**5. Conclusion**

14 Will-be-set-by-IN-TECH

After GEP training, a perfect feature recognition function *ϕ* can be got. Using function *ϕ*, we can calculate the mean mapping value of each operating condition samples from a certain sensor source. For building the multi-source feature evaluation matrix, the samples which are correctly classified are selected to calculate their mean. Multi-source feature evaluation matrix

> *ρ*1(1) *ρ*1(2) ... *ρ*1(11) *ρ*2(1) *ρ*2(2) ... *ρ*2(11)

*ρM*(1) *ρM*(2) ... *ρM*(11)

In Equation 26, each element represents the mean value of each feature component of each operating condition. For example, *ρ*2(1) represents the mean of all correctly classified samples

In order to evaluate the proposed feature-level fusion model, we apply it to bearing fault diagnosis. And data of this bearing fault diagnosis task are also take from a lab of the Case Western Reserve University website [3]. In this work, three experiments over three data sets are conducted as Table 11 shows. Those data are collected under various operating loads from

Data The number of The number of Defect size(inches) Operating Class set training sample testing sample (training/testing) condition label A 80 80 0/0 Normal 1

B 80 80 0/0 Normal 1

C 80 80 0/0 Normal 1

Each data set covers four different operating conditions and four different loads (0, 1, 2 and 3 hp). And each class of data sets has 160 data samples which are divided into two equal

80 80 0.007/0.007 Outer race fault 2 80 80 0.007/0.007 Inner race fault 3 80 80 0.007/0.007 Ball fault 4

80 80 0.007/0.021 Outer race fault 2 80 80 0.007/0.021 Inner race fault 3 80 80 0.007/0.021 Ball fault 4

80 80 0.021/0.007 Outer race fault 2 80 80 0.021/0.007 Inner race fault 3 80 80 0.021/0.007 Ball fault 4

. ... .

. . ⎤ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎦

(26)

is composed by these mean values as Equation 26 shows.

of the first feature from the 2-th operating condition.

motor driven end and fan end accelerometers.

**4.2. Results and discussion**

**Table 11.** Description of three data sets

⎡ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎣

. . . . .

> This chapter has introduced some new methods for bearing fault diagnosis. These new approaches are using information fusion and intelligent algorithms. Bearing fault diagnosis is still an ongoing research subject over a decade and attracting a huge number of researchers in different areas. But most of those current using techniques mainly deal with single-source data. Many researches have shown that an individual decision system with a single data source can only acquire a limited classification capability which may not be enough for a particular application. So, we study a new way for bearing fault diagnosis using information fusion technology and intelligent algorithm.

> Information fusion is a field still under research. Generally, information fusion process may happen in three levels: sensor level, feature level and decision level. Here, we propose a new feature level fusion method and a new decision level fusion method for bearing fault diagnosis. The feature level fusion method is using GEP which is a new intelligent algorithm. And it is a parallel fusion method. The decision level fusion approach is based on a new multiple classifier ensemble method. It analyzes raw vibration signal, and completes the feature extraction by using EMD and fractal feature parameter calculation. From experimental results, we can see that these new fusion model for bearing fault diagnosis task can get good

decision performance which is higher than the performance from traditional single sensor application.

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