Faults Detection of Rolling Element Bearing Due to Misalignment of Rotating Machine
Mohsin Hassan Albdery1,2*, István Szabó 1
1 Department of Machine and Machinery, Institute of Technology, Hungarian University of Agriculture and Life Sciences, Páter K. u. 1, Gödöllő, H-2100, Hungary
2 Mechanical Engineering Doctoral School, Hungarian University of Agriculture and Life Sciences, Páter K. u.
1, Gödöllő, H-2100, Hungary
*Corresponding Author: [email protected]
Accepted: 15 May 2021 | Published: 1 June 2021
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Abstract: Rolling bearing failures are a leading cause of failure in rotating machines, reducing production availability and resulting in costly system downtime. As a result, there is an increasing demand for vibration-based monitoring of bearings, and any method that increases the effectiveness of diagnosing bearing faults should be evaluated. In this paper, we will propose procedures for detecting bearing faults caused by misalignment and data collection for signal processing analysis of all possible operating states of a bearing. The experimental procedures are based on vibration and torque measurements. The test bench's rolling-element bearing is an NTN UCP213-208 pillow block bearing. The experimental work will be conducted using a data acquisition system with a test bench developed by the Department of Machine and Machinery at the Hungarian University of Agricultural and Life Sciences. This work contributes to the development of a predictive method for avoiding rolling element bearing failure.
Keywords: Fault detection, Rolling element bearing, Vibration, Bearing defect, Misalign __________________________________________________________________________
1. Introduction
In recent years, this field has seen significant advancements in advanced fault techniques for diagnosing defects in rolling element bearings via vibration, including time domain, frequency domain, time-frequency domain and acoustic emission technique. Numerous studies on vibration signal analysis techniques have been conducted, and numerous reviews have been written based on defect and fault diagnosis technique categories (Malla et al., 2019). The signal level can accurately predict the machine's or component's future failure and life. Thus, the objective is to prolong the life of the machine by detecting faults early enough that an effective maintenance schedule can be implemented to perform corrective action (Gupta et al., 2017).
By contrast, the frequency domain technique can indicate and pinpoint the precise location of a fault in a rolling element bearing by analyzing vibration peaks at the bearing's characteristic frequencies. This makes it simple to identify a defective bearing (Myers et al.,2004).
This has risen to prominence as a critical subject area due to the ease with which the health of rolling element bearings can be determined via vibration monitoring, as the vibration signature contains critical information about the early stages of fault development within them.Given the potential for severe consequences, it is critical to better understand the early stages of bearing defect using these techniques (YILMAZ et al., 2019). The first step is to acquire data from the rotating machinery system via the sensor; following that, signal pre-processing and feature
extraction must be performed to reduce the dimension of the raw data and extract useful information from it. The performance of machine fault diagnosis is highly dependent on the appropriate extraction and selection of features. The purpose of this article is to propose a procedure for rolling element bearing fault detection.
2. Related Literature review
Feature extraction techniques for the diagnosis of bearing defects range from statistical to model-based methods. They include a range of signal processing algorithms, such as Fast Fourier Transformation, Wavelet transformation, etc. Data collection, extraction of features and defect identification are different phases involved in vibration-based defect diagnostics.
This procedure is illustrated graphically in Figure 1. It starts from collecting data, which involves acquiring signals using a transducer from the system under survey, followed by extracting characteristics related to faults, and finally identifying failures.
Figure 1: An overview of fault diagnosis (Tandon & Choudhury,1999)
The following subsections detail the theoretical models for vibrations studied by earlier researchers in rolling element rolling bearings. In the event of a single point defect on a single inner race, McFadden and Smith (1984) developed a model describing the vibration of rollover element layers. The bearing was considered to be constant with a radial charge. It is the product of a series of pulses at the transitional frequency of the rolling element, with the distribution of load and the amplitude of the transfer function, and the pulse reaction of the exponential decomposition function. The model included the effects of the geometry of the bearings, the shaft rate, and above.
Tandon and Choudhury (1997) developed the first model for predicting vibration frequencies and amplitudes of frequency constituents in the presence of localized defeats in various bearing elements. A brief pulse occurs when one of its connecting elements collides with a defect in another element. A comparative analysis is conducted on the effects on vibration of various pulse types such as rectangular, triangular and halved sinusoidal. The pulse shape and size indicate the damage properties. White(1984) has pointed out in his paper that pulse damage severity, extent and age can be taken into consideration for modelling the bears vibration.
McFadden and Smith(1985) extended their previous model to describe multiple defect vibration. Various defects have been explained by the strengthening and annulment of spectral lines due to different phase angles. Su and Lin (1992) extended the vibration model that McFadden and Smith developed to describe the loaded bearing vibration. They have reported the need for an analysis of the time domain together with the frequency domain to verify a running bearing.
Patel et al. (2010) developed a dynamic model for a ball bearing in the presence of single and multiple defects on the breeds. The paper states that multiple defects cause a high vibration amplitude. Experiments have validated the results, and the theoretical model has a good correlation. The theoretical models for the prediction of ball vibration bearing due to located defects were presented by Patil et al. (2010). The defect was modeled as a half sinusoidal
circumferential wave. The model includes the effect of defect size, angular defect position, etc., on the amplitude of the vibration.
There are many vibration sources in the signal acquired from the experimental system for rolling element roller coils. But there are always certain limitations to a theoretical model. The contribution of all vibratory elements in the model cannot be taken into account and the actual amplitudes of vibration cannot be predicted in this paper. Purohit and Purohit (2006) examined the effects of varying numbers of balls and vibration preload in an analysis model of the rotor bearing system. They concluded that the amplitudes of vibration associated with ball passage frequency are reduced, and the number of balls is increased for greater preloads, leading to reduced vibration amplitude. The analytical model for the prediction of a nonlinear dynamic response of rotor-bearing system based on surface waves was developed by Harsha et al.
(2004). In the case of rolling element bearings with single or multiple defects on different components in the structure, Kiral and Karagülle(2003) have developed a method based on the finite feature for detection. The results were found using time and frequency domain parameters. Shah and Patel (2014) have submitted a review of dynamic modelling and rolling element bearing fault identification methods.
The main contribution to increasing the fault diagnosis process is the selection of vital features of a targeted machine (Kothamasu et al., 2006). Diagnosis of defect Characteristics can be divided into three classes: time domain, frequency domain and time-frequency domain, as addressed in Figure 2. The extraction techniques include the mean, standard divergences, RMS, skewness, kurtosis, maximum, minimum and crest factor selected as the extraction statistic (Ali et al., 2015). In general, all the features for failure diagnosis are unnecessary.
Figure 2: Feature extraction techniques (Ali et al., 2015)
Studies of several literature suggest that it is more accurate to use additional condition monitoring techniques in conjunction with vibration analysis to accurately analyze faults and monitor rolling element bearing conditions than it is to use vibration analysis alone.
3. Experimental works
A test bench for examination of bearings was established in the machine department and machinery at the technology institute of the Hungarian agricultural and life sciences University.
This experimental setup was conveyed (Gárdonyi et al., 2015) to detect the early stage of failure of the rolling element. In many industrial applications, an asynchronous motor, mainly pumps and fans, is often employed. NTN UCP213-208, a pillow block bearing, is the bearing used on the test bench as shown in Figure 3.
Figure 3: 1 NTN UCP213-208, pillow block bearing
The test stand includes an engine unit, rolling bearing elements, as well as a control panel and measuring system. The structure of the stand consists of two independent tables, which can be assembled in every position. The manual control panel and the touch screen allows the drive parameters and data collection set-up. It is designed to test a wide variety of mechanical drives, grips and rotating elements. There are plenty of possibilities for positioning driving and driving units in the rust table of the test stand.
All drive parameters can be fixed through a data collector and accurately defined by a programmable logical controller (PLC), as shown in Figure4 and Figure 5. In order to understand the condition of the bearing, the signals from the bearing must be measured and analysed. In order to achieve this, it is necessary to measure the mechanical parameter that appears as a signal from the bearing with the appropriate sensor accelerometer.
Figure 4: Test bench of Asynchronous motor and bearings
Figure 5: Control board of the measurements system
4. Measurement of misalignment
Alignment measurement will be done by using the Fixturlaser XA system (Figure 6). By moving the front side and the rear pairs of one machine's foot, the shafts are vertically and horizontally aligned within the tolerances. The measurement will be possible by moving the horizontal wing correction. The system contains a tolerance table, and for this purpose, a manual is used.
The system calculates the relative distance of the two shafts at two levels after rotation in different measuring positions. The system is entered the distances between the two measuring planes, the distance from the connections and the machine's feet. The display box then displays the actual position of the foot and the alignment. The machine can be directly adjusted in accordance with the values displayed. In the memory manager, the alignment results can be saved. The memory management measurements can be easily transferred to a PC for additional documentation.
Figure 6: Fixturlaser XA system
5. Measurement of vibration
With the help of a Microlog analyser all types of vibration can be measured for bearing (Figure 7). The FFT port should connect one end of the accelerometer. Then select the direction of axial and radial in the bearing case for another end of the accelerometer. This set-up provides time-domain and frequency-domain curves for the vibration signals. Diagnosis of the causes of the bearing defect following the results of the FFT analyser and the vibration analysis software interface. All alignment changes were made in full speed and full load conditions on a horizontal plane with the motor operating due to the shaft misalignment and another problem near the vibration generation, the best position for the sensor to fit the accelerometer near the placement accelerometer sensor.
Figure 7: Skf Microlog analyzer Ax device during measurement
The procedure for the experimentation is as follows:
i. Accelerometer device is mounted/fixed at bearing housing in two axial and radial direction by using Accelerometer sensors .
ii. Accelerometer sensors will be connected through the cables to the Microlog analyzer device.
iii. Collect vibration data by operating the bearings at various loads and speeds.
iv. MATLAB software will be used to analyse the Microlog analyzer device's data by Signal Processing Onramp tool.
v. Analyze the vibration data using time-domain and frequency- domain techniques.
vi. Mesaring torque through bearing in diffrent cases of misallignment.
Using two accelerometer sensors, the Skf Microlog device is used to collect vibration data from axial- and radial-load covers. Study the effect of machine and engine imbalances on roller bearing as well. Unbalance can produce excessive forces affecting the performance of the bearing. Study the impact on the bearing of misalignment. To analyze HBM Spider8 and Skf Microlog device data, use the MATLAB software. Study the effect on bearing by using Skf Microlog Analyzers and Skf Sensor of machine misalignment and unequilibrium. Simulate vibration due to a rolling-element bearing misalignment and imbalance.
Extraction features are carried out by time and frequency domain applications. The statistical parameters for the signals are extracted (Mean, Kurtosis, Skewness and RMS). The wavelet transform domain parameters are also used. Then the functions obtained can be used to extract hopefully only the most representative (best) characteristics as input data for genetic algorithms
and/or main component analysis. As an optimization process, the GA and PCA have been used to choose the optimal features.The bearing's defects are classified primarily as distributed defect and localized defect. Condition monitoring is used to detect and diagnose bearing defects. It is also used to inspect the quality of bearings. By avoiding failures, condition monitoring decreased the risk of serious accidents. Vibration analysis is a fundamental condition monitoring technique that has been widely developed. These vibration signals provide information about the bearing's health and allow for the detection of impending faults before they develop into critical defects. Vibration monitoring is a highly reliable and sensitive method of determining the severity of a fault.
Vibration peaks are generated in the spectrum at the frequencies of the bearing characteristics;
from this, we can easily determine which bearing element is defective. Figure 8 ( a and b) measured rolling-element bearing during operation for 2000 RPM and for vibration frequencies and displacement for axial and radial positions. It showed there is change happened in signals during operations because of increase in load and speed of the machine. We can also be measuring the vibrations of the rolling-element bearing during unbalance and misalignment of motor, which will be our future work.
(a)
(b)
Figure 8: Vibrations measurement of rolling element bearing
6. Conclusion
This article, what has been done using the test bench developed through laboratory experiments at the Department of Machine and Machinery, Hungarian University of Agricultural and Life
Sciences University, can help detect defects of rolling-element bearing conditions in the early stages. Bearing defects may be very small, but they can significantly impact vibration-critical equipment and shorten its life. These faults can be found in the form of indentations, scratches, pits, and abrasive particles embedded in lubricants. Contamination, mounting, assembly, poor maintenance, operation, and other factors can all cause bearing surfaces to deteriorate. As a result of which, temperature and vibration are unavoidable. This causes machine elements to fail. The test equipment is suitable for measuring bearing and torque vibration signals in the shaft bearing system. The expected outcome will demonstrate the effect of misalignment on bearing. The importance of studying the effect of misalignment on the bearing is to avoid failure, which leads to thermal expansion due to increases the fraction force and defects surface of bearings elements.This procedure helps develop a predictive method using signal processing and other techniques to analyze experimental work and avoid bearing failure.
Acknowledgments
Special thanks to the the Stipendium Hungaricum Scholarship Programme, Institute of Technology and the Mechanical Engineering Doctoral School, Hungarian University of Agriculture and Life Sciences, Gödöllő, Hungary.
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