# A Review on Data-Driven Condition Monitoring of Industrial Equipment
## 2 Data-Driven Condition Monitoring of Industrial Equipment
### Fault Diagnosis and Prediction
#### 2.2.1 Method Based on Shallow Machine Learning
decomposed the stator current signal of a three-phase motor into an intrinsic mode function (IMF) containing the main amplitude and frequency information by means of the empirical mode decomposition (EMD) method. The IMFs were then applied to the Wigner-Ville distribution (WVD) to obtain WVD contour patterns to characterize the characteristic frequencies of bearing defects. The contour pattern is then pattern recognized using an ANN, which can effectively detect outer ring defects in bearings. [PERSON] et al. [105] transformed the induction motor current signal into a two-dimensional park vector (PV) pattern image by the park vector analysis (PVA) method. The different features of the pattern image (Area of PV pattern; Circumference of the pattern; width of the pattern; resultant of the centroid) were then used as inputs to the ANN and the motor bearing state as output from the ANN, enabling the detection and diagnosis of faults caused by mis-mounted bearings. This enables the detection and diagnosis of defects caused by incorrectly mounted bearings. Bangalore and Tjernberg [106] constructed the detected average temperature signals of the wind turbine gearbox bearings into bearing behaviour patterns via the SCADA system. The alarm and warning feature data from the SCADA system were then used to train the ANN model. The results show that the method can efficiently and accurately detect failures due to spalled or damaged bearings. Additionally, in addition to employing a single signal as state information, multiple signals are often used in combination to train ANN-based state detection models. For instance, in [107], [PERSON] et al. transformed the sample entropy (SampEn) into the multiscale entropy of the current and vibration signals of an induction motor into a time series and used it as input to an ANN for misalignment fault detection of induction motors. This approach not only reduces the number of sensors, but also avoids the need for extensive calculations in the frequency domain analysis. [PERSON] et al. [108] used the data set of electric submersible pump (ESP) system variables obtained from real-time sensor monitoring and the corresponding combination of variables calculated (e.g., temperature, pressure, etc.) as input to an ANN, with the input weights of the variables determined by the severity of their impact on the system. Afterwards, the inputs are compared with the operating ranges corresponding to the ESP components in the hidden layer, and finally the output layer determines whether the input conditions will lead to an ESP failure or predict the trend leading to an ESP failure. In [90], the current signal is analyzed by performing a third harmonic analysis to extract the amplitude and frequency features of the fault, and the vibration signal is decomposed into intrinsic mode functions (IMFs) using complete ensemble empirical mode decomposition (CEEMD) to extract the signal features with the best IMFs in the time and frequency domains. Afterwards, the features of the current and vibration signals that are most relevant to the health indicators are combined and reduced to two-dimensional primary metric space data using a PCA model. This dataset was used to train the ANN model and achieved a diagnostic accuracy of 99% on the test data obtained under stator fault of a brushless DC motor. Figure 4 shows the basic processes for condition monitoring using the ANN model, and the methods described next in this paper have the similar process to this method. The relevant references for the ANN methods discussed in this section are summarized in Tables 10-12. Figure 3.1 A typical ANN based condition monitoring system.