*5.3. Analysis of center spring wear fault signal of fluid pressure based on the standard Autogram*

The fault feature information on center spring wear is extracted by standard Autogram, and the colormap presentation based on the Equation (2) is shown in Figure 13.

**Figure 13.** Colormap presentation of the center spring wear fault signal based on standard Autogram.

The maximum *kurtosis* of 5.3 is assigned to the node (3, 1), with center frequency of 312.5 Hz and a bandwidth of 625 Hz. Thus, node (3, 1) is adopted as a data source for further investigation, and the no threshold spectrum, upper threshold spectrum, and lower threshold spectrum are displayed in Figure 14.

Figure 14 shows the fault feature information on center spring wear at fault feature frequency 24.5 Hz with most of its harmonics extracted. The amplitude values obtained based on no threshold processing in Figure 14a are much larger than those in Figure 14b,c, and they are larger than those of original center spring wear signal spectrum in Figure 12. It can be seen that Autogram is effective for extracting the information, and standard Autogram based on no threshold processing has the strongest extraction ability.

**Figure 14.** Spectrums of the center spring wear fault data source node (3, 1) based on standard Autogram. (**a**) No threshold spectrum; (**b**) Upper threshold spectrum; (**c**) Lower threshold spectrum.

*5.4. Analysis of center spring wear fault signal of fluid pressure based on Upper Autogram.*

Upper Autogram is applied to extract the fault feature information, and the colormap presentation based on Equation (3) is shown in Figure 15.

Upper Autogram-Kmax=23.1 @ level 3, Bw=625Hz, fc=937.5Hz

**Figure 15.** Colormap presentation of the center spring wear fault signal based on Upper Autogram.

In Figure 15, the maximum *kurtosisu* is 23.1, and it corresponds to the node (3, 2), with a center frequency of 937.5 Hz and a bandwidth of 625 Hz. Node (3, 2) is used as data source for further investigation, and the no threshold spectrum, upper threshold spectrum, and lower threshold spectrum are shown in Figure 16.

**Figure 16.** Spectrums of the center spring wear fault data source node (3, 2) based on upper Autogram. (**a**) No threshold spectrum; (**b**) Upper threshold spectrum; (**c**) Lower threshold spectrum.

Figure 16c illustrates that the fault feature information at fault feature frequency 24.5 Hz and its harmonics are extracted. However, in Figure 16a,b, most harmonics are not extracted effectively, and there are many background noises. Thus, upper Autogram based on lower threshold processing has the strongest extraction ability.

Compared with standard Autogram based on no threshold processing in Figure 14a and original center spring wear signal in Figure 12, standard Autogram based on lower threshold processing is not so effective and has the weakest extraction ability.

#### *5.5. Analysis of center spring wear fault signalof fluid pressurebased on Lower Autogram.*

Extraction of the fault feature information is executed by lower Autogram, and the colormap presentation can be obtained based on Equation (4). It is illustrated in Figure 17.

In Figure 17, the maximum *kurtosisl* is 1.6, and it corresponds to node (3, 4), with a center frequency of 2187.5 Hz and a bandwidth of 625 Hz. Node (3, 4) is used as data source, and its no threshold spectrum, upper threshold spectrum, and lower threshold spectrum are demonstrated in Figure 18.

**Figure 17.** Colormap presentation of the center spring wear fault signal based on lower Autogram.

**Figure 18.** Spectrums of the center spring wear fault data source node (3, 4) based on lower Autogram. (**a**) No threshold spectrum; (**b**) Upper threshold spectrum; (**c**) Lower threshold spectrum.

The fault feature information at fault feature frequency 24.5 Hz and most of its harmonics are not extracted in the three figures in Figure 18, and their amplitude values are smaller compared with those of original center spring wear signal in Figure 12. Thus, lower Autogram based on three kinds of threshold processing has very weak extraction ability.

Comparing with the standard Autogram result in Figure 14 and the upper Autogram result in Figure 16, lower Autogram has the weakest extraction ability, and it is influenced by background noises. Thus, standard Autogram has the strongest extraction ability and can extract the most fault feature information from the background noises.
