# A Review on Data-Driven Condition Monitoring of Industrial Equipment
## 2 Data-Driven Condition Monitoring of Industrial Equipment
### Data Processing Methods
#### 2.1.3 The Empirical Model Decomposition Approach
Through different simulations and experiments, this method outperforms the WT and FT methods overall in terms of efficiency and accuracy of fault detection. However, as the acquired equipment signals are typically combined with noise or unknown intermittent signals, this may lead to model confusion when utilizing EMD for signal decomposition due to repeated jumps in the local extremes in a short time interval. To tackle this difficulty, improved EMD algorithms have been brought into the field of equipment condition monitoring, such as ensemble EMD (EEMD) [77; 78; 79; 80; 81] and complete ensemble EMD (CEEMD) [82; 83]. In [84], the collected aviation hydraulic pump pressure signals were decomposed into several IMFs using EEMD to reflect the fault characteristics in the time, frequency, and time-frequency domains to define the severity of the faults. Using PCA to scale down the defect characteristics, a support vector regression (SVR) model was then developed to predict the remaining life of the pump. It is demonstrated that the multi-domain fault characteristics retrieved by EEMD can more precisely reflect the severity of the loose piston defect and the remaininguseful life of pump can be estimated by the SVR model. [PERSON] et al. [85] propose a new method for extracting fault features. Initially, the fault vibration signals of the inner ring, outer ring, and rolling element of the bearing are decomposed into IMFs with different physical significance by EEMD. Then, the IMFs with the highest correlation coefficients are chosen to characterize the original fault signals using the correlation coefficient analysis method. The obtained IMF multi-scale fuzzy entropy values are then used to generate a feature vector for the training and construction of the SVM classifier. Several experiments demonstrate that the approach can accurately detect and diagnose the type and severity of bearing faults under varying motor loads and bearing fault severity levels. CEEMD was suggested in [86] to increase algorithm efficiency and reduce computing costs, and it has been successfully used to condition monitoring. In [87], [PERSON] et al. used CEEMD to decompose microphone-recorded induction motor acoustic sound signals. The CEEMD was utilized to temporally decompose motor sound signals acquired under normal and fault operating conditions, and the corresponding spectrum of IMFs obtained by time-frequency distribution of Gabor (TFDG) was used to select specific IMFs containing fault information for fault diagnosis. It is shown that the diagnosis of unbalance condition, bearing faults, and broken rotor bar defects can be accomplished by comparing the spectra of IMFs of sound signals under normal and faulty operating conditions, and that the validity of the results can be verified by analyzing vibration signals. A defect feature extraction optimization approach is proposed in [88] for CEEMD. A genetic algorithm is used to optimize the white noise amplitude of the CEEMD so that the decomposition produces the least mean value of mutual information between the IMFs, thereby suppressing the model mixing phenomena. The method is used to the diagnosis of bearing faults, and the results indicate that the optimized method can adaptively analyze diverse signals and provide more sensitive diagnostic results than the empirically chosen white noise approach. The corresponding references for the EMD methods reviewed in this section are summarized in Tables 7-9 below.