Prediction of Defects in Antifriction Bearings using Vibration Signal Analysis

Transcription

1 Prediction of Defects in Antifriction Bearings using Vibration Signal Analysis M Amarnath, Non-member R Shrinidhi, Non-member A Ramachandra, Member S B Kandagal, Member Antifriction bearing failure is a major factor in failure of rotating machinery. As a fatal defect is detected, it is common to shut down the machinery as soon as possible to avoid catastrophic damages. Performing such an action, which usually occurs at inconvenient times, typically results in substantial time and economical losses. It is, therefore, important to monitor the condition of antifriction bearings and to know the details of severity of defects before they cause serious catastrophic consequences. The vibration monitoring technique is suitable to analyze various defects in bearing. This technique can provide early information about progressing malfunctions. This paper describes the suitability of vibration monitoring and analysis techniques to detect defects in antifriction bearings. Time domain analysis, frequency domain analysis and spike energy analysis have been employed to identify different defects in bearings. The results have demonstrated that each one of these techniques is useful to detect problems in antifriction bearings. Keywords : Antifriction bearings; Defects; Prediction; Vibration signal; Signature analysis NOTATION BD, PD f o, f i,, f R f r n b INTRODUCTION : roller diameter and pitch diameter, respectively, mm : outer race, inner race and roller malfunction frequencies, respectively, Hz : rotational frequency, Hz : number of rollers : angle of contact Condition monitoring of antifriction bearings in rotating machinery using vibration analysis is a very well established method. It offers the advantages of reducing down time and improving maintenance efficiency. The machine need not be stopped for diagnosis. Even new or geometrically perfect bearings may generate vibration due to contact forces, which exist between the various components of bearings. Antifriction bearing defects may be categorized as localized and distributed. The localized defects include cracks, pits and spalls caused by M Amarnath and R Shrinidhi are with S J College of Engineering, Mysore ; A Ramachandra is with Ghossia College of Engineering, Ramanagaram, Bangalore and S B Kandagal is with the Department of Aerospace Engineering, Indian Institute of Science, Bangalore This paper was received on December 8, 00. Written discussion on the paper will be entertained till September 30, fatigue on rolling surfaces 1. The other category, ie, distributed defects include surface roughness, waviness, misaligned races and off size rolling elements. These defects may result from manufacturing error and abrasive wear. Hence, study of vibrations generated by these defects is important for quality inspection as well as for condition monitoring. Antifriction bearing failures result in serious problems, mainly in places where machines are rotating at constant and high speeds. In order to prevent any catastrophic consequences caused by a bearing failure, bearing condition monitoring techniques, such as, temperature monitoring, wear debris analysis, oil analysis, vibration analysis and acoustic emission analysis have been developed to identify existence of flaws in running bearings. Among them vibration analysis is most commonly accepted technique due to its ease of application. Vibration signature monitoring and analysis in one of the main techniques used to predict and diagnose various defects in antifriction bearings 3. Vibration signature analysis provides early information about progressing malfunctions and forms the basic reference signature or base line signature for future monitoring purpose. Defective rolling elements in antifriction bearings generate vibration frequencies at rotational speed of each bearing component and rotational frequencies are related to the motion of rolling elements, cage and races. Initiation and progression of flaws on antifriction bearing generate specific and predictable characteristic of vibration. Components flaws (inner race, outer race and rolling elements) generate a specific defect frequencies

2 calculated from equations, mentioned by Chaudhary and Tandon 4, namely Inner face malfunction frequency n é BD fi = f r + æ è ç ö ù ê 1 PD cosb ë ø ú û (1) Outer race malfunction frequency n é BD fo = f r - æ è ç ö ù ê1 cosb ë PDø ú û () Roller malfunction frequency Vol 85, July 004 f R PD BD f é BD = r - æ è ç ö ê1 ëê PDø ù cos b ú (3) ûú The time domain and frequency domain analyses 5 are widely accepted for detecting malfunctions in bearings. The frequency domain spectrum is more useful since it also identifies the exact nature of defect in the bearings. Spike energy analysis makes use of spike energy meter to measure three parameters of high frequency pulses, namely, pulse amplified, pulse rate and high frequency random vibratory energy associated with bearing defects. These three parameters are electrically combined into a single quantity called gse. EXPERIMENTAL SETUP An experimental test rig built to predict defects in antifriction bearings is shown in Figure 1. The test rig consists of a shaft with central rotor, which is supported on two bearings. An induction motor coupled by a flexible coupling drives the shaft. Self aligning double row ball bearing is mounted at driver end and cylindrical roller bearing is mounted at free end. The cylindrical roller bearing is tested at constant speed of 1400 rpm with radial load of 30 N. Cylindrical roller bearing type NRB NU 305 (with outer race and roller defects), HMT e-45 (with inner race defect) have been used for analysis. 1 roller bearing; rotor; 3 self aligning ball bearing; 5 motor Figure 1 Schematic diagram of test rig 4 flexible coupling and Table 1 Roller bearing details (NRB NU 305) Parameter Value Number of rollers 10 Outer diameter, mm 6 Inner diameter, mm 5 Pitch diameter, mm 44 Roller diameter, mm 8 Contact angle, b 0 The details of the bearings used in the present analysis are given in Table 1. Provision is made on the roller bearing housing to mount accelerometers. Two-channel FFT analyzer (A&D AD 355) was used to monitor vibration signals from good and defective bearings. IRD mechanalysis model 880-spectrum analyzer was used to monitor spike energy pulses associated with bearing defects. Three types of bearing defects, namely, inner race, outer race and roller defects were studied. Experimental tests were carried out on three sets of bearings. Initially new bearing (good bearing) was fixed in the test rig and signals were recorded using FFT analyzer, shock pulse meter and spike energy analyzer. The good bearing was replaced by defective bearing and signals were recorded for each one of the case separately under the same standard condition. Time domain analysis and frequency domain analysis were carried out. Time waveform indicates severity of vibrations for defective bearings and frequency spectrum identifies exact nature of defects in bearings. Spike energy level which is more comprehensive parameter to predict defects in bearings was also recorded for all bearings. RESULTS AND DISCUSSION The vibration signals of good bearing and defective bearing is shown in Figures and 3, respectively. The magnitude of peak to peak time response of good bearing is found to be in the 10 mv range in comparison to 00 mv range for defective bearings. In order to assess the clarity in different defects in bearings the spectrum analysis is shown in Figures 4-7 for good bearing, bearing with inner race defect, bearing with outer race defect and bearing with roller defect, respectively. The details of inner race defect, outer race defect and roller defect are shown in Figures 8-10, respectively. The magnitude of spectrum at various harmonic frequencies for defective bearing is found to be quite distinct in comparison to good bearings. The frequency spectrum of the vibration signal from the inner race defect bearing shows the peaks at 137 Hz, 75 Hz, 413 Hz, 551 Hz and 87 Hz. The fundamental frequency estimated for the inner race defect from the equation (1) is found to be Hz. The frequency 89

3 Figure Time wave form of good bearing Figure 5 Frequency spectrum of inner race defect Figure 3 Time wave form of defective bearing Figure 6 Frequency spectrum of outer race defect Figure 4 Frequency spectrum of good bearing Figure 7 Frequency spectrum of roller defect 90

4 Figure 11 Spike energy spectrum of good bearing Figure 8 Defects on inner race of roller bearing (NRB NU 305) Figure 1 Spike energy spectrum of defective bearing Figure 9 Defects on outer race of roller bearing (NRB NU 305) The frequency spectrum of the vibration signal from the roller defect bearing shows the peaks at 81 Hz, 163 Hz, 44 Hz and 36 Hz, 408 Hz, 489 Hz, 571 Hz, 653 Hz and 698 Hz. The fundamental frequency for the roller defect bearing from the equation (3) is found to be Hz. The results correlate very well. The spike energy spectrum of the bearing is shown in Figures 11 and 1 for good bearing and defective bearing, respectively. The clustered nature of the spectrum of the defective bearing spike energy content factor is of the order of 0.03 in comparison to 0.01 in case of good bearing. The spike energy spectrum can be utilized to assess the severity of the defect in the bearings. CONCLUSIONS Figure 10 Defects on roller of roller bearing (NRB NU 305) spectrum of the vibration signal from outer race defect shows the peaks at 95 Hz, 190 Hz, 86 Hz, 477 Hz, 668 Hz, 763 Hz and 950 Hz. The fundamental frequency for the outer race defect bearing from equation () is found to be Hz. Vol 85, July 004 Time waveform and frequency spectrum provide useful information to analyze defects in antifriction bearings. Time waveform indicates severity of vibration in defective bearings. Frequency domain spectrum identifies amplitudes corresponding to defect frequencies and enables to predict presence of defects on inner race, outer race and rollers of antifriction bearings. Spike energy factor helps to identify the severity of the defect in antifriction bearings. The distinct and different behaviour of vibration signals from bearings with inner 91

5 race defect, outer race defect and roller defect helps in identifying the defects in roller bearings. REFERENCES 1. Y Li and C Zhang. Dynamic Prognostic Prediction of Defect Propagation on Rolling Element Bearing. Journal of Vibration and Acoustics, Trans of ASME, vol 10, no 1, pp S Braun and B Danter. Analysis of Roller/Ball Bearing Vibration. ASME Journal of Mechanical Design, vol 101, 1979, pp Teruo Igarishi and Hiroyoshi. Studies on Vibration and Sound of Defective Rolling Bearings. Bulletin JSME, vol 5, no 04, 1980, pp A Chaudhary and N Tandon. A Theoretical Model to Predict Vibration Response of Rolling Bearings to Distributed Defects Under Radial Load. Journal of Vibration and Acoustics, Transactions of ASME, vol 10, no 1, 1998, pp I J Taylor. Identification of Bearing Defects by Spectral Analysis. Journal of Mechanical Design, Transaction of ASME, vol 10, 1980, pp Submission of Manuscripts for IEI Journals Authors desirous to publish technical papers in the Journal of the Institution under various engineering disciplines are requested to send the manuscript in quadruplicate accompanied by one soft copy in CD or floppy disk (text in MS-Word and figures in JPG or TIFF format) along with original illustrations/ photographs. It may be noted that the evaluation process will not be initiated unless these requirements are fulfilled. 9