starts and stop period. The main principle regarding the working of a journal bearing is that over an infinitesimally small length of shaft perimeter, the theory of a lubricated pair can be applied. The meeting, thickness and speed of the fluid used generate a pressure film. When one surface moves, it drags oil into the gap between the two surfaces. When the space decreases, the oil moves forward. The oil used should be incompressible so that it can develop pressure. The pressure thus created stops oil from entering into the gap that is created. When the oil within the gap reaches the pressure limit, it pushes oil through the smaller space (Journal bearing). Vibration Analysis Vibration analysis is a technique that is used to analyze the damage in bearings due to wear and tear. In a rotating machine there is a rotor and bearings for support. Usually the nonidealities of these mechanisms could stimulate vibrations of the rotating system. These uncontrolled vibrations could lead to extreme wear of the components inside the rotating machine, particularly to the bearings. This can reduce the process quality also. Vibrations are injurious to the machine even when amplitudes are low. This is evident in the case of super harmonic vibrations. Super harmonic vibrations takes place below the first critical speed of the rotating machine. This type of vibration is animated when the rotational speed of the mechanism is a fraction of the normal frequency of the mechanism. When this happens the machine transforms a part of its rotational energy into vibration energy. While designing the rotating machines, the amount of vibrating energy should be reduced. The vibration phenomenon in rotating machines can be studied by the analysis of the coupled rotor bearing systems employing a multibody simulation approach. This approach mainly focuses on the modeling of hydrodynamic journal bearings. The non idealities affecting the rotor bearing system as well as their effect on the super harmonic vibration of the rotating machine is analyzed in this approach. Moreover a comparison is made with a computationally efficient journal bearing model for the purpose of validating the model for further development. The journal bearing model which is selected is improved for taking the waviness of the shaft journal. Then the improved model is implemented and analyzed in a multibody simulation code. A rotor bearing mechanism that includes a flexible tube roll, two journal bearings as well as a supporting structure is used for analysis by the method of multibody simulation technique. Shell thickness variation in the tube roll and waviness of the shaft journal in the bearing assembly are the two modeled non idealities. These non idealities can cause sub harmonic reverberation in the mechanism. In this approach such non idealities can be understood through analysis. This method is ideal for analysis for vibrations on rotating mechanisms (Petri.H). Journal Bearings are of supreme importance to almost all forms of machinery that rotates. They are one of the most common of machine components. As a result of their significance and extensive use, journal bearing breakdown is also one of the leading causes of collapse in revolving machinery. It is so important to machine protection and mechanization process that research on the basis and analysis of journal bearing malfunction has been vigorously undertaken for many years (Shiroshi.J. & Liang.Y.L.S. & Danyluk. S. &Kurfess. T). MCSA Motor current signature analysis can be identified as a system that is used for analyzing and trending dynamic energizing systems. An accurate analysis of the results of MCSA will help to identify the following, viz, incoming winding health, stator winding health, rotor health, air gap static and dynamic eccentricity, load issues, system load and efficiency. Bearing health and coupling health (Penrose,H.W.). Motor signature current analysis or MCSA is considered as one of the most sought after detection methods that are used for fault detection. This method is so popular because it can easily identify the common machine faults like turn to turn short circuit, cracked broken rotor bars, and deterioration of bearings (Mehala.N. & Dahiya.R.2007) Studies and researches in the area of defect analysis have revealed that MCSA can be a good replacement for the usual conventional vibration monitoring (Kar.C & Mohanty.R. 2004). MCSA is now regarded as a conditioning monitoring technique that is used extensively for the purpose of diagnosing problems like broken rotor bars, shorted turns in low voltage stator windings, abnormal levels of air gap eccentricity as well as other mechanical problems. MCSA is ideal in diagnosing problems that are related to induction motor drives (Thomson, W.T&Gilmore, R.J.) MCSA technology became more popular after the 1990’s. This technology includes both Motor circuit analysis and Motor current signature analysis. The applications of these analysis methods are almost endless. The basic purpose behind the development of MCSA technique was to detect faults in rotor bar, which was difficult to asses using traditional methods like vibration analysis (Penrose,H.W.). It is very clear that there is a vital relationship between fault detection and importance to the user of that fault detection. Even though there are more studies on effects of MCSA in induction motors, there is little with regard to MCSA and journal bearings even though it is estimated that bearing faults results in nearly 50% of motor failures (Benbouzid.M.E.H.2000). . MCSA technology can also be used effectively in monitoring and assessing turn fault detections in rotor motors. Turn fault detection is based on the fact unfaulted motors that is powered by symmetrical multistage voltage source will be having no negative current sequence that is flowing in the leads. This type of fault usually results in negative sequence current flow (Kliman. GB..Premerlani, W.J.Koeg1 R.A &.Hoevveler. D.). MCSA is regarded as one of the most powerful method that is widely used in online motor diagnosis for the purpose of detecting fault in motors. The advantage of using MCSA is that there is no estimation for motor parameters and there is plainness of current sensors and their installation. Though MCSA has many advantages, it suffers from certain drawbacks also. Main among them is that it requires high precision of slip frequency information for guaranteeing the dependability of the diagnosis result. Moreover the current data should be sampled after the motor speed arrives at a steady state. Lastly harmonic numbers which are unspecified in equations of abnormal harmonics results in ambiguity. MCSA is more useful than other diagnostic techniques because abnormal harmonic frequencies are independent of the types of drive systems or techniques that are used for control (Jee-Hoon Jung.J.H. & Lee.A & Kwon.G.H.2006). . Simple voltage and current sensors can be used for condition monitoring, fault diagnosis, and detection. However in the MCSA method, the current frequency spectrum is obtained for the purpose of analysis. Analysis is done to find specific frequency components that point out initial fault or degradation of the condition of the machine. The frequencies are related to well known faults of the machine. Therefore the machine condition can be inferred after the processing of the stator current. A distinguishing feature of this method is that the sampling of current is possible when the machine is still working. Fault detection can be done without switching of the machine. Faults that are commonly associated with induction motors as well as bearings are generally classified as isolation faults. By sampling the stator current and analyzing the spectrum, incipient faults can be diagnosed. This process is the base of MCSA method. The frequencies that appear on the spectrum that shows the presence of short circuit faults can be expressed using the following equation. fcc = ff { k+ n (1-s) } p p - pole pairs s - rotor slip ff - fundamental frequency(Hz) fcc - short-circuit related frequency (Hz) k=1,2,3... order of the frequency harmonic. For detecting the fault, a test was developed based on MCSA as shown in the above figure. First the stator current was sampled first in the time domain as well as in the sequence calculation of the frequency spectrum and analysis was made to diagnose specific frequency components that are related to initial faults. There is an associated frequency for each rotor faults. This can be identified in the spectrum. By comparing the harmonic amplitudes of specific frequencies, faults can be detected. It is also possible to determine the degree of faulty condition. As per the above system, current sensors that are connected to a data acquisition board are used for acquiring current samples from the motor under load conditions. These signals are then transformed to a frequency domain using Fast Fourier transform algorithm for making inferences about the faulty conditions of the machine. This is illustrated from the figure below. With regard to the frequency spectrum of the stator current, it was clear that when the machine is attached to the load and in existence of some type of peculiarity problems arise in detecting the short circuits in the stator winding. This is because of the fact that associated frequency is very close to the frequency that is attached to eccentricity. Fault detection can be performed well using higher sample frequency or by extrication of the load from the machine (Pereira.L.A. & Gazzana.D.d.S.& Pereira.L.F.A). It is clear form various literature that are related to bearing faults and their diagnosis that MCSA is the most superior as well as widely sought after method that is used in fault diagnosis. However it is also clear that theoretical analysis as well modeling of machine faults are Infact most necessary to differentiate the pertinent frequency mechanism from others that may be present due to time harmonics, saturation of the machine etc (Nandi.S. & Toliyat. H.A). PEAKVUE Peakvue analysis method is mainly an analysis method that is used exclusively for measuring stress wave activity in a metallic component. The stress waves are associated with lubrication, impact, friction and fatigue cracking. These develop faults in rolling element bearings as well as in gears. For example if there is a defect on the bearing raceway by an impact by the rolling element, it results in the generation of a series of stress waves that usually propagate away from the defect location to other numerous directions. The propagation of the wave introduces a ripple on the surface of the machine which in turn will introduce a response output in a sensor detecting total movement like an accelerometer or strain gage. Some common defects which encourages generation of stress waves are fatigue cracking in bearing raceways as well as in gear teeth, cracked and broken gear teeth and pitting in antifriction bearing races that causes the roller to impact etc. the main activity will be to detect and to quantify the stress wave activity relative to energy as well as repetition rates. This can lead towards recognition of certain faults and also allows the assessment of these detected faults (James C. Robinson.J.C. & Walker.J.W.& Berry.J.E. 2001). . To prevent catastrophic failure in machines as well as to facilitate effective maintenance planning, detection of mechanical faults in bearings and machines is considered very important. In early stages, human senses like sound and touch were used for this purpose. But in its place electronic sensors have taken over. These sensors help to detect faults with more accuracy and quickly. But the real problem is to interpret these signals by the sensors in a correct way. If the interpretation is correct, the maintenance engineer will be able to pinpoint the fault and helps him to take corrective measures quickly. It is important that the services of skilled as well as trained persons are required to perform this job. Nowadays electronic sensors have become more advanced and sophisticated. Therefore the detection process as well as the interpretation of the signals also has become more complicated. Peakvue data analysis is a proven technique that is used for the early detection of failures that are of high frequency and impact related failures. They can be bearing faults due to wear and tear, loss in lubrications, contaminations etc. the Peakvue method provides a measure of true peak accelerations at frequencies that are high. This gives an indication about the impending failure. The data obtained through this method can provide indication towards the real problem. It can also be further diagnosed using spectral measurements (Robinson . J. & Illig .M & Brown. T. & Limburg.R.). The Peakvue analysis method is equally capable to detect faults in slow machinery as well as in fast machinery. With respect to bearings, this methodology focuses on the analysis of stress waves, scuffing, wear and tear etc. moreover it has been proved through research that Peakvue methodology is far more superior than demodulation methodology that is used to identify faults (Robinson.J.C. & Piety.K.R. 1996). Peak Vue can be defined as a method of vibration signal analysis that is used to capture the peal value of the time wave form over a time interval that is defined. Processing can be done on these peak values just like custom immediate values from the wave form into a frequency based spectral plot. Here the signal processing is directed at picking up as well as monitoring stress waves that have short duration. The stress waves are produced primarily at faults where there is metal to metal impacting. Examples of faults are gear defects, bearing defects, and abrasive wear and tear. .When compared with demodulation, Peak Vue maintains true signal levels that are occurring at the impacting events. Peak Vue is a more trendable limit. Moreover it is not limited to the middle frequency range. Peak vue can be put up identical to that of demodulation in transitional frequency bands. I.e. it will be in between 6000 to 30000 rpm (Dominick.J). The main Peak Vue parameter that is used for trending the measurements is called as PK-PK waveform. This particular parameter is made available in the PdM software. This parameter has proved to be reliable indicator for the detection of faults that is caused by impact. It is to be noted that the Pk-Pk waveform parameter is not dependant on the analysis bandwidth. As a result of this lack of dependence, it permits to institute the general alarm levels. Moreover fault alarm levels must be based on amplitudes that are relative to baseline levels for strictures like the digital overall alarm level. While monitoring the gearboxes and bearings, it is better to include two times gear mesh in the bandwidth analysis. This is done for capturing back lashing that can be possible. Other than that it is also important to set the high pass filter, higher than the vibration frequencies that are anticipated. The higher frequencies that are introduced will experience major reduction. This is because of the fact that there will be losses from the gear teeth to the outer area or surface where the sensor is escalated. Therefore it would be better if the high pass filter is set higher than two times the highest gear mesh. It is also recommended that the measurement point should be towards each and every bearing in the gear box. The high pass filter that is set should be equal for each and every measurement point. There will be change in the resolution as well as in the analysis bandwidth. At least two times gear mesh should be incorporated for any type of gears on the shaft that is being checked as well as to provide adequate frequency resolution to determine that the gear mesh is being adapted with either shaft or the gearbox. For bearings that are inside the gear box, the same rules mentioned above are applied. The only difference is that the high pass filter will always remain the same. In the case of bearings, it is recommended that a minimum of 16 revs should be included within the peak vue time wave form. Stress wave capture analysis, where importance is given on confining the peak values connected with each occasion, has confirmed to be very useful tactic for fault exposure, fault succession as well as severity assessment (Robinson.J.C.&Berry. J.E. 2001). Reference: Benbouzid.M.E.H.2000. A Review of Induction Motors Signature Analysis as a Medium for Faults Detection. Dominick.J. PeakVue as Part of a Reliability Based Maintenance Program. (online). Available:http://www.compsys.com/drknow/aplpapr.nsf/19fcc759cbee3c3c8525659800528caa/0c644b02e0d67926852565a200634f3b?OpenDocument Journal Bearing. (online). Available:http://www.answers.com/topic/journal-bearing-2 James C. Robinson.J.C. & Walker.J.W.& Berry.J.E. 2001. Description of peakvue and illustration of its wide Array of applications in fault detection and Problem severity assessment Jee-Hoon Jung.J.H. & Lee.A & Kwon.G.H.2006. Online Diagnosis of Induction Motors Using MCSA Kar.C.& Mohanty.R.2004. Monitoring gear vibrations through motor current signature analysis and wavelet transform. Kliman. GB..Premerlani, W.J.Koeg1 R.A &.Hoevveler. D. A New Approach to On-Line Turn Fault Detection in AIC Motors. Mehala.N. & Dahiya.R.2007. Motor Current Signature Analysis and its Applications in InductionMotor Fault Diagnosis. Vol 2. Iss 1. . Nandi.S. & Toliyat. H.A. condition monitoring and fault diagnosis of electrical machines- a review. Penrose,H.W Practical Motor Current Signature Analysis Taking the Mystery Out of MCSA Penrose,H.W. Applications for motor current signature analysis. Pereira.L.A. & Gazzana.D.d.S.& Pereira.L.F.A. Motor Current Signature Analysis and Fuzzy Logic Applied to the Diagnosis of Short-Circuit Faults in Induction Motors Petri.HNon-Linear Journal Bearing Model for Analysis of Superharmonic Vibrations of Rotor Systems.2008. (online). Available:https://oa.doria.fi/handle/10024/42673 Robinson . J. & Illig .M & Brown. T. & Limburg.R. The model 682a05 bearing fault detector. A New Approach for Predicting Catastrophic Machine Failure. Robinson.J.C.&Berry. J.E. 2001. Description of peakvue and illustration of its wide Array of applications in fault detection and Problem severity assessment. Robinson.J.C. & Piety.K.R. 1996. New Methodology for Bearing Fault Defect:PeakVueAnalysis. (online) Available:http://www.compsys.com/drknow/aplpapr.nsf/06b6f5a4de2eae6285256a3f004d9758/d5fbeecf87af72a485256620007144db!OpenDocument  Shiroshi.J. & Liang.Y.L.S. & Danyluk. S. &Kurfess. T. Vibration Analysis for Bearing Outer Race Condition Diagnostics. (online). Available:http://www.scielo.br/scielo.php?pid=S0100-73861999000300010&script=sci_arttext Thomson, W.T&Gilmore, R.J. Motor current signature analysis to detect faults in induction motor drives- Fundamentals, data interpretation, and industrial case histories What is the definition of journal bearing?. (online). Available:http://www.toolingu.com/definition-560210-24475-journal-bearing.html Read More