**7. References**

168 Fuzzy Inference System – Theory and Applications

recognition accuracy and provides a better generalization capability compared to the individual classifiers. Therefore, it is able to effectively identify not only incipient faults but

1. Comparing the diagnosis results of the proposed hybrid intelligent method with those of individual classifiers, we find that the testing accuracies of the hybrid intelligent method (100% for data set A, 90.83% for data set B and 91.33% for data set C in case 1; 100% in case 2) increase by 0, 18.61%, 17.72% and 16.61% compared with the average accuracies of the six individual classifiers. In addition, although the highest classification accuracy (100%) is obtained by all the classifiers for data set A in case 1, the classification error of the hybrid intelligent method is least among all classifiers. Thus, the proposed hybrid intelligent method is superior to the individual classifiers in the light of the classification accuracies. 2. All the above comparisons prove that the proposed hybrid intelligent method obtains significant improvements in fault diagnosis accuracy compared to the individual classifiers. It reliably recognizes both incipient faults and compound faults of rolling element bearings. The success obtained by the hybrid intelligent method may be attributed to the following three points. 1) Extracting both time- and frequency-domain features better reflects the machinery health conditions. 2) Selecting the sensitive features reflecting the fault characteristics avoids interference of other fault-unrelated features. 3) Combining multiple

intelligent classifiers based on fuzzy inference system raises diagnosis accuracy.

employed to fault diagnosis of other rotating machinery.

the same time recognition of compound faults.

3. The problems studied in this chapter cover single fault diagnosis, incipient fault diagnosis and compound fault diagnosis, and therefore they are typical cases of machinery fault diagnosis. The satisfactory experiment results demonstrate the effectiveness and generalization ability of the hybrid intelligent method. Although the proposed method is applied to fault diagnosis of the rolling element bearings successfully, it may also be

In this chapter, a hybrid intelligent method based on fuzzy inference system is proposed for intelligent fault diagnosis of rotating machinery. In the method, several preprocessing methods, like empirical mode decomposition (EMD), filtration and demodulation, are utilized to mine fault information from vibration signals. In order to remove the redundant and irrelevant information, an improved distance evaluation technique is presented and used to select the sensitive features. Multiple fuzzy inference systems are combined using genetic algorithms (GAs) to enhance fault identification accuracy. The experimental results show that the hybrid intelligent method enables the identification of incipient faults and at

This research is supported by National Natural Science Foundation of China (51005172), the Fundamental Research Funds for the Central Universities and the Preferred Support Funds of the Scientific Research Project of the Ministry of Human Resources and Social Security of

also compound faults of locomotive rolling bearings.

**4. Discussions** 

**5. Conclusions** 

**6. Acknowledgements** 

China for Returned Scholars.


**9** 

*1Iraq 2India* 

*1Baghdad University 2Jamia Millia Islamia,* 

**Some Studies on Noise and Its Effects on Industrial/Cognitive** 

**Task Performance and Modeling** 

Ahmed Hameed Kaleel1 and Zulquernain Mallick2

Present industrial environment is quite different than past. Globalization and market driven forces has made the working environment quite competitive. It is quite obvious that these factors when combined with environmental factors, lead to poor operators/workers performance. Therefore, ergonomists has new challenges in terms of predicting workers

High noise level exposure leads to psychological as well physiological problems. It results in deteriorated cognitive task efficiency, although the exact nature of work performance is still unknown. To predict cognitive task efficiency deterioration, neuro-fuzzy tools were used. It has been established that a neuro-fuzzy computing system helps in identification and analysis of fuzzy models. The last decade has seen substantial growth in development of various neuro-fuzzy systems. Among them, adaptive neuro-fuzzy inference system provides a systematic and directed approach for model building and gives the best possible

Input variables were noise level, cognitive task type, and age of workers. Out-put variable was predicted in terms of reduction in cognitive task efficiency. The cause-effect relationships of these parameters are complex, uncertain, and non-linear in nature therefore, it is quite difficult to properly examine it by conventional methods. Hence, an attempt is made in present study to develop a neuro-fuzzy model to predict the effects of noise pollution on human work efficiency as a function of noise level, cognitive task type, and age of the workers practicing cognitive type of task at (I.T.O power plant station, centrifugal pump industry WPIL India Limited, and Shriram Piston & Rings limited) industries. Categorization of noise and its levels

A total of 155 questionnaires were distributed among the workers of industries under reference. Likert scale has been used to evaluate the answers densities which ranges between "strongly disagree" to "strongly agree". Cognitive workers performance was evaluated based on self administrated questionnaire survey, which consisted of 55-questions, covering all

(high, medium, and low) was based on a survey conducted for this purpose.

possible reported effects of cognitive task on cognitive task performance.

efficiency as well as workers health protection and well being.

design parameters in minimum possible time.

**1. Introduction** 

