Condition Monitoring of Rotating Shaft Using Virtual ... - IIT Guwahati

5th International & 26th All India Manufacturing Technology, Design and Research Conference (AIMTDR 2014) December 12th–14th, 2014, IIT Guwahati, Assam, India

Condition monitoring of rotating shaft using virtual instrumentation Ajay Chaubey1, H.Chelladurai2*,Subir Singh Lamba3 1,2*,3

PDPM Indian Institute Insti of information Technology, Design and 1 Manufacturing,Jabalpur,India,, [email protected],2*[email protected] [email protected],subirs@iii tdmj.ac.in3 Abstract Shaft is a component which is subjected to various forces when it is used in the processes process and utility plants like high speed compressors, steam and gas turbine, generators and pumps etc. Therefore, safety, reliability, efficiency and performance of shaftss become a major concern for better performance of such equipments. To control the vibration n effects during working condition, various parameters such as speed, diameter and bend values have been investigated in the present work and a regression model has been developed to relate the input and output parameters to minimize the vibrationlevels. Error Error analysis has been carried out between regression model and experimental values to know the feasibility of the model. Two methods, namely, Response Surface Methodology (RSM) and Artificial Neural Network (ANN) were used to predict the responses of the rotating shaft. The investigations were focused around three parameters, three levels and central composite face centered design outlined with full replication procedure and regression model was created. ANN is used to predict bend values of the rotating shaft. haft. To acquire lateral vibration (waveform) signal, a virtual instrument simulation test system has been developed using Lab VIEW. VIEW. In this study, an attempt has been made to estimate the bend levels using Multi-Layer Layer Perceptrons (MLP) architecture. The feed feed forward back propagation algorithm is chosen for training and testing the experimental data. Keywords:Vibration, Vibration, Signal Analysis Technique, Data Acquisition, Artificial Neural Network.

1 Introduction Condition Monitoring (CM) is a tool which helps to identify failing of machine operation before it goes into failure. Most of machines are subjected to wear, deformation, and fatigue failure. These effects cause an increase in the clearance between mating parts, misalignment of shaft, bent shaft, eccentric shaft and faulty bearing leading to an increase in the level of vibrations which results in the failure or breakdown of the machine.CM or predictive maintenance program’s major function is to detect the initiation of failures that can influence the maintenance budget, production targets and safety. CM involves periodical or consistent data collection, data analysis, analysis interpretation and diagnosis. Condition monitoring uses various techniques such as vibration analysis, oil analysis, wear particle cle analysis, thermography, ultrasonic analysis andother techniques to assess the equipment condition. Various researches have been published on the behavior of a rotating shaft. Tlaisietal. Tlaisiet (2012) developed a technique for crack detection in the shaft using lateral and torsional vibration measurement in terms of maximum acceleration, velocity and displacement.Jerzyetal. (2003) discussed iscussedthe crack detection methodologies and on-line line monitoring techniques for rotating machinery shaft. shaft.Veer etal.(2005)investigated the he time domain features based on detection of differences between the amplitude distribution of a vibration signal without withou filtering gear mesh frequencies and the distribution of the signal for a gearbox in good condition .Krzysztofet al. has studied that signal

features(SFs)were extracted from frequency domain transforms as well as from time domain signals and their wavelet coefficients (time (time–frequency domainwere used to develop the ANN model model). Manish Yadav et al.(2011) has investigated that RMS, crest factor,probability density moments (skewness, kurtosis) are the most popular statistical time domain parameters to determine termine the sensitive positions on a machine in healthy condition. The condition monitoring of low speed bearing using an ultrasound technique versus vibration measurements was also observed. Yong-Han Han Kim et al.(2006) has reported that detection of faults from ultrasound signals was superior when comparisons were made in the frequency domain.Chelladuraiet helladuraiet al. (2008) has investigatedthat ANNs are trained with a subset of the experimental work data for identified machine conditions.

Figure1.Conceptual model of vibration measuring system To implement a reliable vibration measuring system, a Micro-Electronic Electronic Mechanical System ((MEMS) sensor

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Condition monitoring of rotating shaft using virtual instrumentation

is mounted on rotating machinery. The motive of this work is to reduce hardware components (Such as an amplifier, filter, and analog to digital converter) and to developVirtual Instrumentation (VI) to monitor vibration levels of a rotating shaft .Therefore, an attempt has been made to develop the VI program to process the signals with respect to various rotating speeds of shaft, bend values and diameter of the shaft. Figure.1 shows a conceptual model of a vibration measuring system of rotating machinery. Analysis of vibration data is an important process to identify the vibration level of rotating shaft with different operating conditions.The objective of this study is to model the relevant data provided by the rotation of bent shaft vibration using the statistical technique and the neural networks, and then to compare the outputs of these two methodologies.

2 Experimental setup and conditions The test rig consists of a shaft supported in three ball bearings and driven by a DC motor of 1.5 HP (Figure 2).The experimental setup is composed of a DC electromotor with a voltage regulator to control speed, with a maximum speed of 2200 RPM.The signal is obtained in three directions but in this paper only lateral vibration signal is taken (i.e. along zaxis).LabVIEWprogramming has been developed to acquire the accelerometer signals and store them continuously frame by frame for monitoring the condition of the rotating shaft online at every stage. The data were analyzed using statistical methods in the time domain and amplitude in the frequency domain.

3 Signal processing and vibration analysis Techniques In order to understand the enhancing of signal characteristics and for efficient extraction of useful informationin this study, it is necessary to discuss themathematical details regarding feature extraction. The study focuses on feature extraction from the time and frequency domains. Vibration signals were measured from 0 to 10 kHz with a sampling rate of 25.6 kHz. Sample size was 2048 data points.

3.1 Time Domain features Statistical methods are usually used to examine the random characteristics of a physical system. It isimportant to sum up the data and to obtain meaningful and useful results. The time domain features are: Root Mean Square (RMS), crest factor, skewness and kurtosis. The choice fell on these features due to information gained from experimentation and the literature review [Doreswamy (2013)]. (a)Root mean square: RMS for discrete signals is a kind of averaging the signalsand is time series analysis feature to measure the power content in the vibration signalsand is defined as

RMS ∑ , x x

1

(b) The crest factor:It is defined as the ratio of the peak value to the RMS of a signal.In other words, it is equal to the peak amplitude of a waveform divided by the RMS value. !"# Crestfactor $%&'"# , (2)

Figure 2.Schematic diagram of experimental Set-up

P e a k = c re s t v a lu e =

1 2

(( x

tm a x

) − ( x tm in ) ) , (3) 557-2


Table 1 Design of experiments with input parameter for CCF S.NO 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Speed (mm) 950 400 400 1500 950 1500 950 950 400 1500 950 400 950 950 1500 1500 400 950 950 950

Diameter (mm) 10 12 10 12 10 12 10 10 8 8 8 8 10 10 10 8 12 12 10 10

Bend (mm) 2.75 1.5 2.75 4 2.75 1.5 2.75 1.5 1.5 1.5 2.75 4 2.75 2.75 2.75 4 4 2.75 2.75 4

FFT-Peak 2 in m/s 0.020979 0.003174 0.00849 0.028882 0.021868 0.017456 0.020693 0.014727 0.006506 0.029977 0.02931 0.009823 0.02082 0.020789 0.029453 0.039752 0.010156 0.019471 0.020281 0.022836

(c)Kurtosis:It is a statistical factorthat is sensitive to the signal shape and it is well adapted to the impulse nature of the simulating forces generated by damagecomponent. Kurtosis describes the nature of distribution,i.e., how peaked or flat the distribution is. But a vibration signal contains sharp peaks of higher value, and then its distribution function defined as sharper. The kurtosis measures the contribution of the distribution. It has high peak at the centre and has flatter tails. Kurtosis K +, ∑ - , (4) x x (d)Skewness: It shows the symmetry of the Probability Density Function (PDF) of the amplitude in a time series. A time series having equal number of large as well as small amplitude values has a skewness of zero. A time series having many small values and few large values is positively skewed (right tail).A time series having many large values and few small values is negatively skewed (left tail) [Giovanni B (2001)]. Skewness is the third statistical moment of a distribution, which is given by Skewness K +1 ∑ - , (5) x x 3.2 Frequency Domain features While certain information can easily be interpreted in the time domain, the detailed analysis of rotating machinery vibration data is frequently conducted in the frequency domain. Frequencydomain analysis of the vibration signal is the most widely used approach for detecting bearing defects. The high variability in successive measurements,

carried out in different motor operating conditions, suggests the use of a statistical approach for identifying the reference vibration spectrum model parameters. 4 Development of Mathematical Model 4.1 Response Surface Methodology RSM has widely been used in the field of industry and chemical engineering to study the yield or the output of a system as it varies with the variations in the level of one or more applied factors. In this work, a face centered design has been used because it requires only a few levels of the factors and in practice it is frequently difficult to change factor level. However, central composite face centered designs are not rotatable. The chosen levels of the selected independent variables with their units and notations are presented in Table 2. All the coefficients were obtained by applying central composite face centered design using the design expert statistical software. The number of experiments is estimated as N= 2f +2*f+nc where f=3, nc= 6.The number of experiments in a CCD matrix corresponding to three process variables is calculated as23 +2*3+6 =20 which are shown in Table 1. The response or dependent variables namely amplitude in frequency domainare measured for various shaft rotating conditions. A total number of 20 experiments were performed to include all combinations of the three independent parameters. After determining the significant coefficients at 95% confidence level, the final output model was developed using significant coefficientsand the final mathematical model to estimate amplitude in frequency domain is given by equation (6): Table 2Level of Independent variables Input Parameter Speed, S(rpm) Diameter, D(mm) Bend, B(mm)

Level -1 1500 12 1.5

0 950 10 2.75

1 400 8 4.0

Amplitude=(+0.048356)+(5.42250*10005*S)+(0.0139 18*D)+(8.81309*10003*B)+(2.31727*10006*S*D)+(1. 98218*10-006*S*B)+(2.65800*10004*D*B)+ (8.93749*10-009*S2)+(6.78852*10-004*D2)+ (6) (1.85190*10003*B2) The value of F-ratio in all cases is higher than standard tabulated value. The p-value of regression term less than 0.05 indicates that the model is statistically significant.This p-value less than 0.0001 showed that the developed quadratic model fits very well to the experimental results. The high values of R2 (i.e., R2= 0.99) provides the fraction of variability in a data set that is responsible for the statistical model.

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4.2 Neural Validation

Network

Training

and

The Artificial Neural Network (ANN) is a tool for data processing and consist of a large number of interrelated processing elements i.e. artificial neurons in a designmotivated by the structure of the cerebral cortex of brain (Tsoukalas and Uhrig, 1997). For the present work three different bent shafts of same diameter were used in test rig. For the bent shaft under consideration, the time domain was acquired and is stored in files. We present a framework based on statistical data extraction and Back Propagation Neural Network (BPNN) to find the dominant parameters and the best model for bend detection. There are three classes of vibration data produced, namely, Type A (i.e., bend value 1.5 mm), Type B (i.e., bend value 2.75 mm) and Type C (i.e., bend value 4 mm) having same diameter of shafts. In our experiments vibration data were collected, analysed for various levels of input parameters. Then, the data obtained is divided into 12 samples for each bend shaft at different speed. For this purpose, four parameters are extracted from each sample and are used as the input of each BPNN model. These four parameters are: root mean square, crest factor skewness, and kurtosis, and are frequently used in features of time domain because of their effectiveness to detect the condition of rotating shaft.

4.3 Data Normalization The data is to be normalized before sending for network training. During training of the neural network, a higher valued input variable tends to suppress the influence of the smaller value input. To eliminate this problem and in order to make neural networks perform better, the data must be well managed and scaled before giving as input to the ANN. The features data are normalized in the range 0.1 to 0.9 to minimize the effect of input variable. The range 0.1 and 0.9 is selected in place of zero and one because zero and one cannot be realized by the activation function (sigmoid function and tansig function). All the time domain features of bent shaft are normalized using the following equation.

 ( X i old − min value )   + 0.1 , (7) X i = 0.8 ×   ( max value − min value )  Where Xi old = actual data, max value and min value are the maximum and minimum value of the data respectively and Xi is the normalized data.

4.4 BPNN Model Development and Classification Accuracy Selecting the amount of hidden neurons is important to develop the architecture of different neural network models. It consists of the input layer, hidden layer and output layer. For training and testing purpose, the number of input layer used are forty eight

(12*4), choice of hidden layer and three output layers. The number of output neurons is constant. The difference between each model depends on the number of input neurons and hidden neurons. The size of input layer which is supposed to avoid under fitting and the number of neurons in a neural network model should not be large so that it is away from over fitting. Since the input layer has forty eight neurons(i.e., 12 different speeds and each speed having four statistical parameters (12*4)), therefore, the models with three input layer size also differ in hidden layer size. The next step is to generate target vector data set. The target vector is chosen to be same as the number of type of bend shaft i.e. three. The target vector of type A will have a value of 0.9 for its first element and all other elements will have a value of 0.1. Typical training vectors created after extracting the features from the time domain for three bent shaft values is shown in Table 3. Table 3 Target vector used for Back propagation Serial No. 1 2 3

Bent shaft value Type A Type B Type C

Node1

Target Node2

Node3

0.9 0.1 0.1

0.1 0.9 0.1

0.1 0.1 0.9

5. Results and Discussion The effects of bent shaft valuespeed of rotating shaft and diameter on the vibration response as shown in Figure.3 (a). From the experimental data, empirical/statistical model has been developed (equation 6). Effect of diameter of shaft has been analyzed and it shows that vibration level of the shaft decreases with increase in diameter, as shown in Figure 3(a).The effect of speed of shaft has been analyzed and it shows that vibration level of the shaft increases with increase in speed, as shown in Figure 3(b). At high speed, amplitude increases which means that the rate of vibration increases due to frequency of rotation causing the shaft to blow out or to go out of balance. The resulting centrifugal force will force the shaft to vibrate; it may well go into transverse oscillation which increases the amplitude of vibration in the time domain as well as frequency domain. An increase in bend value of shaft causes the relative amplitude to increase at the same frequency, as shown in Figure 3(c).

5.1 Comparison between model and ANN model

Regression

In order to test and validate the different models, three statistical tests between the estimated vibration features data using MLP and Regression werecarried outas shown in Table 4.

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6 Conclusion

Vibration amplitude, m/s2

Regression model Experimental result

0.03 0.025 0.02

A number of experiments have ave been carried out to investigate the effectiveness of the prediction of bent shaftt for taking condition monitoring of rotating shaft. Table 4Comparison Comparison between Neural Network and Regression Model

0.015 0.01 0.005 0 6

8

10

12

14

Neural network Regression model

SSE 3.4867e006 3.2722e004

MAE

MSE

4.2368e-004

4.9853e-007

0.0033

1.6361e-005

0.145

Regression model

0.14

Experimental result

0.035 0.03 0.025 0.02 0.015 0.01 0.005 0

SSE

Vibration Amplitude, m/s2

Diameter, mm (a)

0.13 0.125

NN

REG

(a) 0

1000

2000

0.145

Speed , rpm (b)

Experimental result

MAE

0.14

Regression model

0.135

0.025

0.13

0.02

0.125 NN

0.015 0.01

REG

(b)

0.005 0.145

0 0

2 4 Bend values (c)

6

Figure 3 (a),(b) and (c) Show changes in vibration amplitude with respect to input parameter Sum of Square Error (SSE) value close to zero indicates that the model has a smaller random error component and the fit will be more useful for prediction of response. This statistics is also known as the fit standard error or the standard error of regression. The obtained value of MAE ((Mean absolute error) is 0.0033 for regression model is higher than the NN that is 4.2368e-004, 004, as shown in Figure 4. A lowerr value of MAE is to measure a highly accurate prediction. On the basis of Mean Square Error (MSE) criterion of network training, although hough the MSE for learning set reduces with the time of learning but predictive ability of the network shows a parabolic dependence.

0.14 MSE

Vibration Amplitude, m/s2

0.135

0.135 0.13 0.125 NN (c)

REG

Figure 4(a) SSE,(b) MAE and (c)MSE of NN and Statistical Method Experiments have been carried out at various levels of bend, diameter and speed of the shaft has been varied from 400 rpm to 1500 rpm. The vibration signals obtained from an accelerometer sensor were also measured and analyzed for comparison purpose. ANN performs well as compared to statistical regression method for prediction of response because the error in

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NN is very less than the Statistical method. Therefore, the neural network prediction is better than the statistical technique.It has been shown that using time domain features not only leads to lower computational burden but also results in more accurate detection of level of bent shaft. In other words, as compared to existing methods, proposed scheme is simple, accurate, reliable and economical. In fact, the ANN provides better results in known prediction. Neural network model and regression model are developed and results are validated with experimental results. Relative amplitude of spectrum increases with speed of rotating shaft and bend value, and decreases with varying diameter of theshaft.A bend shaft with a high level together with a low level diameter(less mass) leads to more vibration in rotating shaft and viceversa. To obtain least vibration condition, it is important to control bend at a low level by setting low level of speed to reduce vibration of a rotating shaft.This methodology is also used for condition monitoring of cutting tools. A bent shaft condition monitoring approach based on a hierarchical algorithm was also tested, and the results obtained demonstrate that it is worth implementing on a factory floor in many applications.

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