Engineering Failure Analysis 30 (2013) 17–26
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Failure detection and optimization of sugar mill boiler using FMEA and Taguchi method A. Mariajayaprakash a,⇑, T. Senthilvelan b a b
Department of Mechanical Engineering, Rajiv Gandhi College of Engineering and Technology, Puducherry, India Department of Mechanical Engineering, Pondicherry Engineering College, Puducherry, India
a r t i c l e
i n f o
Article history: Received 27 June 2012 Accepted 11 December 2012 Available online 4 January 2013 Keywords: Boilers FMEA Cause and effect diagram Taguchi method
a b s t r a c t Sugar industry plays an important role in economic development of country. Cogeneration is an important source of income for sugar industries. Boiler is one of the essential components used in cogeneration process. Unscheduled boiler outages in sugar mills are major problem resulting loss of production. The boiler may be failed due to number of reasons; some of the reasons such as mechanical failure, electrical failure and temperature sensors failure. This paper describes the failures of the fuel feeding system frequently occurred in the cogeneration boiler and gives the solution to rectify these failures by using three important tools, namely, cause and effect diagram, Failure Mode and Effect Analysis and Taguchi method. Ó 2012 Elsevier Ltd. All rights reserved.
1. Introduction India faces a peak electric generating shortage of over 20% and an energy shortage of 12% [1]. One of the methods of savings energy in sugar mills is cogeneration [2]. India is the world’s largest producer of sugar. Boilers are used in various fields such as garment industries, steam power plants, paper industries, and sugar mills. In sugar mills not only sugar is manufactured but also electricity is produced by cogeneration [3–5]. Boiler is the essential component in the sugar mills. This research work is carried out one of the sugar industry located in Tamil Nadu. The cogeneration option is adopted in the above mentioned sugar mill. The major problem occurred in this sugar mill is frequent failure of boiler, which leads loss of production. The main objective of this research work is to identify the failures which are frequently occurred in the boiler and to minimize those failures. Three tools are employed to minimize the failures. The first tool name is cause and effect diagram or Ishikawa diagram. It helps to bring out all the parameters, which leads to failures of boiler. Then, the most significant parameters to cause the boiler failures are identified by using Failure Mode and Effect Analysis (FMEA). Finally, Taguchi method is employed to optimize the parameters thereby failures are minimized. 2. Boiler Boiler is a closed vessel in which water or fluid is heated under pressure. The steam or hot fluid is then circulated out of the boiler for use in various processes [6]. The boiler used in this research work is vertical, water tube, top supported, high pressure boiler. The working pressure, generating capacity and steam temperature of this boiler are 66 kg/cm2, 75 tonnes/h, and 485 °C respectively. The main components used in cogeneration boiler are fuel feeding system, furnace, super heater, attemperator, economizer, air preheator, FD fan, ID fan and dust collector [7]. The fuel feeding system consists of storage ⇑ Corresponding author. Tel.: +91 9786022257. E-mail address:
[email protected] (A. Mariajayaprakash). 1350-6307/$ - see front matter Ó 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.engfailanal.2012.12.010
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Nomenclature r yi DOE
number of tests in a trial response value of observation in the ith test design of experiments m degrees of freedom mA degrees of freedom for factor A kA number of levels for factor A mrequired total degrees of freedom required mLN total degrees of freedom of the available orthogonal array N number of trials ANOVA analysis of variance M overall mean percentage defects VFactor variance of factor SS sum of square expected amount of variation V0 P percent contribution l mean a level of risk me degrees of freedom for the error Ve error variance F(a, 1, me) F ratio required at the level of risk geff effective number of replications n total number of experiments CI confidence interval T average values of defects at different levels
bunker(silo), drum feeder and screw conveyor. The drum feeder and screw conveyor combination is intended to feed controlled and varied quantity of fuel to the boiler from the storage bunker. The drum feeder extracts bagasse from the storage bunker and the quantity extracted is proportional to the speed of rotation of the drum. The extracted bagasse is fed to the screw conveyor which transports the same longitudinally and feeds into the chute connecting the conveyor and pneumatic distributor. There are four such assemblies per boiler. In the above mentioned cogeneration boiler, failures were frequently being occurred in the fuel feeding system. Hence the important components and fuel are described. 2.1. Fuel The fuel that is used in the boiler are bagasse (B), which is a byproduct of the sugar extraction process, palm boom (P) and cane trash (C). The short fall of bagasse is a problem during the process. Hence, we have to look for other fuels such as palm boom, wood chips, cane trash etc. This boiler is designed to burn bagasse, palm boom, cane trash or a mixture [3,8]. 2.2. Drum feeder The drum feeder is designed to withstand the vertical load due to the fuel column in the storage bunker. The drum is located eccentrically in the casing of the feeder shroud plates are provided on top of the drum feeder at the fuel entry location from the storage bunker, to avoid entrapment of fine fuel dust and fibers between the drum and casing which can cause jamming and over loading during inspection. Spikes are welded to the drum and are arranged in an inclined axis to the centre line of rotation of the feeder, to ensure efficient extraction of fuel. The drum feeder extracts fuel from the storage bunker and the quantity extracted is proportional to the speed of rotation of the drum. The drum feeder is driven by variable speed drive which is connected to combustion control of the boiler. The drum feeder capacity is 9500 kg/h. 2.3. Screw conveyor The trough of the screw conveyor is of carbon steel and the bottom portion of the trough is lined with stainless steel plates to ensure smooth flow of fuel to the distributed chute. The lining is plug welded on to casing. Quick opening access doors on the casing are provided to enable access for cleaning the trough. The carbon steel screw has a toothed profile which gives positive conveying of the fibrous material. The flights of the screw are terminated at the outlet end to make sure that there is no packing of bagasse at the discharge end. The shaft ends screw conveyor is located in antifriction bearings at both
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Fig. 1. Cause and effect diagram.
ends. The shaft of the screw conveyor is directly driven by constant speed geared motor. The geared motor is coupled to the screw conveyor shaft through a tyre coupling. The drive is located in a common base frame. The screw conveyor capacity is 12,000 kg/h. 3. Cause and effect diagram A cause and effect diagram is a graphical tool to display the causes of any quality problem. It is also named as Ishikawa diagram or fishbone diagram (due to their resemblance) [9]. An Ishikawa or cause and effect diagram is constructed to identify all the parameters that may affect the operation of drum and is shown in Fig. 1 [10,11]. The identified process parameters are (i) fuel parameters – type, size, foreign material and moisture, (ii) motor parameters – current, relay contact, improper rotation and oil level in gear box, (iii) storage bunker parameters – fuel level and door not properly closed, (iv) operator – maintenance improper, mal function and fatigue. Among the above, the most significant parameters are selected by using the tool Failure Mode and Effect Analysis (FMEA). 4. Failure Mode and Effect Analysis (FMEA) After constructing Ishikawa diagram, it is highly advisable to perform Failure Mode Effect Analysis [12]. Failure Mode and Effect Analysis is an important quality tool used in the manufacturing and other industries to improve the product quality and productivity [13,14]. It is a systematic procedure to identify the potential failure modes, and their causes and effects. In this research work, FMEA is applied to analyze the failures occurred in cogeneration process and is used to find out the most significant parameters affecting that process. It can also be used to asses and optimize maintenance plans. FMEA is usually carried out by a team of experienced and skilled Engineers and expert’s knowledge. The failure modes are identified and ranked with help of Risk Priority Number (RPN). RPN is the product of occurrence (O), severity (S) and detection (D) of failures. That is, RPN = O S D [15]. Each factor is rated on a scale 1–10. Generally, the failure mode having higher RPN will be given more important [16]. On the basis of higher RPN, the most significant parameters affected the drum failure are fuel type, fuel moisture, motor load and silo level. 5. Taguchi’s method There are various methods used for improving the quality in variety of industries. Taguchi method is one of the best optimization technique to achieve high quality without increasing cost. It is a simple, systematic and powerful method to increase the quality [17,18]. The advantage of this method is to reduce both product cost and number of experiments required [19]. Mathematical and statistical techniques are combined in Taguchi method. In this research work, Taguchi’s method is used for improving the quality by reducing the failures in the fuel feeding system during the cogeneration process in the sugar industry. Two important tools employed in Taguchi’s method are signal to noise ratio (S/N ratio) and orthogonal arrays (OA) [20].
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5.1. Factors and levels In Taguchi method, first, significant process parameters and their levels are selected. The ranges of these parameters were selected on the basis of preliminary experiments conducted by using one variable at a time approach [10]. In this research work, four control factors and three levels are chosen for analyzing the drum failure. The most significant process parameters considered as control factors and their levels are shown in Table 1. 5.2. Selection of orthogonal array (OA) In Taguchi method, experimental analysis is based on orthogonal array. Orthogonal array is used to minimize the number of experiments, by which quality characteristics are examined [21]. The appropriate OA is selected on the basis of total degrees of freedom required [22]. By using number of factors, number of levels of each factor and number of interactions DOF is determined. In this research work, the interaction effect between the process parameters is not considered. The degree of freedom for three levels is 2 (DOF = number of levels 1). The required total DOF for four factors and three levels is 8(4 (3 1) = 8).In Taguchi method, the total DOF of selected OA must be greater than or equal to the total DOF required for the experiment. Hence L9 OA having eight DOF is selected in this research work. The Table 2 shows L9 OA which has four columns and nine experiment runs. 5.3. Signal to noise ratio In Taguchi method, signal to noise ratio (S/N ratio) is employed to analysis the quality characteristics of the product or process parameters. It is also called as statistical measure of performance [18]. It is the ratio of the mean (signal) to the standard deviation (Noise). Regardless of the category of the quality characteristic, process parameter settings with the highest S/ N ratio always yield the optimum quality with minimum variance [23].The following three types of S/N ratios are considered to be standard and are widely applied in Taguchi method [24]. (1) Smaller is better n . X :y2i
g ¼ 10 log 1 n
i¼1
(2) nominal is the best n . X :l2 =r2
g ¼ 10 log 1 n
i¼1
(3) higher is better n . X :1=y2i
g ¼ 10 log 1 n
i¼1
Table 1 Process parameters with their ranges and values at three levels. Parameter designation
Process parameters
Range
Level 1
Level 2
Level 3
A B C D
Fuel type Fuel moisture in % Motor load in amps Silo level in %
3 Types 49–51 11.5–40 50–100
B 49 11.5 50
B+C 50 25 75
B+C+P 51 40 100
Table 2 L9 orthogonal array. RUN
A
B
C
D
1 2 3 4 5 6 7 8 9
1 1 1 2 2 2 3 3 3
1 2 3 1 2 3 1 2 3
1 2 3 2 3 1 3 1 2
1 2 3 3 1 2 2 3 1
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g is the S/N ratio, yi is the value of quality characteristic at ith setting, l is the mean, n is the total number of trial runs at ith setting, and r is the standard deviation. In this research work, smaller is better S/N ratio is selected to minimize the drum failures occurred during the cogeneration process. 5.4. Methodology In this research work, observations were made in one of the leading sugar factory located in Tamil Nadu. Cogeneration is adopted in the sugar mill. During the cogeneration process, it was found that the plant was frequently affected by failure of drum and screw conveyor, which are the components of fuel feeding system. The significant process parameters affecting the drum failure were recorded in Table 1. The parameters at different levels are assigned in the selected orthogonal array and that is shown in Table 3. The cogeneration plant was run on the basis of Table 3. It has nine trials. The plant was run three times for the same set of parameters given in Table 3. Single randomization technique is employed. The observations were made for every 1 h. The number of failures were noted and tabulated in Table 4. The percentage of failures has been calculated by the formula given below:
Percentage of drum failure ¼
No: of drum failures Total no: of failures
Since the failures are lower the better type of quality characteristics the S/N ratio were calculated for each of nine trials and the values are tabulated in Table 5. For example, for trial no. 1, the S/N ratio is:
S=N ratio ¼ 10 log½1=3ð2:282 þ 1:952 þ 2:612 Þ ¼ 7:22 The mean response refers to the average value of the performance characteristic for each parameter at different levels. The average values of the casting defects and S/N ratios for each parameter at different levels are calculated and are given in Table 6. The average values of casting defects and S/N ratios for each parameter at different levels are plotted in Figs. 2 and 3, respectively. 5.5. Analysis of variance (ANOVA) The purpose of ANOVA is to investigate which process parameters significantly affect the quality characteristic [25]. The total variation may be decomposed into many components. In this paper, the total variation present in the process is decomposed to the following components: (1) (2) (3) (4) (5)
Variation Variation Variation Variation Variation
due due due due due
to to to to to
factor factor factor factor error.
A. B. C. D.
The total variation is calculated using the values given in Table 4.
Total variation SST ¼ SSA þ SSB þ SSC þ SSD þ SSe Variation due to error SSe ¼ SST ðSSA þ SSB þ SSC þ SSD Þ
Table 3 Experimental L9 array. Trial no.
A Fuel type
B Fuel moisture in %
C Motor load in amps
D Silo level in %
1 2 3 4 5 6 7 8 9
B B B B+C B+C B+C B+C+P B+C+P B+C+P
49 50 51 49 50 51 49 50 51
11.5 25 40 25 40 11.5 40 11.5 25
50 75 100 100 50 75 75 100 50
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No. of failures
1 2 3 4 5 6 7 8 9
1
2
3
7 10 8 12 16 13 13 12 15
6 9 9 10 15 10 12 14 14
8 10 8 13 16 11 11 12 13
Table 5 Percentage of failure values and signal to noise (S/N) ratios against trail numbers. Run
% of failures
1 2 3 4 5 6 7 8 9
1
2
3
2.28 3.26 2.61 3.91 5.21 4.23 4.23 3.91 4.89
1.95 2.93 2.93 3.26 4.89 3.26 3.91 4.56 4.56
2.61 3.26 2.61 4.23 5.21 3.58 3.58 3.91 4.23
Mean
S/N ratio
2.28 3.15 2.72 3.80 5.10 3.69 3.91 4.13 4.56
7.22 9.98 8.69 11.64 14.16 11.39 11.86 12.34 13.19
Table 6 Average values of drum failures and S/N ratios at different levels.
A B C D
Level 1
Level 2
Level 3
Drum failure
S/N ratios
Drum failure
S/N ratios
Drum failure
S/N ratios
2.71 3.33 3.37 3.98
8.63 10.24 10.32 11.52
4.20 4.13 3.83 3.58
12.40 12.16 11.61 11.07
4.20 3.66 3.90 3.55
12.46 11.09 11.57 10.89
4.4 4.2
% OF DEFECTS
Factors
4.0 3.8 3.6 3.4 3.2 3.0 2.8 2.6 A1 A2 A3 B1 B2 B3 C1 C2 C3 D1 D2 D3
Fig. 2. Average values of drum failures for each parameters at different levels.
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-8.5 -9.0
S/N Ratio
-9.5 -10.0 -10.5 -11.0 -11.5 -12.0 -12.5 -13.0
A1 A2 A3 B1 B2 B3 C1 C2 C3 D1 D2 D3
Fig. 3. Average values of S/N ratios for each parameter at different levels.
Table 7 ANOVA for drum failures and S/N ratios. Source
Sum of squares
Degrees of freedom
Variance
F ratio
Drum failure
S/N ratio
Drum failure
S/N ratio
Drum failure
S/N ratio
Drum failure
S/N ratio
A B C D Error
13.21 2.88 1.56 1.05 2.12
28.86 5.50 3.20 0.60 0.11
2 2 2 2 18
2 2 2
6.61 1.44 0.78 0.52 0.12
14.43 2.75 1.60
56.06 12.24 6.64 4.44 1.00
257.98 49.19 28.61
Total
20.83
38.28
26
8
Total degrees of freedom
2
0.06
1.00
4.78
mT ¼ ðmA þ mB þ mC þ mD þ me Þ me ¼ mT ðmA þ mB þ mC þ mD Þ ¼ 26 ð2 þ 2 þ 2 þ 2Þ ¼ 18
The results of analysis of variance (ANOVA) are shown in Table 7. In Table 7, it is clear that the parameters A, B, C and D significantly affect both mean and variation in the drum failures. 6. Results and discussion 6.1. Percent contribution Percent contribution is the function of the sum of squares of each significant item. Percent contribution to the total sum of square can be used to evaluate the importance of a change in the process parameter on these quality characteristics [26,27]. It is calculated using the formulae given below:
Percent contributionðPÞ ¼ ðSS0A =SST Þ 100 SS0A ¼ SSA ðmeÞðmA Þ V A ¼ V 0A þ V error where V 0A is the expected amount of variation due solely to factor A given below:
V A ¼ SSA=mA V 0A ¼ SS0A=mA The percentage of contribution of fuel type, fuel moisture and motor load is shown in Table 8 and Table 9. 6.2. Estimating the mean From Table 8, it is clear that factor D has the least effect on the quality characteristic. The estimation of mean for drum failure is calculated by the following equation [28]:
l ¼ T þ ðA1 TÞ þ ðB1 TÞ þ ðC1 TÞ þ ðD3 TÞ
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Table 8 ANOVA drum failures, including percent contribution. Source
Sum of squares
Degrees of freedom
Variance
F ratio
Expected SS0
Percent contribution (P)
A B C D Error (pooled)
13.21 2.88 1.56 1.05 2.12
2 2 2 2 18
6.61 1.44 0.78 0.52 0.12
56.06 12.24 6.64 4.44 1.00
12.43 2.71 1.47 0.98 3.22
59.70 13.03 7.07 4.72 15.47
Total
20.83
26
20.83
100.00
Table 9 S/N ratios drum failures, including percent contribution. Source
Sum of squares
Degrees of freedom
Variance
F ratio
Expected SS0
Percent contribution (P)
A B C D Error (pooled)
28.86 5.50 3.20 0.60 0.11
2 2 2
14.43 2.75 1.60
257.98 49.19 28.61
2
0.06
1.00
28.05 5.35 3.11 0.60 1.16
73.29 13.98 8.13 1.57 3.04
Total
38.28
8
4.78
38.28
100.00
where T is the average values of drum failure at different levels. The mean for a selected trial condition for parameters at (A1, B1, C1, D3) is 0.49. 6.3. Confidence Interval around the estimated mean An important step in Taguchi’s optimization technique is to conduct confirmation experiments for validating the predicted values. Thus a 95% confidence interval (CI) for the predicted mean of optimum quality characteristic on a confirmation test is estimated using the following two equations [29].
CI3 ¼ ½F ða; 1; meÞVeð1=geff þ 1=rÞ1=2
geff ¼ N=ð1 þ total DOF associated in the estimate of meanÞ where a is the level of risk, Ve is the error variance, me is the degrees of freedom for the error, geff is the effective number of replications and r is number of test trials. Using the values in Table 5, the CI was calculated as follows:
geff ¼ N=1 þ ðtotal DOF associated in the estimate of meanÞ ¼ 27=ð1 þ 2Þ ¼ 9 a ¼ 1 confidence limits ð95%Þ ¼ 0:05 F ratio ¼ ð1; 0:05; 18Þ ¼ 4:41ðtabulatedÞ Confidence Interval CI3 = ±1.86. The 95% confidence interval of the predicted optimum of the drum failure during the process is: 1.37 < 1.86 < 2.35. 6.4. Confirmation experiments Three confirmation experiments were conducted at the optimum setting of the process parameters. The fuel type, was set at the first level (A1), fuel moisture was at the first level (B1), motor load was at the first level (C1) and silo level was kept at the third level (D3). The average of the respondents failures in each experiment is found to be 0.65%; the result was within the CI of the protected optimum of the drum failures. The confirming experiments results gave 0.65% < 2.35% (maximum of CI). Therefore, the selected parameters as well as their appropriate levels are significant enough to obtain the desired result. 7. Result of the analysis The Boiler drum failure observations were made during the cogeneration process, ANOVA was carried out using the results of observations and then the interpretation methods were used to obtain the percent contribution of each parameter and optimum levels of each parameter:
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Table 10 Optimum parameters under economic considerations. Parameter designation
Parameter
Optimum levels
Optimum value
A B C D
Fuel type Fuel moisture in % Motor load in amps Silo level in %
1 1 1 3
B 49 11.5 100
(1) The percent contribution of each parameter to the variation of drum failure and optimum parameter (under economic condition) are shown in Table 8. (2) The optimum levels of various parameters for minimum drum failure of boiler were shown in Table 10. (3) The predicted range of optimum painting defects is 1.37 < 1.86 < 2.35. 8. Conclusion In this study, Taguchi method has been employed for optimizing the process parameters of drum feeder of boiler and the following conclusions are drawn: It is proved that, the quality of drum feeder during the process is improved by Taguchi’s method at the lowest possible cost. Ishikawa diagram or cause and effect diagram is very effective to sort out all the possible causes affecting the quality of drum feeder during the process. The most significant parameters affecting the quality of the drum feeder during the process are identified by using the FMEA tool. The parameter fuel is significantly affect the quality of drum feeder during the process. The optimum levels of fuel type, fuel moisture, motor load and silo level are estimated. The predicted range of optimum of the drum failure during the process is 1.37 < 1.86 < 2.35.
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