Fig. 1. Fault diagnosis framework of rotor machinery based on feature residual values classification approach.
Fig. 1. Fault diagnosis framework of rotor machinery based on feature residual values classification approach.
Fig. 2. Schematic of the procedure for generating the feature residual values.
Fig. 2. Schematic of the procedure for generating the feature residual values.
Fig. 3. First experimental setup: (a‐1) RK‐4 rotor kit with (a‐2) displacement sensor and (a‐3) acceleration sensor, the fault imbedding device for (a‐4) mass unbalance, (a‐5) disk rubbing, (a‐6) misalignment and (a‐7) oil whirl. Second experimental setup: (b‐1) small‐scale steam turbine prototype with (b‐2) measurement equipment, (b‐3) bearing and two shafts, the fault imbedding device for (b‐4) mass unbalance, (b‐5) disk rubbing, (b‐6) shaft rubbing, and (b‐7) misalignment. Third experimental setup: (c‐1) industrial steam turbine with (c‐2) shaft and blades and (c‐3) a bearing.
Fig. 3. First experimental setup: (a‐1) RK‐4 rotor kit with (a‐2) displacement sensor and (a‐3) acceleration sensor, the fault imbedding device for (a‐4) mass unbalance, (a‐5) disk rubbing, (a‐6) misalignment and (a‐7) oil whirl. Second experimental setup: (b‐1) small‐scale steam turbine prototype with (b‐2) measurement equipment, (b‐3) bearing and two shafts, the fault imbedding device for (b‐4) mass unbalance, (b‐5) disk rubbing, (b‐6) shaft rubbing, and (b‐7) misalignment. Third experimental setup: (c‐1) industrial steam turbine with (c‐2) shaft and blades and (c‐3) a bearing.
Fig. 4. Original vibration signal (time‐domain) in four conditions and their corresponding various feature residual trends. (a) normal‐state, (b) misalignment, (c) rub, (d) crack, (e) 1s average of skewness (good case), (f) 1s deviation of crest factor (good case), (g) 1s average of max value (good case), (h) 1s deviation of shape factor (bad case).
Fig. 4. Original vibration signal (time‐domain) in four conditions and their corresponding various feature residual trends. (a) normal‐state, (b) misalignment, (c) rub, (d) crack, (e) 1s average of skewness (good case), (f) 1s deviation of crest factor (good case), (g) 1s average of max value (good case), (h) 1s deviation of shape factor (bad case).
Fig. 5. Comparisons of fault identification accuracy rates of RPC based classifier and common FDA classifier for different experimental cases: the classification results obtained using (a) different baselines for the normal state and (b) identical training data and different target systems.
Fig. 5. Comparisons of fault identification accuracy rates of RPC based classifier and common FDA classifier for different experimental cases: the classification results obtained using (a) different baselines for the normal state and (b) identical training data and different target systems.
Fig. 6. The example process of generating feature residual values on real data set from vibration sensors. (include normal and faulty vibration data).
Fig. 6. The example process of generating feature residual values on real data set from vibration sensors. (include normal and faulty vibration data).
Fig. 7. Visualization of the classification obtained by the RPC based diagnosis algorithm by changing the normal state of the system using four selected features.
Fig. 7. Visualization of the classification obtained by the RPC based diagnosis algorithm by changing the normal state of the system using four selected features.
Table 1. Explanations of training and testing data sets for each state of the rotor machine.
Table 1. Explanations of training and testing data sets for each state of the rotor machine.
Table 2. Calculation sample data obtained from vibration sensors: each table includes fault data (left), estimated values of measured data (center) and the feature residual values obtained by RPC based algorithm (right).
Table 2. Calculation sample data obtained from vibration sensors: each table includes fault data (left), estimated values of measured data (center) and the feature residual values obtained by RPC based algorithm (right).
Table 3. Fault diagnosis results under various classifiers with feature residuals and raw feature values. (The test are conducted by representative experiments from No.2, 4, 5 and 7)
Table 3. Fault diagnosis results under various classifiers with feature residuals and raw feature values. (The test are conducted by representative experiments from No.2, 4, 5 and 7)
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