Mathematical Problems in Engineering

Volume 2013, Article ID 824706, 10 pages

http://dx.doi.org/10.1155/2013/824706

## A New Similarity Measure of Generalized Trapezoidal Fuzzy Numbers and Its Application on Rotor Fault Diagnosis

Department of Automation, China University of Petroleum-Beijing (CUPB), Beijing 102249, China

Received 8 January 2013; Accepted 24 January 2013

Academic Editor: Valentina E. Balas

Copyright © 2013 Xin Zuo et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### Abstract

Fault diagnosis technology plays a vital role in the variety of critical engineering applications. Fuzzy approach is widely employed to cope with decision-making problems because it is in the simplest and most used form. This paper proposed a new similarity measure of generalized trapezoidal fuzzy numbers used for fault diagnosis. The presented similarity measure combines concepts of the geometric distance, the center of gravity point, the perimeter, and the area of the generalized trapezoidal fuzzy numbers for calculating the degree of similarity between generalized trapezoidal fuzzy numbers. This method is proposed to deal with both standardized and nonstandardized generalized trapezoidal fuzzy numbers. Some properties of the proposed similarity measure have been proved, and 12 sets of generalized fuzzy numbers have been used to compare the calculation results of the proposed similarity measures with the existing similarity measures. Comparison results indicate that the proposed similarity measure can overcome the drawbacks of existing similarity measures. Finally, a fault diagnosis experiment is carried out in laboratory based on multifunctional flexible rotor experiment bench. Experimental results demonstrate that the proposed similarity measure is more effective than other methods in terms of rotor fault diagnosis.

#### 1. Introduction

Fault diagnosis technology has gained attention from researchers for its various applications [1–3]. To date, a large number of valuable approaches have been proposed for dealing with fault analysis issues, such as fuzzy theories [4, 5], expert system [6], wavelet analysis [7, 8], data fusion [9, 10], and neural network [11, 12]. Particularly, fuzzy approach is most successfully applied in fault diagnosis because it is in the simplest and most used form [13]. Using the fuzzy theories during the decision-making process, it is necessary to develop suitable technology to measure fuzzy similarity. Ranking the fuzzy numbers plays an important role in decision-making uncertain environment. Many methods [4, 14–20] have been presented for handing fuzzy multiple attributes decision-making problems. But they are only designed for standardized fuzzy numbers, that is, and . However, nonstandardized fuzzy numbers are very common in reality. Wen et al. [21] proposed a new method for ranking nonstandardized fuzzy numbers, that is, and . The method combines the concepts of exponential distance, the perimeter, and the area of the generalized trapezoidal fuzzy numbers for calculating the degree of similarity between nonstandardized generalized trapezoidal fuzzy numbers. But we find that the result of Wen and Zhou’s method is unreasonable. Even though Wen and Zhou use exponential distance to solve the unreasonable negative result caused by geometric distance, the problem caused by exponential distance is obvious: when , we always obtain unreasonable result though coincidence of the area is large and the perimeter and area of generalized trapezoidal fuzzy numbers are almost same.

Based on Wen and Zhou’s method, this paper proposed a new similarity measure combines concepts of the geometric distance, the center of gravity point, the perimeter and the area of generalized trapezoidal fuzzy numbers for calculating the degree of similarity between generalized trapezoidal fuzzy numbers. Some properties of the proposed similarity measure have been proved, and 12 sets of generalized fuzzy numbers have been used to compare the calculation results of the proposed similarity measures with the existing similarity measures. Comparison results indicate that the proposed similarity measure can overcome the drawbacks of the existing similarity measures. Finally, a fault diagnosis experiment is carried out in laboratory based on multifunctional flexible rotor experiment bench to demonstrate the proposed similarity measure. Based on the proposed similarity measure, we present a new fuzzy fault diagnosis algorithm for dealing with rotor fault diagnosis problem, where the values of the evaluating items are represented by generalized trapezoidal fuzzy numbers. The proposed method provides us a useful way for handling fault diagnosis problem, and experimental results demonstrate that the proposed similarity measure is more effective than other methods in terms of rotor fault diagnosis.

The rest of this paper is organized as follows. In Section 2, we briefly review basic concepts of generalized fuzzy numbers. In Section 3, we briefly review some existing similarity measures of fuzzy numbers. In Section 4, we present a new similarity measure between nonstandardized generalized trapezoidal fuzzy numbers. In Section 5, we make a comparison of the calculation results of the proposed similarity measure with existing similarity measures. In Section 6, we apply the proposed similarity measure to deal with rotor fault diagnosis problem. The conclusions are discussed in Section 7.

#### 2. Basic Concepts of Generalized Fuzzy Numbers

In this section, we review basic concepts of generalized fuzzy numbers from Chen [22]. Chen represented generalized trapezoidal fuzzy number , where , , and denote real values, and , as shown in Figure 1. The generalized fuzzy number is a fuzzy subset of the real line . The membership function of a generalized fuzzy number satisfies the following conditions:(1) is a continuous mapping from the universe of discourse to the closed interval in ,(2), where ,(3) is monotone increasing in [],(4), where ,(5) is monotone decreasing in [],(6), where .

If , then the generalized fuzzy number is a normal trapezoidal fuzzy number, denoted as . If and , then the generalized fuzzy number is called a crisp interval. If , then is normal triangular fuzzy number. If , then is a generalized trapezoidal fuzzy number. If and , then is a real number.

#### 3. Existing Similarity Measure between Fuzzy Numbers

In this section, some works have to be done to review some existing similarity measures between fuzzy numbers from Chen [14], Lee [15], S.-J. Chen and S.-M. Chen [4], Wei and Chen [20], and Wen et al. [21]. Consider two trapezoidal fuzzy numbers and , where and .

##### 3.1. Chen’s Similarity Measures between Fuzzy Numbers

Chen [14] presented a distance-based similarity measure between two trapezoidal fuzzy numbers and , where and . The degree of similarity between and is calculated as follows: where , if and are triangular fuzzy numbers, where and . The degree of similarity between and is calculated as follows: The larger the value of , the more similarity between the fuzzy numbers and .

##### 3.2. Lee’s Similarity Measures between Fuzzy Numbers

Lee [15] presented a similarity measure between two trapezoidal fuzzy numbers and , where and . The degree of similarity between and is calculated as follows: where

##### 3.3. Chen and Chen’s Similarity Measures between Fuzzy Numbers

Chen’s and Lee’s similarity measures cannot deal with generalized fuzzy numbers. In order to solve this problem, S.-J. Chen and S.-M. Chen [4] presented a similarity measure between two generalized trapezoidal fuzzy numbers and by using the simple center of gravity method (SCGM). The degree of similarity between and is calculated as follows: where , and is defined as follows: where and are defined as follows: Then, the center of gravity (COG) point () and () of generalized trapezoidal fuzzy numbers and can be calculated as follows: The values of and can be calculated as the same.

##### 3.4. Wei and Chen’s Similarity Measures between Fuzzy Numbers

Wei and Chen [20] presented a new method by combining concepts of the geometric distance, the perimeter, and the height of generalized fuzzy numbers to calculate the degree of similarity between and , where , , , and . The degree of similarity between and is calculated as follows: where , and the perimeters and are defined as follows:

##### 3.5. Wen and Zhou’s Similarity Measures between Fuzzy Numbers

All of former four similarity measures are only designed for standardized fuzzy numbers, that is, and . They cannot be used to calculate the degree of similarity between nonstandardized fuzzy numbers (i.e., and ). This is because they all use geometric distance to measure the degree of similarity , which may lead to unreasonable negative result easily, that is, . However, nonstandardized fuzzy numbers are very common in reality. Therefore, it is necessary to put forward a new similarity measure. In order to deal with both standardized and nonstandardized fuzzy numbers, Wen et al. [21] put forward a new method combines concepts of the exponential distance, the perimeter, and the area of the generalized trapezoidal fuzzy numbers for calculating the degree of similarity between and , where , , , and . The degree of similarity between and is calculated as follows: where , the center of gravity point () is calculated as (8), and the perimeters and the area of generalized trapezoidal fuzzy number are calculated as follows: The center of gravity (COG) point (), the perimeter , and the area of generalized trapezoidal fuzzy number can be calculated as the same.

Even though Wen and Zhou use exponential distance to solve the unreasonable negative result caused by geometric distance , the problem caused by exponential distance is obvious: the effect of exponential distance on is more than the perimeter and the area of the generalized trapezoidal fuzzy numbers. When , we always obtain the unreasonable result , though the coincidence of the area is large and the perimeter and area of generalized trapezoidal fuzzy numbers are almost the same. Therefore, it is necessary to put forward a new similarity measure to solve the existing defects of Wen and Zhou’s similarity measure.

#### 4. A New Similarity Measure between Generalized Trapezoidal Fuzzy Numbers

In this section, a new method is presented to calculate the degree of similarity between generalized fuzzy numbers based on Wen and Zhou’s method. The proposed method combines concepts of the geometric distance, the center of gravity point, the perimeter, and the area of the generalized trapezoidal fuzzy numbers for calculating the degree of similarity between and , where , , , , , and . The proposed method not only can deal with standardized fuzzy numbers but also can deal with nonstandardized fuzzy numbers. In order to deal with nonstandardized fuzzy numbers, normalization process is used to transform it into standardized fuzzy numbers which helps to avoid obtaining unreasonable negative result . Normalization process is shown as follows: Then, the new standardized fuzzy number can be got, where and . The new standardized fuzzy number can be got in the same way, where and . The degree of similarity between and is calculated as follows: The normalization process is unworkable when the generalized trapezoidal fuzzy numbers and are real numbers at the same time, where , , , , , and . In order to deal with this problem, the geometric distance should be transformed into exponential distance. The degree of similarity between and is calculated as follows: where , and the larger the value of , the more similarity between the fuzzy numbers and .

##### 4.1. Properties of the New Similarity Measure

We can verify the following properties of the new similarity measure.

*Property 1. *.

*Proof. *When the generalized trapezoidal fuzzy numbers and are not real numbers at the same time, and because, , and , we can obtain .

When the generalized trapezoidal fuzzy numbers and are real numbers at the same time, and because , and , we can obtain .

The proof is completed.

*Property 2. *Two generalized trapezoidal fuzzy numbers and are identical if and only if .

*Proof. *When the generalized trapezoidal fuzzy numbers and are not real numbers at the same time, if and are identical, then , , , and . Thus, , , , , , , and . The degree of similarity between and is calculated as follows:
When the generalized trapezoidal fuzzy numbers and are real numbers at the same time, if and are identical, thus, , , , and . The degree of similarity between and is calculated as follows:
When the generalized trapezoidal fuzzy numbers and are not real numbers at the same time, if , then
It implies that , , , , , , and . Therefore, , and , the generalized trapezoidal fuzzy numbers and are identical.

When the generalized trapezoidal fuzzy numbers and are real numbers at the same time, if , then
It implies that , , , , , , and . Therefore, , and , the generalized trapezoidal fuzzy numbers and are identical.

The proof is completed.

*Property 3. *.

*Proof. *When the generalized trapezoidal fuzzy numbers and are not real numbers at the same time,
Because , based on (12), we can obtain that , , , , and . Therefore, .

When the generalized trapezoidal fuzzy numbers and are real numbers at the same time,
Because , based on (12), we can obtain that , , , , and . Therefore, .

The proof is completed.

#### 5. The Comparison of the Similarity Measures

In this section, the proposed similarity measure is compared with five existing methods (Chen’s method, Lee’s method, Chen and Chen’s method, Wei and Chen’s method, and Wen and Zhou’s method) using 12 sets of generalized fuzzy numbers shown in Figure 2. Set 1 to set 8 are 8 sets of standardized generalized fuzzy numbers, and set 9 to set 12 are 4 sets of nonstandardized generalized fuzzy numbers. Table 1 compares the calculation results of all six similarity measures.

Some drawbacks of the existing similarity measures are shown in Table 1, which are described as follows.(1)Set 1 and set 2 of Figure 2 are clearly two different sets of generalized fuzzy numbers. The fuzzy numbers and in set 2 are more similar than set 1. However, according to Table 1, Chen’s method obtains the same degree of similarity for set 1 and set 2, which is an incorrect result.(2)Set 3 in Figure 2 clearly indicates that the generalized fuzzy numbers and are not the same, that is, . However, according to Table 1, Chen’s Method and Lee’s method obtain the incorrect results, that is, .(3)Set 4 and set 5 of Figure 2 are clearly two different sets of generalized fuzzy numbers. The fuzzy numbers and in set 5 are more similar than set 4. However, according to Table 1, Chen’s method and Chen and Chen’s method obtain the same degree of similarity for set 4 and set 5, which are incorrect results. Furthermore, Lee’s method obtains an incorrect result, in which the degree of similarity for set 4 is higher than that of set 5.(4)Set 6 in Figure 2 clearly indicates that the generalized fuzzy numbers and are not the same, that is, . However, according to Table 1, Chen’s method and Lee’s method obtain the incorrect results, that is, .(5)Set 4 and set 5 of Figure 2 are clearly two different sets of generalized fuzzy numbers. The fuzzy numbers and in set 5 are more similar than set 4. However, according to Table 1, Chen and Chen’s method obtains an incorrect result, in which the degree of similarity for set 7 is higher than that of set 6.(6)Set 8 in Figure 2 clearly indicates that the degree of similarity between two real values cannot be calculated by Lee’s method because the denominator , which results in the incorrect result . Furthermore, set 8 in Figure 2 clearly indicates that the generalized fuzzy numbers and are not the same, that is, . However, Chen’s method obtains the incorrect results, that is, .(7)Set 9 in Figure 2 clearly indicates that Chen’s method, Chen and Chen’s method and Wei and Chen’s method cannot deal with nonstandardized fuzzy numbers, which all give the incorrect result, that is, . Moreover, set 9 has the same degree of similarity as set 2, because the geometric distance, the center of gravity point, the perimeter, and the area of the generalized trapezoidal fuzzy numbers of the generalized trapezoidal fuzzy numbers in set 9 should be equal to those in set 2 after normalization process. However, Wen and Zhou’s Method obtains an incorrect result, in which the degree of similarity for set 9 is particularly lower than that of set 2.(8)Set 10 and set 11 of Figure 2 clearly indicates that Chen’s method, Chen and Chen’s Method and Wei and Chen’s method cannot deal with nonstandardized fuzzy numbers, which all give the incorrect result, that is, . Moreover, set 10 and set 11 of Figure 2 are clearly two different sets of nonstandardized generalized fuzzy numbers. Nonstandardized generalized fuzzy numbers and in set 11 are more similar than set 10. However, according to Table 1, Wen and Zhou’s method obtains the same degrees of similarity for set 10 and set 11. Finally, set 10 and set 11 in Figure 2 clearly indicates that nonstandardized fuzzy numbers and are approximately similar, that is, . But Wen and Zhou’s method obtains the incorrect results, that is, .(9)Set 12 in Figure 2 clearly indicates that Chen’s method, Lee’s method, and Wei and Chen’s method cannot deal with nonstandardized fuzzy numbers, which all give the incorrect result, that is, . Moreover, set 12 in Figure 2 clearly indicates that nonstandardized generalized fuzzy numbers and are not same, that is, . However, Chen and Chen’s method obtains the incorrect results, that is, .

From Table 1 and Figure 2, it clearly indicates that the proposed similarity measure can overcome the drawbacks of the existing similarity measures.

#### 6. Rotor Fault Diagnosis Based on the Proposed Similarity Measure

In this section, the proposed similarity measure is used to deal with the rotor fault diagnosis problem. The major cause of machine vibration is rotor unbalance and misalignment, which leads to additional dynamic load and to accelerate machine deterioration [23]. The proposed similarity measure is a very helpful method in diagnosing the rotor unbalance and misalignment to avoid any failures or damages that may arise.

The proposed similarity measure will be verified by fault diagnosis experiments on multifunctional flexible rotor experiment bench. The vibration acceleration and vibration displacement sensors are, respectively, installed on supporting rotor bases to collect vibration signals by data collector. Experimental procedures involve following main steps.

##### 6.1. Determine the Rotor Fault Diagnosis Set

Rotor unbalance and misalignment are two major rotor fault features. We determine the rotor fault diagnosis set as , where unbalance and misalignment.

##### 6.2. Structure the Fault Template of Rotor Fault Feature

Rotor speed is 1500 rpm, and we choose , , and amplitude frequency response (fundamental frequency is 25 Hz) and average amplitude of vibration displacement as four types of rotor fault feature parameters. In order to structure the fault template of rotor fault feature, we adopt experimental statistics method to simulate rotor fault operation mode and collect five sets of data for each type of rotor fault feature parameter. Every set of data consists of 40 observations. Let = [,] indicate the 40 observations of the set of data within 16s, where represent two rotor fault features, respectively, represent four types of rotor fault feature parameters, respectively, and represent the set number of the collected data. The arithmetic average and mean deviation of the set of data can be got as follows: Then, the generalized trapezoidal fuzzy number can be constructed based on these collected data as follows: We can obtain 40 generalized trapezoidal fuzzy numbers , where , , 2, 3, 4 and , 2, 3, 4, 5. The result of 40 generalized trapezoidal fuzzy numbers is shown in Table 2.

The generalized trapezoidal fuzzy number for each fault feature parameter can be got as follows: The result of generalized trapezoidal fuzzy numbers for the fault template is shown in Table 3.

##### 6.3. Structure the Diagnosing Rotor Fault Feature

We let multifunctional flexible rotor experiment bench bed run in rotor misalignment mode and collect data for each parameter of rotor fault feature. The generalized trapezoidal fuzzy numbers of diagnosing rotor fault feature can be obtained through the collected data. The result is shown in Table 4.

##### 6.4. Calculate the Degree of Similarity between Fuzzy Numbers and Each Fault Feature Parameter

In the following, Wen and Zhou’s and the proposed methods are used to deal with the rotor fault diagnosis problem, respectively. Based on (11) and (14), the degree of similarity between generalized trapezoidal fuzzy numbers and each fault feature parameter shown in Table 3 can be calculated. The calculating rotor fault diagnosis results of Wen and Zhou’s and the proposed methods are shown in Tables 5 and 6, respectively.

From Table 5, we obtain , , , and . It means that the rotor fault diagnosis result for fault feature parameter is rotor unbalance and the result for the other three types of rotor fault feature parameters are rotor misalignment. Thus, it is unable to determine fault feature from the rotor fault diagnosis result of Wen and Zhou’s method. Comparing with the result of Wen and Zhou’s method, the rotor fault diagnosis results of the proposed method for four types of rotor fault feature parameters are all rotor misalignment. The rotor fault diagnosis result of the proposed method coincides with the fault feature of multifunctional flexible rotor experiment bench. Therefore, experimental results demonstrate that the proposed similarity measure is more effective than Wen and Zhou’s methods in terms of rotor fault diagnosis.

#### 7. Conclusions

This paper presents a new similarity measure used for fault diagnosis technology, which can deal with both standardized and nonstandardized generalized trapezoidal fuzzy numbers. It combines concepts of the geometric distance, the center of gravity point, the perimeter, and the area of generalized trapezoidal fuzzy numbers for calculating the degree of similarity between and . Some properties of the proposed similarity measure have been proved, and 12 sets of generalized fuzzy numbers have been used to compare the calculation results of the proposed similarity measures with the existing similarity measures. From Table 1 and Figure 2, it clearly indicates that the proposed similarity method can overcome the drawbacks of existing similarity measures. Finally, a rotor fault diagnosis example has been given to show the proposed similarity measure.

#### Acknowledgment

The authors would like to thank the anonymous reviewers for critical comments and suggestions leading to an improved presentation.

#### References

- A. T. Ghainani, A. A. M. Zin, and N. M. Ismail, “Fuzzy timing petri net for fault diagnosis in power system,”
*Mathematical Problems in Engineering*, vol. 2012, Article ID 717195, 12 pages, 2012. View at Publisher · View at Google Scholar - C. Roberts, H. P. B. Dassanayake, N. Lehrasab, and C. J. Goodman, “Distributed quantitative and qualitative fault diagnosis: railway junction case study,”
*Control Engineering Practice*, vol. 10, no. 4, pp. 419–429, 2002. View at Publisher · View at Google Scholar · View at Scopus - R. Ubar, S. Kostin, and J. Raik, “Embedded fault diagnosis in digital systems with BIST,”
*Microprocessors and Microsystems*, vol. 32, no. 5-6, pp. 279–287, 2008. View at Publisher · View at Google Scholar · View at Scopus - S.-J. Chen and S.-M. Chen, “Fuzzy risk analysis based on similarity measures of generalized fuzzy numbers,”
*IEEE Transactions on Fuzzy Systems*, vol. 11, no. 1, pp. 45–56, 2003. View at Publisher · View at Google Scholar · View at Scopus - R. Eslamloueyan, “Designing a hierarchical neural network based on fuzzy clustering for fault diagnosis of the Tennessee-Eastman process,”
*Applied Soft Computing*, vol. 11, pp. 1407–1415, 2011. View at Google Scholar - H. Luo, S. L. Yang, X. J. Hu, and X. X. Hu, “Agent oriented intelligent fault diagnosis system using evidence theory,”
*Expert Systems with Applications*, vol. 39, pp. 2524–2531, 2012. View at Google Scholar - Z. Du, X. Jin, and Y. Yang, “Fault diagnosis for temperature, flow rate and pressure sensors in VAV systems using wavelet neural network,”
*Applied Energy*, vol. 86, no. 9, pp. 1624–1631, 2009. View at Publisher · View at Google Scholar · View at Scopus - V. Muralidharan and V. Sugumaran, “Feature extraction using wavelets and classification through decision tree algorithm for fault diagnosis of mono-block centrifugal pump,”
*Measurement*, vol. 46, pp. 353–359, 2013. View at Google Scholar - J. Zhang, “Improved on-line process fault diagnosis through information fusion in multiple neural networks,”
*Computers and Chemical Engineering*, vol. 30, pp. 558–571, 2006. View at Google Scholar - M. Grbovic, W. C. Li, P. Xu, A. K. Usadi, L. M. Song, and S. Vucetic, “Decentralized fault detection and diagnosis via sparse PCA based decomposition and Maximum Entropy decision fusion,”
*Journal of Process Control*, vol. 22, pp. 738–750, 2012. View at Google Scholar - M. Barakat, F. Druaux, D. Lefebvre, M. Khalil, and O. Mustapha, “Self adaptive growing neural network classifier for faults detection and diagnosis,”
*Neurocomputing*, vol. 74, pp. 3865–3876, 2011. View at Google Scholar - M. Weerasinghe, J. Barry Gomm, and D. Williams, “Neural networks for fault diagnosis of a nuclear fuel processing plant at different operating points,”
*Control Engineering Practice*, vol. 6, no. 2, pp. 281–289, 1998. View at Publisher · View at Google Scholar · View at Scopus - X. B. Xu, H. S. Feng, Z. Wang, and C. L. Wen, “An information fusion method of fault diagnosis based on interval basic probability assignment,”
*Chinese Journal of Electronics*, vol. 20, no. 2, pp. 255–260, 2011. View at Google Scholar · View at Scopus - S.-J. Chen, “New methods for subjective mental workload assessment and fuzzy risk analysis,”
*Cybernetics and Systems*, vol. 27, no. 5, pp. 449–472, 1996. View at Google Scholar · View at Scopus - H. S. Lee, “Optimal aggregation method for fuzzy opinions of group decision,” in
*Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics (SMC '99)*, vol. 3, pp. 314–319, October 1999. View at Scopus - S.-J. Chen and S.-M. Chen, “A new method to measure the similarity between fuzzy numbers,” in
*Proceedings of the 10th IEEE International Conference on Fuzzy Systems*, pp. 208–214, Melbourne, Australia, 2011. - J. H. Chen and S.-M. Chen, “A new method for ranking generalized fuzzy numbers for handling fuzzy risk analysis problems,” in
*Proceedings of the 9th Joint Conference on Information Sciences (JCIS '06)*, pp. 1196–1199, Kaohsiung, Taiwan, October 2006. View at Publisher · View at Google Scholar · View at Scopus - S.-J. Chen and S.-M. Chen, “Fuzzy risk analysis based on the ranking of generalized trapezoidal fuzzy numbers,”
*Applied Intelligence*, vol. 26, no. 1, pp. 1–11, 2007. View at Publisher · View at Google Scholar · View at Scopus - S.-H. Wei and S.-M. Chen, “A new similarity measure between generalized fuzzy numbers,” in
*Proceedings of the 3rd International Conference on Soft Computing and Intelligent System and 7th International Symposium on Advanced Intelligent System*, pp. 315–320, Tokyo, Japan, 2006. - S.-H. Wei and S.-M. Chen, “A new approach for fuzzy risk analysis based on similarity measures of generalized fuzzy numbers,”
*Expert Systems with Applications*, vol. 36, no. 1, pp. 589–598, 2009. View at Publisher · View at Google Scholar · View at Scopus - C. L. Wen, Z. Zhou, and X. B. Xu, “A new similarity measure between generalized trapezoidal fuzzy numbers and its application to fault diagnosis,”
*Acta Electronica Sinica*, vol. 39, no. 3, pp. 1–6, 2011. View at Google Scholar · View at Scopus - S.-H Chen, “Ranking generalized fuzzy number with graded mean integration,” in
*Proceedings of the 8th international Fuzzy Systems Association World Congress*, vol. 2, pp. 899–902, Taipei, Taiwan, 1999. - L. Arebi, F. Gu, and A. Ball, “Rotor Misalignment detection using a wireless sensor and a shaft encoder,” in
*Proceedings of the Computing and Engineering Annual Researchers' Conference (CEARC '10)*, pp. 6–13, University of Huddersfield, Huddersfield, UK, 2010.